Cooperative transport of objects of different shapes and sizes

This paper addresses the design of control policies for groups of up to 16 simple autonomous mobile robots (called s-bots) for the cooperative transport of heavy objects of different shapes and sizes. The s-bots are capable of establishing physical connections with each other and with the object (called prey). We want the s-bots to self-assemble into structures which pull or push the prey towards a target location. The s-bots are controlled by neural networks that are shaped by artificial evolution. The evolved controllers perform quite well, independently of the shape and size of the prey, and allow the group to transport the prey towards a moving target. Additionally, the controllers evolved for a relatively small group can be applied to larger groups, making possible the transport of heavier prey. Experiments are carried out using a physics simulator, which provides a realistic simulation of real robots, which are currently under construction

Evolving chess playing programs

This contribution introduces a hybrid GP/ES system for the evolution of chess playing computer programs. We discuss the basic system and examine its performance in comparison to pre-existing algorithms of the type alpha-beta and its improved variants. We can show that evolution is able to outperform these algorithms both in terms of efficiency and strength

Autonomous Self-assembly in a Swarm-bot

Multi-robot systems have been studied in tasks that require the robots to be physically linked. In such a configuration, a group of robots may navigate a terrain that proves too difficult for a single robot. On the contrary, many collective tasks can be accomplished more efficiently by a group of independent robots. This paper is about swarm-bot, a robotic system that can operate in both configurations and autonomously switch from one to the other. We examine the performance of a single robot and of groups of robots selfassembling with an object or another robot. We assess the robustness of the system with respect to different types of rough terrain. Finally, we evaluate the performance of swarms of 16 physical robots. At present, for self-assembly in autonomous, mobile robotics, swarm-bots is the state of the art for what concerns reliability, robustness and speed

Negotiation of goal direction for cooperative transport

In this paper, we study the cooperative transport of a heavy object by a group of robots towards a goal. We investigate the case in which robots have partial and noisy knowledge of the goal direction and can not perceive the goal itself. The robots have to coordinate their motion to apply enough force on the object to move it. Furthermore, the robots should share knowledge in order to collectively improve their estimate of the goal direction and transport the object as fast and as accurately as possible towards the goal. We propose a bio-inspired mechanism of negotiation of direction that is fully distributed. Four different strategies are implemented and their performances are compared on a group of four real robots, varying the goal direction and the level of noise. We identify a strategy that enables effcient coordination of motion of the robots. Moreover, this strategy lets the robots improve their knowledge of the goal direction. Despite significant noise in the robots' communication, we achieve effective cooperative transport towards the goal and observe that the negotiation of direction entails interesting properties of robustness

Evolving aggregation behaviors in a swarm of robots

In this paper, we study aggregation in a swarm of simple robots, called s-bots, having the capability to self-organize and self-assemble to form a robotic system, called a swarm-bot. The aggregation process, observed in many biological systems, is of fundamental importance since it is the prerequisite for other forms of cooperation that involve self-organization and self-assembling. We consider the problem of designing the control system for the swarm-bot using artificial evolution. The results obtained in a simulated 3D environment are presented and analyzed. They show that artificial evolution, exploiting the complex interactions among s-bots and between s-bots and the environment, is able to produce simple but general solutions to the aggregation problem

Towards an Autonomous Evolution of Non-Biological Physical Organisms

We propose an experimental study where simplistic organ- isms rise from inanimate matter and evolve solely through physical interactions. These organisms are composed of three types of macroscopic building blocks floating in an agitated medium. The dynamism of the medium allows the blocks to physically bind with and disband from each other. This results in the emergence of organisms and their reproduction. The process is governed solely by the building blocks' local interactions in the absence of any blueprint or central command. We demonstrate the feasibility of our approach by realistic computer simulations and a hardware prototype. Our results suggest that an autonomous evolution of non-biological organisms can be realized in human-designed environments and, potentially, in natural environments

Cooperation through self-assembly in multi-robot

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, called s-bots, are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots’ controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decisionmaking mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly

Moving targets: Collective decisions and flexible choices in house-hunting ants

Many decisions involve a trade-off between commitment and flexibility. We show here that the collective decisions ants make over new nest sites are sometimes sufficiently flexible that the ants can change targets even after an emigration has begun. Our findings suggest that, in this context, the ants’ procedures are such that they can sometimes avoid ‘negative information cascades’ which might lock them into a poor choice. The ants are more responsive to belated good news of a higher quality nest than they are when the nest they had initially chosen degraded to become worse than an alternative. Our study confirms, in a new way, that ant colonies can be very powerful “search engines”

Evolving Self-Organizing Behaviors for a Swarm-bot

In this paper, we introduce a self-assembling and self-organizing artifact, called a swarm-bot, composed of a swarm of s-bots, mobile robots with the ability to connect to and to disconnect from each other. We discuss the challenges involved in controlling a swarm-bot and address the problem of synthesizing controllers for the swarm-bot using artificial evolution. Specifically, we study aggregation and coordinated motion of the swarm-bot using a physics-based simulation of the system. Experiments, using a simplified simulation model of the s-bots, show that evolution can discover simple but effective controllers for both the aggregation and the coordinated motion of the swarm-bot. Analysis of the evolved controllers shows that they have properties of scalability, that is, they continue to be effective for larger group sizes, and of generality, that is, they produce similar behaviors for configurations different from those they were originally evolved for. The portability of the evolved controllers to real s-bots is tested using a detailed simulation model which has been validated against the real s-bots in a companion paper in this same special issue