AI 中文总结
本研究针对人机交互场景下机器人轨迹生成的约束与效率问题，结合RRT/RRT*算法、扩散模型与一致性蒸馏技术，实现了快速、低加加速度的机器人轨迹生成。
AI 中文摘要
本研究提出了一种人机交互（HRI）场景下机械臂的约束运动规划框架。对于末端位姿完全确定的非冗余机械臂，碰撞避免、自碰撞避免等额外要求难以通过简单的零空间次级任务处理，这使得在需同时考虑安全性与运动学约束的HRI环境中生成可行的关节空间轨迹颇具挑战。为解决该问题，采用快速探索随机树（RRT）与RRT*算法生成考虑碰撞及自碰撞的轨迹，所得数据集用于训练扩散模型，该模型通过引导采样生成满足约束的轨迹。为降低迭代扩散采样所需的推理时间，应用一致性蒸馏技术，并在损失函数中引入关节加权加加速度正则项，通过惩罚关节加速度的突变以生成更平滑的轨迹。仿真结果表明，一致性模型可在100毫秒内生成150条轨迹候选，维持较高的任务成功率，且在应用加加速度正则项时能显著降低关节与末端执行器的加加速度。
英文摘要
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.
Comments8 pages, 4 figures
