Supervisory Control Theory for Controlling Swarm Robotics Systems

Swarm robotics systems have the potential to tackle many interesting problems. Their control software is mostly created by ad-hoc development. This makes it hard to deploy swarm robotics systems in real-world scenarios as it is difficult to analyse, maintain, or extend these systems. Formal methods can contribute to overcome these problems. However, they usually do not guarantee that the implementation matches the specification because the system’s control code is typically generated manually. This thesis studies the application of the supervisory control theory (SCT) framework in swarm robotics systems. SCT is widely applied and well established in the man- ufacturing context. It requires the system and the desired behaviours (specifications) to be defined as formal languages. In this thesis, regular languages are used. Regular languages, in the form of deterministic finite state automata, have already been widely applied for controlling swarm robotics systems, enabling a smooth transition from the ad-hoc development currently in practice. This thesis shows that the control code for swarm robotics systems can be automatically generated from formal specifications. Several case studies are presented that serve as guidance for those who want to learn how to specify swarm behaviours using SCT formally. The thesis provides the tools for the implementation of controllers using formal specifications. Controllers are validated on swarms of up to 600 physical robots through a series of systematic experiments. It is also shown that the same controllers can be automatically ported onto different robotics platforms, as long as they offer the required capabilities. The thesis extends and incorporates techniques to the supervisory control theory framework; specifically, the concepts of global events and the use of probabilistic generators. It can be seen as a step towards making formal methods a standard practice in swarm robotics

Classification and Management of Computational Resources of Robotic Swarms and the Overcoming of their Constraints

Swarm robotics is a relatively new and multidisciplinary research field with many potential applications (e.g., collective exploration or precision agriculture). Nevertheless, it has not been able to transition from the academic environment to the real world. While there are many potential reasons, one reason is that many robots are designed to be relatively simple, which often results in reduced communication and computation capabilities. However, the investigation of such limitations has largely been overlooked. This thesis looks into one such constraint, the computational constraint of swarm robots (i.e., swarm robotics platform). To achieve this, this work first proposes a computational index that quantifies computational resources. Based on the computational index, a quantitative study of 5273 devices shows that swarm robots provide fewer resources than many other robots or devices. In the next step, an operating system with a novel dual-execution model is proposed, and it has been shown that it outperforms the two other robotic system software. Moreover, results show that the choice of system software determines the computational overhead and, therefore, how many resources are available to robotic software. As communication can be a key aspect of a robot's behaviour, this work demonstrates the modelling, implementing, and studying of an optical communication system with a novel dynamic detector. Its detector improves the quality of service by orders of magnitude (i.e., makes the communication more reliable). In addition, this work investigates general communication properties, such as scalability or the effects of mobility, and provides recommendations for the use of such optical communication systems for swarm robotics. Finally, an approach is shown by which computational constraints of individual robots can be overcome by distributing data and processing across multiple robots

Optimal Deceptive and Reference Policies for Supervisory Control

The use of deceptive strategies is important for an agent that attempts not to reveal his intentions in an adversarial environment. We consider a setting in which a supervisor provides a reference policy and expects an agent to follow the reference policy and perform a task. The agent may instead follow a different, deceptive policy to achieve a different task. We model the environment and the behavior of the agent with a Markov decision process, represent the tasks of the agent and the supervisor with linear temporal logic formulae, and study the synthesis of optimal deceptive policies for such agents. We also study the synthesis of optimal reference policies that prevents deceptive strategies of the agent and achieves the supervisor's task with high probability. We show that the synthesis of deceptive policies has a convex optimization problem formulation, while the synthesis of reference policies requires solving a nonconvex optimization problem.Comment: 20 page

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 capabilities and their desired behaviour, the control source code can be automatically generated. However, regular SCT cannot model probabilistic controllers (supervisors). In this paper, we propose a probabilistic supervisory control theory (pSCT) framework. It applies prior work on probabilistic generators in a way that allows controllers to be decomposed into multiple local modular supervisors. Local modular supervisors take advantage of the modularity of formal specifications to reduce the size required to store the control logic. To validate the pSCT framework, we model a distributed swarm robotic version of the graph colouring problem and automatically generate the control source code for the Kilobot swarm robotics platform. We report the results of systematic experiments with swarms of 25 and 100 physical robots

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 capabilities and their desired behaviour, the control source code can be automatically generated. However, regular SCT cannot model probabilistic controllers (supervisors). In this paper, we propose a probabilistic supervisory control theory (pSCT) framework. It applies prior work on probabilistic generators in a way that allows controllers to be decomposed into multiple local modular supervisors. Local modular supervisors take advantage of the modularity of formal specifications to reduce the size required to store the control logic. To validate the pSCT framework, we model a distributed swarm robotic version of the graph colouring problem and automatically generate the control source code for the Kilobot swarm robotics platform. We report the results of systematic experiments with swarms of 25 and 100 physical robots

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 capabilities and their desired behaviour, the control source code can be automatically generated. However, regular SCT cannot model probabilistic controllers (supervisors). In this paper, we propose a probabilistic supervisory control theory (pSCT) framework. It applies prior work on probabilistic generators in a way that allows controllers to be decomposed into multiple local modular supervisors. Local modular supervisors take advantage of the modularity of formal specifications to reduce the size required to store the control logic. To validate the pSCT framework, we model a distributed swarm robotic version of the graph colouring problem and automatically generate the control source code for the Kilobot swarm robotics platform. We report the results of systematic experiments with swarms of 25 and 100 physical robots

Northeastern Illinois University, Board of Governors Universities, Academic Catalog 1993-1994

https://neiudc.neiu.edu/catalogs/1034/thumbnail.jp