An Embedded Evolutionary Controller to Navigate a Population of Autonomous Robots

This chapter studies evolutionary computation applied to the development of embedded controllers to navigate a team of six mobile robots. It describes a genetic system where the population exists in a real environment, where they exchange genetic material and reconfigure themselves as new individuals to form the next generations, providing the means of running genetic evolutions in a real physical platform. The chapter presents the techniques that could be adapted from the literature as well as the novel techniques developed to allow the design of the hardware and software necessary to embedding the distributed evolutionary system. It also describes the environment where the experiments are carried out in real time. These experiments test the influence of different parameters, such as different partner selection and reproduction strategies. This chapter proposes and implements a fully embedded distributed evolutionary system that is able to achieve collision free-navigation in a few hundreds of trials. Evolution can manipulate some morphology aspects of the robot: the configuration of the sensors and the motor speed levels. It also proposes some new strategies that can improve the performance of evolutionary systems in general. Ever more frequently, multi-robot systems have been shown in literature as a more efficient approach to industrial applications in relation to single robot solutions. They are usually more flexible, robust and fault-tolerant solutions (Baldassarre et al., 2003). Nevertheless, they still present state-of-the-art challenges to designers that have difficulties to understand the complexity of robot-to-robot interaction and task sharing in such parallel systems (Barker & Tyrrell, 2005). Often, designers are not able to predict all the situations that the robots are going to face and the resulting solutions are not able to adapt to variations in the working environment. Therefore, new techniques for the automated synthesis of robotic embedded controllers that are able to deal with bottom-up design strategies are being investigated. In this context bioinspired strategies such as Evolutionary Computation are becoming attractive alternatives to traditional design, since it can naturally deal with decentralized distributed solutions, and are more robust to noise and the uncertainty of real world applications (Thakoor et al., 2004). Evolutionary robotics is a promising methodology to automatically design robot control circuits (Nelson et al., 2004a). It is been applied to the design of single robot navigation circuits with some success, where it is able to achieve efficient solutions for simple tasks,