Paper Info
Title
LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information
Abstract
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.
Year
DOI
Venue
2010
10.1177/0278364911406562
I. J. Robotic Res.
Keywords
DocType
Volume
Planning,control,uncertainty
Conference
30
Issue
ISSN
Citations 
7
0278-3649
133
PageRank 
References 
Authors
4.34
23
3
Search Limit
100133
Name
Order
Citations
PageRank
Jur van den Berg1197793.23
Pieter Abbeel26363376.48
Ken Goldberg33785369.80