A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field
The traditional artificial potential field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, t...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2504-446X/9/6/390 |
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| author | Bo Ma Yi Ji Liyong Fang |
| author_facet | Bo Ma Yi Ji Liyong Fang |
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| description | The traditional artificial potential field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, the APF is highly susceptible to local minima, compromising the motion reliability in complex environments. To address these challenges, this paper presents a novel hybrid obstacle avoidance algorithm—deflected simulated annealing–adaptive artificial potential field (DSA-AAPF)—which combines an improved simulated annealing mechanism with an enhanced APF model. The proposed approach integrates a leader–follower distributed formation strategy with the APF framework, where the resultant force formulation is redefined to smooth the UAV trajectories. An adaptive attractive gain function is introduced to dynamically adjust the UAV velocity based on the environmental context, and a fast-converging controller ensures accurate and efficient convergence to the target. Moreover, a directional deflection mechanism is embedded within the simulated annealing process, enabling UAVs to escape the local minima caused by semi-enclosed obstacles through continuous rotational motion. The simulation results, covering the formation reconfiguration, complex obstacle avoidance, and entrapment escape, demonstrate the feasibility, robustness, and superiority of the proposed DSA-AAPF algorithm. |
| format | Article |
| id | doaj-art-e63a8cc8e4c34724896f3e7b9e954f5e |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-e63a8cc8e4c34724896f3e7b9e954f5e2025-08-20T03:27:28ZengMDPI AGDrones2504-446X2025-05-019639010.3390/drones9060390A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential FieldBo Ma0Yi Ji1Liyong Fang2School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSDU-ANU Joint Science College, Shandong University, Weihai 264209, ChinaSchool of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, ChinaThe traditional artificial potential field (APF) method exhibits limitations in its force distribution: excessive attraction when UAVs are far from the target may cause collisions with obstacles, while insufficient attraction near the goal often results in failure to reach the target. Furthermore, the APF is highly susceptible to local minima, compromising the motion reliability in complex environments. To address these challenges, this paper presents a novel hybrid obstacle avoidance algorithm—deflected simulated annealing–adaptive artificial potential field (DSA-AAPF)—which combines an improved simulated annealing mechanism with an enhanced APF model. The proposed approach integrates a leader–follower distributed formation strategy with the APF framework, where the resultant force formulation is redefined to smooth the UAV trajectories. An adaptive attractive gain function is introduced to dynamically adjust the UAV velocity based on the environmental context, and a fast-converging controller ensures accurate and efficient convergence to the target. Moreover, a directional deflection mechanism is embedded within the simulated annealing process, enabling UAVs to escape the local minima caused by semi-enclosed obstacles through continuous rotational motion. The simulation results, covering the formation reconfiguration, complex obstacle avoidance, and entrapment escape, demonstrate the feasibility, robustness, and superiority of the proposed DSA-AAPF algorithm.https://www.mdpi.com/2504-446X/9/6/390artificial potential fieldsimulated annealingmulti-UAV formationpath planning |
| spellingShingle | Bo Ma Yi Ji Liyong Fang A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field Drones artificial potential field simulated annealing multi-UAV formation path planning |
| title | A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field |
| title_full | A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field |
| title_fullStr | A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field |
| title_full_unstemmed | A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field |
| title_short | A Multi-UAV Formation Obstacle Avoidance Method Combined with Improved Simulated Annealing and an Adaptive Artificial Potential Field |
| title_sort | multi uav formation obstacle avoidance method combined with improved simulated annealing and an adaptive artificial potential field |
| topic | artificial potential field simulated annealing multi-UAV formation path planning |
| url | https://www.mdpi.com/2504-446X/9/6/390 |
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