| Controlling the cooperative behaviors of a fleet of autonomous underwater vehicles in a stochastic, complex environment is a formidable challenge in artificial intelligence. The complexity arises from the challenges of limited navigation and communication capabilities of underwater environment. A time critical cooperative operation by acoustic networks of Multiple Cooperative Vehicles (MCVs) necessitates a robust task allocation mechanism and an efficient path planning model. In this work, we present solutions to investigate two aspects of the cooperative schema for multiple underwater vehicles under realistic underwater acoustic communications: a Location-aided Task Allocation Framework (LAAF) algorithm for multi-target task assignment and a mathematical programming model, the Grid-based Multi-Objective Optimal Programming (GMOOP), for finding an optimal vehicle command decision given a set of objectives and constraints. We demonstrate that, the location-aided auction strategies perform significantly better than the generic auction algorithm in terms of effective task allocation time and information bandwidth requirements. In a typical task assignment scenario, the time needed in the LAAF algorithm is only a fraction compared to the generic auction algorithm. On the other hand; the GMOOP path planning technique provides a unique means for multi-objective tasks by cooperative agents with limited communication capabilities. Under different environmental settings, the GMOOP path planning technique is proved to provide a method with balance of sufficient expressive power and flexibility, and its solution algorithms tractable in terms of mission completion time, with a limited increase of overhead in acoustic communication. Prior to this work, existing multi-objective action selection methods were limited to robust networks where constant communication available. The dynamic task allocation, together with the GMOOP path planning controller, provides a comprehensive solution to the search-classify tasks for cooperative AUVs. |
