A Survey on Autonomous and Intelligent Swarms of Uncrewed Aerial Vehicles (UAVs)

UAV swarms have attracted much attention due to their high potential to execute complex missions more robustly and effectively. Essential technologies for swarms are the family of algorithms that allow the individual agents to undertake tasks intelligently, localize their relative positions, perceive surroundings, and plan and track collision-free and low-cost trajectories cooperatively so that the swarm’s overall objectives are efficiently achieved. There is still a lack of corresponding surveys that provide a systematic summary covering the control layer to task allocation and guide application-driven researchers in leveraging these capabilities for diverse UAV swarm applications. This survey debates the essential technologies of UAV swarms, including swarm trajectory planning, task assignment, control approaches, localization, perception, and communications. State-of-the-art algorithms and recent technical advancements have been investigated to expose the potential for developing highly autonomous and intelligent swarm systems. It further explores the use cases of UAV swarms in civil applications and critically analyzes existing technologies. The paper concludes by emphasizing the challenges for autonomous and intelligent UAV swarms and outlining potential future research directions. Overall, this paper provides a contemporary and comprehensive review of UAV swarm technologies and investigates their potential to transform civil application fields and support future technology advancement.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Intelligent Transportation Systems
Published
2025-05-22
DOI
https://doi.org/10.1109/tits.2025.3569500
Citations
23
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
34.20

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Survey on Autonomous and Intelligent Swarms of Uncrewed Aerial Vehicles (UAVs)

Shuai Li, Cai Luo, Zhenpeng Du, Chunbo Luo et al.
23 citations
IEEE Transactions on Intelligent Transportation Systems
Distributed Control Multi-Agent Systems
34.20
article

A Survey on Autonomous and Intelligent Swarms of Uncrewed Aerial Vehicles (UAVs)

Shuai Li, Cai Luo, Zhenpeng Du, Chunbo Luo, Jian Pu, Geyong Min, Jia Wu
article en
23 citations

Abstract

UAV swarms have attracted much attention due to their high potential to execute complex missions more robustly and effectively. Essential technologies for swarms are the family of algorithms that allow the individual agents to undertake tasks intelligently, localize their relative positions, perceive surroundings, and plan and track collision-free and low-cost trajectories cooperatively so that the swarm’s overall objectives are efficiently achieved. There is still a lack of corresponding surveys that provide a systematic summary covering the control layer to task allocation and guide application-driven researchers in leveraging these capabilities for diverse UAV swarm applications. This survey debates the essential technologies of UAV swarms, including swarm trajectory planning, task assignment, control approaches, localization, perception, and communications. State-of-the-art algorithms and recent technical advancements have been investigated to expose the potential for developing highly autonomous and intelligent swarm systems. It further explores the use cases of UAV swarms in civil applications and critically analyzes existing technologies. The paper concludes by emphasizing the challenges for autonomous and intelligent UAV swarms and outlining potential future research directions. Overall, this paper provides a contemporary and comprehensive review of UAV swarm technologies and investigates their potential to transform civil application fields and support future technology advancement.

IEEE Transactions on Intelligent Transportation SystemsVol. 26(10)
University of Exeter (GB), Fudan University (CN), Shanghai Institute for Science of Science (CN), China University of Petroleum, East China (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN), University of Oulu (FI)
Royal Society, Fundamental Research Funds for the Central Universities
Openalex Percentile: Top 0%
Distributed Control Multi-Agent Systems
34.20
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.