Robotic odour search: Evolving a robot's brain with Genetic Programming

Abstract

This paper addresses the problem of controlling a group of mobile robots to track an odour plume to its source. To perform this task in real environments, it is important that the robots are able to adapt to a changing world, and use the experience gained to improve their performance. We address this task with Genetic Programming to evolve the controllers for the robots. Two evolutionary approaches are proposed and compared to a variant of the Silkworm Moth algorithm, that has been modified to take advantage of multi robot systems. The statistically validated results showed that, in the groups of robots where significant differences were found, the evolved controllers were able to find the odour plume faster and converge to its source better than the Silkworm Moth approach.