AUVs' Distributed Mission Planning System for Effective Underwater Chlorophyll Sampling

This paper presents a distributed mission planning system that enables a team of autonomous underwater vehicles (AUVs) for effective underwater chlorophyll sampling. The proposed system is developed based on a distributed algorithm that optimizes the real-time task allocation and mission planning of AUVs in a cooperative fashion while considering environmental and vehicular constraints to ensure efficient and coordinated sampling efforts. A realistic case study of a chlorophyll sampling mission in Australia's Great Barrier is utilized to investigate the performance of the proposed system in a restricted mission time and in the exposure to the uncertainty of the mission. Simulation studies confirm the effectiveness and resilience of the proposed system and showcase its potential as a valuable tool for advancing underwater chlorophyll sampling missions.