无需训练的扩散规划器，让机器人轨迹自动满足安全与运动约束。
Safe Model Predictive Diffusion with Shielding
- 在去噪过程中实时强制执行安全与运动可行性约束
- 在拖车系统上成功率显著提升，计算时间低于1秒
- 适合对安全性要求高的复杂机器人路径规划
为复杂机器人系统生成安全、运动学动力学可行且最优的轨迹是机器人领域的核心挑战。本文提出无需训练的模型预测扩散（Safe MPD），将基于模型的扩散框架与安全防护机制结合，生成从构建起就兼具运动学动力学可行性与安全性的轨迹。通过在去噪过程中对所有样本施加可行性与安全性约束，该方法避免了事后修正常带来的计算不可行与可行性丢失问题。我们在具有挑战性的非凸规划问题中验证了该方法，包括运动学及加速度控制的拖车系统。结果表明，相比现有安全策略，该方法在成功率和安全性上均有显著提升，同时实现亚秒级计算时间。
原文摘要 · Abstract (English)
Generating safe, kinodynamically feasible, and optimal trajectories for complex robotic systems is a central challenge in robotics. This paper presents Safe Model Predictive Diffusion (Safe MPD), a training-free diffusion planner that unifies a model-based diffusion framework with a safety shield to generate trajectories that are both kinodynamically feasible and safe by construction. By enforcing feasibility and safety on all samples during the denoising process, our method avoids the common pitfalls of post-processing corrections, such as computational intractability and loss of feasibility. We validate our approach on challenging non-convex planning problems, including kinematic and acceleration-controlled tractor-trailer systems. The results show that it substantially outperforms existing safety strategies in success rate and safety, while achieving sub-second computation times.
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