Covering path generation for autonomous turf-care vehicle

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

A covering path generation algorithm is developed to generate a lengthwise pattern based on a polygon describing the outer boundary and obstacles (polygon holes) of a geographical area. The algorithm is applied to an autonomous lawn-care robot for application to large grass turfs, for example golf-courses, which require structured and precise cutting patterns. The geographical polygon is recorded by manually driving the vehicle around the contour, resulting in a polygon given as geographical (latitude, longitude) coordinates of the vertices, which together with machine parameters are used to generate a suitable toolpath. The algorithm has been tested on a recorded polygon from a local park turf which is non-convex and has holes, illustrating the algorithm functionality and limitations wrt. optimality. In particular, the algorithm can generate a tool-path for any polygon orientation.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics
PublisherIEEE
Publication date2017
Pages346-351
Article number8014041
ISBN (Print)978-1-5090-5998-0, 978-1-5090-6001-6
ISBN (Electronic)978-1-5090-6000-9, 978-1-5090-5999-7
DOIs
Publication statusPublished - 2017
Event2017 IEEE International Conference on Advanced Intelligent Mechatronics - Munich, Germany
Duration: 3. Jul 20177. Jul 2017

Conference

Conference2017 IEEE International Conference on Advanced Intelligent Mechatronics
CountryGermany
CityMunich
Period03/07/201707/07/2017

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Mai, C., Jouffroy, J., Top, S., & Bjærre, M. (2017). Covering path generation for autonomous turf-care vehicle. In Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics (pp. 346-351). [8014041] IEEE. https://doi.org/10.1109/AIM.2017.8014041
Mai, Christian ; Jouffroy, Jerome ; Top, Søren ; Bjærre, Martin. / Covering path generation for autonomous turf-care vehicle. Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics. IEEE, 2017. pp. 346-351
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abstract = "A covering path generation algorithm is developed to generate a lengthwise pattern based on a polygon describing the outer boundary and obstacles (polygon holes) of a geographical area. The algorithm is applied to an autonomous lawn-care robot for application to large grass turfs, for example golf-courses, which require structured and precise cutting patterns. The geographical polygon is recorded by manually driving the vehicle around the contour, resulting in a polygon given as geographical (latitude, longitude) coordinates of the vertices, which together with machine parameters are used to generate a suitable toolpath. The algorithm has been tested on a recorded polygon from a local park turf which is non-convex and has holes, illustrating the algorithm functionality and limitations wrt. optimality. In particular, the algorithm can generate a tool-path for any polygon orientation.",
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Mai, C, Jouffroy, J, Top, S & Bjærre, M 2017, Covering path generation for autonomous turf-care vehicle. in Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics., 8014041, IEEE, pp. 346-351, 2017 IEEE International Conference on Advanced Intelligent Mechatronics, Munich, Germany, 03/07/2017. https://doi.org/10.1109/AIM.2017.8014041

Covering path generation for autonomous turf-care vehicle. / Mai, Christian; Jouffroy, Jerome; Top, Søren; Bjærre, Martin.

Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics. IEEE, 2017. p. 346-351 8014041.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Mai C, Jouffroy J, Top S, Bjærre M. Covering path generation for autonomous turf-care vehicle. In Proceedings of the 2017 IEEE International Conference on Advanced Intelligent Mechatronics. IEEE. 2017. p. 346-351. 8014041 https://doi.org/10.1109/AIM.2017.8014041