Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 30, Issue 7, Pages 895-913Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364911406562
Keywords
Planning; control; uncertainty
Categories
Funding
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [0905344] Funding Source: National Science Foundation
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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 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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