Multi-objective optimization of confidence-based localization in large-scale underwater robotic swarms.
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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
Localization in large-scale underwater swarm robotic systems has in-creasingly 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 Op-timization 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.