基于势场优化的多无人机群避障
Multi-UAV Swarm Obstacle Avoidance Based on Potential Field Optimization
AI总结：
本文提出一种结合改进多机器人编队避障算法与优化单无人机路径规划的混合算法，有效提升多无人机群在静态环境中的避障能力和编队稳定性。
摘要：
在多无人机场景中，传统的人工势场（APF）方法由于障碍避障路径规划不合理，往往导致冗余飞行路径和频繁的急转方向变化，且在避障过程中容易发生无人机间碰撞。为解决这些问题，本研究提出了一种新型混合算法，将改进的多机器人编队避障（MRF IAPF）算法与优化的单无人机路径规划APF相结合。其核心思想如下：首先，整合MRF IAPF的障碍排斥力、无人机间交互力和目标吸引力三种交互力；其次，引入精细化的单无人机路径优化机制，包括碰撞风险评估和辅助子目标策略。当无人机面临高碰撞威胁时，会生成临时航点以引导避障，确保最终精确到达实际目标。仿真结果表明，与传统基于APF的编队算法相比，所提算法在路径长度优化和航向稳定性方面取得了显著改进，能够有效避障并快速恢复编队配置，从而验证了其在静态环境中未知障碍物的有效性和适用性。
英文摘要：
In multi UAV scenarios,the traditional Artificial Potential Field (APF) method often leads to redundant flight paths and frequent abrupt heading changes due to unreasonable obstacle avoidance path planning,and is highly prone to inter UAV collisions during the obstacle avoidance process.To address these issues,this study proposes a novel hybrid algorithm that combines the improved Multi-Robot Formation Obstacle Avoidance (MRF IAPF) algorithm with an enhanced APF optimized for single UAV path planning.Its core ideas are as follows:first,integrating three types of interaction forces from MRF IAPF obstacle repulsion force,inter UAV interaction force,and target attraction force;second,incorporating a refined single UAV path optimization mechanism,including collision risk assessment and an auxiliary sub goal strategy.When a UAV faces a high collision threat,temporary waypoints are generated to guide obstacle avoidance,ensuring eventual precise arrival at the actual target.Simulation results demonstrate that compared with traditional APF based formation algorithms,the proposed algorithm achieves significant improvements in path length optimization and heading stability,can effectively avoid obstacles and quickly restore the formation configuration,thus verifying its applicability and effectiveness in static environments with unknown obstacles.
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。
