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ET-MATD3-Based UAV Formation Control in Dynamic Obstacle Environments

delete2026-05-08
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PRE
AI
J
Junze Li
L
Lei Liu *
C
Chengqing Liang
D
Dongmei Yan
L
Lei Li
DOI：10.1016/j.jfranklin.2026.108729delete
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Abstract

Abstract

En 中文
• Event-Triggered Mechanism: Learns optimal ETC via AMDP, historical actions & augmented action-value function. • Multi-Objective Optimization: Designed dynamic policy switching for multi-objective safety-formation balance. • Curriculum Learning Strategy: Curriculum learning with progressive tasks cuts training difficulty, boosts efficiency.
Keywords:
Event-Triggered Control
Multi-Objective Optimization
Curriculum Learning
UAV Formation Control
Dynamic Obstacle Environments

Journal

J
Journal of the Franklin Institute
IF:
4.2
Papers:
812
Citations:
0

Organization

H
hohai university
Scholars:
4.7K
Papers: 2.0K
Citations: 0
N
nanjing university of posts and telecommunications
Scholars:
3.2K
Papers: 1.4K
Citations: 0
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