Andrade, GR and Boyle, JH (2020) A minimal biologically-inspired algorithm for robots foraging energy in uncertain environments. Robotics and Autonomous Systems, 128. 103499. ISSN 0921-8890
Abstract
This work details the design and simulation results of a bioinspired minimalist algorithm based on C. elegans, using autonomous agents to forage for attractant energy sources. The robotic agents are energy-constrained and depend on the energy they forage to recharge their batteries, which is significant as the foraging task is one of the canonical testbeds for cooperative robotics.
The algorithm consists of 6 input parameters which were simulated and optimised in 9 unbounded environments of varying difficulty levels, containing attractant sources which robots would then have to forage from to maintain energy levels and survive the entirety of the simulation.
The robots running the algorithm were then optimised using Evolutionary Algorithms and the best solutions in all 9 environments were categorised with the use of clustering techniques. The clustering results highlighted the different strategies which emerged. Ultimately across the 9 environments, 6 different strategies have been identified. The results demonstrate the applicability of the proposed algorithm to localise attractant sources and harvest energy in different scenarios using the same core algorithm.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier. This is an author produced version of an article published in Robotics and Autonomous Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Engineering Systems and Design (iESD) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 May 2020 10:55 |
Last Modified: | 16 Mar 2021 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.robot.2020.103499 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160055 |