Minimum Uncertainty Robot Navigation Using Information-guided POMDP Planning
A ubiquitous problem in robotics is determining policies that move robots with uncertain process and observation models (partially-observed state systems) to a goal configuration while avoiding collision. We propose a new method to solve this minimum uncertainty navigation problem. We use a continuous partially-observable Markov decision process (POMDP) model and optimize an objective function that considers both probability of collision and uncertainty at the goal position. By using information-theoretic heuristics, we are able to find policies that are effective for both minimizing collisions and stopping near the goal configuration. We additionally introduce a filtering algorithm that tracks collision free trajectories and estimates the probability of collision.
Salvatore Candido Seth Hutchinson
Department of Electrical and Computer Engineering at the University of Illinois Electrical and Computer Engineering at the University of Illinois
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
6102-6108
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）