改进势场法，让多无人机编队避障更顺滑、少碰撞。
Multi-UAV Swarm Obstacle Avoidance Based on Potential Field Optimization
- 融合三种力：避障斥力、无人机间作用力、目标吸引力。
- 引入临时路径点与风险评估，减少急转弯和路径冗余。
- 适合复杂静态环境下的多无人机编队避障任务。
在多无人机场景中，传统人工势场法常因路径规划不合理导致飞行路径冗余、转向频繁，且易发生无人机间碰撞。为此，本文提出一种新型混合算法，结合改进的多机器人编队避障（MRF IAPF）与优化的单机路径规划势场法。核心思路为：第一，集成三类交互力——编队避障斥力、无人机间相互作用力、目标吸引力建模；第二，引入精细化单机路径优化机制，包括碰撞风险评估与辅助子目标策略。当某架无人机面临高碰撞风险时，生成临时航点引导避障，确保最终精确抵达真实目标。仿真结果表明，相比传统基于势场的编队算法，所提方法在路径长度优化与航向稳定性方面均有显著提升，能有效避开障碍物并快速恢复编队结构，验证了其在未知静态障碍环境中的适用性与有效性。
原文摘要 · Abstract (English)
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.
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