Motion planning with uncertainty: a landmark approach
Artificial Intelligence - Special volume on planning and scheduling
Neuro-Dynamic Programming
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Efficient vision-based navigation
Autonomous Robots
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
International Journal of Robotics Research
The Skyline algorithm for POMDP value function pruning
Annals of Mathematics and Artificial Intelligence
Motion planning under uncertainty using iterative local optimization in belief space
International Journal of Robotics Research
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
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When a mobile robot does not have perfect knowledge of its position, conventional controllers can experience failures such as collisions because the uncertainty of the position is not considered in choosing control actions. In this paper, we show how global planning and local feedback control can be combined to generate control laws in the space of distributions over position, that is, in information space. We give a novel algorithm for computing "information-constrained" linear quadratic Gaussian (icLQG) policies for controlling a robot with imperfect state information. The icLQG algorithm uses the belief roadmap algorithm to efficiently search for a trajectory that approximates the globally-optimal motion plan in information space, and then iteratively computes a feedback control law to locally optimize the global approximation. The icLQG algorithm is not only robust to imperfect state information but also scalable to high-dimensional systems and environments. In addition, icLQG is capable of answering multiple queries efficiently. We demonstrate performance results for controlling a vehicle on the plane and a helicopter in three dimensions.