Probabilistic Robotics
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
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.
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
Implement SLAM. Improve packaging