Multi-UAV Swarm Obstacle Avoidance Based on Potential Field Optimization

2025-11-20

AI Summary

To address excessive path redundancy, frequent heading discontinuities, and high collision risk in multi-UAV formation navigation through static unknown obstacle environments, this paper proposes a hybrid obstacle-avoidance algorithm integrating an improved artificial potential field (IAPF) method with multi-robot formation control. The approach innovatively couples obstacle repulsive forces, inter-vehicle cooperative forces, and target attractive forces; incorporates a collision-risk assessment model and a dynamic sub-goal guidance mechanism to generate smooth intermediate waypoints, thereby enabling safe obstacle avoidance and rapid formation recovery. Experimental results demonstrate that, compared to conventional APF, the proposed method reduces path length by 23.6%, decreases heading change rate by 41.2%, achieves zero collisions, and significantly improves formation maintenance accuracy—validating its effectiveness in complex static unknown obstacle scenarios.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizes multi-UAV path planning to reduce redundant flight paths
• Minimizes abrupt heading changes and inter-UAV collision risks
• Enables effective obstacle avoidance while maintaining formation stability

Innovation

• Hybrid algorithm combining improved MRF IAPF with APF
• Integrates three interaction forces for swarm navigation
• Uses collision risk assessment with temporary waypoints