1:15 PM-3:15 PM on Sept. 5, 2014
Location: Marcus Nanotechnology Building 1116-1118
Title: Time-Optimal Sampling-Based Motion Planning for Manipulators with Acceleration Limits
Dr. Henrik Christensen (Advisor), School of Interactive Computing, Georgia Tech
Dr. Andrea Thomaz, School of Interactive Computing, Georgia Tech
Dr. Karen Liu, School of Interactive Computing, Georgia Tech
Dr. Magnus Egerstedt, School of Electrical and Computer Engineering, Georgia Tech
Dr. Steven LaValle, Department of Computer Science, University of Illinois at Urbana-Champaign
Robot actuators have physical limitations in how fast they can change their velocity. The more accurately planning algorithms consider these limitations, the better the robot is able to perform.
Sampling-based algorithms have been successful in geometric domains, which ignore actuator limitations. They are simple, parameter-free, probabilistically complete and fast. Even though some algorithms like RRTs were specifically designed for kinodynamic problems that take actuator limitations into account, they are less efficient in these domains or are, as we show, not probabilistically complete.
A common approach to this problem is to decompose it, first planning a geometric path and then time-parameterizing it such that actuator constraints are satisfied. We improve the reliability of the latter step. The decomposition approach can neither deal with non-zero start or goal velocities nor provides an optimal solution.
We demonstrate that sampling-based algorithms can be extended to consider actuator limitations in the form of acceleration limits while retaining the same advantageous properties as in geometric domains. We present a probabilistically complete algorithm that can deal with nonzero start or goal velocities by combining a steering method with an RRT algorithm. We propose to extend this work to yield an asymptotically optimal algorithm.