Preference-Based Multi-Agent Collision Avoidance for Unmanned Aerial Vehicle Swarms with Multi-Objective Optimization

Engineered Science 2026 Open access

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Authors: Yulong Cao, Guhao Zhao, Hao Wang, 赖光明
DOI: 10.30919/es2211
Cited by: 1

Abstract

A preference-based multi-agent collision avoidance algorithm is proposed for unmanned aerial vehicle swarms, achieving 50% collision rate reduction with real-time performance through the integration of strategy selection network, multi-objective evaluator, and bayesian personalized ranking-based preference learning.

Fields of Study & Subjects

Robotic Path Planning Algorithms Autonomous Vehicle Technology and Safety Air Traffic Management and Optimization

Publication details

Journal / source
Engineered Science
Publisher
Engineered Science Publisher
Publication date
1 January 2026
Language
EN