Evolving flocking in embodied agents based on local and global application of Reynolds’ rules

Autoři: Rita Parada Ramos aff001;  Sancho Moura Oliveira aff002;  Susana Margarida Vieira aff001;  Anders Lyhne Christensen aff002
Působiště autorů: Instituto Superior Técnico (IST), Lisbon, Portugal aff001;  Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal aff002;  Instituto de Telecomunicações, Lisbon, Portugal aff003;  Embodied Systems for Robotics and Learning at the Mærsk Mc-Kinney Møller Institute, University of Southern Denmark (SDU), Odense, Denmark aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0224376


In large scale systems of embodied agents, such as robot swarms, the ability to flock is essential in many tasks. However, the conditions necessary to artificially evolve self-organised flocking behaviours remain unknown. In this paper, we study and demonstrate how evolutionary techniques can be used to synthesise flocking behaviours, in particular, how fitness functions should be designed to evolve high-performing controllers. We start by considering Reynolds’ seminal work on flocking, the boids model, and design three components of a fitness function that are directly based on his three local rules to enforce local separation, cohesion and alignment. Results show that embedding Reynolds’ rules in the fitness function can lead to the successful evolution of flocking behaviours. However, only local, fragmented flocking behaviours tend to evolve when fitness scores are based on the individuals’ conformity to Reynolds’ rules. We therefore modify the components of the fitness function so that they consider the entire group of agents simultaneously, and find that the resulting behaviours lead to global flocking. Furthermore, the results show that alignment need not be explicitly rewarded to successfully evolve flocking. Our study thus represents a significant step towards the use of evolutionary techniques to synthesise collective behaviours for tasks in which embodied agents need to move as a single, cohesive group.

Klíčová slova:

Artificial neural networks – Collective animal behavior – Foraging – Inertia – Predation – Robotic behavior – Robotics – Robots


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2019 Číslo 10
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