Application of Coarse-Coding Techniques for Evolvable Multirobot Controllers

Robots, in their most general embodiment, can be complex systems trying to negotiate and manipulate an unstructured environment. They ideally require an ‘intelligence’ that reflects our own. Artificial evolutionary algorithms are often used to generate a high-level controller for single and multi robot scenarios. But evolutionary algorithms, for all their advantages, can be very computationally intensive. It is therefore very desirable to minimize the number of generations required for a solution. In this chapter, we incorporate the Artificial Neural Tissue (ANT) approach for robot control from previous work with a novel Sensory Coarse Coding (SCC) model. This model is able to exploit regularity in the sensor data of the environment. Determining how the sensor suite of a robot should be configured and utilized is critical for the robots operation. Much as nature evolves body and brain simultaneously, we should expect improved performance resulting from artificially evolving the controller and sensor configuration in unison. Simulation results on an example task, resource gathering, show that the ANT+SCC system is capable of finding fitter solutions in fewer generations. We also report on hardware experiments for the same task that show complex behaviors emerging through self-organized task decomposition.