AI 中文总结
针对多机器人运动规划难题，介绍基于模型的扩散最优控制（MDOC），它不依赖数据，通过结合动力学模型与控制障碍函数约束投影，利用基于冲突搜索的安全机制，在模拟实验中展现出优于基线规划器的性能。
AI 中文摘要
在连续环境中的多机器人运动规划具有挑战性，因为联合轨迹空间组合增长以及难以执行动态可行性和严格安全约束。近期方法将轨迹规划重铸为概率推理，从使用扩散模型的轨迹后验中采样，但其依赖大量示范数据集且采样时难以严格执行动力学和安全约束。为此，我们引入基于模型的扩散最优控制（MDOC），它无需数据就能高效生成动态可行轨迹。通过基于冲突搜索，其安全机制可自然扩展到多机器人规划设置。模拟实验表明，该集成方法在样本效率、几何平滑度和成功率上优于代表性基线规划器，减少计算时间并生成无碰撞轨迹。
英文摘要
Multi-Robot Motion Planning in continuous environments, where robots must generate dynamically feasible, collision-free trajectories, is challenging due to the combinatorial growth of the joint trajectory space and the difficulty of enforcing dynamic feasibility and hard safety constraints. Recent approaches recast trajectory planning as probabilistic inference, sampling from a posterior over trajectories using diffusion models whose score functions are learned from demonstration data. While showing promising performance, these approaches are limited: they often rely on sizable demonstration datasets and struggle to rigorously enforce dynamics and hard safety constraints during sampling. To this end, we introduce Model-Based Diffusion Optimal Control (MDOC), a model-based diffusion planner that efficiently produces dynamically feasible trajectories without relying on data. Crucially, we show that MDOC's safety mechanism -- combining known dynamics models with Control Barrier Function-constrained projections -- naturally scales to multi-robot planning settings through Conflict-Based Search. Across simulation experiments, this integrated method consistently outperforms representative baseline planners in sample efficiency, geometric smoothness, and success rate, while reducing computation time and producing collision-free trajectories.
CommentsPublished in Robotics: Science and Systems (RSS), 2026
Journal refProceedings of Robotics: Science and Systems XXII, Sydney, Australia, July 2026
DOI：10.15607/RSS.2026.XXII.041
