动态环境下安全机器人操纵器遥操作的预测相对速度转向方法
Predictive Relative-Velocity Steering for Safe Robotic Manipulator Teleoperation in Dynamic Environments
- Zhejiang University（浙江大学）
- University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
- Tsinghua University（清华大学）
- Institute for Embodied Intelligence and Robotics, Tsinghua University（清华大学具身智能与机器人研究所）
- TetraBOT
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对动态环境下机器人遥操作的安全问题，提出轻量级模块化预测相对速度转向框架，可提升末端执行器避障率并缓解死锁，仿真与物理实验验证了其有效性。
AI中文摘要：
遥操作领域的最新进展已使机器人操纵器能够执行灵巧的、类似人类手臂的运动。然而，人类操作者可能无法及时且有效地避开突然出现的障碍物，尤其是在存在网络延迟或注意力有限的情况下，这会带来安全风险。为解决该问题，我们提出了一种轻量级且模块化的主动避障框架，直接在末端执行器速度指令层面运行。在对点云进行预处理后，该框架首先基于带集成超调保护的碰撞时间（TTC）预测潜在碰撞，随后使用罗德里格斯旋转公式（Rodrigues' rotation formula）旋转相对速度向量。这种偏转仅改变相对速度的方向，同时保留其大小，从而缓解了传统人工势场（APF）方法常见的死锁问题。预测模块可补偿复杂遥操作流程引入的点云处理延迟，而轻量级设计可满足遥操作所需的高频控制。在多种场景下的仿真表明，与基线方法相比，所提方法实现了更高的末端执行器避障率；在物理机器人系统上的实验进一步验证了其避障有效性。
英文摘要：
Recent advances in teleoperation have enabled robotic manipulators to perform dexterous, human-arm-like motions. However, human operators may fail to avoid suddenly appearing obstacles promptly and effectively, particularly under network latency or limited attention, thereby creating safety risks. To address this issue, we propose a lightweight and modular framework for proactive collision avoidance, operating directly at the end-effector velocity-command level. After preprocessing the point cloud, the framework first predicts potential collisions based on time-to-collision (TTC) with integrated overshoot protection, and subsequently rotates the relative-velocity vector using Rodrigues' rotation formula. The deflection changes only the direction of the relative velocity while preserving its magnitude, thereby mitigating the deadlock problem commonly encountered by conventional artificial potential field (APF) methods. The prediction module compensates for point-cloud processing latency introduced by complex teleoperation pipelines, while the lightweight design enables the high-frequency control required for teleoperation. Simulations across diverse scenarios show that the proposed method achieves a higher end-effector collision avoidance rate than the baseline methods. Experiments on a physical robotic system further validate its collision-avoidance effectiveness.
