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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Time-Optimal Sampling-Based Motion Planning for Manipulators with Acceleration Limits
Tobias Kunz
Robotics PhD Candidate
School of Interactive Computing
College of Computing
Georgia Institute of Technology
http://www.cc.gatech.edu/~tobias/
Date: Monday, March 9, 2015
Time: 11:15 am - 1:15 pm
Location: College of Computing Building, Room 345
Committee:
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
Abstract:
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, which 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. However, 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 an asymptotically optimal planner by combining a steering method with the RRT* algorithm. In addition, we present hierarchical rejection sampling to improve the efficiency of informed kinodynamic planning in high-dimensional spaces.
