Summary
This chapter introduced some concepts for robot navigation in an unstructured environment, which is to say, in the real world, where the designers of the robot don’t have control over the content of the space. We started by introducing SLAM, along with some of the strengths and weaknesses of map-based navigation. We talked about how Roomba navigates, by random interaction and statistical models. The method selected for our toy-gathering robot project, Albert, combined two algorithms that both relied mostly on vision sensors.
The first was the Floor Finder, a technique I learned when it was used by the winning entry in the DARPA Grand Challenge. The Floor Finder algorithm uses the near vision (next to the robot) to teach the far vision (away from the robot) what the texture of the floor is. We can then divide the room into things that are safe to drive on, and things that are not safe. This deals with our obstacle avoidance. Our navigation technique used a trained...
