Zheng Yuanyuan, Zhang Hanqi, Zheng Gang, Hong Yuanjian, Wei Zhonghua, Sun Peng
School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China.
College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Biomimetics (Basel). 2025 Jun 11;10(6):392. doi: 10.3390/biomimetics10060392.
Although existing motion transfer methods for bionic robot arms are based on kinematic equivalence or simplified dynamic models, they frequently fail to tackle dynamic compliance and real-time adaptability in complex human-like motions. To address this shortcoming, this study presents a motion transfer method from the human arm to a bionic robot arm based on the hybrid PSO-RF (Particle Swarm Optimization-Random Forest) algorithm to improve joint space mapping accuracy and dynamic compliance. Initially, a high-precision optical motion capture (Mocap) system was utilized to record human arm trajectories, and Kalman filtering and a Rauch-Tung-Striebel (RTS) smoother were applied to reduce noise and phase lag. Subsequently, the joint angles of the human arm were computed through geometric vector analysis. Although geometric vector analysis offers an initial estimation of joint angles, its deterministic framework is subject to error accumulation caused by the occlusion of reflective markers and kinematic singularities. To surmount this limitation, this study designed five action sequences for the establishment of the training database for the PSO-RF model to predict joint angles when performing different actions. Ultimately, an experimental platform was built to validate the motion transfer method, and the experimental verification showed that the system attained high prediction accuracy (R = 0.932 for the elbow joint angle) and real-time performance with a latency of 0.1097 s. This paper promotes compliant human-robot interaction by dealing with joint-level dynamic transfer challenges, presenting a framework for applications in intelligent manufacturing and rehabilitation robotics.
尽管现有的仿生机器人手臂运动传递方法基于运动学等效或简化动力学模型，但它们在处理复杂类人运动中的动态柔顺性和实时适应性方面常常失败。为了解决这一缺点，本研究提出了一种基于混合粒子群优化-随机森林（PSO-RF）算法的从人手臂到仿生机器人手臂的运动传递方法，以提高关节空间映射精度和动态柔顺性。首先，利用高精度光学运动捕捉（Mocap）系统记录人手臂轨迹，并应用卡尔曼滤波和Rauch-Tung-Striebel（RTS）平滑器来减少噪声和相位滞后。随后，通过几何向量分析计算人手臂的关节角度。尽管几何向量分析提供了关节角度的初始估计，但其确定性框架会受到反射标记遮挡和运动学奇异性引起的误差积累的影响。为了克服这一限制，本研究设计了五个动作序列，用于建立PSO-RF模型的训练数据库，以预测执行不同动作时的关节角度。最终，搭建了一个实验平台来验证该运动传递方法，实验验证表明该系统获得了较高的预测精度（肘关节角度的R = 0.932）和0.1097 s延迟的实时性能。本文通过应对关节级动态传递挑战促进了人机柔顺交互，为智能制造和康复机器人技术的应用提供了一个框架。
