基于GCN的多无人机抗干扰路径规划
Anti-Jamming Path Planning Using GCN for Multi-UAV
AI总结：
本文针对干扰源位置未知场景，提出基于GCN预测干扰区域并结合多智能体控制算法实现多无人机分散避扰与目标集结的抗干扰路径规划方法。
摘要：
本文关注无人机（UAV）日益增长的重要性以及无人机集群在多个领域中协同作业的出现。然而，无人机集群的有效性可能因干扰技术而严重受损，因此需要鲁棒的抗干扰策略。虽然现有方法如跳频和物理路径规划已被探索，但在干扰源位置未知的情况下，针对无人机集群的路径规划研究仍存在空白。为此，本文提出一种新方法，使无人机集群利用集体智能预测干扰区域、规避干扰并高效抵达目标位置。该方法利用图卷积网络（GCN），基于每架无人机收集的信息预测干扰区域的位置和强度。随后采用多智能体控制算法分散无人机集群、避开干扰，并在到达目标后重新集结。通过仿真验证了所提方法的有效性，展示了干扰区域的准确预测以及通过避障算法实现的成功规避，最终达成任务目标。所提方法具有鲁棒性、可扩展性和计算高效性，适用于无人机集群在潜在敌对环境中运行的多种场景。
英文摘要：
This paper addresses the increasing significance of UAVs (Unmanned Aerial Vehicles) and the emergence of UAV swarms for collaborative operations in various domains. However, the effectiveness of UAV swarms can be severely compromised by jamming technology, necessitating robust antijamming strategies. While existing methods such as frequency hopping and physical path planning have been explored, there remains a gap in research on path planning for UAV swarms when the jammer's location is unknown. To address this, a novel approach, where UAV swarms leverage collective intelligence to predict jamming areas, evade them, and efficiently reach target destinations, is proposed. This approach utilizes Graph Convolutional Networks (GCN) to predict the location and intensity of jamming areas based on information gathered from each UAV. A multi-agent control algorithm is then employed to disperse the UAV swarm, avoid jamming, and regroup upon reaching the target. Through simulations, the effectiveness of the proposed method is demonstrated, showcasing accurate prediction of jamming areas and successful evasion through obstacle avoidance algorithms, ultimately achieving the mission objective. Proposed method offers robustness, scalability, and computational efficiency, making it applicable across various scenarios where UAV swarms operate in potentially hostile environments.
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。
