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Multiple cooperative vehicles, joined in an acoustic communication network, can perform time-critical, cooperative operations given a robust task allocation mechanism and an efficient path planning model. In this paper, we present solutions for the task-allocation and path-planning problems of the cooperative schema for multiple AUVs: a Location-Aided task Allocation Framework (LAAF) algorithm for multi-target task assignment and the Grid-based Multi-Objective Optimal Programming (GMOOP) mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Our research is based on an existing mobile ad-hoc network underwater acoustic simulator and two routing protocols (blind flooding and dynamic source routing). The LAAF and GMOOP controllers combine within a “task-planact” structure to generate an optimized local system output in a timely manner to achieve fleet-wide cooperation. Our preliminary results demonstrate that the location-aided auction strategies perform significantly better than a generic auction algorithm in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path planning technique provides an efficient method for multi-objective tasks by cooperative agents with limited communication capabilities with its results can be referenced in . Prior to this work, existing multi-objective action selection methods were limited to robust networks where constant, reliable communication was available. Both the LAAF and GMOOP algorithms were robust to poor acoustic network conditions and ongoing changing environments. LAAF dynamic task allocation and the GMOOP path planning controller provide an effective solution for cooperative search-classify missions with multiple AUVs under marginal communication conditions.