A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge

Shuangyao Huang; Haibo Zhang; Zhiyi Huang

A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge

Multiagent Systems 2025-07-16 v1 Machine Learning Robotics

Authors: Shuangyao Huang , Haibo Zhang , Zhiyi Huang

Abstract

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.

Keywords

multi-agent reinforcement learning collision avoidance UAV swarm domain knowledge reward design

Cite

@article{arxiv.2507.10913,
  title  = {A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge},
  author = {Shuangyao Huang and Haibo Zhang and Zhiyi Huang},
  journal= {arXiv preprint arXiv:2507.10913},
  year   = {2025}
}

Comments

Under review at AAAI 2026

A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge

Abstract

Keywords

Cite

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