人类共享环境中长距离多机器人路径规划的扩散方法
Diffusion for Long-Horizon Multi-Robot Path Planning in Human-Shared Environments
浏览论文内容
中文总结 AI 辅助
研究人类共享环境中多机器人长距离路径规划问题，提出MRRD框架，结合滚动时域、并行扩散推理及冲突搜索机制，纳入时间条件与引导项，实验证明该框架能实时扩展到15个机器人，性能优于现有基线。
中文摘要 AI 辅助
在人类共享环境中进行多机器人路径规划，需要在强大的机器人间协调与具备社会意识的行为之间取得微妙平衡。虽然扩散模型擅长生成可预测的、类似人类的路径，但现有的生成式规划器通常限于固定时长路径且计算延迟高，限制了其对不同目标距离的适应性并阻碍实时部署。我们提出了多机器人滚动扩散（MRRD）框架，通过密集人群为大型机器人团队实现实时、长距离导航。MRRD结合滚动时域方案以适应人类运动的有限预测范围，并行扩散推理以可扩展地生成类似人类的路径，以及基于冲突的搜索机制来解决机器人间碰撞。它还纳入基于紧迫性的时间条件来生成不同速度的路径，并采用差异化引导项以最大化对人类的社会意识和机器人间的高效协调。在拥挤环境中的实验结果表明，MRRD能实时成功扩展到15个机器人，在安全性和任务成功率方面显著优于现有基线。
英文摘要
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.
发表机构
- Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
机构由 AI 辅助整理，请以论文原文为准。
