利用领域知识的无人机集群协同避碰学习框架
A Learning Framework For Cooperative Collision Avoidance of UAV Swarms Leveraging Domain Knowledge
AI总结：
本文提出一种基于领域知识驱动奖励的多智能体强化学习框架，通过将障碍物建模为二维场极大值并近似等高线来实现无人机集群的协同避碰，支持大规模集群训练并在复杂环境中表现出色。
摘要：
本文提出了一种多智能体强化学习（MARL）框架，利用领域知识驱动的奖励实现无人机集群的协同避碰。该奖励源自图像处理领域的知识，通过在二维场上近似等高线来构建。通过将障碍物建模为场上的极大值，由于等高线不会穿过峰值或相交，因此能固有地避免碰撞。此外，等高线平滑且节能。由于最小化了智能体交互，并消除了最先进MARL方法中对复杂信用分配方案或观测共享机制的需求，该框架能够支持大规模集群的训练。此外，通过强化训练，无人机获得了适应等高线可能不可行或不存在的复杂环境的能力。进行了大量实验以评估本框架相对于最先进MARL算法的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
