This paper presents a novel solution to address the challenges in achieving energy efficiency and cooperation for collision avoidance in UAV swarms. The proposed method combines Artificial Potential Field (APF) and Particle Swarm Optimization (PSO) techniques. APF provides environmental awareness and implicit coordination to UAVs, while PSO searches for collision-free and energy-efficient trajectories for each UAV in a decentralized manner under the implicit coordination. This decentralized approach is achieved by minimizing a novel cost function that leverages the advantages of the active contour model from image processing. Additionally, future trajectories are predicted by approximating the minima of the novel cost function using calculus of variation, which enables proactive actions and defines the initial conditions for PSO. We propose a two-branch trajectory planning framework that ensures UAVs only change altitudes when necessary for energy considerations. Extensive experiments are conducted to evaluate the effectiveness and efficiency of our method in various situations.

翻译：本文提出一种新颖解决方案，以应对无人机集群在实现能量高效与协作避障时所面临的挑战。该方法融合了人工势场法与粒子群优化技术。人工势场法为无人机提供环境感知能力与隐式协调机制，而粒子群优化则在隐式协调框架下，以去中心化方式为每架无人机搜索无碰撞且能量高效的轨迹。这种去中心化方法通过最小化一种新型代价函数实现，该函数借鉴了图像处理中主动轮廓模型的优势。此外，通过变分法近似求解新型代价函数的极小值来预测未来轨迹，该预测既可触发主动行动，也为粒子群优化定义了初始条件。我们提出双支路轨迹规划框架，确保无人机仅在能量考量必要时才改变飞行高度。通过多场景下的广泛实验，充分验证了该方法在不同情境下的有效性与高效性。