Bin Xu

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— We propose an efficient method for updating a path that was computed using level-set methods. Our approach is suitable for autonomous vehicles navigating in a static environment for which an a priori map of the environment is inaccurate. When the autonomous vehicle detects a new obstacle, our algorithm replans an optimal route without recomputing the(More)
—For an autonomous vehicle navigating in a static environment for which an a priori map is inaccurate, we propose a hybrid receding horizon control method to determine optimal routes when new obstacles are detected. The hybrid method uses the level sets of the solution to either a global or local Eikonal equation in the formulation of the receding horizon(More)
— We address the minimal risk motion planning problem in a two dimensional environment in the presence of both moving and static obstacles. Our approach is inspired by recent results due to Vladimirsky [20] in which path planning on time-varying maps is addressed using a new level-set approach, and for which computational costs are remarkably low. Toward(More)
We investigate path planning algorithms that are based on level set methods for applications in which the environment is static, but where an a priori map is inaccurate and the environment is sensed in real-time. Our principal contribution is not a new path planning algorithm, but rather a formal analysis of path planning algorithms based on level set(More)
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