OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD
Quadcopter is a type of Unmanned Aerial Vehicle (UAV) technology, characterized by simple mechanical structure, ease of flying and good maneuvering. In its usage, the quadcopter is required to evade obstacles in its path. Thus, an obstacle avoidance system in a 3D space with both static and dynamic...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Informatics Department, Engineering Faculty
2022-07-01
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| Series: | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
| Subjects: | |
| Online Access: | https://kursorjournal.org/index.php/kursor/article/view/267 |
| _version_ | 1797743913571713024 |
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| author | Hendy Prasetyo Trihastuti Agustinah |
| author_facet | Hendy Prasetyo Trihastuti Agustinah |
| author_sort | Hendy Prasetyo |
| collection | DOAJ |
| description | Quadcopter is a type of Unmanned Aerial Vehicle (UAV) technology, characterized by simple mechanical structure, ease of flying and good maneuvering. In its usage, the quadcopter is required to evade obstacles in its path. Thus, an obstacle avoidance system in a 3D space with both static and dynamic obstacles is. Avoidance direction is determined by considering nearest distance based on the dimensions of the obstacle. Due to limited battery capacity, the quadcopter also needs to consider energy efficiency in obstacle avoidance. The obstacle’s properties and movement direction are also needed in considering the correct avoidance direction. Using a modified Local Mean K-Nearest Centroid Neighbor (LMKNCN) algorithm results in a 97.5% accuracy for avoidance direction decision. The learning process between training data and testing data yielded a computation duration of 0.142341 seconds. The simulations showed that the quadcopter is able to avoid static and dynamic obstacles to reach its destination without collisions. |
| first_indexed | 2024-03-12T15:02:21Z |
| format | Article |
| id | doaj.art-3e0e667c926a4e499a35ff083c67e384 |
| institution | Directory Open Access Journal |
| issn | 0216-0544 2301-6914 |
| language | English |
| last_indexed | 2024-03-12T15:02:21Z |
| publishDate | 2022-07-01 |
| publisher | Informatics Department, Engineering Faculty |
| record_format | Article |
| series | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi |
| spelling | doaj.art-3e0e667c926a4e499a35ff083c67e3842023-08-13T20:42:15ZengInformatics Department, Engineering FacultyJurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi0216-05442301-69142022-07-0111310.21107/kursor.v11i3.267OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHODHendy Prasetyo0Trihastuti Agustinah1Institut Teknologi Sepuluh Nopember (ITS)Institut Teknologi Sepuluh Nopember (ITS)Quadcopter is a type of Unmanned Aerial Vehicle (UAV) technology, characterized by simple mechanical structure, ease of flying and good maneuvering. In its usage, the quadcopter is required to evade obstacles in its path. Thus, an obstacle avoidance system in a 3D space with both static and dynamic obstacles is. Avoidance direction is determined by considering nearest distance based on the dimensions of the obstacle. Due to limited battery capacity, the quadcopter also needs to consider energy efficiency in obstacle avoidance. The obstacle’s properties and movement direction are also needed in considering the correct avoidance direction. Using a modified Local Mean K-Nearest Centroid Neighbor (LMKNCN) algorithm results in a 97.5% accuracy for avoidance direction decision. The learning process between training data and testing data yielded a computation duration of 0.142341 seconds. The simulations showed that the quadcopter is able to avoid static and dynamic obstacles to reach its destination without collisions.https://kursorjournal.org/index.php/kursor/article/view/267Energy EfficientObstacle AvoidanceMachine LearningModified LMKNCNMovement TrendsQuadcopter Navigation |
| spellingShingle | Hendy Prasetyo Trihastuti Agustinah OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi Energy Efficient Obstacle Avoidance Machine Learning Modified LMKNCN Movement Trends Quadcopter Navigation |
| title | OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD |
| title_full | OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD |
| title_fullStr | OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD |
| title_full_unstemmed | OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD |
| title_short | OBSTACLE AVOIDANCE IN QUADCOPTER NAVIGATION USING MODIFIED LOCAL MEAN K-NEAREST CENTROID NEIGHBOR METHOD |
| title_sort | obstacle avoidance in quadcopter navigation using modified local mean k nearest centroid neighbor method |
| topic | Energy Efficient Obstacle Avoidance Machine Learning Modified LMKNCN Movement Trends Quadcopter Navigation |
| url | https://kursorjournal.org/index.php/kursor/article/view/267 |
| work_keys_str_mv | AT hendyprasetyo obstacleavoidanceinquadcopternavigationusingmodifiedlocalmeanknearestcentroidneighbormethod AT trihastutiagustinah obstacleavoidanceinquadcopternavigationusingmodifiedlocalmeanknearestcentroidneighbormethod |