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

Abstract

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.