Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/71498
Title: Optimal design of a suspended cable driven system for locomotion device
Authors: Chawalit Khanakornsuksan
Theeraphong Wongratanaphisan
Authors: Chawalit Khanakornsuksan
Theeraphong Wongratanaphisan
Keywords: Engineering
Issue Date: 1-Jan-2020
Abstract: © 2020, Levrotto and Bella. All rights reserved. Cable-driven systems are promising alternatives to articulating machines/robots, especially, when a large range of motion is required. They have many advantages such as low inertia, high load bearing capacity, easy installation and low-cost. The purpose of this study is two-fold: 1) to introduce a new design concept of an automated gait machine using cable suspension configuration and 2) to provide a methodology for optimizing the design using the particle swarm optimization (PSO) technique in order to minimize cable tension forces. The design consists of a platform where user stands on and three cables suspending and actuating the standing platform to generate path. Walking paths for user covering a range of leg’s length are used to generate required motion of the platform. Cable forces required to produce motion are calculated from kinematic and dynamic analysis. The workspace for optimization includes motion paths that consists of position and orientation of the foot and also with external wrenches due ground reaction forces along gait cycle. Through unconstrained optimization, the maximum tension force is around 0.48 times of the user’s weight. However, the resulting footprint of the system is very large. To obtain realistic design, constraints on cable attachment locations are added to the PSO algorithm. The trade-off to added constraints is the increase in the maximum cable forces to user’s weight ratio to 0.63.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85098264407&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/71498
ISSN: 15908844
Appears in Collections:CMUL: Journal Articles

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