This paper addresses the collision avoidance problem of UAV swarms in three-dimensional (3D) space. The key challenges are energy efficiency and cooperation of swarm members. We propose to combine Artificial Potential Field (APF) with Particle Swarm Planning (PSO). APF provides environmental awareness and implicit coordination to UAVs. PSO searches for the optimal trajectories for each UAV in terms of safety and energy efficiency by minimizing a fitness function. The fitness function exploits the advantages of the Active Contour Model in image processing for trajectory planning. Lastly, vehicle-to-vehicle collisions are detected in advance based on trajectory prediction and are resolved by cooperatively adjusting the altitude of UAVs. Simulation results demonstrate that our method can save up to 80\% of energy compared to state-of-the-art schemes.

翻译：本文针对三维空间中无人机集群的避碰问题展开研究。核心挑战在于集群成员的能效性与协同性。我们提出将人工势场法与粒子群规划相结合。人工势场法为无人机提供环境感知与隐式协同能力，粒子群规划则通过最小化适应度函数为每架无人机搜索兼顾安全性与能效性的最优轨迹。该适应度函数借鉴了图像处理中活动轮廓模型的优势用于轨迹规划。最后，基于轨迹预测提前检测无人机间的碰撞风险，并通过协同调整飞行高度解决冲突。仿真结果表明，与现有最优方案相比，本方法可节省高达80%的能耗。