CoChat
Sign Up
Cited by 5Modular Robots and Swarm IntelligenceDistributed Control Multi-Agent SystemsRobotic Path Planning Algorithms

A Decentralized Potential Field-Based Self-Organizing Control Framework for Trajectory, Formation, and Obstacle Avoidance of Fully Autonomous Swarm Robots

Open access
Abdel-Nasser, Mohammed, El-Ferik, Sami, Rashad, Ramy, Saif, Abdul-Wahid A.·2025·Robotics
DOI 10.3390/robotics14120192
Open this paper in CoChat — free account

Abstract

In this work, we propose a fully decentralized, self-organizing control framework for a swarm of autonomous ground mobile robots. The system integrates potential field-based mechanisms for simultaneous trajectory tracking, formation control, and obstacle avoidance, all based on local sensing and neighbor interactions without centralized coordination. Each robot autonomously computes attractive, repulsive, and formation forces to navigate toward target positions while maintaining inter-robot spacing and avoiding both static and dynamic obstacles. Inspired by biological swarm behavior, the controller emphasizes robustness, scalability, and flexibility. The proposed method has been successfully validated in the ARGoS simulator, which provides realistic physics, sensor modeling, and a robust environment that closely approximates real-world conditions. The system was tested with up to 15 robots and is designed to scale to larger swarms (e.g., 100 robots), demonstrating stable performance across a range of scenarios. Results obtained using ARGoS confirm the swarm’s ability to maintain formation, avoid collisions, and reach a predefined goal area within a configurable 1 m radius. This zone serves as a spatial convergence region suitable for multi-robot formation, even in the presence of unknown fixed obstacles and movable agents. The framework can seamlessly handle the addition or removal of swarm members without reconfiguration.

Integrity signals

Registry

Crossref · 10.3390/robotics14120192

Cites

65 of 68 references verified0 citation errors · 3 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cited works that were retracted

Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.

  • Cites a retracted work: 10.1051/e3sconf/202450503013

    Major

    The cited work was retracted on 2025-07-07 (notice 10.1051/e3sconf/202450500001), before this paper was published (2025-12-18). Its authors could have known.

    Cited work 10.1051/e3sconf/202450503013 · retracted 2025-07-07 · notice 10.1051/e3sconf/202450500001 · this paper published 2025-12-18

1 of 1

Cites

1 retracted

Flagged first, then by citations

3 of the 68 references couldn’t be matched to a known paper.

Cited by

Sorted by citations

Authors
Abdel-Nasser, M., El-Ferik, S., Rashad, R., & Saif, A.-W. A.
Affiliations
  • King Fahd University of Petroleum and Minerals (SA)
  • Department of Control and Instrumentation Engineering, KFUPM, Dhahran 31261, Saudi Arabia
Corresponding author
Saif, Abdul-Wahid A.
ORCID
Venue
Robotics
Publisher
MDPI AG
Published
2025-12-18
Volume
14
Issue
12
Pages
192
ISSN
2218-6581 (ISSN-L)
DOI
10.3390/robotics14120192
OpenAlex
W4417466454
Semantic Scholar
b291fdb6b957ddda02ead992da1f19b03251386a
Cited by
5
References
68
Citations by year
2026: 5
FWCI
1.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Robotics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
King Fahd University of Petroleum and Minerals, through IRC-SML (Project #INML2505)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › Modular Robots and Swarm Intelligence
  • Physical Sciences › Computer Science › Computer Networks and Communications › Distributed Control Multi-Agent Systems
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
Keywords
swarm robotics, decentralized control, potential field method, formation control, trajectory tracking, obstacle avoidance, self-organizing control, multi-robot systems, unmanned ground vehicles

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review