Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Unity 2018 Artificial Intelligence Cookbook

You're reading from   Unity 2018 Artificial Intelligence Cookbook Over 90 recipes to build and customize AI entities for your games with Unity

Arrow left icon
Product type Paperback
Published in Aug 2018
Publisher
ISBN-13 9781788626170
Length 334 pages
Edition 2nd Edition
Languages
Tools
Arrow right icon
Authors (2):
Arrow left icon
Jorge Palacios Jorge Palacios
Author Profile Icon Jorge Palacios
Jorge Palacios
Jorge Elieser P Garrido Jorge Elieser P Garrido
Author Profile Icon Jorge Elieser P Garrido
Jorge Elieser P Garrido
Arrow right icon
View More author details
Toc

Table of Contents (12) Chapters Close

Preface 1. Behaviors - Intelligent Movement 2. Navigation FREE CHAPTER 3. Decision Making 4. The New NavMesh API 5. Coordination and Tactics 6. Agent Awareness 7. Board Games and Applied Search AI 8. Learning Techniques 9. Procedural Content Generation 10. Miscellaneous 11. Other Books You May Enjoy

Avoiding agents

In crowd-simulation games, it would be unnatural to see agents behaving entirely like particles in a physics-based system. The goal of this recipe is to create an agent capable of mimicking our peer-evasion movement.

Getting ready

We need to create a tag called Agent and assign it to those game objects that we would like to avoid, and we also need to have the Agent script component attached to them:

Example of how the Inspector window of a dummy agent should appear

Take a look on the following:

  • Tag: Agent (created by us)
  • Agent component is attached (the one created by us)

How to do it...

This recipe will entail creating a new agent behavior:

  1. Create the AvoidAgent behavior, which is composed of a collision avoidance radius and the list of agents to avoid:
using UnityEngine; 
using System.Collections; 
using System.Collections.Generic; 
 
public class AvoidAgent : AgentBehaviour 
{ 
    public float collisionRadius = 0.4f; 
    GameObject[] targets; 
} 
  1. Implement the Start function in order to set the list of agents according to the tag we created earlier:
void Start () 
{ 
    targets = GameObject.FindGameObjectsWithTag("Agent"); 
} 
  1. Define the GetSteering function:
public override Steering GetSteering() 
{ 
    // body 
} 
  1. Add the following variables to the compute distances and velocities from agents that are nearby:
Steering steering = new Steering(); 
float shortestTime = Mathf.Infinity; 
GameObject firstTarget = null; 
float firstMinSeparation = 0.0f; 
float firstDistance = 0.0f; 
Vector3 firstRelativePos = Vector3.zero; 
Vector3 firstRelativeVel = Vector3.zero; 
  1. Find the closest agent that is prone to collision with the current one:
foreach (GameObject t in targets) 
{ 
    Vector3 relativePos; 
    Agent targetAgent = t.GetComponent<Agent>(); 
    relativePos = t.transform.position - transform.position; 
    Vector3 relativeVel = targetAgent.velocity - agent.velocity; 
    float relativeSpeed = relativeVel.magnitude; 
    float timeToCollision = Vector3.Dot(relativePos, relativeVel); 
    timeToCollision /= relativeSpeed * relativeSpeed * -1; 
    float distance = relativePos.magnitude; 
    float minSeparation = distance - relativeSpeed * timeToCollision; 
    if (minSeparation > 2 * collisionRadius) 
        continue; 
    if (timeToCollision > 0.0f && timeToCollision < shortestTime) 
    { 
        shortestTime = timeToCollision; 
        firstTarget = t; 
        firstMinSeparation = minSeparation; 
        firstRelativePos = relativePos; 
        firstRelativeVel = relativeVel; 
    } 
} 
  1. If there is one that is prone to collision, then move away:
if (firstTarget == null) 
    return steering; 
if (firstMinSeparation <= 0.0f || firstDistance < 2 * collisionRadius) 
    firstRelativePos = firstTarget.transform.position; 
else 
    firstRelativePos += firstRelativeVel * shortestTime; 
firstRelativePos.Normalize(); 
steering.linear = -firstRelativePos * agent.maxAccel; 
return steering; 

How it works...

Given a list of agents, we take into consideration which one is closest, and, if it is close enough, we make it so that the agent tries to escape from the expected route of that first one according to its current velocity, so that they don't collide.

There's more...

This behavior works well when combined with other behaviors using blending techniques (some are included in this chapter); otherwise, it's a starting point for your own collision avoidance algorithms.

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime