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. Presented at the 14th International distributed autonomous robotic systems symposium 2018 (DARS 2018), 15-17 October 2018, Boulder, USA.
Ultra-Short Base Line (USBL) is the most commonly adopted localization method in industry due to its flexibility. However, the maximum number of underwater targets that can be simultaneously localized by USBL is very limited (up to 10 using the most advanced technology). A large-scale hierarchical localization approach has been investigated for stationary underwater sensor network. The main concept behind a hierarchical localization approach is that a successfully localized ordinary node with high precision can serve as a reference node for neighboring nodes localization. The authors introduced the concept of confidence value which is associated with the localization process and a predetermined confidence threshold. Confidence values of localized ordinary nodes were solely dependent on the localization error. An optimized confidence value-based localization algorithm for large scale underwater mobile sensor networks is proposed. To dynamically determine the confidence value of each sensor node on current localization estimate. To promote a localized ordinary node to a reference node for neighboring ordinary nodes localization; based on its confidence value. The proposed algorithm harnesses a single USBL system proprioceptive sensors for large-scale swarm localization.