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A probabilistic framework for online motion planning of vehicles in dynamic environments is proposed. We develop a sampling-based motion planner that incorporates prediction of obstacle motion. A key feature is the introduction of task specifications as artificial measurements. This allows us to cast the exploration phase in the planner as a nonlinear, possibly multimodal, estimation problem, which...
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