This paper presents a cooperative dynamic task assignment framework for a certain class of Autonomous Underwater Vehicles (AUVs) employed to control outbreak of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier Reef. The problem of monitoring and controlling the COTS is transcribed into a constrained task assignment problem in which eradicating clusters of COTS, by the injection system of COTSbot AUVs, is considered as a task. A probabilistic map of the operating environment including seabed terrain, clusters of COTS, and coastlines is constructed. Then, a novel heuristic algorithm called Heuristic Fleet Cooperation (HFC) is developed to provide a cooperative injection of the COTSbot AUVs to the maximum possible COTS in an assigned mission time. Extensive simulation studies together with quantitative performance analysis are conducted to demonstrate the effectiveness and robustness of the proposed cooperative task assignment algorithm in eradicating the COTS in the Great Barrier Reef.
翻译:本文为澳大利亚大堡礁中用于控制龙角海星爆发的某类自主水下机动车辆(AUV)提供了一个合作动态任务分配框架,监测和控制COTS的问题被转嫁到一个受限制的任务分配问题,在这个问题上,COTSbot AUV的注射系统将消除COTS集群视为一项任务,绘制了运行环境的概率图,包括海底地形、COTS群和海岸线。然后,开发了一种名为Hyuristic船队合作(HFC)的新型超常算法,以便在指定任务时间内将COTSbot AVs的合作注入尽可能多的COTS。还进行了广泛的模拟研究,并进行了定量绩效分析,以证明拟议合作任务分配算法在大堡礁中消除COTS的有效性和稳健性。