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A Quality-Diversity Approach to Evolving a Repertoire of Diverse Behaviour-Trees in Robot Swarms

Montague, Kirsty; Hart, Emma; Paechter, Ben; Nitschke, Geoff


Geoff Nitschke


João Correia

Stephen Smith

Raneem Qaddoura


Designing controllers for a swarm of robots such that collabo-rative behaviour emerges at the swarm level is known to be challenging. Evolutionary approaches have proved promising, with attention turning more recently to evolving repertoires of diverse behaviours that can be used to compose heterogeneous swarms or mitigate against faults. Here we extend existing work by combining a Quality-Diversity algorithm (MAP-Elites) with a Genetic-Programming (GP) algorithm to evolve repertoires of behaviour-trees that define the robot controllers. We compare this approach with two variants of GP, one of which uses an implicit diversity method. Our results show that the QD approach results in larger and more diverse repertoires than the other methods with no loss in quality with respect to the best solutions found. Given that behaviour-trees have the added advantage of being human-readable compared to neural controllers that are typically evolved, the results provide a solid platform for future work in composing heterogeneous swarms.

Presentation Conference Type Conference Paper (Published)
Conference Name EVOStar 2023
Start Date Apr 12, 2023
End Date Apr 14, 2023
Acceptance Date Jan 18, 2023
Online Publication Date Apr 9, 2023
Publication Date Apr 10, 2023
Deposit Date Feb 20, 2023
Publicly Available Date Apr 10, 2024
Publisher Springer
Pages 145-160
Series Title Lecture Notes in Computer Science
Series Number 13989
Series ISSN 0302-9743
Book Title Applications of Evolutionary Computation: 26th European Conference, EvoApplications 2023, Held as Part of EvoStar 2023, Brno, Czech Republic, April 12–14, 2023, Proceedings
ISBN 9783031302282
Keywords Swarm-robotics, Quality-Diversity, Genetic-Programming


A Quality-Diversity Approach To Evolving A Repertoire Of Diverse Behaviour-Trees In Robot Swarms (accepted version) (706 Kb)

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