Simulation and Implementation of Obstacle Avoidance and Synchronization Tasks using Probabilistic and Timed Supervisory Control Theory in Swarm Robotics

Due to difficulties in formal implementation of swarm robotic systems, controlling software of such systems is developed in an ad-hoc manner and with trial and error. So, it is hard to reuse these systems for other similar problems. Moreover, testing, analyzing and verifying the correctness of the controller are difficult too. There is no guarantee that the implementation matches the specifications. To address these problems, supervisory control theory as a formal approach is suggested. In this paper, probabilistic and timed supervisory control theory (ptSCT) is implemented on ARGoS platform in swarm robotic. The proposed approach automatically calculates ptSCT, and then generates the equivalent controlling software codes. The generated controlling software can be used for both simulation and running on real robots without any changes. For comparison purposes, two tasks namely obstacle avoidance and synchronization of robots are designed using both SCT and proposed ptSCT. The approach is successfully validated in both tasks using up to 64 E-Puck robots. The experimental results show the advantages of the ptSCT, in terms of simplicity, reusability, and automatic code generation.