Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots.
This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots. The neurocontrollers cause the dynamic specialization of the robots by allocating roles between them based solely on their interaction. We show that the best evolved controller proves to be successful when tested on a real hardware platform, the swarm-bot. The performance achieved is similar to the one achieved by existing modular or behavior-based approaches, also due to the effect of an emergent recovery mechanism that was neither explicitly rewarded by the fitness function, nor observed during the evolutionary simulation. Our results suggest that direct access to the orientations or intentions of the other agents is not a necessary condition for robot coordination: Our robots coordinate without direct or explicit communication, contrary to what is assumed by most research works in collective robotics. This work also contributes to strengthening the evidence that evolutionary robotics is a design methodology that can tackle real-world tasks demanding fine sensory-motor coordination.
Ampatzis
Christos
C
European Space Agency, Advanced Concepts Team, ESTEC, Keplerlaan I, Postbus 2fff99, 2200 AG, Noordwijk, The Netherlands. christos.ampatzis@esa.int
Tuci
Elio
E
Trianni
Vito
V
Christensen
Anders Lyhne
AL
Dorigo
Marco
M
D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D003198:Computer Simulation; D003201:Computers; D004867:Equipment Design; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012371:Robotics / Q000379:methods*