Multi Agent Navigation on the GPU

We present a unique and elegant graphics hardware realization of multi agent simulation. Specifically, we adapted Velocity Obstacles that suits well parallel computation on single instruction, multiple thread, SIMT, type architecture. We explore hash based nearest neighbors search to considerably optimize the algorithm when mapped on to the GPU. Moreover, to alleviate inefficiencies of agent level concurrency, primarily exposed in small agent count (≤32) scenarios, we exploit nested data parallel in unrolling the inner velocity iteration, demonstrating an appreciable performance increase. Simulation of ten thousand agents created with our system runs on current hardware at a real time rate of eighteen frames per second. Our software implementation builds on NVIDIA’s CUDA. CR Categories: I.3.1 [Computer Graphics]: Hardware Architecture—Graphics processors; I.2.8 [Artificial Intelligence]: Problem Solving, Control Methods, and Search— Graph and Tree Search Strategies; I.2.11 [Artificial Intelligence]: Distributed Artificial Intelligence—Intelligent Agents