Evolving Homogeneous Neurocontrollers for a Group of Heterogeneous Robots: Coordinated Motion, Cooperation, and Acoustic Communication

Abstract

This article describes a simulation model in which artificial evolution is used to design homogeneous control structures and adaptive communication protocols for a group of three autonomous simulated robots. The agents are required to cooperate in order to approach a light source while avoiding collisions. The robots are morphologically different: Two of them are equipped with infrared sensors, one with light sensors. Thus, the two morphologically identical robots should take care of obstacle avoidance; the other one should take care of phototaxis. Since all of the agents can emit and perceive sound, the group's coordination of actions is based on acoustic communication. The results of this study are a proof of concept: They show that dynamic artificial neural networks can be successfully synthesized by artificial evolution to design the neural mechanisms required to underpin the behavioral strategies and adaptive communication capabilities demanded by this task. Postevaluation analyses unveil operational aspects of the best evolved behavior. Our results suggest that the building blocks and the evolutionary machinery detailed in the article should be considered in future research work dealing with the design of homogeneous controllers for groups of heterogeneous cooperating and communicating robots.

Evolving Communication in Embodied Agents: Assessment and Open Challenges

“…coordinated and/or synchronized collective behaviors, as in Chapter ?? (see also Sperati et al, 2008;Trianni et al, 2007;Tuci et al, 2008), and collective decision behaviors, as in Chapter ? ?, see also Marocco and Nolfi, 2007;Uno et al, 2007).…”

An investigation of the evolutionary origin of reciprocal communication using simulated autonomous agents

“…Moreover, we do not review those ER models, in which the mechanisms for social interactions are entirely evolved, but the focus of that is on the ontogeny than on issues concerning the phylogeny of communication (e.g. Di Paolo 2000;Baldassarre et al 2003;Trianni and Dorigo 2006;Tuci et al 2008;Williams et al 2008).…”

Evolving Self-Assembly in Autonomous Homogeneous Robots: Experiments with Two Physical Robots

“…In particular, we train via artificial evolution a dynamic neural network that, when downloaded to real robots, allows them to coordinate their actions in order to decide who will grip whom. Dynamic neural networks have been used in the past as a means to achieve specialization in a robot group (see [34,38] for examples). Similarly, we study self-assembly in a setup where the robots interact and eventually differentiate by allocating distinct roles.…”