Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles
This research presents an unmanned ground vehicle for identifying infested trees by bark beetles in mountain forests. The ground vehicle uses sensors for autonomous navigation and obstacle avoidance. The identification of infested trees is carried out by classifying the resin stains on the bark of u...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/12/12/944 |
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| author | Jonathan Flores Sergio Salazar Iván González-Hernández Yukio Rosales-Luengas Rogelio Lozano |
| author_facet | Jonathan Flores Sergio Salazar Iván González-Hernández Yukio Rosales-Luengas Rogelio Lozano |
| author_sort | Jonathan Flores |
| collection | DOAJ |
| description | This research presents an unmanned ground vehicle for identifying infested trees by bark beetles in mountain forests. The ground vehicle uses sensors for autonomous navigation and obstacle avoidance. The identification of infested trees is carried out by classifying the resin stains on the bark of unhealthy trees with a computer vision algorithm. This approach proposes tracking bark beetle spread in forest trees with image data of the infested trees considering resin sprouts as early indicators of the infestation in contrast to aerial monitoring, which only detects trees in advanced stages. Terrain autonomous vehicle direction is controlled by changing the velocities of left- and right-side wheels. A rotating LiDAR sensor is used to detect trees and avoid objects. The dynamic model of the vehicle is presented, and a control algorithm is proposed for path-following. Moreover, the stability of the system is proven using a Lyapunov function. In order to demonstrate the performance of the control and classification algorithms, experimental results from an outdoor forest environment are presented. |
| format | Article |
| id | doaj-art-ec8a1766099b4240aa5c4b76665ee6c1 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-ec8a1766099b4240aa5c4b76665ee6c12024-12-27T14:37:15ZengMDPI AGMachines2075-17022024-12-01121294410.3390/machines12120944Unmanned Ground Vehicle for Identifying Trees Infested with Bark BeetlesJonathan Flores0Sergio Salazar1Iván González-Hernández2Yukio Rosales-Luengas3Rogelio Lozano4Program of Aerial and Submarine Autonomous Navigation Systems, Department of Research and Multidisciplinary Studies, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoProgram of Aerial and Submarine Autonomous Navigation Systems, Department of Research and Multidisciplinary Studies, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoProgram of Aerial and Submarine Autonomous Navigation Systems, Department of Research and Multidisciplinary Studies, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoProgram of Aerial and Submarine Autonomous Navigation Systems, Department of Research and Multidisciplinary Studies, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoProgram of Aerial and Submarine Autonomous Navigation Systems, Department of Research and Multidisciplinary Studies, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City 07360, MexicoThis research presents an unmanned ground vehicle for identifying infested trees by bark beetles in mountain forests. The ground vehicle uses sensors for autonomous navigation and obstacle avoidance. The identification of infested trees is carried out by classifying the resin stains on the bark of unhealthy trees with a computer vision algorithm. This approach proposes tracking bark beetle spread in forest trees with image data of the infested trees considering resin sprouts as early indicators of the infestation in contrast to aerial monitoring, which only detects trees in advanced stages. Terrain autonomous vehicle direction is controlled by changing the velocities of left- and right-side wheels. A rotating LiDAR sensor is used to detect trees and avoid objects. The dynamic model of the vehicle is presented, and a control algorithm is proposed for path-following. Moreover, the stability of the system is proven using a Lyapunov function. In order to demonstrate the performance of the control and classification algorithms, experimental results from an outdoor forest environment are presented.https://www.mdpi.com/2075-1702/12/12/944UGVtree classificationbark beetle identification |
| spellingShingle | Jonathan Flores Sergio Salazar Iván González-Hernández Yukio Rosales-Luengas Rogelio Lozano Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles Machines UGV tree classification bark beetle identification |
| title | Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles |
| title_full | Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles |
| title_fullStr | Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles |
| title_full_unstemmed | Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles |
| title_short | Unmanned Ground Vehicle for Identifying Trees Infested with Bark Beetles |
| title_sort | unmanned ground vehicle for identifying trees infested with bark beetles |
| topic | UGV tree classification bark beetle identification |
| url | https://www.mdpi.com/2075-1702/12/12/944 |
| work_keys_str_mv | AT jonathanflores unmannedgroundvehicleforidentifyingtreesinfestedwithbarkbeetles AT sergiosalazar unmannedgroundvehicleforidentifyingtreesinfestedwithbarkbeetles AT ivangonzalezhernandez unmannedgroundvehicleforidentifyingtreesinfestedwithbarkbeetles AT yukiorosalesluengas unmannedgroundvehicleforidentifyingtreesinfestedwithbarkbeetles AT rogeliolozano unmannedgroundvehicleforidentifyingtreesinfestedwithbarkbeetles |