Title
Design frame of a leg exoskeleton for load-carrying augmentation
Abstract
This paper presents a design frame of a leg exoskeleton. The performance of an exoskeleton was analyzed by studying a model of a linear coupled 1-DOF human-exoskeleton system. The results showed that a low-impedance, anthropomorphic and well attached mechanical structure is benefit for the control of exoskeleton, and a direct force feedback control could also be implemented to further reduce the impedance. Three kinds of applied sensor interface were compared. A leg exoskeleton called ELEBOT was developed for validating control algorithm. The ELEBOT included four main parts: mechanical structure, hydraulic actuator, sensor and control system and power. The function of such a load-carrying robot is to reduce the injuries associated with the back and lower limbs after a long heavy walk. Experiment results with a simple force controller confirmed that ELEBOT could efficiently assist people walking with 30 kg payload now.
Year
DOI
Venue
2009
10.1109/ROBIO.2009.5420684
ROBIO
Keywords
Field
DocType
exoskeleton mechanical structure,control system,force sensors,hydraulic actuators,interface,motion control,robots,sensor interface,design frame,low-impedance,experiment result,force feedback control,simple force controller,linear coupled 1-dof human-exoskeleton system,direct force feedback control,hydraulic actuator,load-carrying augmentation,mechanical structure,control algorithm,force control,materials handling,exoskeleton,elebot exoskeleton,applied sensor interface,leg exoskeleton,force feedback,force controller,sensor system,power,1-dof human-exoskeleton system,dynamic model,exoskeletons,force,torque,leg
Motion control,Control theory,Torque,Control theory,Simulation,Control engineering,Exoskeleton,Engineering,Control system,Haptic technology,Payload,Hydraulic cylinder
Conference
ISBN
Citations 
PageRank 
978-1-4244-4775-6
4
0.62
References 
Authors
9
5
Name
Order
Citations
PageRank
Heng Cao172.07
Zhengyang Ling240.95
Jun Zhu3989.61
Yu Wang451.67
Wei Wang540.62