Research on a Full-body Power-assisted Exoskeleton Control Method Based on GSO-RF Intent Recognition Algorithm

doi:10.3901/JME.2024.17.091

Abstract

Abstract: To address the complex structure and single function of existing power-assisted exoskeletons, a full-body power-assisted exoskeleton is designed based on the ergonomic theory, and the upper and lower limbs are integrated to improve the assisted effect. To address the problem of low accuracy of motion intention recognition of the power-assisted exoskeleton, a motion intention recognition method based on posture signals and electromyography signals is proposed. The random forest (RF) algorithm is used to classify the human-robot interaction signals and determine the wearer's movement intention; the Glowworm swarm optimization (GSO) algorithm is used to optimize the random forest to improve the classification accuracy and reduce the classification time. To improve the trajectory tracking accuracy of the exoskeleton control system and ensure the suppleness of the exoskeleton movement, a motion control strategy based on the guided adaptive fuzzy inversion algorithm is proposed. The exoskeleton experimental platform is built to carry out motion intention recognition and trajectory tracking experiments. The results show that the accuracy of motion intention recognition can reach 96.6%, and the real-time performance is good, the proposed control strategy is effective and feasible, and the trajectory tracking effect is good. The booster performance of the exoskeleton has been proven through booster effectiveness tests.

Key words: full-body power-assisted exoskeleton, motion intent recognition, human-robot interaction, motion control

CLC Number:

TG156

YUAN Xiaoqing, WU Tao, YUAN Xun, WANG Wendong. Research on a Full-body Power-assisted Exoskeleton Control Method Based on GSO-RF Intent Recognition Algorithm[J]. Journal of Mechanical Engineering, 2024, 60(17): 91-101.

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