Automatic Parameter Tuning of Motion Planning Algorithms

Cano, J. , Yang, Y., Bodin, B., Nagarajan, V. and O’Boyle, M. (2019) Automatic Parameter Tuning of Motion Planning Algorithms. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 01-05 Oct 2018, pp. 8103-8109. ISBN 9781538680940 (doi:10.1109/IROS.2018.8594183)

177441.pdf - Accepted Version



Motion planning algorithms attempt to find a good compromise between planning time and quality of solution. Due to their heuristic nature, they are typically configured with several parameters. In this paper we demonstrate that, in many scenarios, the widely used default parameter values are not ideal. However, finding the best parameters to optimise some metric(s) is not trivial because the size of the parameter space can be large. We evaluate and compare the efficiency of four different methods (i.e. random sampling, AUC-Bandit, random forest, and bayesian optimisation) to tune the parameters of two motion planning algorithms, BKPIECE and RRT-connect. We present a table-top-reaching scenario where the seven degrees-of-freedom KUKA LWR robotic arm has to move from an initial to a goal pose in the presence of several objects in the environment. We show that the best methods for BKPIECE (AUC-Bandit) and RRT-Connect (random forest) improve the performance by 4.5x and 1.26x on average respectively. Then, we generate a set of random scenarios of increasing complexity, and we observe that optimal parameters found in simple environments perform well in more complex scenarios. Finally, we find that the time required to evaluate parameter configurations can be reduced by more than 2/3 with low error. Overall, our results demonstrate that for a variety of motion planning problems it is possible to find solutions that significantly improve the performance over default configurations while requiring very reasonable computation times.

Item Type:Conference Proceedings
Additional Information:This work was supported by the AnyScale Applications project under the EPSRC grant EP/L000725/1, and partially by the EPSRC grant PAMELA EP/K008730/1.
Glasgow Author(s) Enlighten ID:Cano Reyes, Dr Jose
Authors: Cano, J., Yang, Y., Bodin, B., Nagarajan, V., and O’Boyle, M.
College/School:College of Science and Engineering > School of Computing Science
Published Online:07 January 2019
Copyright Holders:Copyright © 2018 IEEE
First Published:First published in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): 8103-8109
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
614231AnyScale ApplicationsJeremy SingerEngineering and Physical Sciences Research Council (EPSRC)EP/L000725/1COM - COMPUTING SCIENCE