摘要： 针对现有康复机器人仿真研究中生物力学特性与机器人控制策略失配、人机耦合仿真自动化不足等问题，本研究创新性地整合了机器人运动学分析、训练轨迹规划设计以及肌肉骨骼模型生物力学特征，构建了一种基于OpenSim和MATLAB的上肢康复机器人人机联合仿真系统，并提出了一套自动化人机耦合仿真流程。系统实现了对匹配完成模型的同步关节角度调节与运动播放可视化展示。在机器人仿真层中，提供了正向运动学、逆向运动学的计算，并针对不同应用场景提供了四种轨迹规划算法，计算结果经格式转换后传递至生物力学仿真层。在生物力学仿真层中，结合残差缩减与计算肌肉控制补偿未建模外力（即间接补偿机器人外力数据误差）并优化肌肉激活度求解，同时支持对仿真计算结果中的肌肉激活程度、肌肉纤维长度等生物信息进行可视化展示，帮助康复医师更加精准地判断患者的康复效果。实验验证证明，与传统人工处理方法相比，RSVH系统将仿真准备时间减少约40%，并且简化了跨平台仿真操作的复杂度。更得益于其多任务并行执行能力，RSVH系统在仿真效率与自动化程度上显著优于单任务处理模式的现有系统。

Abstract: Addressing issues in existing rehabilitation robot simulation research, such as the mismatch between biomechanical characteristics and robot control strategies, and insufficient automation in human-robot coupling simulations, this study innovatively integrates robot kinematics analysis, training trajectory planning and design, and biomechanical characteristics of musculoskeletal models to construct a human-robot joint simulation system for upper limb rehabilitation robots based on OpenSim and MATLAB, and proposes an automated human-robot coupling simulation process. The system enables synchronous joint angle adjustment and motion playback visualization for matched models. At the robot simulation layer, it provides forward and inverse kinematics calculations, and offers four trajectory planning algorithms for different application scenarios. The computational results are converted in format and then transmitted to the biomechanical simulation layer. In the biomechanical simulation layer, residual reduction is combined with computational muscle control to compensate for unmodeled external forces (i.e., indirectly compensate for robot external force data errors) and optimize muscle activation solutions. It also supports the visualization of biological information such as muscle activation levels and muscle fiber lengths in simulation results, helping rehabilitation physicians more accurately assess patient recovery outcomes. The system is validated through joint kinematic and dynamic simulation experiments focused on the flexion of the right upper limb elbow. Compared to traditional methods, this innovative system significantly improves efficiency and automation while simplifying the complexity of cross-platform simulation operations.