Evolution of Swarm Robotics Systems with Novelty Search

TL;DR

This paper demonstrates that novelty search, an evolutionary technique rewarding behavioral diversity, effectively evolves neural controllers for swarm robotics, especially in deceptive or complex tasks, offering an alternative to traditional fitness-based methods.

Contribution

The study applies novelty search combined with NEAT to swarm robotics, showing its advantages over fitness-based evolution in deception resistance, diversity, and solution simplicity.

Findings

Novelty search is unaffected by deception.

It effectively bootstraps evolution in swarm tasks.

It finds diverse, low-complexity solutions.

Abstract

Novelty search is a recent artificial evolution technique that challenges traditional evolutionary approaches. In novelty search, solutions are rewarded based on their novelty, rather than their quality with respect to a predefined objective. The lack of a predefined objective precludes premature convergence caused by a deceptive fitness function. In this paper, we apply novelty search combined with NEAT to the evolution of neural controllers for homogeneous swarms of robots. Our empirical study is conducted in simulation, and we use a common swarm robotics task - aggregation, and a more challenging task - sharing of an energy recharging station. Our results show that novelty search is unaffected by deception, is notably effective in bootstrapping the evolution, can find solutions with lower complexity than fitness-based evolution, and can find a broad diversity of solutions for the same task. Even in non-deceptive setups, novelty search achieves solution qualities similar to those obtained in traditional fitness-based evolution. Our study also encompasses variants of novelty search that work in concert with fitness-based evolution to combine the exploratory character of novelty search with the exploitatory character of objective-based evolution. We show that these variants can further improve the performance of novelty search. Overall, our study shows that novelty search is a promising alternative for the evolution of controllers for robotic swarms.