Metamodeling Techniques Applied to the Design of Reconfigurable Control Applications

In order to realize autonomous manufacturing systems in environments characterized by high dynamics and high complexity of task, it is necessary to improve the control system modelling and performance. This requires the use of better and reusable abstractions. In this paper, we explore the metamodel techniques as a foundation to the solution of this problem. The increasing popularity of model-driven approaches and a new generation of tools to support metamodel techniques are changing software engineering landscape, boosting the adoption of new methodologies for control application development

Supervisory control theory applied to swarm robotics

Currently, the control software of swarm robotics systems is created by ad hoc development. This makes it hard to deploy these systems in real-world scenarios. In particular, it is difficult to maintain, analyse, or verify the 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. Also, there is cultural resistance to apply formal methods; they may be perceived as an additional step that does not add value to the final product. To address these problems, we propose supervisory control theory for the domain of swarm robotics. The advantages of supervisory control theory, and its associated tools, are a reduction in the amount of ad hoc development, the automatic generation of control code from modelled specifications, proofs of properties over generated control code, and the reusability of formally designed controllers between different robotic platforms. These advantages are demonstrated in four case studies using the e-puck and Kilobot robot platforms. Experiments with up to 600 physical robots are reported, which show that supervisory control theory can be used to formally develop state-of-the-art solutions to a range of problems in swarm robotics

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Supervisory control theory applied to swarm robotics

Currently, the control software of swarm robotics systems is created by ad hoc development. This makes it hard to deploy these systems in real-world scenarios. In particular, it is difficult to maintain, analyse, or verify the 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. Also, there is cultural resistance to apply formal methods; they may be perceived as an additional step that does not add value to the final product. To address these problems, we propose supervisory control theory for the domain of swarm robotics. The advantages of supervisory control theory, and its associated tools, are a reduction in the amount of ad hoc development, the automatic generation of control code from modelled specifications, proofs of properties over generated control code, and the reusability of formally designed controllers between different robotic platforms. These advantages are demonstrated in four case studies using the e-puck and Kilobot robot platforms. Experiments with up to 600 physical robots are reported, which show that supervisory control theory can be used to formally develop state-of-the-art solutions to a range of problems in swarm robotics

Dynamic Reconfiguration in Modular Self-Reconfigurable Robots Using Multi-Agent Coalition Games

In this thesis, we consider the problem of autonomous self-reconfiguration by modular self-reconfigurable robots (MSRs). MSRs are composed of small units or modules that can be dynamically configured to form different structures, such as a lattice or a chain. The main problem in maneuvering MSRs is to enable them to autonomously reconfigure their structure depending on the operational conditions in the environment. We first discuss limitations of previous approaches to solve the MSR self-reconfiguration problem. We will then present a novel framework that uses a layered architecture comprising a conventional gait table-based maneuver to move the robot in a fixed configuration, but using a more complex coalition game-based technique for autonomously reconfiguring the robot. We discuss the complexity of solving the reconfiguration problem within the coalition game-based framework and propose a stochastic planning and pruning based approach to solve the coalition-game based MSR reconfiguration problem. We tested our MSR self-reconfiguration algorithm using an accurately simulated model of an MSR called ModRED (Modular Robot for Exploration and Discovery) within the Webots robot simulator. Our results show that using our coalition formation algorithm, MSRs are able to reconfigure efficiently after encountering an obstacle. The average “reward” or efficiency obtained by an MSR also improves by 2-10% while using our coalition formation algorithm as compared to a previously existing multi-agent coalition formation algorithm. To the best of our knowledge, this work represents two novel contributions in the field of modular robots. First, ours is one of the first research techniques that has combined principles from human team formation techniques from the area of computational economics with dynamic self-reconfiguration in modular self-reconfigurable robots. Secondly, the modeling of uncertainty in coalition games using Markov Decision Processes is a novel and previously unexplored problem in the area of coalition formation. Overall, this thesis addresses a challenging research problem at the intersection of artificial intelligence, game theory and robotics and opens up several new directions for further research to improve the control and reconfiguration of modular robots

Formal Synthesis of Controllers for Safety-Critical Autonomous Systems: Developments and Challenges

In recent years, formal methods have been extensively used in the design of autonomous systems. By employing mathematically rigorous techniques, formal methods can provide fully automated reasoning processes with provable safety guarantees for complex dynamic systems with intricate interactions between continuous dynamics and discrete logics. This paper provides a comprehensive review of formal controller synthesis techniques for safety-critical autonomous systems. Specifically, we categorize the formal control synthesis problem based on diverse system models, encompassing deterministic, non-deterministic, and stochastic, and various formal safety-critical specifications involving logic, real-time, and real-valued domains. The review covers fundamental formal control synthesis techniques, including abstraction-based approaches and abstraction-free methods. We explore the integration of data-driven synthesis approaches in formal control synthesis. Furthermore, we review formal techniques tailored for multi-agent systems (MAS), with a specific focus on various approaches to address the scalability challenges in large-scale systems. Finally, we discuss some recent trends and highlight research challenges in this area

Design of a Control System for a Reconfigurable Engine Assembly Line

Today’s automotive manufacturing environment is dynamic. It is characterized by short life cycles of products especially in powertrain, due in part to changing Government regulations for fuel economy. In the USA, the National Highway Traffic and Safety Administration (NHTSA), Corporate Average Fuel Economy (CAFE) mandates an average of 29 miles per gallon (mpg), gradually increasing to 35.5 mpg by 2016 and 54.5 mpg towards 2025. Life cycles of engines and transmissions have consequently shortened, driving automakers to develop and manufacture more efficient powertrains. Not long ago, plants produced engines for decades, with minor modifications warranting slight manufacturing line rework. Conversely, today’s changing trends require machines and complete engine line overhauls rendering initial setups obsolete. Automakers compete to satisfy government regulations for best mileage and also lower manufacturing cost, thus the adoption of Reconfigurable Manufacturing Systems (RMS). Production lines follow modularity in designs, for hardware and software, to adapt to new business conditions, economically and time-wise. Information Technology (IT) and Controls are growing closer with the line of demarcation disappearing in manufacturing. Controls are benefiting from opportunities in IT, hardware and software. The advent of agent-based technology which are autonomous, cooperative and extendible in different production activities, helped to develop controls for RMS in academia. Component-based software suitable for RMS modularity and plug-and-play hardware/software components has gained decades of popularity in the software industry. This thesis implements distributed controls imbedding component-based technology and IEC 61311-3 function block standard for automotive engine assembly, which will contribute to these developments. The control architecture provides reconfigurability which is lacking in current manufacturing systems. The research imbeds: 1- Reconfigurability - Fitting RMS-designed hardware towards new manufacturing, 2- Reusability - Building software library for reuse across assembly lines, and 3- Plug-and-Play - Embedding easy to assemble software components (function blocks)