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An online learning supported line-grasping control method for a de-icing robot based on the KNN\u2013 Q( \u03bb) algorithm is presented in this paper. The proposed KNN\u2013 Q( \u03bb) algorithm combines reinforcement learning with the k-nearest neighbor algorithm to perceive continuous states and uses a collective decision making mechanism in the action selection process to output continuous actions. Benefiting from the ability of online learning, the KNN\u2013 Q( \u03bb)-based line-grasping control method may tolerate possible robot model errors, robot arm attitude errors and environment interferences to line-grasping control. Simulation experiment results show that as a continuous-states-continuous-actions reinforcement learning algorithm, the KNN\u2013 Q( \u03bb) algorithm outperforms traditional reinforcement learning algorithms for the application of line-grasping control in terms of leaning rate and robustness. <\/jats:p>","DOI":"10.1177\/0959651812444240","type":"journal-article","created":{"date-parts":[[2012,6,7]],"date-time":"2012-06-07T01:21:20Z","timestamp":1339032080000},"page":"936-946","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["<i>KNN<\/i>\u2013<i>Q<\/i>(<i>\u03bb<\/i>) algorithm-based line-grasping control of a de-icing robot"],"prefix":"10.1177","volume":"226","author":[{"given":"Shuning","family":"Wei","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, People\u2019s Republic of China"},{"name":"College of Electrical Engineering, Guangxi University, People\u2019s Republic of China"}]},{"given":"Yaonan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Engineering, Hunan University, People\u2019s Republic of China"}]},{"given":"Yong","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, People\u2019s Republic of China"}]}],"member":"179","published-online":{"date-parts":[[2012,5,23]]},"reference":[{"issue":"4","key":"bibr1-0959651812444240","first-page":"7","volume":"32","author":"Li ZH","year":"2008","journal-title":"Power Syst Technol"},{"issue":"39","key":"bibr2-0959651812444240","first-page":"36","volume":"3","author":"Hu XH","year":"2006","journal-title":"Mater Protect"},{"key":"bibr3-0959651812444240","doi-asserted-by":"publisher","DOI":"10.1002\/rob.20295"},{"first-page":"108","volume-title":"Proceedings of the 6th IFAC symposium on intelligent autonomous vehicles","author":"Tavares L","key":"bibr4-0959651812444240"},{"first-page":"85","volume-title":"Proceedings of the 35th international symposium on robotics","author":"Rocha J","key":"bibr5-0959651812444240"},{"first-page":"3978","volume-title":"Proceedings of the IEEE international conference on robotics and automation","author":"Debenest P","key":"bibr6-0959651812444240"},{"issue":"2","key":"bibr7-0959651812444240","first-page":"111","volume":"29","author":"Zhang YC","year":"2007","journal-title":"Robot"},{"issue":"009","key":"bibr8-0959651812444240","first-page":"33","volume":"25","author":"Chen ZW","year":"2006","journal-title":"Transducer Microsyst Technol"},{"key":"bibr9-0959651812444240","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"first-page":"1563","volume-title":"Proceedings of 2005 IEEE conference on control applications","author":"Zhou FY","key":"bibr10-0959651812444240"},{"first-page":"3970","volume-title":"Proceedings of the IEEE international conference on robotics and automation","author":"Pouliot N","key":"bibr11-0959651812444240"},{"key":"bibr12-0959651812444240","doi-asserted-by":"publisher","DOI":"10.1029\/94JB03098"},{"key":"bibr13-0959651812444240","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2008.08.021"},{"key":"bibr14-0959651812444240","doi-asserted-by":"publisher","DOI":"10.1109\/34.888718"},{"key":"bibr15-0959651812444240","unstructured":"Watkins C. 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