Abstract
This paper presents a robust cooperative distributed task allocation and mission planning system that enables a fleet of a certain class of autonomous underwater vehicles (AUVs) for persistent underwater ecological monitoring and preservation. The proposed system is developed based on a distributed algorithm that optimizes task allocation and mission planning in real-time while considering environmental constraints, sensor capabilities, communication limitations, as well as system failures. A realistic case study of monitoring and control of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier is utilized to investigate how the proposed system can contribute to the inspection and eradication of the maximum possible COTS in a limited mission time in the exposure of uncertainty of mission failure caused by internal/external faults. The effectiveness and robustness of our approach are validated through extensive qualitative and quantitative simulations.
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