Log InSearch
Open Access
zourenling@163.comRenling Zou, Department of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China. E-mail: zourenling@163.com.
A novel lower limb exoskeleton was designed based on human biomechanics.
The positive and inverse kinematic solutions of the exoskeleton were determined using the D-H method and geo metric method, respectively.
Geometrical relationships of the exoskeleton linkage members were utilized to derive workspace expressions for different gait stages.
The efficacy of the online gait generation algorithm was assessed by providing initial conditions.
Simulation experiments were conducted to analyse the dynamic self-balancing capabilities of the exoskeleton during flat walking.
Open Accesszourenling@163.comRenling Zou, Department of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200082, China. E-mail: zourenling@163.com.
A novel lower limb exoskeleton was designed based on human biomechanics.
The positive and inverse kinematic solutions of the exoskeleton were determined using the D-H method and geo metric method, respectively.
Geometrical relationships of the exoskeleton linkage members were utilized to derive workspace expressions for different gait stages.
The efficacy of the online gait generation algorithm was assessed by providing initial conditions.
Simulation experiments were conducted to analyse the dynamic self-balancing capabilities of the exoskeleton during flat walking.
Objective: This study presents the design of an innovative lower limb exoskeleton featuring dynamic self-balancing capabilities. It employs Zero Moment Point and Model Predictive Control online gait algorithms to plan stable walking patterns, thereby facilitating precise gait training for individuals with lower limb motor impairments and enhancing the overall training effectiveness. Methods: The positive and negative kinematic solutions of the lower limb exoskeleton were determined using the Denavit-Hartenberg method and the geometric method, respectively. The geometric relationships of the exoskeleton’s linkage components were employed to derive workspace expres sions for various gait phases. By utilizing Zero Moment Point and Model Predictive Control online gait algorithms, simulation experiments were conducted to validate the dynamic self-balancing capability of the exoskeleton while walking on flat terrain. Results: In the evaluation of the online gait generation algorithm’s validity, the generated gait trajectory aligned with the planned trajectory. When examining dynamic self-balancing walking capability, the trajectories from initial simulation experiments on flat terrain closely matched the intended trajectories. Conclusion: The online gait generation algorithm presented in this study is capable of producing a stable walking pattern for continuous bipedal gait. This newly designed lower limb exoskeleton can achieve stable dynamic self-balancing walking.
Keywords: Lower limb exoskeleton, online gait generation algorithm, dynamic self-balancing, rehabilitation training
ISSN: 2957-5478
Volume 3, Issue 1
March 2025
Pages: 1-76
Log In
On This Page
CITE