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Example of a simple particle filter for robot location, Stanford's Intro to AI
This project forked from jtosey/particle_filter_demo
Example of a simple particle filter for robot location, Stanford's Intro to AI
I forked the original python demo in an attempt to make the filter as close to the algorithm presented in class as possible. I can now compare the algorithm, line-for-line, with the lecture notes presented in 11-20. In this demo, the robot location is a green turtle, and the estimated location of the robot (once the particles converge) is shown as a red turtle. I hope this helps you! I found by playing around with this code gave me a much better understand exactly how this algorithm works. I encourage you to do the same. The code works substantially the same way, but probably performs a little worse and is a little less efficient than Martin's original implementation, but is more faithful to the 11-20. With only a single sensor, and a fairly symmetric world, the robot sometimes converges to the wrong location even with thousands of particles because it can get the same correlation of readings in multiple parts of the maze. I've extended the filter with an algorithm that continuously checks whether the estimate has converged, and whether it continues to produce measurements that are strongly correlated with the robot. If it has a converged solution (low geographcial dispersion) but the weighted measurements show poor correlation, we have to assume that it converged to the wrong locale. In this case we reset the filter to new random initial conditions and start again. - jrt ---- Original comments: 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. 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: python particle_filter.py Feel free to experiment with different mazes, particles counts, etc. Enjoy! mjl
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