Swift augmented human–robot dynamics modeling for rehabilitation planning analyses

IF 2.6 2区 工程技术 Q2 MECHANICS Multibody System Dynamics Pub Date : 2024-03-14 DOI:10.1007/s11044-024-09975-3
Vahid Akbari, Omid Mahdizadeh, S. Ali A. Moosavian, Mahdi Nabipour
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Abstract

With the widespread implementation of robotics exoskeletons in rehabilitation, modeling and dynamics analysis of such highly nonlinear coupled systems has become significantly important. In this paper, a swift numerical human–robot dynamics modeling has been developed to achieve accurate and realistic interpretation. This takes into consideration the separation and impact between multiple bodies for rehabilitation planning. To this end, first, a novel parallel algorithm combined with sequential interaction conditions is proposed based on the numerical recursive Newton–Euler method. The approach begins by deriving separated numerical models for the complicated system: i.e. both the human and the robot. These models are then augmented, with a primary focus on reducing the error of the interaction conditions, including forces and positions. The accuracy of the proposed model, with a computational complexity of O(n), is assessed by comparing to a previously validated nonrecursive analytical model with a higher computational complexity of O(n^4). Additionally, the quality of the connection between the human and the robot is assessed to establish a suitable control objective and an effective interaction strategy for rehabilitation planning. The study employs a lower-limb walking assistive robot developed in the ARAS lab (RoboWalk) to validate the proposed method. The algorithm is empirically implemented on the RoboWalk test stand, ensuring the integrity of the proposed dynamics modeling. The human–robot interaction forces are estimated with an accuracy of 2 N, in the presence of friction and measurement noise. Finally, the effectiveness of the model-based controller is assessed by using the proposed method, providing valuable tools for the enhancement of overall performance of such a complex dynamics system.

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用于康复规划分析的 Swift 增强型人机动力学建模
随着机器人外骨骼在康复领域的广泛应用,此类高度非线性耦合系统的建模和动力学分析变得尤为重要。本文开发了一种快速的人与机器人动力学数值建模,以实现准确而逼真的解释。其中考虑到了康复规划中多体之间的分离和影响。为此,首先在数值递归牛顿-欧拉法的基础上,提出了一种结合顺序交互条件的新型并行算法。该方法首先为复杂的系统(即人类和机器人)推导出分离的数值模型。然后对这些模型进行增强,主要重点是减少包括力和位置在内的交互条件的误差。建议模型的计算复杂度为 O(n),通过与之前验证的计算复杂度更高(O(n^4))的非递归分析模型进行比较,评估了建议模型的准确性。此外,还对人与机器人之间的连接质量进行了评估,以便为康复规划确定合适的控制目标和有效的交互策略。研究采用了 ARAS 实验室开发的下肢行走辅助机器人(RoboWalk)来验证所提出的方法。该算法是在 RoboWalk 测试台上根据经验实施的,以确保所建议的动力学建模的完整性。在存在摩擦和测量噪声的情况下,人机交互力的估计精度为 2 N。最后,使用所提出的方法对基于模型的控制器的有效性进行了评估,为提高此类复杂动力学系统的整体性能提供了宝贵的工具。
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来源期刊
CiteScore
6.00
自引率
17.60%
发文量
46
审稿时长
12 months
期刊介绍: The journal Multibody System Dynamics treats theoretical and computational methods in rigid and flexible multibody systems, their application, and the experimental procedures used to validate the theoretical foundations. The research reported addresses computational and experimental aspects and their application to classical and emerging fields in science and technology. Both development and application aspects of multibody dynamics are relevant, in particular in the fields of control, optimization, real-time simulation, parallel computation, workspace and path planning, reliability, and durability. The journal also publishes articles covering application fields such as vehicle dynamics, aerospace technology, robotics and mechatronics, machine dynamics, crashworthiness, biomechanics, artificial intelligence, and system identification if they involve or contribute to the field of Multibody System Dynamics.
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