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arxiv: 1907.07647 · v1 · pith:D47RKGKTnew · submitted 2019-07-17 · 💻 cs.RO

Fly Safe: Aerial Swarm Robotics using Force Field Particle Swarm Optimisation

Lauren Parker , James Butterworth , Shan Luo This is my paper

Pith reviewed 2026-05-24 20:11 UTC · model grok-4.3

classification 💻 cs.RO
keywords force field particle swarm optimisationcollision avoidancemicro aerial vehiclesswarm roboticsparticle swarm optimisationtarget searchaerial swarms
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The pith

Repellent force fields added to particle swarm optimisation eliminate collisions in drone swarms while preserving search speed.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Force Field Particle Swarm Optimisation to make particle swarm methods safe for groups of Micro Aerial Vehicles. Standard PSO ignores collisions between agents during search, so the new method adds a velocity term from designated repellent force fields around each particle. This drives collisions to zero in simulation while the time needed to reach a target stays comparable to the unmodified algorithm. Tests with varying swarm sizes confirm scalability, and the approach runs successfully on physical MAV hardware. Readers would care because collision-free swarm motion opens practical uses in search, monitoring, and exploration tasks.

Core claim

FFPSO designates repellent force fields to particles such that these fields provide an additional velocity component into the original PSO equations, reducing the number of particle collisions during search to 0 whilst also being able to locate a target of interest in a similar amount of time. Scalability is shown by measuring crashes and goal time across different swarm sizes, and the algorithm is demonstrated on a swarm of real MAVs.

What carries the argument

Repellent force fields that supply an extra velocity term in the PSO update equations to push particles apart.

If this is right

  • Collisions between particles fall to zero throughout the entire search process.
  • Time required to locate the goal remains similar to unmodified PSO across tested swarm sizes.
  • The method transfers from simulation to real MAV flight without reported loss of the zero-collision property.
  • Performance holds when the number of agents increases, supporting larger swarms.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same repulsion term could be adapted for obstacle avoidance in environments with fixed barriers rather than only other particles.
  • Field strength might be varied dynamically during a mission to balance safety against search thoroughness in changing conditions.
  • The approach could be combined with other swarm coordination rules such as formation maintenance without re-deriving the core velocity update.
  • Real-world wind or sensor noise might require retuning the force parameters beyond what simulation experiments covered.

Load-bearing premise

The added repulsion velocity term can be parameterised so global search performance stays comparable to standard PSO without instability or trapping in local minima when the equations run on real MAV dynamics.

What would settle it

A physical MAV swarm trial in which FFPSO produces any collisions or takes substantially longer than standard PSO to reach the target.

Figures

Figures reproduced from arXiv: 1907.07647 by James Butterworth, Lauren Parker, Shan Luo.

Figure 1
Figure 1. Figure 1: Snapshots of FFPSO in simulation. (a) shows the [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: The strength of the force field w.r.t. the distance [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The real world setting. The motion capture cameras [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The number of time steps taken to find both goals [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Flight paths of the three CFs on an example of a test [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: A couple of graphs showing the effectiveness of the [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

Particle Swarm Optimisation (PSO) is a powerful optimisation algorithm that can be used to locate global maxima in a search space. Recent interest in swarms of Micro Aerial Vehicles (MAVs) begs the question as to whether PSO can be used as a method to enable real robotic swarms to locate a target goal point. However, the original PSO algorithm does not take into account collisions between particles during search. In this paper we propose a novel algorithm called Force Field Particle Swarm Optimisation (FFPSO) that designates repellent force fields to particles such that these fields provide an additional velocity component into the original PSO equations. We compare the performance of FFPSO with PSO and show that it has the ability to reduce the number of particle collisions during search to 0 whilst also being able to locate a target of interest in a similar amount of time. The scalability of the algorithm is also demonstrated via a set of experiments that considers how the number of crashes and the time taken to find the goal varies according to swarm size. Finally, we demonstrate the algorithms applicability on a swarm of real MAVs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript proposes Force Field Particle Swarm Optimisation (FFPSO), an extension of standard PSO for aerial swarms of MAVs. Repellent force fields are assigned to particles to contribute an additional velocity term in the PSO update equations, with the central claim that this eliminates collisions (reported as zero) while preserving comparable time to locate a target. Simulation results across swarm sizes and a real-MAV hardware demonstration are presented to support scalability and applicability.

Significance. If the performance claims hold, the work supplies a practical, tunable modification that addresses collision avoidance in PSO-based robotic search without apparent loss of global search capability. The explicit statement of the modified velocity equations and the inclusion of both simulation scaling experiments and real-robot validation are strengths that support reproducibility and transferability assessment.

minor comments (2)
  1. [simulation experiments] The simulation results section would benefit from reporting the number of independent trials and any variance (e.g., standard deviation) on the time-to-goal metric to substantiate the 'similar amount of time' claim across swarm sizes.
  2. [algorithm description] A brief sensitivity analysis or range for the force-field repulsion gain parameter would clarify robustness of the zero-collision result to tuning choices.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of the manuscript, the recognition of its practical contribution to collision avoidance in PSO-based MAV swarms, and the recommendation for minor revision. No major comments appear in the report, so we provide no point-by-point rebuttals below.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper introduces FFPSO as an explicit algorithmic modification to standard PSO by adding a repulsion velocity term from designated force fields. The velocity-update equations are stated directly, performance claims (zero collisions, comparable search time) are measured via simulation across swarm sizes and a real-MAV demonstration, and no equations, parameters, or results are shown to reduce to their own inputs by definition or self-citation chain. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities beyond the force-field concept itself; full paper would be needed to enumerate fitted repulsion strengths or simulation assumptions.

invented entities (1)
  • repellent force fields no independent evidence
    purpose: supply additional velocity component to prevent particle collisions
    Introduced in the abstract as the core modification to standard PSO.

pith-pipeline@v0.9.0 · 5717 in / 1036 out tokens · 17565 ms · 2026-05-24T20:11:39.346009+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

  1. [1]

    A Survey on Aerial Swarm Robotics,

    S. Chung, A. A. Paranjape, P. Dames, S. Shen, and V . Kumar, “A Survey on Aerial Swarm Robotics,” IEEE Transactions on Robotics , vol. 34, no. 4, pp. 837–855, aug 2018

    work page 2018

  2. [2]

    Particle swarm optimization,

    J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Pro- ceedings of ICNN’95 - International Conference on Neural Networks , vol. 4, nov 1995, pp. 1942–1948 vol.4

    work page 1995

  3. [3]

    A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications,

    Y .-D. Zhang, S. Wang, and G. Ji, “A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications,” Math- ematical Problems in Engineering , vol. 2015, pp. 1–38, 2015

    work page 2015

  4. [4]

    Multiple UA Vs Routes Planning Based on Particle Swarm Optimization Algorithm,

    G. Wang, Q. Li, and L. Guo, “Multiple UA Vs Routes Planning Based on Particle Swarm Optimization Algorithm,” in2010 2nd International Symposium on Information Engineering and Electronic Commerce, jul 2010, pp. 1–5

    work page 2010

  5. [5]

    An Improved Particle Swarm Opti- mization Algorithm Used for Unmanned Aerial Vehicle Fixed-point Reconnaissance Route Planning,

    Z. Wang, X. Chen, and L. Xu, “An Improved Particle Swarm Opti- mization Algorithm Used for Unmanned Aerial Vehicle Fixed-point Reconnaissance Route Planning,” International Journal of Simulation – Systems, Science & Technology, vol. 16, no. 4B, pp. 221–225, 2015

    work page 2015

  6. [6]

    AR.Drone UA V control parameters tuning based on particle swarm optimization algorithm,

    T. T. Mac, C. Copot, T. T. Duc, and R. D. Keyser, “AR.Drone UA V control parameters tuning based on particle swarm optimization algorithm,” in 2016 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR) , may 2016, pp. 1–6

    work page 2016

  7. [7]

    Robust Control Tuning by PSO of Aerial Robots Hose Transportation,

    J. Estevez and M. Gra ˜na, “Robust Control Tuning by PSO of Aerial Robots Hose Transportation,” in Bioinspired Computation in Artificial Systems, J. M. Ferr ´andez Vicente, J. R. ´Alvarez-S´anchez, F. de la Paz L ´opez, F. J. Toledo-Moreo, and H. Adeli, Eds. Cham: Springer International Publishing, 2015, pp. 291–300

    work page 2015

  8. [8]

    Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UA V Formation Recon- figuration,

    H. Duan, Q. Luo, Y . Shi, and G. Ma, “Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UA V Formation Recon- figuration,”IEEE Computational Intelligence Magazine , vol. 8, no. 3, pp. 16–27, aug 2013

    work page 2013

  9. [9]

    Exact Robot Navigation Using Arti- ficial Potential Functions,

    E. Rimon and D. Koditschek, “Exact Robot Navigation Using Arti- ficial Potential Functions,” Robotics and Automation, IEEE , vol. 8, no. 5, pp. 501–518, 1992

    work page 1992

  10. [10]

    Evolutionary artificial potential fields and their application in real time robot path planning,

    P. Vadakkepat, K. C. Tan, and W. Ming-Liang, “Evolutionary artificial potential fields and their application in real time robot path planning,” in Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512) , vol. 1, jul 2000, pp. 256–263 vol.1

    work page 2000

  11. [11]

    New potential functions for mobile robot path planning,

    S. S. Ge and Y . J. Cui, “New potential functions for mobile robot path planning,” IEEE Transactions on Robotics and Automation , vol. 16, no. 5, pp. 615–620, oct 2000

    work page 2000

  12. [12]

    Stability analysis of swarms,

    V . Gazi and K. M. Passino, “Stability analysis of swarms,” IEEE Transactions on Automatic Control , vol. 48, no. 4, pp. 692–697, apr 2003

    work page 2003

  13. [13]

    A formation behavior for large-scale micro-robot force deployment,

    D. D. Dudenhoeffer and M. P. Jones, “A formation behavior for large-scale micro-robot force deployment,” in 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165) , vol. 1, dec 2000, pp. 972–982 vol.1

    work page 2000

  14. [14]

    Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines,

    D. H. Kim, H. O. Wang, G. Ye, and S. Shin, “Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines,” in 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) , vol. 1, dec 2004, pp. 159–164 V ol.1

    work page 2004

  15. [15]

    Assistive collision avoidance for quadrotor swarm teleoperation,

    D. Zhou and M. Schwager, “Assistive collision avoidance for quadrotor swarm teleoperation,” in 2016 IEEE International Conference on Robotics and Automation (ICRA) , may 2016, pp. 1249–1254

    work page 2016

  16. [16]

    A centralized control of move- ments using a collision avoidance algorithm for a swarm of au- tonomous agents,

    K. Loayza, P. Lucas, and E. Pel ´aez, “A centralized control of move- ments using a collision avoidance algorithm for a swarm of au- tonomous agents,” in 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), oct 2017, pp. 1–6

    work page 2017

  17. [17]

    Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization,

    J. Pugh and A. Martinoli, “Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization,” in 2007 IEEE Swarm Intelligence Symposium, apr 2007, pp. 332–339

    work page 2007

  18. [18]

    Flocks, Herds and Schools: A Distributed Behavioral Model,

    C. W. Reynolds, “Flocks, Herds and Schools: A Distributed Behavioral Model,” in Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques , ser. SIGGRAPH ’87. New York, NY , USA: ACM, 1987, pp. 25–34

    work page 1987

  19. [19]

    Outdoor flocking and formation flight with autonomous aerial robots,

    G. V ´as´arhelyi, C. Vir ´agh, G. Somorjai, N. Tarcai, T. Sz ¨or´enyi, T. Nepusz, and T. Vicsek, “Outdoor flocking and formation flight with autonomous aerial robots,” IEEE International Conference on Intelligent Robots and Systems , 2014

    work page 2014

  20. [20]

    Flocking algorithm for autonomous flying robots,

    C. Vir ´agh, G. V ´as´arhelyi, N. Tarcai, T. Sz ¨or´enyi, G. Somorjai, T. Nepusz, and T. Vicsek, “Flocking algorithm for autonomous flying robots,” Bioinspiration & Biomimetics , vol. 9, 2013

    work page 2013

  21. [21]

    Reynolds flocking in reality with fixed-wing robots: Communication range vs. maximum turning rate,

    S. Hauert, S. Leven, M. Varga, F. Ruini, A. Cangelosi, J. Zufferey, and D. Floreano, “Reynolds flocking in reality with fixed-wing robots: Communication range vs. maximum turning rate,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2011, pp. 5015–5020

    work page 2011

  22. [22]

    Self- organized flocking in mobile robot swarms,

    A. E. Turgut, H. C ¸ elikkanat, F. G ¨okc ¸e, and E. \cSahin, “Self- organized flocking in mobile robot swarms,” Swarm Intelligence , vol. 2, no. 2, pp. 97–120, dec 2008. [Online]. Available: https://doi.org/10.1007/s11721-008-0016-2

    work page doi:10.1007/s11721-008-0016-2 2008

  23. [23]

    ARGoS: A Modular, Parallel, Multi-Engine Simulator for Multi-Robot Systems,

    C. Pinciroli, V . Trianni, R. O’Grady, G. I. Pini, A. Brutschy, M. Bram- billa, N. Mathews, E. Ferrante, G. Di Caro, F. Ducatelle, M. Bi- rattari, L. M. Gambardella, and M. Dorigo, “ARGoS: A Modular, Parallel, Multi-Engine Simulator for Multi-Robot Systems,” Swarm Intelligence, vol. 6, no. 4, pp. 271–295, 2012

    work page 2012

  24. [24]

    Lehman and K

    J. Lehman and K. Stanley, Novelty Search and the Problem with Objectives, 2011, pp. 37–56

    work page 2011