Multi-UAV Swarm Obstacle Avoidance Based on Potential Field Optimization

TL;DR

This paper introduces a hybrid potential field algorithm for multi-UAV obstacle avoidance that reduces path redundancy, improves stability, and prevents collisions by integrating formation-based forces with refined path planning.

Contribution

It combines an improved multi-robot formation obstacle avoidance method with an enhanced APF for single UAV path optimization, addressing limitations of traditional approaches.

Findings

Significant reduction in path length compared to traditional methods

Enhanced heading stability during obstacle avoidance

Effective obstacle avoidance and formation restoration in static environments

Abstract

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.