| 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 tasks. An optimal plan computed prior to the operation will no longer be optimal when the vehicles execute the plan. This dissertation presents 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 planning. This property allows the vehicles to evolve their task plans and routes in response to the changing environment in real time, and under varying computational time windows.*.;*This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation). The CD requires the following system requirements: Windows MediaPlayer or RealPlayer. |
