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Conference Paper: Local path planning based on Ridge Regression Extreme Learning Machines for an outdoor robot

TitleLocal path planning based on Ridge Regression Extreme Learning Machines for an outdoor robot
Authors
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856
Citation
The 2015 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2015), Zhuhai, China, 6-9 December 2015. In Conference Proceedings, 2015, p. 745-750 How to Cite?
AbstractFor mobile robot local path planning under outdoor environment, Ridge Regression Extreme Learning Machines (RRELM) is adopted, it is a fast machine learning classification method to apply in path planning. Firstly, the laser rangefinder data are extracted and marked to describe the outdoor environment. Secondly, ridge regression theory is utilized to improve the generalization ability of ELM for local path planning. Meanwhile, the start-goal point constraint is considered for planning. Additionally, abrupt dynamic obstacle is regarded as a kind of disturbance to plan the path by RRELM. Then the optimal path is estimated by the distance evaluation function among feasible paths. Finally, a great deal of outdoor robot simulation experiments are shown that RRELM find out the safety path for outdoor robot, and the generalization ability, smoothness and rapidity of RRELM for path planning are better than SVM and ELM, furthermore, the performance of RRELM for the dynamic environment is also efficient. © 2015 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/235012

 

DC FieldValueLanguage
dc.contributor.authorYu, L-
dc.contributor.authorLong, Z-
dc.contributor.authorXi, N-
dc.contributor.authorJia, Y-
dc.contributor.authorDing, C-
dc.date.accessioned2016-10-14T13:50:42Z-
dc.date.available2016-10-14T13:50:42Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE International Conference on Robotics and Biomimetics (IEEE-ROBIO 2015), Zhuhai, China, 6-9 December 2015. In Conference Proceedings, 2015, p. 745-750-
dc.identifier.urihttp://hdl.handle.net/10722/235012-
dc.description.abstractFor mobile robot local path planning under outdoor environment, Ridge Regression Extreme Learning Machines (RRELM) is adopted, it is a fast machine learning classification method to apply in path planning. Firstly, the laser rangefinder data are extracted and marked to describe the outdoor environment. Secondly, ridge regression theory is utilized to improve the generalization ability of ELM for local path planning. Meanwhile, the start-goal point constraint is considered for planning. Additionally, abrupt dynamic obstacle is regarded as a kind of disturbance to plan the path by RRELM. Then the optimal path is estimated by the distance evaluation function among feasible paths. Finally, a great deal of outdoor robot simulation experiments are shown that RRELM find out the safety path for outdoor robot, and the generalization ability, smoothness and rapidity of RRELM for path planning are better than SVM and ELM, furthermore, the performance of RRELM for the dynamic environment is also efficient. © 2015 IEEE.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000856-
dc.relation.ispartofIEEE International Conference on Robotics and Biomimetics Proceedings-
dc.rightsIEEE International Conference on Robotics and Biomimetics Proceedings. Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.titleLocal path planning based on Ridge Regression Extreme Learning Machines for an outdoor robot-
dc.typeConference_Paper-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ROBIO.2015.7418858-
dc.identifier.scopuseid_2-s2.0-84964523281-
dc.identifier.hkuros269344-
dc.identifier.hkuros978-146739674-5-
dc.identifier.spage745-
dc.identifier.epage750-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161019-

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