Automatic Parameter Tuning of Motion Planning Algorithms

Jose Cano Reyes, Yiming Yang, Bruno Bodin, Vijayanand Nagarajan, Michael O'Boyle

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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 degreesof-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.
Original languageEnglish
Title of host publicationIEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
Place of PublicationMadrid, Spain
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages8103-8109
Number of pages7
ISBN (Electronic)978-1-5386-8094-0
ISBN (Print)978-1-5386-8095-7
DOIs
Publication statusPublished - 7 Jan 2019
Event2018 IEEE/RSJ International Conference on Intelligent Robots and Systems - Madrid, Spain
Duration: 1 Oct 20185 Oct 2018
https://www.iros2018.org/

Publication series

Name
PublisherIEEE
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Conference

Conference2018 IEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS 2018
Country/TerritorySpain
CityMadrid
Period1/10/185/10/18
Internet address

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