用扩散模型加速机器人安全轨迹生成，100毫秒内出150条可行路径
Accelerating Human-Aware Robot Trajectory Generation via Diffusion and Consistency Distillation
- 结合RRT*与扩散模型，通过引导采样生成满足避障约束的轨迹
- 一致性蒸馏使推理速度提升至100毫秒内生成150条轨迹，成功率高
- 加入关节加速度惩罚项，显著降低轨迹抖动，适合实时人机协作场景
针对非冗余机械臂在人机交互（HRI）环境中同时满足碰撞规避与自碰撞规避等多重约束的难题，本文提出一种约束运动规划框架。通过RRT和RRT*算法生成包含碰撞与自碰撞规避的轨迹数据集，并用于训练扩散模型，实现通过引导采样生成满足约束的轨迹。为降低迭代扩散采样的推理时间，引入一致性蒸馏技术；同时，在损失函数中加入联合加权加加速度正则项，以抑制关节加速度突变，提升轨迹平滑性。仿真结果表明，该一致性模型可在100毫秒内生成150条轨迹候选，保持高任务成功率，并在应用加加速度正则后显著降低关节与末端执行器的加加速度。
原文摘要 · Abstract (English)
This research proposes a constrained motion planning framework for robot manipulators in human-robot interaction (HRI). For a non-redundant manipulator with a fully specified end-effector pose, additional requirements such as collision avoidance and self-collision avoidance are difficult to handle as simple null-space secondary tasks. This limitation makes it challenging to generate feasible joint-space trajectories in HRI environments where safety and kinematic constraints must be considered simultaneously. To address this limitation, collision- and self-collision-aware trajectories are generated using Rapidly-exploring Random Tree (RRT) and RRT* algorithms, and the resulting dataset is used to train a diffusion model that generates constraint-satisfying trajectories through guided sampling. To reduce the inference time required for iterative diffusion sampling, consistency distillation is applied, and a joint-weighted jerk regularization term is incorporated into the loss function to promote smoother trajectories by penalizing abrupt changes in joint acceleration. Simulation results show that the consistency model generates 150 trajectory candidates in less than 100 ms, maintains a high episode success rate, and substantially reduces joint and end-effector jerk when jerk regularization is applied.
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