Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations,\r\nand task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication\r\nconstraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic\r\ncommunication conditions: a location-aided task allocation framework (LAAF) algorithm for multitarget task assignment and the\r\ngrid-basedmultiobjective optimal programming (GMOOP) mathematical model for finding an optimal vehicle command decision\r\ngiven a set of objectives and constraints. Both the LAAF andGMOOP algorithms are well suited in poor acoustic network condition\r\nand dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind\r\nflooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than\r\nthe well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption.\r\nWe also demonstrate that the GMOOP path-planning technique provides an efficient method for executingmultiobjective tasks by\r\ncooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods\r\nthat are limited to networks where constant, reliable communication is assumed to be available
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