Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs
In this paper we address the problem of trajectory following in an unknown environment with an unmanned aerial vehicle (UAV). The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 2D or 3D sensors, such as...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10210384/ |
| _version_ | 1797743443157450752 |
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| author | Jurica Goricanec Ana Milas Lovro Markovic Stjepan Bogdan |
| author_facet | Jurica Goricanec Ana Milas Lovro Markovic Stjepan Bogdan |
| author_sort | Jurica Goricanec |
| collection | DOAJ |
| description | In this paper we address the problem of trajectory following in an unknown environment with an unmanned aerial vehicle (UAV). The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 2D or 3D sensors, such as LiDARs. We present a novel algorithm based on the conventional Artificial Potential Field (APF) called Augmented Artificial Potential Field (AAPF) that corrects the planned path to avoid obstacles. Our proposed algorithm uses a combination of two attractive forces and both normal and rotational repulsive forces to avoid obstacles and handle local minima problems. The smooth trajectory following achieved with the MPC tracker allows us to quickly change and re-plan the UAV path. Comparative simulation experiments have shown that our approach solves local minima problems in trajectory following and generates more efficient paths to avoid potential collisions with static obstacles compared to our previously developed algorithm for obstacle avoidance. The laboratory experimental evaluation results indicate that the algorithm can be deployed on a real UAV with limited computational power and real-time processing requirements. |
| first_indexed | 2024-03-12T14:55:29Z |
| format | Article |
| id | doaj.art-44780db6be29443b86d19ae39f3d8fd8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T14:55:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-44780db6be29443b86d19ae39f3d8fd82023-08-14T23:00:27ZengIEEEIEEE Access2169-35362023-01-0111834928350610.1109/ACCESS.2023.330310910210384Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVsJurica Goricanec0https://orcid.org/0000-0001-8335-5728Ana Milas1https://orcid.org/0000-0003-0289-0791Lovro Markovic2https://orcid.org/0000-0003-4242-0831Stjepan Bogdan3https://orcid.org/0000-0003-2636-3216Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, CroatiaIn this paper we address the problem of trajectory following in an unknown environment with an unmanned aerial vehicle (UAV). The main goal is to safely follow the planned trajectory by avoiding obstacles. The proposed approach is suitable for aerial vehicles equipped with 2D or 3D sensors, such as LiDARs. We present a novel algorithm based on the conventional Artificial Potential Field (APF) called Augmented Artificial Potential Field (AAPF) that corrects the planned path to avoid obstacles. Our proposed algorithm uses a combination of two attractive forces and both normal and rotational repulsive forces to avoid obstacles and handle local minima problems. The smooth trajectory following achieved with the MPC tracker allows us to quickly change and re-plan the UAV path. Comparative simulation experiments have shown that our approach solves local minima problems in trajectory following and generates more efficient paths to avoid potential collisions with static obstacles compared to our previously developed algorithm for obstacle avoidance. The laboratory experimental evaluation results indicate that the algorithm can be deployed on a real UAV with limited computational power and real-time processing requirements.https://ieeexplore.ieee.org/document/10210384/Artificial potential fieldsobstacle avoidanceUAVtrajectory followingpath planning |
| spellingShingle | Jurica Goricanec Ana Milas Lovro Markovic Stjepan Bogdan Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs IEEE Access Artificial potential fields obstacle avoidance UAV trajectory following path planning |
| title | Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs |
| title_full | Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs |
| title_fullStr | Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs |
| title_full_unstemmed | Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs |
| title_short | Collision-Free Trajectory Following With Augmented Artificial Potential Field Using UAVs |
| title_sort | collision free trajectory following with augmented artificial potential field using uavs |
| topic | Artificial potential fields obstacle avoidance UAV trajectory following path planning |
| url | https://ieeexplore.ieee.org/document/10210384/ |
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