Toward Efficient Task Assignment and Motion Planning for Large Scale Underwater Mission

An Autonomous Underwater Vehicle (AUV) needs to possess a certain degree of autonomy for any particular underwater mission to fulfil the mission objectives successfully and ensure its safety in all stages of the mission in a large scale operating field. In this paper, a novel combinatorial conflict-free-task assignment strategy consisting of an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method takes advantage of the heuristic search potency of the Particle Swarm Optimization (PSO) algorithm to address the discrete nature of routing-task assignment approach and the complexity of NPhard path planning problem. The proposed hybrid method, is highly efficient as a consequence of its reactive guidance framework that guarantees successful completion of missions particularly in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of a vehicle’s autonomy by relying on its reactive nature and capability of providing fast feasible solutions. KeywordsUnderwater vehicle, Path planning, Route planning, Autonomy, Evolutionary optimization