SwarmDiff: 拡散トランスフォーマによる混雑環境下の群ロボット軌道計画
SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer
群ロボットのマクロ状態を確率密度関数で表し、条件付き拡散モデルとDiffusion Transformerでリスクを考慮した軌道を生成する階層的フレームワークを提案。
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: Kang Ding, Chunxuan Jiao, Yunze Hu, Kangjie Zhou, Pengying Wu, Yao Mu, Chang Liu
分類: cs.RO, cs.MA
原文アブストラクト
Swarm robotic trajectory planning faces challenges in computational efficiency, scalability, and safety, particularly in complex, obstacle-dense environments. To address these issues, we propose SwarmDiff, a hierarchical and scalable generative framework for swarm robots. We model the swarm's macroscopic state using Probability Density Functions (PDFs) and leverage conditional diffusion models to generate risk-aware macroscopic trajectory distributions, which then guide the generation of individual robot trajectories at the microscopic level. To ensure a balance between the swarm's optimal transportation and risk awareness, we integrate Wasserstein metrics and Conditional Value at Risk (CVaR). Additionally, we introduce a Diffusion Transformer (DiT) to improve sampling efficiency and generation quality by capturing long-range dependencies. Extensive simulations and real-world experiments demonstrate that SwarmDiff outperforms existing methods in computational efficiency, trajectory validity, and scalability, making it a reliable solution for swarm robotic trajectory planning.
