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We present a fast, analytical method for estimating the probability of collision of a motion plan for a mobile robot operating under the assumptions of Gaussian motion and sensing uncertainty. Estimating the probability of collision is an integral step in many algorithms for motion planning under uncertainty and is crucial for characterizing the safety of motion plans. Our method is computationally...
Computing globally optimal motion plans requires exploring the configuration space to identify reachable free space regions as well as refining understanding of already explored regions to find better paths. We present the rapidly-exploring roadmap (RRM), a new method for single-query optimal motion planning that allows the user to explicitly consider the trade-off between exploration and refinement...
We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents toward desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel...
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