G-MAPP: GPU加速によるマルチエージェント計画と知覚を用いたリアクティブ動作生成
G-MAPP: GPU-accelerated Multi-Agent Planning and Perception for Reactive Motion Generation
GPUを用いて環境モデリングとベクトル場ベースの計画を高速化し、動的で複雑な環境におけるリアクティブな動作生成を実現するフレームワークを提案した。CPU版と比較して最大5倍の高速化を達成し、実機実験で衝突回避を確認した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Tanmay Bishnoi, Riddhiman Laha, Tobias Löw, Jose Alex Chandy, Luis F. C. Figueredo, Sami Haddadin
分類: cs.RO
原文アブストラクト
Reactive motion generation in unstructured environments remains an open challenge in robotics. Due to the computational complexity of collision-free motion generation, existing methods either generate global trajectories for static scenarios, or employ models that make conservative assumptions about the environment. This paper identifies the primary bottleneck as the runtime performance demand of planning on high-fidelity environments, and the temporal integration between the perception and planning modules. Therefore, we propose a framework that does not compromise on runtime performance and world representations for perception and planning by accelerating world modeling and vector-field based planning using the GPU. This allows us to achieve faster parallel state exploration for quasi-global trajectory planning, and tighter coupling of the perception-action loop in real-time for dynamic cluttered environments with off-the-shelf depth sensors. We quantitatively evaluate the computation-time and success rate differences for the CPU and GPU versions of our planner, and perform qualitative evaluations of our coupled framework using real-world experiments on a 7-DoF Franka Emika robot. Experimental results demonstrate that our GPU-based framework achieves up to a 5x speedup over the CPU version and successfully avoids collisions across both trivial and challenging physical world scenarios.
