A Cooperative Dynamic Task Assignment Framework for COTSBot AUVs

TL;DR

This paper introduces a cooperative dynamic task assignment framework for AUVs to effectively control Crown-Of-Thorns Starfish outbreaks in the Great Barrier Reef, utilizing a probabilistic environment map and a novel heuristic algorithm.

Contribution

It proposes a new cooperative task assignment algorithm, HFC, for AUVs to optimize COTS eradication in complex underwater environments.

Findings

The HFC algorithm effectively maximizes COTS eradication within mission time.

Simulation results show high robustness and efficiency of the proposed framework.

The framework outperforms existing methods in cooperative underwater pest control.

Abstract

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.