用扩散模型实现15个机器人在人群中的实时长时程协同导航
Diffusion for Long-Horizon Multi-Robot Path Planning in Human-Shared Environments
- 采用滚动时域+并行扩散生成，突破传统路径长度与延迟限制
- 支持15机器人实时协同，在拥挤环境中安全率与成功率显著领先
- 兼顾避障、社交行为与速度调节，适合大型机器人团队部署
在人类共享环境中进行多机器人路径规划需在机器人间协调与社交合理性之间取得平衡。尽管扩散模型能生成类人路径，但现有生成式规划器通常受限于固定时长路径和高计算延迟，难以适应不同目标距离，阻碍实时应用。本文提出多机器人滚动扩散（MRRD）框架，通过滚动时域适应人类运动预测局限性，利用并行扩散推理实现可扩展的人类路径生成，并结合基于冲突的搜索机制解决机器人间碰撞问题。同时引入基于紧迫性的时序条件控制路径速度变化，采用差异化引导项以增强对人类的社交感知和机器人间的高效协作。在密集环境中的实验表明，MRRD可实现实时运行下15个机器人的协同导航，显著优于现有基线方法，在安全性与任务成功率上均有大幅提升。
原文摘要 · Abstract (English)
Multi-robot path planning in human-shared environments requires a delicate balance between robust inter-robot coordination and socially aware behavior. While diffusion models excel at generating predictable, human-like paths, existing generative planners are often restricted to paths of fixed duration and high computational latency, limiting their adaptability to varying goal distances and hindering real-time deployment. We present Multi-Robot Rolling Diffusion (MRRD), a novel framework that enables real-time, long-horizon navigation for large robot teams through dense crowds. MRRD combines a rolling-horizon scheme to accommodate the limited prediction horizon of human motion, parallelized diffusion inference for scalable generation of human-like paths, and a conflict-based-search mechanism for resolving inter-robot collisions. It further incorporates urgency-based temporal conditioning to generate paths with varying speeds and employs differentiated guidance terms to maximize both social awareness around humans and efficient coordination between robots. Experimental results in crowded environments demonstrate that MRRD successfully scales to 15 robots in real-time, significantly outperforming existing baselines in both safety and mission success rates.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。