Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task
Joint fatigue-based optimal posture prediction for maximizing endurance time in box carrying task
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DOI：
10.1007/s11044-022-09832-1
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发表时间：
2022-06-20
影响因子：
3.4
通讯作者：
Yang, James
中科院分区：
文献类型：
--
作者：
Barman, Shuvrodeb;Xiang, Yujiang;Yang, James
在这项研究中，三室控制器疲劳模型集成了逆动力学优化程序来预测最佳姿势，关节疲劳，和耐力时间的箱子携带任务。所采用的二维人体模型具有10个自由度。对于箱子搬运任务，脚固定在地面上，并给出手的位置和箱子的重量。在基于关节疲劳的姿势预测公式中，设计变量是关节角度、三隔室控制值和总的箱子携带持续时间（耐力时间）。目标是在任务和疲劳约束下最大化总时间，包括舱室单位约束、剩余容量约束和一种新的耦合失效约束。优化成功地预测了最佳姿势，关节扭矩，耐力时间，关节疲劳进展，和关节故障条件。所提出的新的联合疲劳为基础的配方预测最佳的姿势，最大限度地提高耐力时间与一个给定的箱子重量的箱子携带任务。最后，模拟计算效率高，在普通计算机上约5秒的CPU时间内即可获得最佳结果。
In this study, the three-compartment controller fatigue model is integrated with an inverse dynamics optimization routine to predict the optimal posture, joint fatigue, and endurance time for a box carrying task. The two-dimensional human model employed has 10 degrees of freedom. For the box carrying task, the feet are fixed on the ground, and the hand location and box weight are given. In the joint fatigue-based posture prediction formulation, the design variables are joint angles, three-compartment control values, and total box carrying duration (endurance time). The objective is to maximize the total time subject to task and fatigue constraints, including compartment unity constraint, residual capacity constraint, and a novel coupled failure constraint. The optimization successfully predicts the optimal posture, joint torque, endurance time, joint fatigue progression, and joint failure conditions. The proposed novel joint fatigue-based formulation predicts the optimal posture for maximizing the endurance time with a given box weight for a box-carrying task. Finally, the simulation is computationally efficient, and the optimal results are achieved in about 5 seconds CPU time on a regular computer.