Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866915105537523712 |
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| author | Hoang, Van Truong Phung, Manh Duong |
| author_facet | Hoang, Van Truong Phung, Manh Duong |
| contents | This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness functions that not only ensure the formation but also include constraints for optimal and safe UAV operation. To optimize the fitness function and obtain a suboptimal path, we employ the teaching-learning-based optimization algorithm and then further enhance it with mechanisms such as mutation, elite strategy, and multi-subject combination. A number of simulations and experiments have been conducted to evaluate the proposed method. The results demonstrate that the algorithm successfully generates valid paths for the UAVs to fly in a triangular formation for an inspection task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_09357 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization Hoang, Van Truong Phung, Manh Duong Robotics Systems and Control This work addresses the path planning problem for a group of unmanned aerial vehicles (UAVs) to maintain a desired formation during operation. Our approach formulates the problem as an optimization task by defining a set of fitness functions that not only ensure the formation but also include constraints for optimal and safe UAV operation. To optimize the fitness function and obtain a suboptimal path, we employ the teaching-learning-based optimization algorithm and then further enhance it with mechanisms such as mutation, elite strategy, and multi-subject combination. A number of simulations and experiments have been conducted to evaluate the proposed method. The results demonstrate that the algorithm successfully generates valid paths for the UAVs to fly in a triangular formation for an inspection task. |
| title | Path Planning for a UAV Swarm Using Formation Teaching-Learning-Based Optimization |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2501.09357 |