Abstract
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.
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