Acting under uncertainty: discrete Bayesian models for mobile-robot navigation

Discrete Bayesian models have been used to model uncertainty for mobile-robot navigation, but the question of how actions should be chosen remains largely unexplored. This paper presents the optimal solution to the problem, formulated as a partially observable Markov decision process. Since solving for the optimal control policy is intractable, in general, it goes on to explore a variety of heuristic control strategies. The control strategies are compared experimentally, both in simulation and in runs on a robot.

[1]  Edward J. Sondik,et al.  The optimal control of par-tially observable Markov processes , 1971 .

[2]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[3]  Edward J. Sondik,et al.  The Optimal Control of Partially Observable Markov Processes over a Finite Horizon , 1973, Oper. Res..

[4]  Hans P. Moravec,et al.  High resolution maps from wide angle sonar , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[5]  John N. Tsitsiklis,et al.  The Complexity of Markov Decision Processes , 1987, Math. Oper. Res..

[6]  Chelsea C. White,et al.  A survey of solution techniques for the partially observed Markov decision process , 1991, Ann. Oper. Res..

[7]  Hugh F. Durrant-Whyte,et al.  Mobile robot localization by tracking geometric beacons , 1991, IEEE Trans. Robotics Autom..

[8]  W. Lovejoy.  A survey of algorithmic methods for partially observed Markov decision processes , 1991 .

[9]  Leslie Pack Kaelbling,et al.  Acting Optimally in Partially Observable Stochastic Domains , 1994, AAAI.

[10]  Martin L. Puterman,et al.  Markov Decision Processes: Discrete Stochastic Dynamic Programming , 1994 .

[11]  R. Simmons,et al.  Probabilistic Navigation in Partially Observable Environments , 1995 .

[12]  Reid G. Simmons,et al.  Probabilistic Robot Navigation in Partially Observable Environments , 1995, IJCAI.

[13]  Stuart J. Russell,et al.  Approximating Optimal Policies for Partially Observable Stochastic Domains , 1995, IJCAI.

[14]  Leslie Pack Kaelbling,et al.  Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.

[15]  Wolfram Burgard,et al.  The Mobile Robot Rhino , 1995, SNN Symposium on Neural Networks.

[16]  Leslie Pack Kaelbling,et al.  An Efficient Algorithm for Dynamic Programming in Partially Observable Markov Decision Processes , 1995 .

[17]  Illah R. Nourbakhsh,et al.  DERVISH - An Office-Navigating Robot , 1995, AI Mag..

[18]  Reid G. Simmons,et al.  Unsupervised learning of probabilistic models for robot navigation , 1996, Proceedings of IEEE International Conference on Robotics and Automation.