Fast Reciprocal Collision Avoidance Under Measurement Uncertainty

by   Guillermo Angeris, et al.
Stanford University

We present a fully distributed collision avoidance algorithm based on convex optimization for a team of mobile robots. This method addresses the practical case in which agents sense each other via measurements from noisy on-board sensors with no inter-agent communication. Under some mild conditions, we provide guarantees on mutual collision avoidance for a broad class of policies including the one presented. Additionally, we provide numerical examples of computational performance and show that, in both 2D and 3D simulations, all agents avoid each other and reach their desired goals in spite of their uncertainty about the locations of other agents.


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