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http://dx.doi.org/10.12673/jant.2022.26.6.427

K-Means Clustering Algorithm and CPA based Collinear Multiple Static Obstacle Collision Avoidance for UAVs  

Hyeji Kim (Department of Aeronautical Systems Engineering, Hanseo University)
Hyeok Kang (Department of Aeronautical Systems Engineering, Hanseo University)
Seongbong Lee (Department of Aeronautical Systems Engineering, Hanseo University)
Hyeongseok Kim (Department of Aeronautical Systems Engineering, Hanseo University)
Dongjin Lee (Department of Aeronautical Systems Engineering, Hanseo University)
Publication Information
Journal of Advanced Navigation Technology / v.26, no.6, 2022 , pp. 427-433 More about this Journal
Abstract
Obstacle detection, collision recognition, and avoidance technologies are required the collision avoidance technology for UAVs. In this paper, considering collinear multiple static obstacle, we propose an obstacle detection algorithm using LiDAR and a collision recognition and avoidance algorithm based on CPA. Preprocessing is performed to remove the ground from the LiDAR measurement data before obstacle detection. And we detect and classify obstacles in the preprocessed data using the K-means clustering algorithm. Also, we estimate the absolute positions of detected obstacles using relative navigation and correct the estimated positions using a low-pass filter. For collision avoidance with the detected multiple static obstacle, we use a collision recognition and avoidance algorithm based on CPA. Information of obstacles to be avoided is updated using distance between each obstacle, and collision recognition and avoidance are performed through the updated obstacles information. Finally, through obstacle location estimation, collision recognition, and collision avoidance result analysis in the Gazebo simulation environment, we verified that collision avoidance is performed successfully.
Keywords
Closest Point of Approach; Collision Avoidance; K-means Clustering; LiDAR; Unmanned Aerial Vehicles;
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