Cooperation through self-assembly in multi-robot systems

TLDR

Mobile robots called s‑bots must physically connect to self‑assemble so they can operate in environments that prevent individual task execution. The study explores self‑assembly for cooperative object transport and develops an integrated decision‑making mechanism enabling robots to autonomously decide when self‑assembly is needed. An evolutionary‑computed artificial neural network was synthesized to integrate sensory‑motor coordination and decision‑making for autonomous self‑assembly. The experiments demonstrate that s‑bot hardware enables autonomous self‑assembly, the control architecture successfully guides cooperative transport, some controller features are disruptive, and evolutionary synthesis produces ANN that integrate coordination and decision‑making.

Abstract

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) decision-making 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.