用模型驱动的扩散优化控制，让多机器人高效生成无碰撞轨迹。
Model-Based Diffusion Optimal Control for Multi-Robot Motion Planning
- 基于动力学模型和屏障函数约束，不依赖演示数据采样轨迹。
- 在仿真中样本效率、成功率和计算速度均优于基线方法。
- 适合需要高安全性和动态可行性的多机器人协同场景。
连续环境中多机器人运动规划因联合轨迹空间组合爆炸，以及动态可行性与硬性安全约束的难以满足而面临挑战。现有方法将轨迹规划视为概率推断，通过从扩散模型学习的得分函数进行后验采样，但通常依赖大量演示数据，且难以严格保证动态与安全约束。为此，我们提出模型驱动的扩散最优控制（MDOC），一种无需依赖数据即可高效生成动态可行轨迹的方法。关键在于，其安全机制结合已知动力学模型与控制屏障函数约束投影，自然扩展至多机器人场景，通过冲突检测搜索实现。仿真结果表明，该方法在样本效率、几何平滑度、成功率上持续优于代表性基线，同时降低计算时间并生成无碰撞轨迹。
原文摘要 · Abstract (English)
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.
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