2002 · 468 citations · 14 references
BusinessAutonomous SystemsComputer ScienceMarkov Decision ProcessesMobile RoboticsRoboticsAutonomous NavigationBayesian Decision TheoryBayesian ModelingNavigation PlanningPartially Observable Markov Decision ProcessesRobot NavigationDiscrete ModelsExploration Under UncertaintyMarkov Decision ModelsMobile NavigationMobile Robot NavigationNavigation Under UncertaintyProbabilistic RoboticsUncertainty-aware 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.
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