Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots

Self-Organization and Artificial Life

Self-organization can be broadly defined as the ability of a system to display ordered spatiotemporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, artificial life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of lifelike phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.

Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors

Research in swarm robotics has shown that automatic design is an effective approach to realize robot swarms. In automatic design methods, the collective behavior of a swarm is obtained by automatically configuring and fine-tuning the control software of individual robots. In this paper, we present TuttiFrutti: an automatic design method for robot swarms that belongs to AutoMoDe—a family of methods that produce control software by assembling preexisting software modules via optimization. The peculiarity of TuttiFrutti is that it designs control software for e-puck robots that can display and perceive colors using their RGB LEDs and omnidirectional camera. Studies with AutoMoDe have been so far restricted by the limited capabilities of the e-pucks. By enabling the use of colors, we significantly enlarge the variety of collective behaviors they can produce. We assess TuttiFrutti with swarms of e-pucks that perform missions in which they should react to colored light. Results show that TuttiFrutti designs collective behaviors in which the robots identify the colored light displayed in the environment and act accordingly. The control software designed by TuttiFrutti endowed the swarms of e-pucks with the ability to use color-based information for handling events, communicating, and navigating.

Online Evolution of Adaptive Robot Behaviour

The authors propose and evaluate a novel approach to the online synthesis of neural controllers for autonomous robots. The authors combine online evolution of weights and network topology with neuromodulated learning. The authors demonstrate our method through a series of simulation-based experiments in which an e-puck-like robot must perform a dynamic concurrent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. The authors demonstrate that the online evolutionary process, both with and without neuromodulation, is capable of generating controllers well adapted to the periodic task changes. The authors show that when neuromodulated learning is combined with evolution, neural controllers are synthesised faster than by evolution alone. An analysis of the evolved solutions reveals that neuromodulation allows for a more effective expression of a given topology's potential due to the active modification of internal dynamics. Neuromodulated networks learn abstractions of the task and different modes of operation that are triggered by external stimulus.

Swarm robotics reviewed

We present a review of recent activities in swarm robotic research, and analyse existing literature in the field to determine how to get closer to a practical swarm robotic system for real world applications. We begin with a discussion of the importance of swarm robotics by illustrating the wide applicability of robot swarms in various tasks. Then a brief overview of various robotic devices that can be incorporated into swarm robotic systems is presented. We identify and describe the challenges that should be resolved when designing swarm robotic systems for real world applications. Finally, we provide a summary of a series of issues that should be addressed to overcome these challenges, and propose directions for future swarm robotic research based on our extensive analysis of the reviewed literature.

Engineering the evolution of self-organizing behaviors in swarm robotics: a case study

Evolutionary robotics (ER) is a powerful approach for the automatic synthesis of robot controllers, as it requires little a priori knowledge about the problem to be solved in order to obtain good solutions. This is particularly true for collective and swarm robotics, in which the desired behavior of the group is an indirect result of the control and communication rules followed by each individual. However, the experimenter must make several arbitrary choices in setting up the evolutionary process, in order to define the correct selective pressures that can lead to the desired results. In some cases, only a deep understanding of the obtained results can point to the critical aspects that constrain the system, which can be later modified in order to re-engineer the evolutionary process towards better solutions. In this article, we discuss the problem of engineering the evolutionary machinery that can lead to the desired result in the swarm robotics context. We also present a case study about self-organizing synchronization in a swarm of robots, in which some arbitrarily chosen properties of the communication system hinder the scalability of the behavior to large groups. We show that by modifying the communication system, artificial evolution can synthesize behaviors that scale properly with the group size.