机器人可安全自动识别未知负载惯性参数，保障操作全程无碰撞。
Provably-Safe, Online System Identification
- 在线规划安全激励轨迹，实时满足机器人约束与避障
- 在传感器噪声有界条件下，给出末端惯性参数的严格上界
- 适合需要高安全性与精度的工业机械臂应用
精确的操控任务需要准确掌握负载的惯性参数。然而，在保证机器人输入和状态约束、避免与环境碰撞的前提下，对未知负载进行参数识别仍是一个重大挑战。本文提出一个集成框架，使机械臂能够在保障操作安全的前提下，自动、安全地识别负载参数。该框架包含两个协同组件：一是在线轨迹规划与控制框架，生成可被跟踪且满足机器人约束、避开障碍物的可证明安全的激励轨迹；二是鲁棒系统辨识方法，假设传感器噪声有界，计算末端惯性参数的严格上界。在多个未知负载的复杂任务中对机械臂的实验验证表明，该框架在保持整个识别过程安全的同时，能有效建立准确的参数边界。代码已公开于项目主页：https://roahmlab.github.io/OnlineSafeSysID/。
原文摘要 · Abstract (English)
Precise manipulation tasks require accurate knowledge of payload inertial parameters. Unfortunately, identifying these parameters for unknown payloads while ensuring that the robotic system satisfies its input and state constraints while avoiding collisions with the environment remains a significant challenge. This paper presents an integrated framework that enables robotic manipulators to safely and automatically identify payload parameters while maintaining operational safety guarantees. The framework consists of two synergistic components: an online trajectory planning and control framework that generates provably-safe exciting trajectories for system identification that can be tracked while respecting robot constraints and avoiding obstacles and a robust system identification method that computes rigorous overapproximative bounds on end-effector inertial parameters assuming bounded sensor noise. Experimental validation on a robotic manipulator performing challenging tasks with various unknown payloads demonstrates the framework's effectiveness in establishing accurate parameter bounds while maintaining safety throughout the identification process. The code is available at our project webpage: https://roahmlab.github.io/OnlineSafeSysID/.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。