Multi-objective optimization of confidence-based localization in large-scale underwater robotic swarms.

Sabra, Adham; Fung, Wai-keung; Churn, Philip

Abstract

Localization in large-scale underwater swarm robotic systems has increasingly attracted research and industry communities’ attention. An optimized confidence-based localization algorithm is proposed for improving localization coverage and accuracy by promoting robots with high confidence of location estimates to references for their neighboring robots. Confidence update rules based on Bayes filters are proposed based on localization methods’ error characteristics where expected localization error is generated based on measurements such as operational depth and traveled distance. Parameters of the proposed algorithm are then optimized using the Evolutionary Multi-objective Optimization algorithm NSGA-II for localization error and trilateration utilization minimization while maximizing localization confidence and Ultra-Short Base Line utilization. Simulation studies show that a wide localization coverage can be achieved using a single Ultra-Short Base Line system and localization mean error can be reduced by over 45% when algorithm’s parameters are optimized in an underwater swarm of 100 robots.

Citation

SABRA, A., FUNG, W.-K. and CHURN, P. 2018. Multi-objective optimization of confidence-based localization in large-scale underwater robotic swarms. In Correll, N., Schwager, M. and Otte, M. (eds.) Distributed autonomous robotic systems: proceedings of the 14th International distributed autonomous robotic systems symposium 2018 (DARS 2018), 15-17 October 2018, Boulder, USA. Springer proceedings in advanced robotics, 9. Cham: Springer [online], pages 109-123. Available from: https://doi.org/10.1007/978-3-030-05816-6_8

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 14th International distributed autonomous robotic systems symposium 2018 (DARS 2018)
• Start Date: Oct 15, 2018
• End Date: Oct 17, 2018
• Acceptance Date: Aug 7, 2018
• Online Publication Date: Jan 30, 2019
• Publication Date: Feb 24, 2019
• Deposit Date: Feb 12, 2019
• Publicly Available Date: Jan 31, 2020
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Pages: 109-123
• Series Title: Springer proceedings in advanced robotics
• Series Number: 9
• Series ISSN: 2511-1256
• ISBN: 9783030058159
• DOI: https://doi.org/10.1007/978-3-030-05816-6_8
• Keywords: Underwater swarm localization; Confidence values; Multi-objective optimization
• Public URL: http://hdl.handle.net/10059/3291
• Related Public URLs: http://hdl.handle.net/10059/3292
• Contract Date: Feb 12, 2019