Evolution, Self-organization and Swarm Robotics

Abstract

Summary. The activities of social insects are often based on a self-organising process, that is, "a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system"(see [4], p. 8). In a self-organising system such as an ant colony, there is neither a leader that drives the activities of the group, nor are the individual ants informed about a global recipe or blueprint to be executed. On the contrary, each single ant acts autonomously following simple rules and locally interacting with the other ants. As a consequence of the numerous interactions among individuals, a coherent behaviour can be observed at the colony level.A similar organisational structure is definitely beneficial for a swarm of autonomous robots. In fact, a coherent group behaviour can be obtained providing each robot with simple individual rules. Moreover, the features that characterise a self-organising system-such as decentralisation, flexibility and robustness-are highly desirable also for a swarm of autonomous robots. The main problem that has to be faced in the design of a self-organising robotic system is the definition of the individual rules that lead to the desired collective behaviour. The solution we propose to this design problem relies on artificial evolution as the main tool for the synthesis of self-organising behaviours. In this chapter, we provide an overview of successful applications of evolutionary techniques to the evolution of self-organising behaviours for a group of simulated autonomous robots. The obtained results show that the methodology is viable, and that it produces behaviours that are efficient, scalable and robust enough to be tested in reality on a physical robotic platform.

Evolving coordinated group behaviours through maximisation of mean mutual information

“…In fact, there is a fundamental problem-referred to as the design problem-that arises in the development of self-organising behaviours for a group of robots (see also Funes et al 2003;Trianni et al 2008, for a detailed discussion of this topic). This problem consists in defining the appropriate individual rules that will lead to a certain global pattern, and it is particularly challenging due to the indirect relationship between control rules and individual behaviour, and between interacting individuals and the desired global pattern.…”

Embodied Evolution in Collective Robotics: A Review

“…Recent works in reinforcement learning have developed theoretical tools to break down complexity by operating a move from considering many agents to a collection of single agents, each of which being optimized separately (Dibangoye et al, 2015), leading to theoretically well-founded contributions, but with limited practical validation involving very few robots and simple tasks . Lacking theoretical foundations, but instead based on the experimental validation, swarm robotics controllers have been developed with black-box optimization methods ranging from brute-force optimization using a simplified (hence tractable) representation of a problem (Werfel et al, 2014) and evolutionary robotics (Hauert et al, 2008;Trianni et al, 2008;Gauci et al, 2012;Silva et al, 2016).…”

On-Board Evolutionary Algorithm and Off-Line Rule Discovery for Column Formation in Swarm Robotics

“…It is natural to borrow the strength of online evolutionary adaptation algorithms, which have proven to be highly effective in multi-robot systems [11], [12]. We use the (1+1) ROAA [7], which allows the robot to search among good controllers without using communication.…”

“…In [10], reinforcement learning is used in team coordination tasks such as RoboCup setups. [11] tackles classic evolutionary robotics in the context of swarms, where an optimization is executed off-line and then the result is used as the solution. On the contrary, [12] is recognized as the seminal work in embodied evolutionary robotics, as the optimization is executed on-board, i.e., while the robots are running.…”