Co-evolution framework of swarm self-assembly robots

In this paper, we present a co-evolution framework of configuration and control for swarm self-assembly robots, Sambots, in changing environments. The framework can generate different patterns composed of a set of Sambot robots to adapt to the uncertainties in complex environments. Sambot robots are able to autonomously aggregate and disaggregate into a multi-robot organism. To obtain the optimal pattern for the organism, the configuration and control of locomoting co-evolve by means of genetic programming. To finish self-adaptive tasks, we imply a unified locomotion control model based on Central Pattern Generators (CPGs). In addition, taking modular assembly modes into consideration, a mixed genotype is used, which encodes the configuration and control. Specialized genetic operators are designed to maintain the evolution in the simulation environment. By using an orderly method of evaluation, we can select some resulting patterns of better performance. Simulation experiments demonstrate that the proposed system is effective and robust in simultaneously constructing the adaptive structure and locomotion pattern. The algorithmic research and application analysis bring about deeper insight into swarm intelligence and evolutionary robotics.