A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916843988451328 |
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| author | Huang, Shuangyao Zhang, Haibo Huang, Zhiyi |
| author_facet | Huang, Shuangyao Zhang, Haibo Huang, Zhiyi |
| contents | This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10913 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge Huang, Shuangyao Zhang, Haibo Huang, Zhiyi Multiagent Systems Machine Learning Robotics This paper presents a multi-agent reinforcement learning (MARL) framework for cooperative collision avoidance of UAV swarms leveraging domain knowledge-driven reward. The reward is derived from knowledge in the domain of image processing, approximating contours on a two-dimensional field. By modeling obstacles as maxima on the field, collisions are inherently avoided as contours never go through peaks or intersect. Additionally, counters are smooth and energy-efficient. Our framework enables training with large swarm sizes as the agent interaction is minimized and the need for complex credit assignment schemes or observation sharing mechanisms in state-of-the-art MARL approaches are eliminated. Moreover, UAVs obtain the ability to adapt to complex environments where contours may be non-viable or non-existent through intensive training. Extensive experiments are conducted to evaluate the performances of our framework against state-of-the-art MARL algorithms. |
| title | A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge |
| topic | Multiagent Systems Machine Learning Robotics |
| url | https://arxiv.org/abs/2507.10913 |