Robotics | 35 articles | Tech News, Tutorials & Expert Insights

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23 Oct 2015

15 min read

Internet-Connected Smart Water Meter

23 Oct 2015

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6220

article-image-dynamic-path-planning-robot

Packt

19 Oct 2015

8 min read

Dynamic Path Planning of a Robot

Packt

19 Oct 2015

8 min read

In this article by Richard Grimmett, the author of the book Raspberry Pi Robotic Blueprints, we will see how to do dynamic path planning. Dynamic path planning simply means that you don't have a knowledge of the entire world with all the possible barriers before you encounter them. Your robot will have to decide how to proceed while it is in motion. This can be a complex topic, but there are some basics that you can start to understand and apply as you ask your robot to move around in its environment. Let's first address the problem of where you want to go and need to execute a path without barriers and then add in the barriers. (For more resources related to this topic, see here.) Basic path planning In order to talk about dynamic path planning—planning a path where you don't know what barriers you might encounter—you'll need a framework to understand where your robot is as well as to determine the location of the goal. One common framework is an x-y grid. Here is a drawing of such a grid: There are three key points to remember, as follows: The lower left point is a fixed reference position. The directions x and y are also fixed and all the other positions will be measured with respect to this position and directions. Another important point is the starting location of your robot. Your robot will then keep track of its location using its x coordinate or position with respect to some fixed reference position in the x direction and its y coordinate or position with respect to some fixed reference position in the y direction to the goal. It will use the compass to keep track of these directions. The third important point is the position of the goal, also given in x and y coordinates with respect to the fixed reference position. If you know the starting location and angle of your robot, then you can plan an optimum (shortest distance) path to this goal. To do this, you can use the goal location and robot location and some fairly simple math to calculate the distance and angle from the robot to the goal. To calculate the distance, use the following equation: You can use the preceding equation to tell your robot how far to travel to the goal. The following equation will tell your robot the angle at which it needs to travel: Thefollowing is a graphical representation of the two pieces of information that we just saw: Now that you have a goal, angle, and distance, you can program your robot to move. To do this, you will write a program to do the path planning and call the movement functions that you created earlier in this article. You will need, however, to know the distance that your robot travels in a set of time so that you can tell your robot in time units, not distance units, how far to travel. You'll also need to be able to translate the distance that might be covered by your robot in a turn; however, this distance may be so small as to be of no importance. If you know the angle and distance, then you can move your robot to the goal. The following are the steps that you will program: Calculate the distance in units that your robot will need to travel to reach the goal. Convert this to the number of steps to achieve this distance. Calculate the angle that your robot will need to travel to reach the goal. You'll use the compass and your robot turn functions to achieve this angle. Now call the step functions for the proper number of times to move your robot for the correct distance. This is it. Now, we will use some very simple python code that executes this using functions to move the robot forward and turn it. In this case, it makes sense to create a file called robotLib.py with all of the functions that do the actual settings to step the biped robot forward and turn the robot. You'll then import these functions using the from robotLib import * statement and your python program can call these functions. This makes the path planning python program smaller and more manageable. You'll do the same thing with the compass program using the command: from compass import *. For more information on how to import the functions from one python file to another, see http://www.tutorialspoint.com/python/python_modules.htm. The following is a listing of the program: In this program, the user enters the goal location, and the robot decides the shortest direction to the desired angle by reading the angle. To make it simple, the robot is placed in the grid heading in the direction of an angle of 0. If the goal angle is less than 180 degrees, the robot will turn right. If it is greater than 180 degrees, the robot will turn left. The robot turns until the desired angle and its measured angle are within a few degrees. Then the robot takes the number of steps in order to reach the goal. Avoiding Obstacles Planning paths without obstacles is, as has been shown, quite easy. However, it becomes a bit more challenging when your robot needs to walk around the obstacles. Let's look at the case where there is an obstacle in the path that you calculated previously. It might look as follows: You can still use the same path planning algorithm to find the starting angle; however, you will now need to use your sonar sensor to detect the obstacle. When your sonar sensor detects the obstacle, you will need to stop and recalculate a path to avoid the barrier, then recalculate the desired path to the goal. One very simple way to do this is when your robot senses a barrier, turn right at 90 degrees, go a fixed distance, and then recalculate the optimum path. When you turn back to move toward the target, you will move along the optimum path if you sense no barrier. However, if your robot encounters the obstacle again, it will repeat the process until it reaches the goal. In this case, using these rules, the robot will travel the following path: To sense the barrier, you will use the library calls to the sensor. You're going to add more accuracy with this robot using the compass to determine your angle. You will do this by importing the compass capability using from compass import *. You will also be using the time library and time.sleep command to add a delay between the different statements in the code. You will need to change your track.py library so that the commands don't have a fixed ending time, as follows: Here is the first part of this code, two functions that provide the capability to turn to a known angle using the compass, and a function to calculate the distance and angle to turn the tracked vehicle to that angle: The second part of this code shows the main loop. The user enters the robot's current position and the desired end position in x and y coordinates. The code that calculates the angle and distance starts the robot on its way. If a barrier is sensed, the unit turns at 90 degrees, goes for two distance units, and then recalculates the path to the end goal, as shown in the following screenshot: Now, this algorithm is quite simple; however, there are others that have much more complex responses to the barriers. You can also see that by adding the sonar sensors to the sides, your robot could actually sense when the barrier has ended. You could also provide more complex decision processes about which way to turn to avoid an object. Again, there are many different path finding algorithms. See http://www.academia.edu/837604/A_Simple_Local_Path_Planning_Algorithm_for_Autonomous_Mobile_Robots for an example of this. These more complex algorithms can be explored using the basic functionality that you have built in this article. Summary We have seen how to add path planning to your tracked robot's capability. Your tracked robot can now not only move from point A to point B, but can also avoid the barriers that might be in the way. Resources for Article: Further resources on this subject: Debugging Applications with PDB and Log Files[article] Develop a Digital Clock[article] Color and motion finding [article]

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3813

Packt

15 Oct 2015

13 min read

The Internet of Things

Packt

15 Oct 2015

13 min read

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1752

article-image-storing-and-generating-graphs-stored-data

Packt

01 Oct 2015

20 min read

Storing and Generating Graphs of the Stored Data

Packt

01 Oct 2015

20 min read

In this article by Matthijs Kooijman, the author of the book Building Wireless Sensor Networks Using Arduino, we will explore some of the ways to persistently store the collected sensor data and visualize the data using convenient graphs. First, you will see how to connect your coordinator to the Internet and send its data to the Beebotte cloud platform. You will learn how to create a custom dashboard in this platform that can show you the collected data in a convenient graph format. Second, you will see how you can collect and visualize the data on your own computer instead of sending it to the Internet directly. (For more resources related to this topic, see here.) For the first part, you will need some shield to connect your coordinator Arduino to the Internet in addition to the hardware that has been recommended for the coordinator in the previous chapter. This article provides examples for the following two shields: The Arduino Ethernet shield (https://www.arduino.cc/en/Main/ArduinoEthernetShield) The Adafruit CC3000 Wi-Fi shield (https://www.adafruit.com/products/1491) If possible, the Ethernet shield is recommended as its library is small, and it is easier to keep a reliable connection using a wired connection. Additionally, the CC3000 shield conflicts with the SparkFun XBee shield and this would require some modification on the latter's part to make them work together. For the second part, no additional hardware is needed. There are other Ethernet or Wi-Fi shields that you can use. Storing your data in the cloud When it comes to storing your data online somewhere, there are literally dozens of online platforms that offer some kind of data storage service aimed at collecting sensor data. Each of these has different features, complexity, and cost, and you are encouraged to have a look at what is available. Even though a lot of platforms are available, almost none of them are really suited for a hobby sensor network like the one presented in this article. Most platforms support the basic collection of data and offer some web API to access the data but there were two requirements that ruled out most of the platforms: It has to be affordable for a home user with just a bit of data. Ideally, there is a free version to get started. It has to support creating a dashboard that can show data and graphs and also show input elements that can be used to talk back to the network. (This will be used in the next chapter to create an online thermostat). When this article was written, only two platforms seemed completely suitable: Beebotte (https://beebotte.com/) and Adafruit IO (https://io.adafruit.com/). The examples in this article use Beebotte because at the time of writing, Adafruit IO was not publicly available yet and Beebotte currently has some additional features. However, you are encouraged to check out Adafruit IO as an alternative. As both the platforms use the MQTT protocol (explained in the next section), you should be able to reuse the example code with just minimal changes for Adafruit IO. Introducing Beebotte Beebotte, like most of these services, can be seen as a big online database that stores any data you send to it and allows retrieving any data you are interested in. Additionally, you can easily create dashboards that allow you to look at your data and even interact with it through various configurable widgets. By the end of this chapter, you might have a dashboard that looks like this: Before showing you how to talk to Beebotte from your Arduino, some important concepts in the Beebotte system will be introduced: channels, resources, security tokens, and access protocols. The examples in this article serve to get started with Beebotte, but will certainly not cover all features and details. Be sure to check out the extensive documentation on the Beebotte site at https://beebotte.com/overview. Channels and resources All data collected by Beebotte is organized into resources, each representing a single series of data. All data stored in a resource signifies the same thing, such as the temperature in your living room or on/off status of the air conditioner but at different points in time. This kind of data is also often referred to as time-series data. To keep your data organized, Beebotte supports the concept of channels. Essentially, a channel is just a group of resources that somehow belong together. Typically, a channel represents a single device or data source but you are free to group your resources in any way that you see fit. In this example, every sensor module in the network will get its own channel, each containing a resource to store the temperature data and a resource to store the humidity data. Security To be able to access the data stored in your Beebotte account or publish new data, every connection needs to be authenticated. This happens using a secret token or key (similar to a password) that you configure in your Arduino code and proves to the Beebotte server that you are allowed to access the data. There are two kinds of secrets currently supported by Beebotte: Your account secret key: This is a single key for your account, which allows access to all resources and all channels in your account. It additionally allows the creation and deletion of the channels and resources. Channel tokens: Each channel has an associated channel token, which allows reading and writing data from that channel only. Additionally, the channel token can be regenerated if the token is ever compromised. This example uses the account secret key to authenticate the connection. It would be better to use a more limited channel token (to limit the consequences if the token is leaked) but in this example, as the coordinator forwards data for multiple sensor nodes (each of which have their own channel), a channel token does not provide enough access. As an alternative, you could consider using a single channel containing all the resources (named, for example, "Livingroom_Temperature" to still allow grouping) so that you can use the slightly more secure channel tokens. In the future, Beebotte might also support more flexible limited tokens that support writing to more than one channel. The examples in this article use an unencrypted connection, so make sure at least your Wi-Fi connection is encrypted (using WPA or WPA2). If you are working with particularly sensitive information, be sure to consider using SSL/TLS for the connection. Due to limited microcontroller speeds, running SSL/TLS directly on the microcontroller does not seem feasible, so this would need external cryptographic hardware or support on the Wi-Fi / Ethernet shield that is being used. At the time of writing, there does not seem to be any shield that directly supports this but it seems that at least the ESP2866-based shields and the Arduino Yún could be made to support this and the upcoming Arduino Wi-Fi shield 101 might support it as well (but this is out of the scope for this article). Access protocols To store new data and access the existing data over the Internet, a few different access methods are supported by Beebotte: HTTP / REST: The hypertext transfer protocol (HTTP) is the protocol that powers the web. Originally, it was used to let a web browser request a web page from a server, but now HTTP is also commonly used to let all kinds of devices send and request arbitrary data (instead of webpages) to and from servers as well. In this case, the server is commonly said to export an HTTP or REST (Relational state transfer) API.HTTP APIs are convenient, since HTTP is a very widespread protocol and HTTP libraries are available for most programming languages and environments. WebSockets: A downside of HTTP is that it is not very convenient for sending events from the server to the client. A server can only send data to the client after the client sends a request, which means the client must poll for new events continuously.To overcome this, the WebSockets standard was created. WebSockets is a protocol on top of HTTP that keeps a connection (socket) open indefinitely, ready for the server to send new data whenever it wants to, instead of having to wait for the client to request new data. MQTT: The message queuing telemetry transport protocol (MQTT) is a so-called publish/subscribe (pubsub) protocol. The idea is that multiple devices can connect to a central server and each can publish a message to a given topic and also subscribe to any number of topics. Whenever a message is published to a topic, it is automatically forwarded to all the devices that have subscribed to the same topic.MQTT, like WebSockets, keeps a connection open continuously so that both the client and server can send data at any time, making this protocol especially suitable for real-time data and events. MQTT can not be used to access historical data, though. A lot of alternative platforms only support the HTTP access protocol, which works fine to push and access the data and would be suitable for the examples in this chapter but is less suitable to control your network from the Internet, as used in the next chapter. To prepare for this, the examples in this chapter will already use the MQTT protocol, which supports both the use cases efficiently. Sending your data to Beebotte Now that you have learned about some important Beebotte concepts, you are ready to send your collected sensor data to Beebotte. First, you will prepare Beebotte and your Arduino to connect to each other. Then, you will write a sketch for your coordinator to send the data. Finally, you will see how to access and visualize the stored data. Preparing Beebotte Before you can start sending data to Beebotte, you will have to prepare the proper channels and resources to store the data. This example uses two channels: "Livingroom" and "Study", referring to the rooms where the sensors have been placed. You should, of course, use names that reflect your setup and adapt things if you have more or fewer sensors. The first step is to register an account on beebotte.com. Once you have done this, you can access your Control Panel, which will initially show you an empty list of channels: You can create a new channel by clicking the Create New channel button. In the resulting window, you can fill in a name and description for the channel and define the resources. This should look something like this: After creating a channel for every sensor node that you have built, you have prepared Beebotte to receive all the sensor data. The next step is to modify the coordinator sketch to actually send the data. Connecting your Arduino to the Internet In order to let your coordinator send its data to Beebotte, it must be connected to the Internet somehow. There are plenty of shields out there that add wired Ethernet or wireless Wi-Fi connectivity to your Arduino. Wi-Fi shields are typically a lot more expensive than the Ethernet ones but the recently introduced ESP2866 cheap Wi-Fi chipset will likely change that. (However, at the time of writing, no ready-to-use Arduino shield was available yet.) As the code to connect your Arduino to the Internet will be significantly different for each shield, every part of the code will not be discussed in this article. Instead, we will focus on the code that connects to the MQTT server and publishes the data, assuming that the Internet connection is already set up. In the code bundle for this article, two complete examples are available for use with the Arduino Ethernet shield and Adafruit CC3000 shield. These examples should be usable as a starting point to use the other hardware as well. Some things to keep in mind when selecting your hardware have been listed, as follows: Check carefully for conflicting pins. For example, the Adafruit CC3000 Wi-Fi shield uses pins 2 and 3[t1] to communicate with the shield but you might be using these pins to talk to the XBee module as well (particularly, when using the SparkFun XBee shield). The libraries for the various Wi-Fi shields take up a lot of code space on the Arduino. For example, using the SparkFun or Adafruit CC3000 library along with the Adafruit MQTT library fills up most of the available code space on an Arduino Uno. The Ethernet library is a bit (but not much) smaller. It is not always easy to keep a reliable Wi-Fi connection. In theory, this is a matter of simply reconnecting when the Wi-Fi connection is failing but in practice it can be tricky to implement this completely reliably. Again, this is easier with wired Ethernet as it does not have the same disconnection issues as Wi-Fi. If you use a different hardware than recommended (including the recently announced Arduino Ethernet shield 2), you will likely need a different Arduino library and need to make changes to the example code provided. Writing the sketch To send the data to Beebotte, the Coordinator.ino sketch from the previous chapter needs to be modified. As noted before, only the MQTT code will be shown here. However, the code to establish the Internet connection is included in the full examples in the code bundle (in the Coordinator_Ethernet.ino and Coordinator_CC3000.ino sketches). This example uses the "Adafruit MQTT library" for the MQTT protocol, which can be installed through the Arduino library manager or can be found here: https://github.com/adafruit/Adafruit_MQTT_Library. Depending on the Internet shield, you might need more libraries as well (see the comments in the example sketches). Do not forget to add the appropriate includes for the libraries that you are using. For the MQTT library, this is: #include <Adafruit_MQTT.h> #include <Adafruit_MQTT_Client.h> To set up the MQTT library, you first need to define some settings: const char MQTT_SERVER[] PROGMEM = "mqtt.beebotte.com"; const char MQTT_CLIENTID[] PROGMEM = ""; const char MQTT_USERNAME[] PROGMEM = "your_key_here"; const char MQTT_PASSWORD[] PROGMEM = ""; const int MQTT_PORT = 1883; This defines the connection settings to use for the MQTT connection. These are appropriate for Beebotte; if you are using some other platform, check its documentation for the appropriate settings. Note that here the username must be set to your account's secret key, for example: const char MQTT_USERNAME[] PROGMEM = "840f626930a07c87aa315e27b22448468844edcad03fe34f551ac747533f544f"; If you are using a channel token, it must be set to the token prefixed with "token:", such as: const char MQTT_USERNAME[] PROGMEM = "token:1438006626319_UNJoxdmKBoMFIPt7"; The password is unused and can be empty, just like the client identifier. The port is just the default MQTT port number, 1883. Note that all the string variables are marked with the PROGMEM keyword. This tells the compiler to store the string variables in program memory, just like the F() macro you have seen before does (which also uses the PROGMEM keyword underwater). However, the F() macro can only be used in the functions, which is why these variables use the PROGMEM keyword directly. This also means that the extra checking offered by the F() macro is not available, so be careful not to mix up the normal and PROGMEM strings here as this will not result in any compiler error and instead things will be broken when you run the code. Using the configuration constants defined earlier, you can now define the main mqtt object: Adafruit_MQTT_Client mqtt(&client, MQTT_SERVER, MQTT_PORT, MQTT_CLIENTID, MQTT_USERNAME, MQTT_PASSWORD); There are a few different flavors of this object. For example, there are flavors optimized for the specific hardware and corresponding libraries, and there is also a generic flavor that works with any hardware whose library exposes a generic Client object (like most libraries currently do). The latter flavor, Adafruit_MQTT_Client, is used in this example and should be fine. The &client [t2] part of this line refers to a previously created Client object (not shown here as it depends on the Internet shield used), which is used by the MQTT library to set up the MQTT connection. To actually connect to the MQTT server, a function called connect() will be defined. This function is called to connect once at startup and to reconnect every time when the publishing of some data fails. In the CC3300 version, this function associates to the WiFi access point and then sets up the connection to the MQTT server. On the Ethernet version, where the network is always available after initial initialization, the connect() function only sets up the MQTT connection. The latter version is shown as follows: void connect() { client.stop(); // Ensure any old connection is closed uint8_t ret = mqtt.connect(); if (ret == 0) DebugSerial.println(F("MQTT connected")); else DebugSerial.println(mqtt.connectErrorString(ret)); } This calls mqtt.connect() to connect to the MQTT server and writes a debug message to report the success or failure. Note that mqtt.connect() returns a number as an error code (with 0 meaning OK), which is translated to a human readable error message using the mqtt.connectErrorString() function. Now, to actually publish a single value, there is the publish() function: void publish(const __FlashStringHelper *resource, float value) { // Use JSON to wrap the data, so Beebotte will remember the data // (instead of just publishing it to whoever is currently listening). String data; data += "{"data": "; data += value; data += ", "write": true}"; DebugSerial.print(F("Publishing ")); DebugSerial.print(data); DebugSerial.print(F( " to ")); DebugSerial.println(resource); // Publish data and try to reconnect when publishing data fails if (!mqtt.publish(resource, data.c_str())) { DebugSerial.println(F("Failed to publish, trying reconnect...")); connect(); if (!mqtt.publish(resource, data.c_str())) DebugSerial.println(F("Still failed to publish data")); } } This function takes two parameters: the name of the resource to publish to and the value to publish. Note the type of the resource parameter: __FlashStringHelper* is similar to the more common char* string type but indicates that the string is stored in the program memory instead of RAM. This is also the type returned by the F() macro that you have seen before. Just like the MQTT server configuration values that used the PROGMEM keyword before, the MQTT library also expects the MQTT topic names to be stored in the program memory. The actual value is sent using the JSON (JavaScript object notation) format. For example, for a temperature of 20 degrees, it constructs {"data": 20.00, "write": true}. This format allows, in addition to transmitting the value, indicating that Beebotte should store the value so that it can be retrieved later. If the write value is false, or not present, Beebotte will only forward the value to any other devices currently subscribed to the appropriate topic without saving it for later. This example uses some quick and dirty string concatenation to generate the JSON. If you want something more robust and elegant, have a look at the ArduinoJson library at https://github.com/bblanchon/ArduinoJson. If publishing the data fails, it is likely that the WiFi or MQTT connection has failed and so it attempts to reconnect and publish the data once more. As before, there is a processRxPacket() function that gets called when a radio packet is received through the XBee module: void processRxPacket(ZBRxResponse& rx, uintptr_t) { Buffer b(rx.getData(), rx.getDataLength()); uint8_t type = b.remove<uint8_t>(); XBeeAddress64 addr = rx.getRemoteAddress64(); if (addr == 0x0013A20040DADEE0 && type == 1 && b.len() == 8) { publish(F("Livingroom/Temperature"), b.remove<float>()); publish(F("Livingroom/Humidity"), b.remove<float>()); return; } if (addr == 0x0013A20040E2C832 && type == 1 && b.len() == 8) { publish(F("Study/Temperature"), b.remove<float>()); publish(F("Study/Humidity"), b.remove<float>()); return; } DebugSerial.println(F("Unknown or invalid packet")); printResponse(rx, DebugSerial); } Instead of simply printing the packet contents as before, it figures out who the sender of the packet is and which Beebotte resource corresponds to it and calls the publish() function that was defined earlier. As you can see, the Beebotte resources are identified using "Channel/Resource", resulting in a unique identifier for each resource (which is later used in the MQTT message as the topic identifier). Note that the F() macro is used for the resource names to store them in program memory as this is what publish() and the MQTT library expect. If you run the resulting sketch and everything connects correctly, the coordinator will forward any sensor values that it receives to the Beebotte server. If you wait for (at most) five minutes to pass (or reset the sensor Arduino to have it send a reading right away) and then go to the appropriate channel in your Beebotte control panel, it should look something like this: Visualizing your data To easily allow the visualizing of your data, Beebotte supports dashboards. A dashboard is essentially a web page where you can add graphs, gauges, tables, buttons, and so on (collectively called widgets). These widgets can then display or control the data in one or more previously defined resources. To create such a dashboard, head over to the My Dashboards section of your control panel and click Create Dashboard to start building one. Once you set a name for the dashboard, you can start adding widgets to it. To display the temperature and humidity for all the sensors that you are using, you could use the Multi-line Chart widget. As the temperature and humidity values will be fairly far apart, it makes sense to put them each in a separate chart. Adding the temperature chart could look like this: If you also add a chart for the humidity, it should look like this: Here, only the living room sensor has been powered on, so no data is shown for the study yet. Also, to make the graphs a bit more interesting, some warm and humid air was breathed onto the sensor, causing the big spike in both the charts. There are plenty of other widget types that will prove useful to you. The Beebotte documentation provides information on the supported types, but you are encouraged to just play with the widgets a bit to see what they can add to your project. In the next chapter, you will see how to use the control widgets, which allow sending events and data back to the coordinator to control it. Accessing your data You have been accessing your data through a Beebotte dashboard so far. However, when these dashboards are not powerful enough for your needs or you want to access your data from an existing application, you can also access the recent and historical data through the Beebotte's HTTP or WebSocket API's. This gives you full control over the processing and display of the data without being limited in any way by what Beebotte offers in its dashboards. As creating a custom (web) application is out of the scope of this article, the HTTP and WebSocket API will not be discussed in detail. Instead, you should be able to find extensive documentation on this API on the Beebotte site at https://beebotte.com/overview. Summary In this article we learnt some of the ways to store the collected sensor data and visualize the data using convenient graphs. We also learnt as to how to save our data on the Cloud. Resources for Article: Further resources on this subject: TV Set Constant Volume Controller[article] Internet Connected Smart Water Meter[article] Getting Started with Arduino [article]

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3778

article-image-tv-set-constant-volume-controller

Packt

25 Sep 2015

19 min read

TV Set Constant Volume Controller

Packt

25 Sep 2015

19 min read

In this article by Fabizio Boco, author of Arduino iOS Bluprints, we learn how to control a TV set volume using Arduino and iOS. I don't watch TV much, but when I do, I usually completely relax and fall asleep. I know that TV is not meant for putting you off to sleep, but it does this to me. Unfortunately, commercials are transmitted at a very high volume and they wake me up. How can I relax if commercials wake me up every five minutes? Can you believe it? During one of my naps between two commercials, I came up with a solution based on iOS and Arduino. It's nothing complex. An iOS device listens to the TV set's audio, and when the audio level becomes higher than a preset threshold, the iOS device sends a message (via Bluetooth) to Arduino, which controls the TV set volume, emulating the traditional IR remote control. Exactly the same happens when the volume drops below another threshold. The final result is that the TV set volume is almost constant, independent of what is on the air. This helps me sleep longer! The techniques that you are going to learn in this article are useful in many different ways. You can use an IR remote control for any purpose, or you can control many different devices, such as a CD/DVD player, a stereo set, Apple TV, a projector, and so on, directly from an Arduino and iOS device. As always, it is up to your imagination. (For more resources related to this topic, see here.) Constant Volume Controller requirements Our aim is to design an Arduino-based device, which can make the TV set's volume almost constant by emulating the traditional remote controller, and an iOS application, which monitors the TV and decides when to decrease or increase the TV set's volume. Hardware Most TV sets can be controlled by an IR remote controller, which sends signals to control the volume, change the channel, and control all the other TV set functions. IR remote controllers use a carrier signal (usually at 38 KHz) that is easy to isolate from noise and disturbances. The carrier signal is turned on and off by following different rules (encoding) in order to transmit the 0 and 1 digital values. The IR receiver removes the carrier signal (with a pass low filter) and decodes the remaining signal by returning a clear sequence of 0 and 1. The IR remote control theory You can find more information about the IR remote control at http://bit.ly/1UjhsIY. Our circuit will emulate the IR remote controller by using an IR LED, which will send specific signals that can be interpreted by our TV set. On the other hand, we can receive an IR signal with a phototransistor and decode it into an understandable sequence of numbers by designing a demodulator and a decoder. Nowadays, electronics is very simple; an IR receiver module (Vishay 4938) will manage the complexity of signal demodulation, noise cancellation, triggering, and decoding. It can be directly connected to Arduino, making everything very easy. In the project in this article, we need an IR receiver to discover the coding rules that are used by our own IR remote controller (and the TV set). Additional electronic components In this project, we need the following additional components: IR LED Vishay TSAL6100 IR Receiver module Vishay TSOP 4838 Resistor 100Ω Resistor 680Ω Electrolytic capacitor 0.1μF Electronic circuit The following picture shows the electrical diagram of the circuit that we need for the project: The IR receiver will be used only to capture the TV set's remote controller signals so that our circuit can emulate them. However, an IR LED is constantly used to send commands to the TV set. The other two LEDs will show when Arduino increases or decreases the volume. They are optional and can be omitted. As usual, the Bluetooth device is used to receive commands from the iOS device. Powering the IR LED in the current limits of Arduino From the datasheet of the TSAL6100, we know that the forward voltage is 1.35V. The voltage drop along R1 is then 5-1.35 = 3.65V, and the current provided by Arduino to power the LED is about 3.65/680=5.3 mA. The maximum current that is allowed for each PIN is 40 mA (the recommended value is 20 mA). So, we are within the limits. In case your TV set is far from the LED, you may need to reduce the R1 resistor in order to get more current (and the IR light). Use a new value of R1 in the previous calculations to check whether you are within the Arduino limits. For more information about the Arduino PIN current, check out http://bit.ly/1JosGac. The following diagram shows how to mount the circuit on a breadboard: Arduino code The entire code of this project can be downloaded from https://www.packtpub.com/books/content/support. To understand better the explanations in the following paragraphs, open the downloaded code while reading them. In this project, we are going to use the IR remote library, which helps us code and decode IR signals. The library can be downloaded from http://bit.ly/1Isd8Ay and installed by using the following procedure: Navigate to the release page of http://bit.ly/1Isd8Ay in order to get the latest release and download the IRremote.zip file. Unzip the file whatever you like. Open the Finder and then the Applications folder (Shift + Control + A). Locate the Arduino application. Right-click on it and select Show Package Contents. Locate the Java folder and then libraries. Copy the IRremote folder (unzipped in step 2) into the libraries folder. Restart Arduino if you have it running. In this project, we need the following two Arduino programs: One is used to acquire the codes that your IR remote controller sends to increase and decrease the volume The other is the main program that Arduino has to run to automatically control the TV set volume Let's start with the code that is used to acquire the IR remote controller codes. Decoder setup code In this section, we will be referring to the downloaded Decode.ino program that is used to discover the codes that are used by your remote controller. Since the setup code is quite simple, it doesn't require a detailed explanation; it just initializes the library to receive and decode messages. Decoder main program In this section, we will be referring to the downloaded Decode.ino program; the main code receives signals from the TV remote controller and dumps the appropriate code, which will be included in the main program to emulate the remote controller itself. Once the program is run, if you press any button on the remote controller, the console will show the following: For IR Scope: +4500 -4350 … For Arduino sketch: unsigned int raw[68] = {4500,4350,600,1650,600,1600,600,1600,…}; The second row is what we need. Please refer to the Testing and tuning section for a detailed description of how to use this data. Now, we will take a look at the main code that will be running on Arduino all the time. Setup code In this section, we will be referring to the Arduino_VolumeController.ino program. The setup function initializes the nRF8001 board and configures the pins for the optional monitoring LEDs. Main program The loop function just calls the polACI function to allow the correct management of incoming messages from the nRF8001 board. The program accepts the following two messages from the iOS device (refer to the rxCallback function): D to decrease the volume I to increase the volume The following two functions perform the actual increasing and decreasing of volume by sending the two up and down buffers through the IR LED: void volumeUp() { irsend.sendRaw(up, VOLUME_UP_BUFFER_LEN, 38); delay(20); } void volumeDown() { irsend.sendRaw(down, VOLUME_DOWN_BUFFER_LEN, 38); delay(20); irsend.sendRaw(down, VOLUME_DOWN_BUFFER_LEN, 38); delay(20); } The up and down buffers, VOLUME_UP_BUFFER_LEN and VOLUME_DOWN_BUFFER_LEN, are prepared with the help of the Decode.ino program (see the Testing and Tuning section). iOS code In this article, we are going to look at the iOS application that monitors the TV set volume and sends the volume down or volume up commands to the Arduino board in order to maintain the volume at the desired value. The full code of this project can be downloaded from https://www.packtpub.com/books/content/support. To understand better the explanations in the following paragraphs, open the downloaded code while reading them. Create the Xcode project We will create a new project as we already did previously. The following are the steps that you need to follow: The following are the parameters for the new project: Project Type: Tabbed application Product Name: VolumeController Language: Objective-C Devices: Universal To set a capability for this project, perform the following steps: Select the project in the left pane of Xcode. Select Capabilities in the right pane. Turn on the Background Modes option and select Audio and AirPlay (refer to the following picture). This allows an iOS device to listen to audio signals too when the iOS device screen goes off or the app goes in the background: Since the structure of this project is very close to the Pet Door Locker, we can reuse a part of the user interface and the code by performing the following steps: Select FirstViewController.h and FirstViewController.m, right-click on them, click on Delete, and select Move to Trash. With the same procedure, deleteSecondViewControllerand Main.storyboard. Open the PetDoorLocker project in Xcode. Select the following files and drag and drop them to this project (refer to the following picture). BLEConnectionViewController.h BLEConnectionViewController.m Main.storyboardEnsure that Copy items if needed is selected and then click on Finish. Copy the icon that was used for the BLEConnectionViewController view controller. Create a new View Controller class and name it VolumeControllerViewController. Open the Main.storyboard and locate the main View Controller. Delete all the graphical components. Open the Identity Inspector and change the Class to VolumeControllerViewController. Now, we are ready to create what we need for the new application. Design the user interface for VolumeControllerViewController This view controller is the main view controller of the application and contains just the following components: The switch that turns on and off the volume control The slider that sets the desired volume of the TV set Once you have added the components and their layout constraints, you will end up with something that looks like the following screenshot: Once the GUI components are linked with the code of the view controller, we end with the following code: @interface VolumeControllerViewController () @property (strong, nonatomic) IBOutlet UISlider *volumeSlider; @end and with: - (IBAction)switchChanged:(UISwitch *)sender { … } - (IBAction)volumeChanged:(UISlider *)sender { … } Writing code for BLEConnectionViewController Since we copied this View Controller from the Pet Door Locker project, we don't need to change it apart from replacing the key, which was used to store the peripheral UUID, from PetDoorLockerDevice to VolumeControllerDevice. We saved some work! Now, we are ready to work on the VolumeControllerViewController, which is much more interesting. Writing code for VolumeControllerViewController This is the main part of the application; almost everything happens here. We need some properties, as follows: @interface VolumeControllerViewController () @property (strong, nonatomic) IBOutlet UISlider *volumeSlider; @property (strong, nonatomic) CBCentralManager *centralManager; @property (strong, nonatomic) CBPeripheral *arduinoDevice; @property (strong, nonatomic) CBCharacteristic *sendCharacteristic; @property (nonatomic,strong) AVAudioEngine *audioEngine; @property float actualVolumeDb; @property float desiredVolumeDb; @property float desiredVolumeMinDb; @property float desiredVolumeMaxDb; @property NSUInteger increaseVolumeDelay; @end Some are used to manage the Bluetooth communication and don't need much explanation. The audioEngine is the instance of AVAudioEngine, which allows us to transform the audio signal captured by the iOS device microphone in numeric samples. By analyzing these samples, we can obtain the power of the signal that is directly related to the TV set's volume (the higher the volume, the greater the signal power). Analog-to-digital conversion The operation of transforming an analog signal into a digital sequence of numbers, which represent the amplitude of the signal itself at different times, is called analog-to-digital conversion. Arduino analog inputs perform exactly the same operation. Together with the digital-to-analog conversion, it is a basic operation of digital signal processing and storing music in our devices and playing it with a reasonable quality. For more details, visit http://bit.ly/1N1QyXp. The actualVolumeDb property stores the actual volume of the signal measured in dB (short for decibel). Decibel (dB) The decibel (dB) is a logarithmic unit that expresses the ratio between two values of a physical quantity. Referring to the power of a signal, its value in decibel is calculated with the following formula: Here, P is the power of the signal and P0[PRK1] is a reference power. You can find out more about decibel at http://bit.ly/1LZQM0m. We have to point out that if P < P0[PRK2] , the value of PdB[PRK3] if lower of zero. So, decibel values are usually negative values, and 0dB indicates the maximum power of the signal. The desiredVolumeDb property stores the desired volume measured in dB, and the user controls this value through the volume slider in the main tab of the app; desiredVolumeMinDb and desiredVolumeMaxDb are derived from the desiredVolumeDb. The most significant part of the code is in the viewDidLoad method (refer to the downloaded code). First, we instantiate the AudioEngine and get the default input node, which is the microphone, as follows: _audioEngine = [[AVAudioEngine alloc] init]; AVAudioInputNode *input = [_audioEngine inputNode]; The AVAudioEngine is a very powerful class, which allows digital audio signal processing. We are just going to scratch its capabilities. AVAudioEngine You can find out more about AVAudioEngine by visiting http://apple.co/1kExe35 (AVAudioEngine in practice) and http://apple.co/1WYG6Tp. The AVAudioEngine and other functions that we are going to use require that we add the following imports: #import <AVFoundation/AVFoundation.h> #import <Accelerate/Accelerate.h> By installing an audio tap on the bus for our input node, we can get the numeric representation of the signal that the iOS device is listening to, as follows: [input installTapOnBus:0 bufferSize:8192 format:[input inputFormatForBus:0] block:^(AVAudioPCMBuffer* buffer, AVAudioTime* when) { … … }]; As soon as a new buffer of data is available, the code block is called and the data can be processed. Now, we can take a look at the code that transforms the audio data samples into actual commands to control the TV set: for (UInt32 i = 0; i < buffer.audioBufferList->mNumberBuffers; i++) { Float32 *data = buffer.audioBufferList->mBuffers[i].mData; UInt32 numFrames = buffer.audioBufferList->mBuffers[i].mDataByteSize / sizeof(Float32); // Squares all the data values vDSP_vsq(data, 1, data, 1, numFrames*buffer.audioBufferList->mNumberBuffers); // Mean value of the squared data values: power of the signal float meanVal = 0.0; vDSP_meanv(data, 1, &meanVal, numFrames*buffer.audioBufferList->mNumberBuffers); // Signal power in Decibel float meanValDb = 10 * log10(meanVal); _actualVolumeDb = _actualVolumeDb + 0.2*(meanValDb - _actualVolumeDb); if (fabsf(_actualVolumeDb) < _desiredVolumeMinDb && _centralManager.state == CBCentralManagerStatePoweredOn && _sendCharacteristic != nil) { //printf("Decrease volumen"); NSData* data=[@"D" dataUsingEncoding:NSUTF8StringEncoding]; [_arduinoDevice writeValue:data forCharacteristic:_sendCharacteristic type:CBCharacteristicWriteWithoutResponse]; _increaseVolumeDelay = 0; } if (fabsf(_actualVolumeDb) > _desiredVolumeMaxDb && _centralManager.state == CBCentralManagerStatePoweredOn && _sendCharacteristic != nil) { _increaseVolumeDelay++; } if (_increaseVolumeDelay > 10) { //printf("Increase volumen"); _increaseVolumeDelay = 0; NSData* data=[@"I" dataUsingEncoding:NSUTF8StringEncoding]; [_arduinoDevice writeValue:data forCharacteristic:_sendCharacteristic type:CBCharacteristicWriteWithoutResponse]; } } In our case, the for cycle is executed just once because we have just one buffer and we are using only one channel. The power of a signal, represented by N samples, can be calculated by using the following formula: Here, v is the value of the nth signal sample. Because the power calculation has to performed in real time, we are going to use the following functions, which are provided by the Accelerated Framework: vDSP_vsq: This function calculates the square of each input vector element vDSP_meanv: This function calculates the mean value of the input vector elements The Accelerated Framework The Accelerated Framework is an essential tool that is used for digital signal processing. It saves you time in implementing the most used algorithms and mostly providing implementation of algorithms that are optimized in terms of memory footprint and performance. More information on the Accelerated Framework can be found at http://apple.co/1PYIKE8 and http://apple.co/1JCJWYh. Eventually, the signal power is stored in _actualVolumeDb. When the modulus of _actualVolumeDb is lower than the _desiredVolumeMinDb, the TV set's volume is too high, and we need to send a message to Arduino to reduce it. Don't forget that _actualVolumeDb is a negative number; the modulus decreases this number when the TV set's volume increases. Conversely, when the TV set's volume decreases, the _actualVolumeDb modulus increases, and when it gets higher than _desiredVolumeMaxDb, we need to send a message to Arduino to increase the TV set's volume. During pauses in dialogues, the power of the signal tends to decrease even if the volume of the speech is not changed. Without any adjustment, the increasing and decreasing messages are continuously sent to the TV set during dialogues. To avoid this misbehavior, we send the volume increase message. Only after this does the signal power stay over the threshold for some time (when _increaseVolumeDelay is greater than 10). We can take a look at the other view controller methods that are not complex. When the view belonging at the view controller appears, the following method is called: -(void)viewDidAppear:(BOOL)animated { [super viewDidAppear:animated]; NSError* error = nil; [self connect]; _actualVolumeDb = 0; [_audioEngine startAndReturnError:&error]; if (error) { NSLog(@"Error %@",[error description]); } } In this function, we connect to the Arduino board and start the audio engine in order to start listening to the TV set. When the view disappears from the screen, the viewDidDisappearmethod is called, and we disconnect from the Arduino and stop the audio engine, as follows: -(void)viewDidDisappear:(BOOL)animated { [self viewDidDisappear:animated]; [self disconnect]; [_audioEngine pause]; } The method that is called when the switch is operated (switchChanged) is pretty simple: - (IBAction)switchChanged:(UISwitch *)sender { NSError* error = nil; if (sender.on) { [_audioEngine startAndReturnError:&error]; if (error) { NSLog(@"Error %@",[error description]); } _volumeSlider.enabled = YES; } else { [_audioEngine stop]; _volumeSlider.enabled = NO; } } The method that is called when the volume slider changes is as follows: - (IBAction)volumeChanged:(UISlider *)sender { _desiredVolumeDb = 50.*(1-sender.value); _desiredVolumeMaxDb = _desiredVolumeDb + 2; _desiredVolumeMinDb = _desiredVolumeDb - 3; } We just set the desired volume and the lower and upper thresholds. The other methods that are used to manage the Bluetooth connection and data transfer don't require any explanation, because they are exactly like in the previous projects. Testing and tuning We are now ready to test our new amazing system and spend more and more time watching TV (or taking more and more naps!) Let's perform the following procedure: Load the Decoder.ino sketch and open the Arduino IDE console. Point your TV remote controller to the TSOP4838 receiver and press the button that increases the volume. You should see something like the following appearing on the console: For IR Scope: +4500 -4350 … For Arduino sketch: unsigned int raw[68] = {4500,4350,600,1650,600,1600,600,1600,…}; Copy all the values between the curly braces. Open the Arduino_VolumeController.ino and paste the values for the following: unsigned int up[68] = {9000, 4450, …..,}; Check whether the length of the two vectors (68 in the example) is the same and modify it, if needed. Point your TV remote controller to the TSOP4838 receiver and press the button that decreases the volume. Copy the values and paste them for: unsigned int down[68] = {9000, 4400, ….,}; Check whether the length of the two vectors (68 in the example) is the same and modify it, if needed. Upload the Arduino_VolumeController.ino to Arduino and point the IR LED towards the TV set. Open the iOS application, scan for the nRF8001, and then go to the main tab. Tap on connect and then set the desired volume by touching the slider. Now, you should see the blue LED and the green LED flashing. The TV set's volume should stabilize to the desired value. To check whether everything is properly working, increase the volume of the TV set by using the remote control; you should immediately see the blue LED flashing and the volume getting lower to the preset value. Similarly, by decreasing the volume with the remote control, you should see the green LED flashing and the TV set's volume increasing. Take a nap, and the commercials will not wake you up! How to go further The following are some improvements that can be implemented in this project: Changing channels and controlling other TV set functions. Catching handclaps to turn on or off the TV set. Adding a button to mute the TV set. Muting the TV set on receiving a phone call. Anyway, you can use the IR techniques that you have learned for many other purposes. Take a look at the other functions provided by the IRremote library to learn the other provided options. You can find all the available functions in the IRremote.h that is stored in the IRremote library folder. On the iOS side, try to experiment with the AV Audio Engine and the Accelerate Framework that is used to process signals. Summary This artcle focused on an easy but useful project and taught you how to use IR to transmit and receive data to and from Arduino. There are many different applications of the basic circuits and programs that you learned here. On the iOS platform, you learned the very basics of capturing sounds from the device microphone and the DSP (digital signal processing). This allows you to leverage the processing capabilities of the iOS platform to expand your Arduino projects. Resources for Article: Further resources on this subject: Internet Connected Smart Water Meter[article] Getting Started with Arduino[article] Programmable DC Motor Controller with an LCD [article]

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3396

article-image-internet-connected-smart-water-meter

Packt

22 Sep 2015

13 min read

Internet Connected Smart Water Meter

Packt

22 Sep 2015

13 min read

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0
6934

Packt

18 Aug 2015

10 min read

The ROS Filesystem levels

Packt

18 Aug 2015

10 min read

In this article by Enrique Fernández, Luis Sánchez Crespo, Anil Mahtani, and Aaron Martinez, authors of the book Learning ROS for Robotics Programming - Second Edition, you will see the different levels of filesystems in ROS. (For more resources related to this topic, see here.) When you start to use or develop projects with ROS, you will see that although this concept can sound strange in the beginning, you will become familiar with it with time. Similar to an operating system, an ROS program is divided into folders, and these folders have files that describe their functionalities: Packages: Packages form the atomic level of ROS. A package has the minimum structure and content to create a program within ROS. It may have ROS runtime processes (nodes), configuration files, and so on. Package manifests: Package manifests provide information about a package, licenses, dependencies, compilation flags, and so on. A package manifest is managed with a file called package.xml. Metapackages: When you want to aggregate several packages in a group, you will use metapackages. In ROS Fuerte, this form for ordering packages was called Stacks. To maintain the simplicity of ROS, the stacks were removed, and now, metapackages make up this function. In ROS, there exist a lot of these metapackages; one of them is the navigation stack. Metapackage manifests: Metapackage manifests (package.xml) are similar to a normal package but with an export tag in XML. It also has certain restrictions in its structure. Message (msg) types: A message is the information that a process sends to other processes. ROS has a lot of standard types of messages. Message descriptions are stored in my_package/msg/MyMessageType.msg. Service (srv) types: Service descriptions, stored in my_package/srv/MyServiceType.srv, define the request and response data structures for services provided by each process in ROS. The workspace Basically, the workspace is a folder where we have packages, edit the source files or compile packages. It is useful when you want to compile various packages at the same time and is a good place to have all our developments localized. A typical workspace is shown in the following screenshot. Each folder is a different space with a different role: The Source space: In the Source space (the src folder), you put your packages, projects, clone packages, and so on. One of the most important files in this space is CMakeLists.txt. The src folder has this file because it is invoked by CMake when you configure the packages in the workspace. This file is created with the catkin_init_workspace command. The Build space: In the build folder, CMake and catkin keep the cache information, configuration, and other intermediate files for our packages and projects. The Development (devel) space: The devel folder is used to keep the compiled programs. This is used to test the programs without the installation step. Once the programs are tested, you can install or export the package to share with other developers. You have two options with regard to building packages with catkin. The first one is to use the standard CMake workflow. With this, you can compile one package at a time, as shown in the following commands: $ cmake packageToBuild/ $ make If you want to compile all your packages, you can use the catkin_make command line, as shown in the following commands: $ cd workspace $ catkin_make Both commands build the executables in the build space directory configured in ROS. Another interesting feature of ROS are its overlays. When you are working with a package of ROS, for example, Turtlesim, you can do it with the installed version, or you can download the source file and compile it to use your modified version. ROS permits you to use your version of this package instead of the installed version. This is very useful information if you are working on an upgrade of an installed package. Packages Usually, when we talk about packages, we refer to a typical structure of files and folders. This structure looks as follows: include/package_name/: This directory includes the headers of the libraries that you would need. msg/: If you develop nonstandard messages, put them here. scripts/: These are executable scripts that can be in Bash, Python, or any other scripting language. src/: This is where the source files of your programs are present. You can create a folder for nodes and nodelets or organize it as you want. srv/: This represents the service (srv) types. CMakeLists.txt: This is the CMake build file. package.xml: This is the package manifest. To create, modify, or work with packages, ROS gives us tools for assistance, some of which are as follows: rospack: This command is used to get information or find packages in the system. catkin_create_pkg: This command is used when you want to create a new package. catkin_make: This command is used to compile a workspace. rosdep: This command installs the system dependencies of a package. rqt_dep: This command is used to see the package dependencies as a graph. If you want to see the package dependencies as a graph, you will find a plugin called package graph in rqt. Select a package and see the dependencies. To move between packages and their folders and files, ROS gives us a very useful package called rosbash, which provides commands that are very similar to Linux commands. The following are a few examples: roscd: This command helps us change the directory. This is similar to the cd command in Linux. rosed: This command is used to edit a file. roscp: This command is used to copy a file from a package. rosd: This command lists the directories of a package. rosls: This command lists the files from a package. This is similar to the ls command in Linux. The package.xml file must be in a package, and it is used to specify information about the package. If you find this file inside a folder, probably this folder is a package or a metapackage. If you open the package.xml file, you will see information about the name of the package, dependencies, and so on. All of this is to make the installation and the distribution of these packages easy. Two typical tags that are used in the package.xml file are <build_depend> and <run _depend>. The <build_depend> tag shows what packages must be installed before installing the current package. This is because the new package might use a functionality of another package. Metapackages As we have shown earlier, metapackages are special packages with only one file inside; this file is package.xml. This package does not have other files, such as code, includes, and so on. Metapackages are used to refer to others packages that are normally grouped following a feature-like functionality, for example, navigation stack, ros_tutorials, and so on. You can convert your stacks and packages from ROS Fuerte to Hydro and catkin using certain rules for migration. These rules can be found at http://wiki.ros.org/catkin/migrating_from_rosbuild. In the following screenshot, you can see the content from the package.xml file in the ros_tutorials metapackage. You can see the <export> tag and the <run_depend> tag. These are necessary in the package manifest. If you want to locate the ros_tutorials metapackage, you can use the following command: $ rosstack find ros_tutorials The output will be a path, such as /opt/ros/hydro/share/ros_tutorials. To see the code inside, you can use the following command line: $ vim /opt/ros/hydro/share/ros_tutorials/package.xml Remember that Hydro uses metapackages, not stacks, but the rosstack find command line works to find metapackages. Messages ROS uses a simplified message description language to describe the data values that ROS nodes publish. With this description, ROS can generate the right source code for these types of messages in several programming languages. ROS has a lot of messages predefined, but if you develop a new message, it will be in the msg/ folder of your package. Inside that folder, certain files with the .msg extension define the messages. A message must have two principal parts: fields and constants. Fields define the type of data to be transmitted in the message, for example, int32, float32, and string, or new types that you have created earlier, such as type1 and type2. Constants define the name of the fields. An example of a msg file is as follows: int32 id float32 vel string name In ROS, you can find a lot of standard types to use in messages, as shown in the following table list: Primitive type Serialization C++ Python bool (1) unsigned 8-bit int uint8_t(2) bool int8 signed 8-bit int int8_t int uint8 unsigned 8-bit int uint8_t int(3) int16 signed 16-bit int int16_t int uint16 unsigned 16-bit int uint16_t int int32 signed 32-bit int int32_t int uint32 unsigned 32-bit int uint32_t int int64 signed 64-bit int int64_t long uint64 unsigned 64-bit int uint64_t long float32 32-bit IEEE float float float float64 64-bit IEEE float double float string ascii string (4) std::string string time secs/nsecs signed 32-bit ints ros::Time rospy.Time duration secs/nsecs signed 32-bit ints ros::Duration rospy.Duration A special type in ROS is the header type. This is used to add the time, frame, and so on. This permits you to have the messages numbered, to see who is sending the message, and to have more functions that are transparent for the user and that ROS is handling. The header type contains the following fields: uint32 seq time stamp string frame_id You can see the structure using the following command: $ rosmsg show std_msgs/Header Thanks to the header type, it is possible to record the timestamp and frame of what is happening with the robot. In ROS, there exist tools to work with messages. The rosmsg tool prints out the message definition information and can find the source files that use a message type. In the upcoming sections, we will see how to create messages with the right tools. Services ROS uses a simplified service description language to describe ROS service types. This builds directly upon the ROS msg format to enable request/response communication between nodes. Service descriptions are stored in .srv files in the srv/ subdirectory of a package. To call a service, you need to use the package name, along with the service name; for example, you will refer to the sample_package1/srv/sample1.srv file as sample_package1/sample1. There are tools that exist to perform functions with services. The rossrv tool prints out the service descriptions and packages that contain the .srv files, and finds source files that use a service type. If you want to create a service, ROS can help you with the service generator. These tools generate code from an initial specification of the service. You only need to add the gensrv() line to your CMakeLists.txt file. Summary In this article, we saw the different types of filesystems present in the ROS architecture. Resources for Article: Further resources on this subject: Building robots that can walk [article] Avoiding Obstacles Using Sensors [article] Managing Test Structure with Robot Framework [article]

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0
5390

Packt

08 Jul 2015

10 min read

Editors and IDEs

Packt

08 Jul 2015

10 min read

In this article by Daniel Blair, the author of the book Learning Banana Pi, you are going to learn about some editors and the programming languages that are available on the Pi and Linux. These tools will help you write the code that will interact with the hardware through GPIO and on the Pi as a server. (For more resources related to this topic, see here.) Choosing your editor There are many different integrated development environments (generally abbreviated as IDEs) to choose from on Linux. When working on the Banana Pi, you're limited to the software that will run on an ARM-based CPU. Hence, options such as Sublime Text are not available. Some options that you may be familiar with are available for general purpose code editing. Some tools are available for the command line, while others are GUI tools. So, depending on whether you have a monitor or not, you will want to choose an appropriate tool. The following screenshot shows some JavaScript being edited via nano on the command line: Command-line editors The command line is a powerful tool. If you master it, you will rarely need to leave it. There are several editors available for the command line. There has been an ongoing war between the users of two editors: GNU Emacs and Vim. There are many editors like nano (which is my preference), but the war tends to be between the two aforementioned editors. The Emacs editor This is my least favorite command-line editor (just my preference). Emacs is a GNU-flavored editor for the command line. It is often installed by default, but you can easily install it if it is missing by running a quick command, as follows: sudo apt-get install emacs Now, you can edit a file via the CLI by using the following code: emacs <command-line arguments> <your file> The preceding code will open the file in Emacs for you to edit. You can also use this to create new files. You can save and close the editor with a couple of key combinations: Ctrl + X and Ctrl + S Ctrl + X and Ctrl + C Thus, your document will be saved and closed. The Vim editor Vim is actually an extension of Vi, and it is functionally the same thing. Vim is a fine editor. Many won't personally go out of their way to not use it. However, people do find it a bit difficult to remember all the commands. If you do get good at it, though, you can code very quickly. You can install Vim with the command line: sudo apt-get install vim Also, there is a GUI version available that allows interaction with the mouse; this is functionally the same program as the Vim command line. You don't have to be confined to the terminal window. You can install it with an identical command: sudo apt-get install vim-gnome You can edit files easily with Vim via the command line for both Vim and Vim-Gnome, as follows: vim <your file> gvim <your file> The gnome version will open the file in a window There is a handy tutorial that you can use to learn the commands of Vim. You can run the tutorial with the help of the following command: vimtutor This tutorial will teach you how to run this editor, which is awesome because the commands can be a bit complicated at first. The following screenshot shows Vim editing the file that we used earlier: The nano editor The nano editor is my favorite editor for the command line. This is probably because it was the first editor that I was exposed to when I started to learn Linux and experiment with the servers and eventually, the Raspberry Pi and Banana Pi. The nano editor is generally considered the easiest to use and is installed by default on the Banana Pi images. If, for some reason, you need to install it, you can get it quickly with the help of the following command: sudo apt-get install nano The editor is easy to use. It comes with several commands that you will use frequently. To save and close the editor, use the following key combinations: Ctrl + O Ctrl + X You can get help at any time by pressing Ctrl + G. Graphic editors With the exception of gVim, all the editors we just talked about live on the command line. If you are more accustomed to graphical tools, you may be more comfortable with a full-featured IDE. There are a couple of choices in this regard that you may be familiar with. These tools are a little heavier than the command-line tools because you will need to not only run the software, but also render the window. This is not as much of a big deal on the Banana Pi as it is on the Raspberry Pi, because we have more RAM to play with. However, if you have a lot of programs running already, it might cause some performance issues. Eclipse Eclipse is a very popular IDE that is available for everything. You can use it to develop all kinds of systems and use all kinds of programming languages. This is a tool that can be used to do professional development. There are a lot of plugins available in this IDE. It is also used to develop apps for Android (although Android Studio is also available now). Eclipse is written in Java. Hence, in order to make it work, you will require a Java Runtime Environment. The Banana Pi should come equipped with the Java development and runtime environments. If this is not the case, they are not difficult to install. In order to grab the proper version of Eclipse and avoid browsing all the specific versions on the website, you can just install it via the command line by entering the following code: sudo apt-get install eclipse Once Eclipse is installed, you will find it in the application menu under programming tools. The following screenshot shows the Eclipse IDE running on the Banana Pi: The Geany IDE Geany is a lighter weight IDE than Eclipse although the former is not quite fully featured. It is a clean UI that can be customized and used to write a lot of different programming languages. Geany was one of the first IDEs I ever used when first exploring Linux when I was a kid. Geany does not come preinstalled on the Banana Pi images, but it is easy to get via the command line: sudo apt-get install geany Depending on what you plan to do code-wise on the Banana Pi, Geany may be your best bet. It is GUI-based and offers quite a bit of functionality. However, it is a lot faster to load than Eclipse. It may seem familiar for Windows users, and they might find it easier to operate since it resembles Windows software. The following screenshot shows Geany on Linux: Both of these editors, Geany and Eclipse, are not specific to a particular programming language, but they both are slightly better for certain languages. Geany tends to be better for web languages such as HTML, PHP, JavaScript, and CSS, while Eclipse tends to be better for compiled languages such as C++, Go, and Java as well as PHP and Ruby with plugins. If you plan to write scripts or languages that are intended to be run from the command line such as Bash, Ruby, or Python, you may want to stick to the command line and use an editor such as Vim or nano. It is worth your time to play around with the editors and find your preferences. Web IDEs In addition to the command line and GUI editors, there are a couple of web-based IDEs. These essentially turn your Pi into a code server, which allows you to run and even execute certain types of code on an IDE written in web languages. These IDEs are great for learning code, but they are not really replacements for the solutions that were listed previously. Google Coder Google Coder is an educational web IDE that was released as an open source project by Google for the Raspberry Pi. Although there is a readily available image for the Raspberry Pi, we can manually install it for the Banana Pi. The following screenshot shows the Google Coder's interface: The setup is fairly straightforward. We will clone the Git repo and install it with Node.js. If you don't have Git and Node.js installed, you can install them with a quick command in the terminal, as follows: sudo apt-get install nodejs npm git Once it is installed, we can clone the coder repo by using the following code: git clone https://github.com/googlecreativelab/coder After it is cloned, we will move into the directory and install it with the help of the following code: cd ~/coder/coder-base/ npm install It may take several minutes to install, even on the Banana Pi. Next, we will edit the config.js file, which will be used to configure the ports and IP addresses. nano config.js The preceding code will reveal the contents of the file. Change the top values to match the following: exports.listenIP = '127.0.0.1'; exports.listenPort = '8081'; exports.httpListenPort = '8080'; exports.cacheApps = true; exports.httpVisiblePort = '8080'; exports.httpsVisiblePort = '8081'; After you change the settings you need, run a server by using Node.js: nodejs server.js You should now be able to connect to the Pi in a browser either on it or on another computer and use Coder. Coder is an educational tool with a lot of different built-in tutorials. You can use Coder to learn JavaScript, CSS, HTML, and jQuery. Adafruit WebIDE Adafruit has developed its own Web IDE, which is designed to run on the Raspberry Pi and BeagleBone. Since we are using the Banana Pi, it will only run better. This IDE is designed to work with Ruby, Python, and JavaScript, to name a few. It includes a terminal via which you can send commands to the Pi from the browser. It is an interesting tool if you wish to learn how to code. The following screenshot shows the interface of the WebIDE: The installation of WebIDE is very simple compared to that of Google Coder, which took several steps. We will just run one command: curl https://raw.githubusercontent.com/adafruit/Adafruit-WebIDE/alpha/scripts/install.sh | sudo sh After a few minutes, you will see an output that indicates that the server is starting. You will be able to access the IDE just like Google Coder—through a browser from another computer or from itself. It should be noted that you will be required to create a free Bit Bucket account to use this software. Summary In this article, we explored several different programming languages, command-line tools, graphical editors, and even some web IDEs. These tools are valuable for all kinds of projects that you may be working on. Resources for Article: Further resources on this subject: Prototyping Arduino Projects using Python [article] Raspberry Pi and 1-Wire [article] The Raspberry Pi and Raspbian [article]

0
0
2075

article-image-getting-current-weather-forecast

Packt

06 Jul 2015

4 min read

Getting the current weather forecast

Packt

06 Jul 2015

4 min read

In this article by Marco Schwartz, author of the book Intel Galileo Blueprints, we will configure Forecast.io. Forecast.io is a web API that returns weather forecasts of your exact location, and it updates them by the minute. The API has stunning maps, weather animations, temperature unit options, forecast lines, and more. (For more resources related to this topic, see here.) We will use this API to integrate global weather measurements with our local measurements. To do so, first go to the following link: http://forecast.io/ Then, look for the Forecast API link at the bottom of the page. It should be under the Developers section of the footer: You need to create your own Forecast.io account. You can do so by clicking on the Register button on the top right-hand side portion of the page. It will take you to the registration page where you need to provide an e-mail address and a strong password. Then, you will be required to get an API key, which will be displayed in the Forecast.io interface: Write this down somewhere, as you will need it soon. We will then use a Node.js module to get the forecast. The steps are described in more detail at the following link: https://github.com/mateodelnorte/forecast.io Next, you need to determine the latitude and longitude that you are currently in. Head on to the following link to generate it automatically: http://www.ip2location.com/ Then, we modify the main.js file: var Forecast = require('forecast.io'); var util = require('util'); This will set our API key with the one used/returned before: var options = { APIKey: 'your_api_key' }, forecast = new Forecast(options); Next, we'll define a new API route for the forecast. This is also where you need to put your longitude and latitude: app.get('/api/forecast', function(req, res) { forecast.get('latitude', 'longitude', function (err, result, data) { if (err) throw err; console.log('data: ' + util.inspect(data)); res.json(data); }); }); We will also modify the Interface.jade file with a new container: h3.row .col-md-4 div Forecast .col-md-4 div#summary Summary In the JavaScript file, we will refresh the field in the interface. We simply get the summary of the current weather conditions: $.get('/api/forecast', function(json_data) { $('#summary').html('Summary: ' + json_data.currently.summary); }); Again, the complete code can be found at the following link: https://github.com/marcoschwartz/galileo-blueprints-book After downloading, you can now build and upload the application to the board. Next comes the fun part, as we will test our creation. Go to the IP address of your board with port 3000: http://192.168.1.103:3000/ You will be able to see the interface as follows: Congratulations! You have been able to retrieve the data from Forecast.io and display it in your browser. If you're not getting the expected result, don't worry. You can go back and check everything. Ensure that you have downloaded and installed the correct software on your board. Also ensure that you correctly entered your API key in the application. Of course, you can modify your own interface as you wish. You can also add more fields from the answer response of Forecast.io. For instance, you can add a Fahrenheit measurement counterpart. Alternately, you can even add forecasts for the next hour and for the next 24 hours. Just ensure that you check the Forecast.io documentation for all the fields that you wish to use. Summary In this article, we configured Forecast.io that is a web API that returns weather forecasts of your exact location and it updates the same every minute. Resources for Article: Further resources on this subject: Controlling DC motors using a shield [article] Getting Started with Intel Galileo [article] Dealing with Interrupts [article]

0
0
1322

Packt

16 Jun 2015

4 min read

Color and motion finding

Packt

16 Jun 2015

4 min read

In this article by Richard Grimmet, the author of the book, Raspberry Pi Robotics Essentials, we'll look at how to detect the Color and motion of an object. (For more resources related to this topic, see here.) OpenCV and your webcam can also track colored objects. This will be useful if you want your biped to follow a colored object. OpenCV makes this amazingly simple by providing some high-level libraries that can help us with this task. To accomplish this, you'll edit a file to look something like what is shown in the following screenshot: Let's look specifically at the code that makes it possible to isolate the colored ball: hue_img = cv.CvtColor(frame, cv.CV_BGR2HSV): This line creates a new image that stores the image as per the values of hue (color), saturation, and value (HSV), instead of the red, green, and blue (RGB) pixel values of the original image. Converting to HSV focuses our processing more on the color, as opposed to the amount of light hitting it. threshold_img = cv.InRangeS(hue_img, low_range, high_range): The low_range, high_range parameters determine the color range. In this case, it is an orange ball, so you want to detect the color orange. For a good tutorial on using hue to specify color, refer to http://www.tomjewett.com/colors/hsb.html. Also, http://www.shervinemami.info/colorConversion.html includes a program that you can use to determine your values by selecting a specific color. Run the program. If you see a single black image, move this window, and you will expose the original image window as well. Now, take your target (in this case, an orange ping-pong ball) and move it into the frame. You should see something like what is shown in the following screenshot: Notice the white pixels in our threshold image showing where the ball is located. You can add more OpenCV code that gives the actual location of the ball. In our original image file of the ball's location, you can actually draw a rectangle around the ball as an indicator. Edit the file to look as follows: The added lines look like the following: hue_image = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV): This line creates a hue image out of the RGB image that was captured. Hue is easier to deal with when trying to capture real world images; for details, refer to http://www.bogotobogo.com/python/OpenCV_Python/python_opencv3_Changing_ColorSpaces_RGB_HSV_HLS.php. threshold_img = cv2.inRange(hue_image, low_range, high_range): This creates a new image that contains only those pixels that occur between the low_range and high_range n-tuples. contour, hierarchy = cv2.findContours(threshold_img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE): This finds the contours, or groups of like pixels, in the threshold_img image. center = contour[0]: This identifies the first contour. moment = cv2.moments(center): This finds the moment of this group of pixels. (x,y),radius = cv2.minEnclosingCircle(center): This gives the x and y locations and the radius of the minimum circle that will enclose this group of pixels. center = (int(x),int(y)): Find the center of the x and y locations. radius = int(radius): The integer radius of the circle. img = cv2.circle(frame,center,radius,(0,255,0),2): Draw a circle on the image. Now that the code is ready, you can run it. You should see something that looks like the following screenshot: You can now track your object. You can modify the color by changing the low_range and high_range n-tuples. You also have the location of your object, so you can use the location to do path planning for your robot. Summary Your biped robot can walk, use sensors to avoid barriers, plans its path, and even see barriers or target. Resources for Article: Further resources on this subject: Develop a Digital Clock [article] Creating Random Insults [article] Raspberry Pi and 1-Wire [article]

0
0
2323

article-image-integration-chefbot-hardware-and-interfacing-it-ros-using-python

Packt

02 Jun 2015

18 min read

Integration of ChefBot Hardware and Interfacing it into ROS, Using Python

Packt

02 Jun 2015

18 min read

In this article by Lentin Joseph, author of the book Learning Robotics Using Python, we will see how to assemble this robot using these parts and also the final interfacing of sensors and other electronics components of this robot to Tiva C LaunchPad. We will also try to interface the necessary robotic components and sensors of ChefBot and program it in such a way that it will receive the values from all sensors and control the information from the PC. Launchpad will send all sensor values via a serial port to the PC and also receive control information (such as reset command, speed, and so on) from the PC. After receiving sensor values from the PC, a ROS Python node will receive the serial values and convert it to ROS Topics. There are Python nodes present in the PC that subscribe to the sensor's data and produces odometry. The data from the wheel encoders and IMU values are combined to calculate the odometry of the robot and detect obstacles by subscribing to the ultrasonic sensor and laser scan also, controlling the speed of the wheel motors by using the PID node. This node converts the linear velocity command to differential wheel velocity. After running these nodes, we can run SLAM to map the area and after running SLAM, we can run the AMCL nodes for localization and autonomous navigation. In the first section of this article, Building ChefBot hardware, we will see how to assemble the ChefBot hardware using its body parts and electronics components. (For more resources related to this topic, see here.) Building ChefBot hardware The first section of the robot that needs to be configured is the base plate. The base plate consists of two motors and its wheels, caster wheels, and base plate supports. The following image shows the top and bottom view of the base plate: Base plate with motors, wheels, and caster wheels The base plate has a radius of 15cm and motors with wheels are mounted on the opposite sides of the plate by cutting a section from the base plate. A rubber caster wheel is mounted on the opposite side of the base plate to give the robot good balance and support for the robot. We can either choose ball caster wheels or rubber caster wheels. The wires of the two motors are taken to the top of the base plate through a hole in the center of the base plate. To extend the layers of the robot, we will put base plate supports to connect the next layers. Now, we can see the next layer with the middle plate and connecting tubes. There are hollow tubes, which connect the base plate and the middle plate. A support is provided on the base plate for hollow tubes. The following figure shows the middle plate and connecting tubes: Middle plate with connecting tubes The connecting tubes will connect the base plate and the middle plate. There are four hollow tubes that connect the base plate to the middle plate. One end of these tubes is hollow, which can fit in the base plate support, and the other end is inserted with a hard plastic with an option to put a screw in the hole. The middle plate has no support except four holes: Fully assembled robot body The middle plate male connector helps to connect the middle plate and the top of the base plate tubes. At the top of the middle plate tubes, we can fit the top plate, which has four supports on the back. We can insert the top plate female connector into the top plate support and this is how we will get the fully assembled body of the robot. The bottom layer of the robot can be used to put the Printed Circuit Board (PCB) and battery. In the middle layer, we can put Kinect and Intel NUC. We can put a speaker and a mic if needed. We can use the top plate to carry food. The following figure shows the PCB prototype of robot; it consists of Tiva C LaunchPad, a motor driver, level shifters, and provisions to connect two motors, ultrasonic, and IMU: ChefBot PCB prototype The board is powered with a 12 V battery placed on the base plate. The two motors can be directly connected to the M1 and M2 male connectors. The NUC PC and Kinect are placed on the middle plate. The Launchpad board and Kinect should be connected to the NUC PC via USB. The PC and Kinect are powered using the same 12 V battery itself. We can use a lead-acid or lithium-polymer battery. Here, we are using a lead-acid cell for testing purposes. We will migrate to lithium-polymer for better performance and better backup. The following figure shows the complete assembled diagram of ChefBot: Fully assembled robot body After assembling all the parts of the robot, we will start working with the robot software. ChefBot's embedded code and ROS packages are available in GitHub. We can clone the code and start working with the software. Configuring ChefBot PC and setting ChefBot ROS packages In ChefBot, we are using Intel's NUC PC to handle the robot sensor data and its processing. After procuring the NUC PC, we have to install Ubuntu 14.04.2 or the latest updates of 14.04 LTS. After the installation of Ubuntu, install complete ROS and its packages. We can configure this PC separately, and after the completion of all the settings, we can put this in to the robot. The following are the procedures to install ChefBot packages on the NUC PC. Clone ChefBot's software packages from GitHub using the following command: $ git clone https://github.com/qboticslabs/Chefbot_ROS_pkg.git We can clone the code in our laptop and copy the chefbot folder to Intel's NUC PC. The chefbot folder consists of the ROS packages of ChefBot. In the NUC PC, create a ROS catkin workspace, copy the chefbot folder and move it inside the src directory of the catkin workspace. Build and install the source code of ChefBot by simply using the following command This should be executed inside the catkin workspace we created: $ catkin_make If all dependencies are properly installed in NUC, then the ChefBot packages will build and install in this system. After setting the ChefBot packages on the NUC PC, we can switch to the embedded code for ChefBot. Now, we can connect all the sensors in Launchpad. After uploading the code in Launchpad, we can again discuss ROS packages and how to run it. Interfacing ChefBot sensors with Tiva C LaunchPad We have discussed interfacing of individual sensors that we are going to use in ChefBot. In this section, we will discuss how to integrate sensors into the Launchpad board. The Energia code to program Tiva C LaunchPad is available on the cloned files at GitHub. The connection diagram of Tiva C LaunchPad with sensors is as follows. From this figure, we get to know how the sensors are interconnected with Launchpad: Sensor interfacing diagram of ChefBot M1 and M2 are two differential drive motors that we are using in this robot. The motors we are going to use here is DC Geared motor with an encoder from Pololu. The motor terminals are connected to the VNH2SP30 motor driver from Pololu. One of the motors is connected in reverse polarity because in differential steering, one motor rotates opposite to the other. If we send the same control signal to both the motors, each motor will rotate in the opposite direction. To avoid this condition, we will connect it in opposite polarities. The motor driver is connected to Tiva C LaunchPad through a 3.3 V-5 V bidirectional level shifter. One of the level shifter we will use here is available at: https://www.sparkfun.com/products/12009. The two channels of each encoder are connected to Launchpad via a level shifter. Currently, we are using one ultrasonic distance sensor for obstacle detection. In future, we could expand this number, if required. To get a good odometry estimate, we will put IMU sensor MPU 6050 through an I2C interface. The pins are directly connected to Launchpad because MPU6050 is 3.3 V compatible. To reset Launchpad from ROS nodes, we are allocating one pin as the output and connected to reset pin of Launchpad. When a specific character is sent to Launchpad, it will set the output pin to high and reset the device. In some situations, the error from the calculation may accumulate and it can affect the navigation of the robot. We are resetting Launchpad to clear this error. To monitor the battery level, we are allocating another pin to read the battery value. This feature is not currently implemented in the Energia code. The code you downloaded from GitHub consists of embedded code. We can see the main section of the code here and there is no need to explain all the sections because we already discussed it. Writing a ROS Python driver for ChefBot After uploading the embedded code to Launchpad, the next step is to handle the serial data from Launchpad and convert it to ROS Topics for further processing. The launchpad_node.py ROS Python driver node interfaces Tiva C LaunchPad to ROS. The launchpad_node.py file is on the script folder, which is inside the chefbot_bringup package. The following is the explanation of launchpad_node.py in important code sections: #ROS Python client import rospy import sys import time import math #This python module helps to receive values from serial port which execute in a thread from SerialDataGateway import SerialDataGateway #Importing required ROS data types for the code from std_msgs.msg import Int16,Int32, Int64, Float32, String, Header, UInt64 #Importing ROS data type for IMU from sensor_msgs.msg import Imu The launchpad_node.py file imports the preceding modules. The main modules we can see is SerialDataGateway. This is a custom module written to receive serial data from the Launchpad board in a thread. We also need some data types of ROS to handle the sensor data. The main function of the node is given in the following code snippet: if __name__ =='__main__': rospy.init_node('launchpad_ros',anonymous=True) launchpad = Launchpad_Class() try: launchpad.Start() rospy.spin() except rospy.ROSInterruptException: rospy.logwarn("Error in main function") launchpad.Reset_Launchpad() launchpad.Stop() The main class of this node is called Launchpad_Class(). This class contains all the methods to start, stop, and convert serial data to ROS Topics. In the main function, we will create an object of Launchpad_Class(). After creating the object, we will call the Start() method, which will start the serial communication between Tiva C LaunchPad and PC. If we interrupt the driver node by pressing Ctrl + C, it will reset the Launchpad and stop the serial communication between the PC and Launchpad. The following code snippet is from the constructor function of Launchpad_Class(). In the following snippet, we will retrieve the port and baud rate of the Launchpad board from ROS parameters and initialize the SerialDateGateway object using these parameters. The SerialDataGateway object calls the _HandleReceivedLine() function inside this class when any incoming serial data arrives on the serial port. This function will process each line of serial data and extract, convert, and insert it to the appropriate headers of each ROS Topic data type: #Get serial port and baud rate of Tiva C Launchpad port = rospy.get_param("~port", "/dev/ttyACM0") baudRate = int(rospy.get_param("~baudRate", 115200)) ################################################################# rospy.loginfo("Starting with serial port: " + port + ", baud rate: " + str(baudRate)) #Initializing SerialDataGateway object with serial port, baud rate and callback function to handle incoming serial data self._SerialDataGateway = SerialDataGateway(port, baudRate, self._HandleReceivedLine) rospy.loginfo("Started serial communication") ###################################################################Subscribers and Publishers #Publisher for left and right wheel encoder values self._Left_Encoder = rospy.Publisher('lwheel',Int64,queue_size = 10) self._Right_Encoder = rospy.Publisher('rwheel',Int64,queue_size = 10) #Publisher for Battery level(for upgrade purpose) self._Battery_Level = rospy.Publisher('battery_level',Float32,queue_size = 10) #Publisher for Ultrasonic distance sensor self._Ultrasonic_Value = rospy.Publisher('ultrasonic_distance',Float32,queue_size = 10) #Publisher for IMU rotation quaternion values self._qx_ = rospy.Publisher('qx',Float32,queue_size = 10) self._qy_ = rospy.Publisher('qy',Float32,queue_size = 10) self._qz_ = rospy.Publisher('qz',Float32,queue_size = 10) self._qw_ = rospy.Publisher('qw',Float32,queue_size = 10) #Publisher for entire serial data self._SerialPublisher = rospy.Publisher('serial', String,queue_size=10) We will create the ROS publisher object for sensors such as the encoder, IMU, and ultrasonic sensor as well as for the entire serial data for debugging purpose. We will also subscribe the speed commands for the left-hand side and the right-hand side wheel of the robot. When a speed command arrives on Topic, it calls the respective callbacks to send speed commands to the robot's Launchpad: self._left_motor_speed = rospy.Subscriber('left_wheel_speed',Float32,self._Update_Left_Speed) self._right_motor_speed = rospy.Subscriber('right_wheel_speed',Float32,self._Update_Right_Speed) After setting the ChefBot driver node, we need to interface the robot to a ROS navigation stack in order to perform autonomous navigation. The basic requirement for doing autonomous navigation is that the robot driver nodes, receive velocity command from ROS navigational stack. The robot can be controlled using teleoperation. In addition to these features, the robot must be able to compute its positional or odometry data and generate the tf data for sending into navigational stack. There must be a PID controller to control the robot motor velocity. The following ROS package helps to perform these functions. The differential_drive package contains nodes to perform the preceding operation. We are reusing these nodes in our package to implement these functionalities. The following is the link for the differential_drive package in ROS: http://wiki.ros.org/differential_drive The following figure shows how these nodes communicate with each other. We can also discuss the use of other nodes too: The purpose of each node in the chefbot_bringup package is as follows: twist_to_motors.py: This node will convert the ROS Twist command or linear and angular velocity to individual motor velocity target. The target velocities are published at a rate of the ~rate Hertz and the publish timeout_ticks times velocity after the Twist message stops. The following are the Topics and parameters that will be published and subscribed by this node: Publishing Topics: lwheel_vtarget (std_msgs/Float32): This is the the target velocity of the left wheel(m/s). rwheel_vtarget (std_msgs/Float32): This is the target velocity of the right wheel(m/s). Subscribing Topics: Twist (geometry_msgs/Twist): This is the target Twist command for the robot. The linear velocity in the x direction and angular velocity theta of the Twist messages are used in this robot. Important ROS parameters: ~base_width (float, default: 0.1): This is the distance between the robot's two wheels in meters. ~rate (int, default: 50): This is the rate at which velocity target is published(Hertz). ~timeout_ticks (int, default:2): This is the number of the velocity target message published after stopping the Twist messages. pid_velocity.py: This is a simple PID controller to control the speed of each motors by taking feedback from wheel encoders. In a differential drive system, we need one PID controller for each wheel. It will read the encoder data from each wheels and control the speed of each wheels. Publishing Topics: motor_cmd (Float32): This is the final output of the PID controller that goes to the motor. We can change the range of the PID output using the out_min and out_max ROS parameter. wheel_vel (Float32): This is the current velocity of the robot wheel in m/s. Subscribing Topics: wheel (Int16): This Topic is the output of a rotary encoder. There are individual Topics for each encoder of the robot. wheel_vtarget (Float32): This is the target velocity in m/s. Important parameters: ~Kp (float ,default: 10): This parameter is the proportional gain of the PID controller. ~Ki (float, default: 10): This parameter is the integral gain of the PID controller. ~Kd (float, default: 0.001): This parameter is the derivative gain of the PID controller. ~out_min (float, default: 255): This is the minimum limit of the velocity value to motor. This parameter limits the velocity value to motor called wheel_vel Topic. ~out_max (float, default: 255): This is the maximum limit of wheel_vel Topic(Hertz). ~rate (float, default: 20): This is the rate of publishing wheel_vel Topic. ticks_meter (float, default: 20): This is the number of wheel encoder ticks per meter. This is a global parameter because it's used in other nodes too. vel_threshold (float, default: 0.001): If the robot velocity drops below this parameter, we consider the wheel as stopped. If the velocity of the wheel is less than vel_threshold, we consider it as zero. encoder_min (int, default: 32768): This is the minimum value of encoder reading. encoder_max (int, default: 32768): This is the maximum value of encoder reading. wheel_low_wrap (int, default: 0.3 * (encoder_max - encoder_min) + encoder_min): These values decide whether the odometry is in negative or positive direction. wheel_high_wrap (int, default: 0.7 * (encoder_max - encoder_min) + encoder_min): These values decide whether the odometry is in the negative or positive direction. diff_tf.py: This node computes the transformation of odometry and broadcast between the odometry frame and the robot base frame. Publishing Topics: odom (nav_msgs/odometry): This publishes the odometry (current pose and twist of the robot. tf: This provides transformation between the odometry frame and the robot base link. Subscribing Topics: lwheel (std_msgs/Int16), rwheel (std_msgs/Int16): These are the output values from the left and right encoder of the robot. chefbot_keyboard_teleop.py: This node sends the Twist command using controls from the keyboard. Publishing Topics: cmd_vel_mux/input/teleop (geometry_msgs/Twist): This publishes the twist messages using keyboard commands. After discussing nodes in the chefbot_bringup package, we will look at the functions of launch files. Understanding ChefBot ROS launch files We will discuss the functions of each launch files of the chefbot_bringup package. robot_standalone.launch: The main function of this launch file is to start nodes such as launchpad_node, pid_velocity, diff_tf, and twist_to_motor to get sensor values from the robot and to send command velocity to the robot. keyboard_teleop.launch: This launch file will start the teleoperation by using the keyboard. This launch starts the chefbot_keyboard_teleop.py node to perform the keyboard teleoperation. 3dsensor.launch : This file will launch Kinect OpenNI drivers and start publishing RGB and depth stream. It will also start the depth stream to laser scanner node, which will convert point cloud to laser scan data. gmapping_demo.launch: This launch file will start SLAM gmapping nodes to map the area surrounding the robot. amcl_demo.launch: Using AMCL, the robot can localize and predict where it stands on the map. After localizing on the map, we can command the robot to move to a position on the map, then the robot can move autonomously from its current position to the goal position. view_robot.launch: This launch file displays the robot URDF model in RViz. view_navigation.launch: This launch file displays all the sensors necessary for the navigation of the robot. Summary This article was about assembling the hardware of ChefBot and integrating the embedded and ROS code into the robot to perform autonomous navigation. We assembled individual sections of the robot and connected the prototype PCB that we designed for the robot. This consists of the Launchpad board, motor driver, left shifter, ultrasonic, and IMU. The Launchpad board was flashed with the new embedded code, which can interface all sensors in the robot and can send or receive data from the PC. After discussing the embedded code, we wrote the ROS Python driver node to interface the serial data from the Launchpad board. After interfacing the Launchpad board, we computed the odometry data and differential drive controlling using nodes from the differential_drive package that existed in the ROS repository. We interfaced the robot to ROS navigation stack. This enables to perform SLAM and AMCL for autonomous navigation. We also discussed SLAM, AMCL, created map, and executed autonomous navigation on the robot. Resources for Article: Further resources on this subject: Learning Selenium Testing Tools with Python [article] Prototyping Arduino Projects using Python [article] Python functions – Avoid repeating code [article]

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