ClearPath: Highly Parallel Collision Avoidance for Multi-agent Simulation

We present a new local collision avoidance algorithm between multiple agents for real-time simulations. Our approach extends the notion of velocity obstacles from robotics and formulates the conditions for collision free navigation as a quadratic optimization problem. We use a discrete optimization method to efficiently compute the motion of each agent. This resulting algorithm can be parallelized by exploiting data-parallelism and thread-level parallelism. The overall approach, ClearPath, is general and can robustly handle dense scenarios with tens or hundreds of thousands of heterogeneous agents in a few milliseconds. As compared to prior collision avoidance algorithms, we observe more than an order of magnitude performance improvement [Stephen. J. Guy, Jatin Chhugani, Changkyu Kim, Nadathur Satish, Ming C. Lin, Dinesh Manocha, and Pradeep Dubey].

http://gamma.cs.unc.edu/CA/

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Science & Technology

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