分散モデルベース拡散によるスケーラブルなマルチロボット軌道最適化
Distributed Model-Based Diffusion For Scalable Multi-Robot Trajectory Optimization
マルチロボットの軌道最適化を、サーバーと各ロボットが協調する分散拡散プロセスで解く手法を提案し、複雑なタスクを高速に解決できることを示した。
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著者: Haejoon Lee, Xinyi Wang, Taekyung Kim, Dimitra Panagou
分類: cs.RO
原文アブストラクト
Trajectory optimization for multi-robot systems remains a critical challenge, particularly when navigating highly non-convex, non-linear, and non-differentiable environments. While Model-Based Diffusion (MBD) has recently emerged as a promising sampling-based optimization paradigm for single-robot trajectory generation, extending it to multi-robot systems results in a centralized, high-dimensional inference problem that (i) suffers from poor sample efficiency due to the curse of dimensionality and (ii) requires global access to all robots' dynamics, constraints, and objectives. To address this, we propose Distributed Model-Based Diffusion (DMBD), a distributed server-robot framework that decomposes the reverse diffusion process into local conditional reverse diffusion processes. This decomposition enables each robot to iteratively perform denoising independently within its own control subspace while conditioning on the current trajectory estimates of the other robots that are aggregated and broadcast by the server. Extensive simulations in goal swapping, multi-floor coverage, parking, and rush-hour scenarios demonstrate that DMBD achieves strong scalability, solving many challenging coordination tasks in sub-seconds and significantly outperforming existing baselines.
