Supervisory control theory applied to swarm robotics

Probabilistic Supervisory Control Theory (pSCT) Applied to Swarm Robotics

Swarm robotics studies large groups of robots that work together to accomplish common tasks. Much of the used source code is developed in an ad-hoc manner, meaning that the correctness of the controller is not always verifiable. In previous work, supervisory control theory (SCT) and associated design tools have been used to address this problem. Given a formal description of the swarm’s agents ...

An Overview of Swarm Robotics: Swarm Intelligence Applied to Multi-robotics

As an emergent research area by which swarm intelligence is applied to multi-robot systems; swarm robotics (a very particular and peculiar sub-area of collective robotics) studies how to coordinate large groups of relatively simple robots through the use of local rules. It focuses on studying the design of large amount of relatively simple robots, their physical bodies and their controlling beh...

Adaptive Control in Swarm Robotics

Swarm robotic systems are mainly inspired by swarms of socials insects and the collective emergent behavior that arises from their cooperation at the lower lever. Despite the limited sensory ability, computational power, and communication means of each swarm member, the swarm as a group manages to achieve difficult tasks such as searching for food in terrains with obstacles that individual robo...

Introduction to Swarm Robotics

Performance in Supervisory Control Theory

A supervisor is generated with Supervisory Control Theory [3], resulting in a nonblocking and minimally restrictive controller. Time is introduced time using timed automata theory. A compact data structure is used to keep track of the time information in the timed automata: Heaps of Pieces [2]. The fastest sequence of events is found, using graph search algorithms of Dijkstra and A [1]. This is...