This paper proposes a novel, more computationally efficient method for optimizing robot excitation trajectories for dynamic parameter identification, emphasizing self-collision avoidance. This addresses the system identification challenges for getting high-quality training data associated with co-manipulated robotic arms that can be equipped with a variety of tools, a common scenario in industrial but also clinical and research contexts. Utilizing the Unified Robotics Description Format (URDF) to implement a symbolic Python implementation of the Recursive Newton-Euler Algorithm (RNEA), the approach aids in dynamically estimating parameters such as inertia using regression analyses on data from real robots. The excitation trajectory was evaluated and achieved on par criteria when compared to state-of-the-art reported results which didn't consider self-collision and tool calibrations. Furthermore, physical Human-Robot Interaction (pHRI) admittance control experiments were conducted in a surgical context to evaluate the derived inverse dynamics model showing a 30.1\% workload reduction by the NASA TLX questionnaire.

翻译：本文提出一种新颖且计算效率更高的方法，用于优化机器人动态参数辨识中的激励轨迹，特别强调自碰撞规避。该方法解决了共操纵机械臂在配备多种工具时（工业、临床及研究场景中常见）获取高质量训练数据所面临的系统辨识挑战。利用统一机器人描述格式（URDF）实现递归牛顿-欧拉算法（RNEA）的符号化Python实现，通过真实机器人数据的回归分析助力惯性等参数的动态估计。该激励轨迹经评估，在与未考虑自碰撞和工具标定的前沿文献结果对比时达到同等标准。进一步在手术场景中开展物理人机交互（pHRI）导纳控制实验，评估所推导的逆动力学模型，根据NASA TLX量表显示工作负荷降低30.1%。