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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Abstract

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
PublisherSpringer
Pages253-261
Number of pages9
Volume1420
Edition1
ISBN (Electronic)978-981-16-9573-5
ISBN (Print)978-981-16-9572-8
DOIs
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

Conference

Conference5th International Conference on Computational Vision and Bio-Inspired Computing
Abbreviated titleICCVBIC 2021
Period25/11/2126/11/21

Bibliographical note

The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-981-16-9573-5_19


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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.

Keywords

  • Decentralised
  • Multi-agent
  • Autonomous
  • Wildfires

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