Evolving Behavior Allocations in Robot Swarms

Hallauer, Scott; Nitschke, Geoff; Hart, Emma

Abstract

Behavioral diversity is known to benefit problem-solving in biological social systems such as insect colonies and human societies, as well as in artificial distributed systems including large-scale software and swarm-robotics systems. We investigate methods of evolving robot swarms in which individuals have heterogeneous behaviours. Two approaches are investigated to create swarm of size n. The first encodes a repertoire of n behaviours on a single individual, and hence evolves the swarm directly. The second approach uses two phases. First, a large repertoire of diverse behaviours is evolved and then another evolutionary algorithm is used to search for an optimal allocation of behaviours to the swarm. Results indicate that the two phase approach of generate then allocate produces significantly more effective collective behaviors (in terms of task accomplishment) than the direct evolution of behaviorally heterogeneous swarms.

Citation

Hallauer, S., Nitschke, G., & Hart, E. (2023, December). Evolving Behavior Allocations in Robot Swarms. Presented at IEEE Symposium Series on Computational Intelligence (SSCI 2023), Mexico City, Mexico

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE Symposium Series on Computational Intelligence (SSCI 2023)
• Start Date: Dec 5, 2023
• End Date: Dec 8, 2023
• Acceptance Date: Sep 15, 2023
• Online Publication Date: Jan 1, 2024
• Publication Date: 2024
• Deposit Date: Sep 29, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1526-1531
• Book Title: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)
• DOI: https://doi.org/10.1109/SSCI52147.2023.10371934
• Keywords: Swarm-Robotics, Behavioral Quality-Diversity
• Public URL: http://researchrepository.napier.ac.uk/Output/3203804