Improving Generalization in Collision Avoidance for Multiple Unmanned Aerial Vehicles via Causal Representation Learning.

Che Lin, Gaofei Han, Qingling Wu, Boxi Wang, Jiafan Zhuang, Wenji Li, Zhifeng Hao, Zhun Fan

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

A Multi-UAV Cooperative Path-Planning Method for Complex Obstacle Environments.Sensors (Basel, Switzerland) · 2026
Article
A Hierarchical Visual Navigation Algorithm for UAVs Integrating Artificial Potential Field and Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2026
Article

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5 · Who and what money

Authors and funding

8 authors.

Che LinDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.ORCID 0009-0004-5272-4892

Gaofei HanDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.

Qingling WuDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.

Boxi WangDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.

Jiafan ZhuangDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.ORCID 0000-0003-3708-4634

Wenji LiDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.

Zhifeng HaoDepartment of Electronic Engineering, Shantou University, Shantou 515063, China.

Zhun FanShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518000, China.

Funding

the National Natural Science Foundation of China 62176147, 62406186, 62476163the National Science and Technology Major Project 2021ZD0111502the Science and Technology Planning Project of Guangdong Province of China 2022A1515110660the STU Scientific Research Foundation for Talents NTF21001, NTF22030

6 · The paper itself

Abstract

Deep-reinforcement-learning-based multi-UAV collision avoidance and navigation methods have made significant progress. However, the fundamental challenge of those methods is their restricted capability to generalize beyond the specific scenario in which they are trained on. We find that the cause of the generalization failures is attributed to spurious correlation. To solve this generalization problem, we propose a causal representation learning method to identify the causal representations from images. Specifically, our method can neglect factors of variation that are irrelevant to the deep reinforcement learning task through causal intervention. Subsequently, the causal representations are fed into the policy network for action prediction. Extensive testing reveals that our proposed method exhibits better generalization results compared to state-of-the-art methods in different testing scenes.

Indexed as

causal interventioncausal representation learningdeep reinforcement learninggeneralization failuremulti-UAV collision avoidance

Identifiers

PMID40968817

DOI10.3390/s25113303

PMCPMC12158032

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.