Planning to learn: Integrating model learning into a trajectory planner for mobile robots
Name
Greytak-2009-Planning to learn Integrating model learning into a trajectory planner for mobile robots.pdf
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151.97 KB
Format
Adobe PDF
Checksum (MD5)
408dccf4b1d0a8b9e1c0799f26cf7136
Author(s)
Hover, Franz S.
Greytak, Matthew B.
Date Issued
August 2009
Journal
International Conference on Information and Automation, 2009. ICIA '09
Publisher
Institute of Electrical and Electronics Engineers
Citation
Greytak, M., and F. Hover. “Planning to learn: Integrating model learning into a trajectory planner for mobile robots.” Information and Automation, 2009. ICIA '09. International Conference on. 2009. 18-23. © Copyright 2010 IEEE
Version
Final published version
Abstract
For a mobile robot that performs online model learning, the learning rate is a function of the robot's trajectory. The tracking errors that arise when the robot executes a motion plan depend on how well the robot has learned its own model. Therefore a planner that seeks to minimize collisions with obstacles will choose plans that decrease modeling errors if it can predict the learning rate for each plan. In this paper we present an integrated planning and learning algorithm for a simple mobile robot that finds safe, efficient plans through a grid world to a goal point using a standard optimal planner, A*. Simulation results show that with this algorithm the robot practices maneuvers in the open regions of the configuration space, if necessary, before entering the constrained regions of the space. The robot performs mission-specific learning, acquiring only the information it needs to complete the task safely.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICINFA.2009.5204888
