Community Research and Development Information Service - CORDIS

FP5

SWARM-BOTS Report Summary

Project ID: IST-2000-31010
Funded under: FP5-IST
Country: Belgium

Algorithms for autonomous self-assembling

Probably the most characteristic capacity of the swarm-bot system is that it can self-assemble; that is, to move from a situation characterised by the activity of a number n>1 of s-bots to a situation in which these n s-bots physically connect to each other to form a swarm-bot. Self-assembling can enhance the efficiency of a group of autonomous cooperating s-bots in several different contexts.

Generally speaking, self-assembling is advantageous anytime it allows a group of agents to cope with environmental conditions which prevent them from carrying out their task individually. For example, s-bots, designed for all-terrain navigation, could make use of self-assembling to move in a particularly rough terrain by reducing the risk of toppling over, as well as to bridge the gap between the two sides of a trough larger than the body of a single robot, reducing the risk of falling in. In the context of object transport, a group of self-assembled s-bots might be capable of pushing/pulling an object which, due to its characteristics (e.g., mass, size, and shape), can not be transported by a single s-bot.

To develop controllers capable of letting s-bots self-assemble we used a perceptron-type neural network whose weights were evolved using an evolutionary algorithm. These controllers were synthesised in simulation using up to 5 simulated s-bots and then ported to the real s-bots. In short, self-assembly works as follows. The start of the process is triggered by the presence of an s-bot, which turns on its red lights. The s-bots which are closer to the red s-bot perceive the red light and approach it until they are close enough to connect by grasping the red s-bot ring with their gripper. If the connection is successful, they turn their red lights on so as to attract other s-bots. If an s-bot encounters difficulties during the approach phase, it launches a recovery procedure, which consists of the s-bot moving backward and approaching the red s-bot again.

Experiments have shown that this procedure can reliably control the s-bots so that they connect to each other or to a special luminous object, called s-toy, with red lights turned on. The procedure is scalable, as it works for increasing numbers of s-bots (experiments with up to 16 s-bots were run successfully), and robust, as it can control self-assembling s-bots moving on both flat and moderately rough terrain.

Related information

Result In Brief

Reported by

Université Libre de Bruxelles
Ave. F. Roosevelt 50
1050 Brussels
Belgium
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