Abstract
An integrated multiple autonomous underwater vehicle (multi-AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. Each target is to be visited by one and only one AUV, and a shortest path between a starting point and the destination is found in the presence of the variable current environment and dynamic targets. Firstly, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in dynamic ocean environment. The working process involves special definition of the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan a shortest path for each AUV to visit the corresponding target in dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.
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Huang, H., Zhu, D. & Ding, F. Dynamic Task Assignment and Path Planning for Multi-AUV System in Variable Ocean Current Environment. J Intell Robot Syst 74, 999–1012 (2014). https://doi.org/10.1007/s10846-013-9870-2
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DOI: https://doi.org/10.1007/s10846-013-9870-2