Abstract In this paper we present LQG-MP (linear-quadratic Gaussian motion planning), a new approach to robot motion planning 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 C k -continuous and apply LQG-MP to precomputed roadmaps using a variant of Dijkstra's algorithm to efficiently find high-quality paths.
For example, Berg et al. present LQG-MP (linear-quadratic Gaussian motion planning) REF , 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 robots path.
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
The International Journal of Robotics Research
Mathematics Computer Science