Example of a simple particle filter for robot location, Stanford's Intro to AI
This is a very simple particle filter example prompted by Stanford's Intro to AI lectures.
A robot is placed in a maze. It has no idea where it is, and its only sensor can measure the approximate distance to the nearest beacon (yes, I know it's totally weird, but it's easy to implement). Also, it shows that even very simple sensors can be used, no need for a high resolution laser scanner.
In the arena display the robot is represented by a small green turtle and its beliefs are red/blue dots. The more a belief matches the current sensor reading, the more the belief's colour changes to red. Beacons are little cyan dots. For illustration purposes, we also compute the mean of all reasonably confident particles, then check whether that point is actually a center of a cluster. This point is represented by a gray circle, which becomes green when the algorithm thinks it actually determined the robot's position.
The robot then starts to randomly move around the maze. As it moves, its beliefs are updated using the particle filter algorithm. After a couple of moves, the beliefs converge around the robot. It finally knows where it is!
Particle filters really are totally cool...
Start the simulation with:
Feel free to experiment with different mazes, particles counts, etc.