Stigmergy-based collision-avoidance algorithm for self-organising swarms

Paolo Grasso, Mauro Innocente

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

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Real-time multi-agent collision-avoidance algorithms comprise a key enabling technology for the practical use of self-organising swarms of drones. This paper proposes a decentralised reciprocal collision-avoidance algorithm, which is based on stigmergy and scalable. The algorithm is computationally inexpensive, based on the gradient of the locally measured dynamic cumulative signal strength field which results from the signals emitted by the swarm. The signal strength acts as a repulsor on each drone, which then tends to steer away from the noisiest regions (cluttered environment), thus avoiding collisions. The magnitudes of these repulsive forces can be tuned to control the relative importance assigned to collision avoidance with respect to the other phenomena affecting the agent’s dynamics. We carried out numerical experiments on a self-organising swarm of drones aimed at fighting wildfires autonomously. As expected, it has been found that the collision rate can be reduced either by decreasing the cruise speed of the agents and/or by increasing the sampling frequency of the global signal strength field. A convenient by-product of the proposed collision-avoidance algorithm is that it helps maintain diversity in the swarm, thus enhancing exploration.
Original languageEnglish
Title of host publicationComputational Vision and Bio-Inspired Computing
Subtitle of host publicationAdvances in Intelligent Systems and Computing
EditorsS Syms, João Manuel R. S. Tavares, Valentina Emilia Balas
Number of pages9
ISBN (Electronic)978-981-16-9573-5
ISBN (Print)978-981-16-9572-8
Publication statusE-pub ahead of print - 31 Mar 2022
Event5th International Conference on Computational Vision and Bio-Inspired Computing - Online
Duration: 25 Nov 202126 Nov 2021


Conference5th International Conference on Computational Vision and Bio-Inspired Computing
Abbreviated titleICCVBIC 2021

Bibliographical note

The final publication is available at Springer via 10.1007/978-981-16-9573-5_19

Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.

This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.


  • Decentralised
  • Multi-agent
  • Autonomous
  • Wildfires


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