The International Journal of Robotics Research · 2011 · 400 citations · 31 references
EngineeringRobot PlanningGlobal PlanningGaussian ModelsAutonomous SystemsTrajectory PlanningUncertainty QuantificationSystems EngineeringRobot LearningImperfect State InformationHealth SciencesPath PlanningRobot Motion PlanningComputer ScienceAi PlanningMotion PlanningRoute PlanningAutomationMotion UncertaintyPlanningRoboticsLinear-quadratic ControllerTrajectory Optimization
The paper introduces LQG‑MP, a motion‑planning framework that incorporates sensor models and controller dynamics into path planning. LQG‑MP models motion uncertainty with a linear‑quadratic Gaussian controller, precomputes state‑distribution along candidate paths (generated via RRT or roadmaps), selects the best path, and refines it with Kalman smoothing for continuity. Simulation experiments on a car‑like robot, multi‑robot differential‑drive systems, and a 6‑DOF manipulator demonstrate that LQG‑MP can efficiently generate high‑quality, collision‑avoiding paths.
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 robot’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 C k -continuous and apply LQG-MP to precomputed roadmaps using a variant of Dijkstra’s algorithm to efficiently find high-quality paths.
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Sebastian Thrun · Communications of the ACM · 2002 · 7.9K citations
Artificial Intelligence, Path Planning, Imperfect Real-world Environments +13
Randomized Kinodynamic Planning
Steven M. LaValle, James Kuffner · The International Journal of Robotics Research · 2001 · 3.2K citations