Engineering central pattern generated behaviors for the deployment of robotic systems

In order to face with various contexts and situations, autonomous robots should be endowed with many different sensors and behaviors. These requirements pose new challenges such as the coordination of multiple parallel activities and the efficient use of the limited sensorial and cognitive resources. These challenges are often tackled by relying on a priori and well defined coordination schema among behaviors, and on fixed periodic or ad hoc monitoring strategies. Our working hypothesis is that adaptive control strategies, inspired by natural cyclic processes, can be used to cope with these problems. However, the development of these flexible strategies may result hard to be modeled and implemented. Recently, the possibility of abstracting an implementation view into an architectural design is getting more achievable. Hence, in this paper, we propose a modeling framework that allows developers to model simple behavior-based robotic systems enhanced by the use of Central Pattern Generators (CPGs) for modulating the sensor-motor loops. Different from other approaches, the use of CPGs, here, is to efficiently exploit the limited sensorial and cognitive resources, and to coordinate the multiple activities the robot is endowed with, by balancing sensors elaboration and action execution. The framework can use these models to generate robotic control executable code for analysis purposes. In this paper, we focus on parallel behaviors managing analysis.