Evidence map›Paper›PMID 40006370›Full record

ArticleSensors (Basel, Switzerland)2025

Collision Avoidance Mechanism for Swarms of Drones.

Dariusz Marek, Piotr Biernacki, Jakub Szyguła, Adam Domański, Marcin Paszkuta, Marta Szczygieł, Marcel Król, Konrad Wojciechowski

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.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Dariusz MarekDepartment of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.ORCID 0000-0002-4074-8502
Piotr BiernackiPolish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland.ORCID 0000-0002-0159-4782
Jakub SzygułaDepartment of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.ORCID 0000-0002-5728-6105
Adam DomańskiDepartment of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.ORCID 0000-0002-9452-8361
Marcin PaszkutaPolish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland.ORCID 0000-0002-7136-0797
Marta SzczygiełPolish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland.ORCID 0000-0003-2924-0924
Marcel KrólDepartment of Distributed Systems and Informatic Devices, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.ORCID 0009-0007-4831-8113
Konrad WojciechowskiPolish-Japanese Academy of Information Technology, Koszykowa 86, 02-008 Warsaw, Poland.ORCID 0000-0003-4679-2667

Funding

National Centre for Research and Development POIR.04.01.04-00-0078/20
6 · The paper itself

Abstract

This article presents a new approach to collision avoidance in drone swarms, designed for operations in large drone swarms and dynamic environments. The mechanism uses distributed communication, where drones share information about their positions and planned trajectories to predict and avoid collisions. The proposed mechanism enables drones to autonomously cooperate and maintain safe distances in complex scenarios. It is based on the concept of repulsion vectors. The avoidance response is determined by the level of immersion in the protective sphere of obstacles, including other drones. The advantage of the algorithm lies in its simplicity and low computational complexity, allowing it to be used even in small and inexpensive drones. The algorithm was tested in a developed simulation environment, created to handle swarms of over 20 drones and to demonstrate the scalability of the proposed solution. Two scenarios were analyzed: (i) two swarms, each with nine drones, flying on a collision course; (ii) a swarm of 25 drones changing formation. The results showed that the mechanism is effective in avoiding collisions, maintaining safe distances and adapting to changing conditions. The proposed mechanism represents a significant advancement in swarm coordination, offering a robust and scalable solution for real-world applications.

Indexed as

collision avoidancehardware-in-the-loop (HITL)positioning accuracysoftware-in-the-loop (SITL)swarm of drones

Identifiers

PMID40006370
PMCPMC11858889

What OpenQuestion holds

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Registered trials

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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.