Consensus Based Distributive Task Allocation for Multi-AUV in Searching and Detecting

In this paper, marine environment exploration based on multi-AUV (multiple Autonomous Underwater Vehicles) systems is studied mainly concerning about task allocation strategies in targets searching and detecting. The advantages of the proposed strategies lie on two aspects: (1) All AUVs in the system can reach consensus or near consensus on task loads, which makes AUVs in the system be fully taken use of. (2) Each AUV is considered as a reasonable individual and the strategy can be implemented respectively, which reduces communication requirement among AUVs. To reach consensus of targets searching task, a mathematical model that depict the territory competition process among predators in natural district is constructed. With consideration about different performances of AUVs, a protocol is proposed to obtain balanced targets searching load of each AUV system. On the other hand, for targets detecting task, due to the discrete property of targets displacements, the definition of Pareto consensus is put forward to describe the consensus state of detecting task allocation. Based on the definition, a distributive heuristic algorithm based on simulated annealing and K-means clustering is presented to obtain Pareto consensus among AUVs. At last, simulations are carried out to demonstrate the feasibility and effectiveness of results obtained.