Extended Evolutionary Fast Learn-to-Walk Approach for Four-Legged Robots

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dc.contributor.author Anshar, M
dc.contributor.author Williams, M-A
dc.date.accessioned 2009-12-21T02:32:36Z
dc.date.issued 2007-12
dc.identifier.citation Journal of Bionic Engineering, 2007, 4 (4), pp. 255 - 263
dc.identifier.issn 1672-6529
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/4303
dc.description.abstract Robot locomotion is an active research area. In this paper we focus on the locomotion of quadruped robots. An effective walking gait of quadruped robots is mainly concerned with two key aspects, namely speed and stability. The large search space of potential parameter settings for leg joints means that hand tuning is not feasible in general. As a result walking parameters are typically determined using machine learning techniques. A major shortcoming of using machine learning techniques is the significant wear and tear of robots since many parameter combinations need to be evaluated before an optimal solution is found. This paper proposes a direct walking gait learning approach, which is specifically designed to reduce wear and tear of robot motors, joints and other hardware. In essence we provide an effective learning mechanism that leads to a solution in a faster convergence time than previous algorithms. The results demonstrate that the new learning algorithm obtains a faster convergence to the best solutions in a short run. This approach is significant in obtaining faster walking gaits which will be useful for a wide range of applications where speed and stability are important. Future work will extend our methods so that the faster convergence algorithm can be applied to a two legged humanoid and lead to less wear and tear whilst still developing a fast and stable gait. © 2007 Jilin University.
dc.language eng
dc.relation.isbasedon 10.1016/S1672-6529(07)60039-0
dc.title Extended Evolutionary Fast Learn-to-Walk Approach for Four-Legged Robots
dc.type Journal Article
dc.parent Journal of Bionic Engineering
dc.journal.volume 4
dc.journal.volume 4
dc.journal.number 4 en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 255 en_US
dc.identifier.endpage 264 en_US
dc.cauo.name FEIT.School of Software en_US
dc.conference Verified OK en_US
dc.for 0915 Interdisciplinary Engineering
dc.personcode 020389
dc.percentage 100 en_US
dc.classification.name Interdisciplinary Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition December 30, 2007 en_US
dc.description.keywords convergence
dc.description.keywords genetic
dc.description.keywords learning
dc.description.keywords legged-robots
dc.description.keywords locomotion
dc.description.keywords walking gaits
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history Closed (ID: 3)

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