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This paper introduces a market-based cooperative planning system for a team of autonomous vehicles operating in a dynamic environment. The system combines the flexibility of evolution-computation techniques with the distributed nature of market strategy to compute task and paths plans. Optimization is based on a team utility function which accounts for uncertainty in knowledge of the environment. Multiple vehicles will cooperate on the same task if doing so increases the predicted team utility value. The team utility function and the associated stochastic model that predicts future system states are described. The minimum required information exchange among the vehicles is identified. Simulation results, using the Boeing Company developed Open Experimental Platform, demonstrate the effectiveness of the planning.

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This work considers algorithms for maritime search and surveillance missions. Search and identification of magnetic anomalies are evaluated. A combination of a particle filter and a neural network are used to identify and classify anomalies. Communication among vehicles is assumed to update a centralized occupancy based map which represents a discretized belief of target locations. Control decisions are based on a nearest neighbor search of the surrounding cells of the occupancy map. Simulation is performed using a planar kinematic model and actual aeromagnetic data.

In a highly dynamic environment, an adaptive real-time mission planner is essential for controlling a team of autonomous vehicles to execute a set of assigned tasks. The optimal plan computed prior to the start of the operation might be no longer optimal when the vehicles execute the plan. This paper proposes a framework and algorithms for solving real-time task and path planning problems by combining Evolutionary Computation (EC) based techniques with a Market-based planning architecture. The planning system takes advantage of the flexibility of EC-based techniques and the distributed structure of Market-based algorithms. This property allows the vehicles to evolve their task plans and routes in response to the changing environment in real time.