Biomechanical Trajectory Optimization of Human Sit-to-Stand Motion With Stochastic Motion Planning Framework

被引:3
作者
Sharma, Bibhu [1 ,2 ]
Pillai, Branesh M. [1 ,2 ]
Suthakorn, Jackrit [1 ,2 ]
机构
[1] Mahidol Univ, Fac Engn, Dept Biomed Engn, Nakhon Pathom 73170, Thailand
[2] Mahidol Univ, Fac Engn, Ctr Biomed & Robot Technol BART LAB, Nakhon Pathom 73170, Thailand
来源
IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS | 2022年 / 4卷 / 04期
关键词
Biomechanics; optimal control; reinforcement learning; MOVEMENT; FORCES; CONTACT; TORQUE;
D O I
10.1109/TMRB.2022.3205509
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Trajectory optimization has been an important approach in biomechanics for the analysis and prediction of the limb movement. Such approaches have paved the way for the motion planning of biped and quadruped robots as well. Most of these methods are deterministic, utilizing first-order iterative gradient-based algorithms incorporating the constrained differentiable objective functions. However, the limitation of prevailing methods concerning differentiability hinders the implementation of non-differentiable objective functions such as metabolic energy expenditure (MEE) function, which is highly relevant for physiological systems and can even be implemented across the muscular space. This paper consolidates the implementation of the prevalent direct collocation-based optimal control method with the stochastic trajectory optimization method based on Policy Improvement with Path Integral (PI2) for comprehending the human sit-to-stand (STS) motion. PI2 method, which utilizes reinforcement learning of Dynamic Movement Primitive (DMP) to learn a goal-based trajectory is implemented and validated by comparing with the experimental result in joint-space and muscle-space.
引用
收藏
页码:1022 / 1033
页数:12
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