Evidence map›Paper›PMID 42197752›Full record

ReviewSensors (Basel, Switzerland)2026

Recent Developments and Applications of Drone Swarm: Techniques, Strategies, and Challenges.

Ravi Raj, Andrzej Kos

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ravi RajDepartment of Mining, Industrial and ICT Engineering (EMIT), Manresa School of Engineering (EPSEM), Universitat Politècnica de Catalunya (UPC), BarcelonaTech, 08242 Manresa, Barcelona, Spain.ORCID 0000-0001-8073-1812
Andrzej KosFaculty of Computer Science, Electronics, and Telecommunications, AGH University of Krakow, Aleja Adama Mickiewicza 30, 30-059 Krakow, Poland.ORCID 0000-0003-3089-241X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dynamic and complex environment, together with challenging assignments, requires that unmanned aerial vehicle (UAV) systems evolve toward cooperation, autonomy, and cognition. UAV swarms illustrate a revolutionary development in aerial robotics, which utilizes coordinated autonomy to improve operational efficiency. This study offers a detailed examination of UAV swarm systems, the latest developments, and their different applications. The main domains, such as intelligent path planning, work allocation, coordinated control, and safety issues, are analyzed, focusing on the integration of Artificial Intelligence (AI) and Deep Learning (DL) to enhance decision-making and agility. We address the constraints and potential advances in the field of swarm intelligence to facilitate additional research endeavors. The ongoing advancement of drone swarm technologies and its exploration of military uses highlight the increasing importance of anti-drone swarm strategies. Therefore, studying these strategies will have substantial practical importance in preventing and countering drone swarm combat. Thus, this article provides detailed drone swarm applications and the importance of anti-drone swarm techniques in strategic operations. Furthermore, this comprehensive study of the literature aims to offer innovative perspectives on the latest advances in UAV swarm intelligence technology. Future research trends and challenges are discussed to find the research gap.

Indexed as

artificial intelligence (AI)deep learning (DL)dronesswarm intelligenceunmanned aerial vehicles (UAV)

Identifiers

PMID42197752
PMCPMC13210634

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.