Power-assisted Optimization Model of Heterogeneous Sensor Exoskeleton Devices Based on Swarm Intelligence Algorithm and Dynamics Optimization

With the ongoing integration of bionic technology and mechatronics, exoskeleton devices are finding applications in industry, healthcare, and logistics. This study is centered on enhancing the performance of heterogeneous sensor exoskeleton devices by presenting a six-degree-of-freedom upper limb ex...

Full description

Saved in:
Bibliographic Details
Published in:Sensors and materials Vol. 36; no. 1; p. 305
Main Authors: Liu, Mingxian, Zhou, Xinbo, Bao, Jing
Format: Journal Article
Language:English
Published: Tokyo MYU Scientific Publishing Division 01.01.2024
Subjects:
Algorithms
Bionics
Exoskeletons
Industrial applications
Model accuracy
Multiple objective analysis
Optimization models
Swarm intelligence
Trigonometric functions
ISSN:0914-4935, 2435-0869
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With the ongoing integration of bionic technology and mechatronics, exoskeleton devices are finding applications in industry, healthcare, and logistics. This study is centered on enhancing the performance of heterogeneous sensor exoskeleton devices by presenting a six-degree-of-freedom upper limb exoskeleton robot model based on the Denavit–Hartenberg (MDH) approach. The model's accuracy is verified using MATLAB. We construct a multi-objective optimization model that prioritizes workspace expansion. To realize this model, we propose an improved Harris Hawks algorithm (SCA-HHO) based on the sine cosine algorithm. The algorithm's effectiveness is compared with popular swarm intelligence methods (PSO, AOA, WOA) through cross-sectional simulations. SCA-HHO achieves average improvements of 6.30, 1.48, and 0.88% in objective function values compared with the swarm intelligence algorithms, respectively. This indicates SCA-HHO's superior suitability for solving the model proposed in this paper.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0914-4935
2435-0869
DOI:10.18494/SAM4649