LQG-MP: Optimized Path Planning for Robots with Motion Uncertainty and Imperfect State Information

This paper presents 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 execution of the robot’s path. LQGMP 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 needed ensemble of candidate paths from which the best path is selected; in this paper we report results using the RRT-algorithm. We study the performance of LQG-MP with simulation experiments in three scenarios involving a kinodynamic car-like robot, multi-robot planning with differential-drive robots, and a 6-DOF manipulator.