The complexity of Markov decision processes
Mathematics of Operations Research
Motion planning with uncertainty: a landmark approach
Artificial Intelligence - Special volume on planning and scheduling
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Collision Detection
Planning Algorithms
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
Creating High-quality Paths for Motion Planning
International Journal of Robotics Research
The Belief Roadmap: Efficient Planning in Belief Space by Factoring the Covariance
International Journal of Robotics Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
icLQG: combining local and global optimization for control in information space
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Stochastic mobility-based path planning in uncertain environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Using linear landmarks for path planning with uncertainty in outdoor environments
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Collision-probability constrained PRM for a manipulator with base pose uncertainty
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Motion planning under uncertainty using iterative local optimization in belief space
International Journal of Robotics Research
Opportunities and challenges with autonomous micro aerial vehicles
International Journal of Robotics Research
Integrated task and motion planning in belief space
International Journal of Robotics Research
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In this paper we present LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning that takes into account the sensors and the controller that will be used during the execution of the robotâ聙聶s path. LQG-MP is based on the linear-quadratic controller with Gaussian models of uncertainty, and explicitly characterizes in advance (i.e. before execution) the a priori probability distributions of the state of the robot along its path. These distributions can be used to assess the quality of the path, for instance by computing the probability of avoiding collisions. Many methods can be used to generate the required ensemble of candidate paths from which the best path is selected; in this paper we report results using rapidly exploring random trees (RRT). We study the performance of LQG-MP with simulation experiments in three scenarios: (A) a kinodynamic car-like robot, (B) multi-robot planning with differential-drive robots, and (C) a 6-DOF serial manipulator. We also present a method that applies Kalman smoothing to make paths Ck-continuous and apply LQG-MP to precomputed roadmaps using a variant of Dijkstraâ聙聶s algorithm to efficiently find high-quality paths.