Probabilistic Robotics

What Is It?

This is a collection of modules written to demonstrate ideas from the book 'Probabilistic Robotics' by Thrun, Burgard, and Fox. The aim is to implement Simultaneous Localization and Mapping (SLAM) for a simulated robot in a simple environment. At this time the simulated robot is capable of Monte Carlo Localization based on rangefinder data and autonomous goal-finding based on a hybrid automaton

Usage

Run robot_probha.py to watch the robot navigate to a goal using Monte Carlo Localization. Motion control is based on a hybrid automaton. Yellow dots indicate sonar pings, red arrow is the 'flee vector' pointing away from obstacles, and blue arrows are the particle cloud.

File List

locate.py mapdef.py mcl.py ogmap.py robot.py robot_prob.py robot_ha.py robot_probha.py hybrid_automaton.py navigator.py ray_trace.c ray_trace.pyx ray_trace_setup.py ray_trace.so sonar.py utils.py

TO DO

Implement SLAM. Improve packaging