E2CoPre：基于轨迹预测的无人机群节能协同避碰方法
E2CoPre: Energy Efficient and Cooperative Collision Avoidance for UAV Swarms with Trajectory Prediction
AI总结：
针对无人机群避碰的能效与协作难题，提出结合APF与PSO的E2CoPre方法，借助主动轮廓模型代价函数、变分法轨迹预测及双分支规划实现去中心化节能避碰，经多场景实验验证有效。
摘要：
本文提出了一种新颖方案，以解决无人机群避碰过程中实现能效与协作所面临的挑战。所提方法结合了人工势场（Artificial Potential Field，APF）和粒子群优化（Particle Swarm Optimization，PSO）技术。APF为无人机提供环境感知与隐式协调能力，PSO则在该隐式协调下以去中心化方式为每架无人机搜索无碰撞且节能的轨迹。这种去中心化方法通过最小化一个新型代价函数实现，该函数借鉴了图像处理中主动轮廓模型的优势。此外，研究通过变分法逼近该新型代价函数的最小值来预测未来轨迹，从而实现主动行动并为PSO定义初始条件。本文提出了一种双分支轨迹规划框架，确保无人机仅在出于能效考虑有必要时才改变高度。研究开展了大量实验，以评估所提方法在多种场景下的有效性与效率。
英文摘要：
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.
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。
