用学习方法解决颈椎康复机器人正运动学难题，提升精度与实用性。
Learning-based Estimation of Forward Kinematics for an Orthotic Parallel Robotic Mechanism
- 基于神经网络和库普曼算子的机器学习方法求解正运动学
- 仿真与实物实验表明预测误差低于0.8毫米、0.3度，性能可靠
- 适合康复机器人、精密机械系统中难以解析求解的运动学研究
本文提出一种三自由度并联机器人结构，由三条相同的五自由度链连接圆形末端执行器，旨在为颈椎病患者提供辅助治疗。该系统的逆运动学可解析求解，而正运动学则采用基于学习的方法求解，包括基于库普曼算子的方法和神经网络方法。目标是预测末端执行器的位姿轨迹。训练数据来源于逆运动学解析解。方法在仿真和实际硬件平台上均进行了测试。结果验证了学习方法在处理通常难以解析求解的并联机构正运动学问题上的有效性。
原文摘要 · Abstract (English)
This paper introduces a 3D parallel robot with three identical five-degree-of-freedom chains connected to a circular brace end-effector, aimed to serve as an assistive device for patients with cervical spondylosis. The inverse kinematics of the system is solved analytically, whereas learning-based methods are deployed to solve the forward kinematics. The methods considered herein include a Koopman operator-based approach as well as a neural network-based approach. The task is to predict the position and orientation of end-effector trajectories. The dataset used to train these methods is based on the analytical solutions derived via inverse kinematics. The methods are tested both in simulation and via physical hardware experiments with the developed robot. Results validate the suitability of deploying learning-based methods for studying parallel mechanism forward kinematics that are generally hard to resolve analytically.
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