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Maximum likelihood estimate sharing for collective perception in static environments for swarm robotics

Published online by Cambridge University Press:  01 June 2023

Ahmed Abdelli Open the ORCID record for Ahmed Abdelli [Opens in a new window] ,
Ali Yachir ,
Abdenour Amamra  and
Belkacem Khaldi
Ahmed Abdelli*
Affiliation:
Ecole Militaire Polytechnique, Algiers, Algeria
Ali Yachir
Affiliation:
Ecole Militaire Polytechnique, Algiers, Algeria
Abdenour Amamra
Affiliation:
Ecole Militaire Polytechnique, Algiers, Algeria
Belkacem Khaldi
Affiliation:
LabRI-SBA Laboratory, Ecole Superieure en Informatique - 08 May 1945 - Sidi Bel Abbes, Sidi Bel Abbes, Algeria
*
Corresponding author: Ahmed Abdelli; Email: ahmedabdelli@kaist.ac.kr
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Abstract

Collective decision-making by a swarm of robots is of paramount importance. In particular, the problem of collective perception wherein a swarm of robots aims to achieve consensus on the prevalent feature in the environment. Recently, this problem has been formulated as a discrete collective estimation scenario to estimate their proportion rather than deciding about the prevalent one. Nevertheless, the performance of the existing strategies to resolve this scenario is either poor or depends on higher communication bandwidth. In this work, we propose a novel decision-making strategy based on maximum likelihood estimate sharing (MLES) to resolve the discrete collective estimation scenario. Experimentally, we compare the tradeoff speed versus accuracy of MLES with state-of-the-art methods in the literature, such as direct comparison (DC) and distributed Bayesian belief sharing (DBBS). Interestingly, MLES achieves an accurate consensus nearly 20% faster than DBBS, its communication bandwidth requirement is the same as DC but six times less than DBBS, and its computational complexity is $O(1)$. Furthermore, we investigate how noisy sensors affect the effectiveness of the strategies under consideration, with MLES showing better sustainability.

Keywords

swarm roboticsself-organizationcollective decision-makingcollective perceptiondiscrete collective estimation
Type
Research Article
Information
Robotica , Volume 41 , Issue 9 , September 2023 , pp. 2754 - 2773
Copyright
© The Author(s), 2023. Published by Cambridge University Press

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