Ningbo Key Laboratory of Aging Health Equipment and Service Technology, Ningbo Polytechnic, Ningbo, China.
Hebei Province Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao, China.
Sci Rep. 2024 Oct 26;14(1):25476. doi: 10.1038/s41598-024-77137-8.
The inverse kinematics problem of exoskeleton rehabilitation robots is challenging due to the lack of a standard analytical model, resulting in a complex and varied solution process. This complexity is especially pronounced in redundant upper limb exoskeleton robots, where inefficient solutions hinder the robot's ability to adapt to the kinematic shape of the upper limb. This paper proposes a modeling and solution method based on multi-objective optimization to address the inverse kinematics of upper limb exoskeleton robots. We analyzed and validated this method using a redundant upper limb exoskeleton rehabilitation robot system developed by ourselves. First, we established a multi-objective inverse kinematics solution model by defining the end-position function, joint motion comfort function, system energy consumption function, motion safety, and human-like constraints. Then, the solution was designed based on the Improved Equilibrium Optimization (IEO) algorithm and validated its computational performance in terms of optimization ability, accuracy, and robustness. Finally, we experimentally tested the inverse kinematics solution model with the IEO algorithm on discrete objectives and continuous training trajectories using a redundant upper limb exoskeleton rehabilitation robot system. The results show that by incorporating the joint comfort function, system energy consumption function, and human-like constraints into the inverse kinematics model, we can not only quickly solve the inverse kinematics of redundant upper limb exoskeleton robots but also significantly improve the robot's motion shape. Furthermore, it has better solution accuracy and stronger robustness than other algorithms when solving this inverse kinematics model based on the IEO algorithm.
外骨骼康复机器人的逆运动学问题具有挑战性，因为缺乏标准的分析模型，导致解决方案过程复杂且多样化。这种复杂性在冗余上肢外骨骼机器人中尤为明显，低效的解决方案会阻碍机器人适应上肢运动形态的能力。本文提出了一种基于多目标优化的建模和解法，用于解决上肢外骨骼机器人的逆运动学问题。我们使用我们自主开发的冗余上肢外骨骼康复机器人系统对该方法进行了分析和验证。首先，我们通过定义末端位置函数、关节运动舒适度函数、系统能耗函数、运动安全性和类人约束，建立了多目标逆运动学求解模型。然后，基于改进平衡优化（IEO）算法设计了解法，并从优化能力、准确性和鲁棒性方面验证了其计算性能。最后，我们使用冗余上肢外骨骼康复机器人系统对离散目标和连续训练轨迹上的 IEO 算法的逆运动学求解模型进行了实验测试。结果表明，通过将关节舒适度函数、系统能耗函数和类人约束纳入逆运动学模型中，不仅可以快速求解冗余上肢外骨骼机器人的逆运动学问题，还可以显著改善机器人的运动形态。此外，与其他算法相比，基于 IEO 算法求解该逆运动学模型时，具有更好的求解精度和更强的鲁棒性。
