Space-Time Continuous Models of Swarm Robotic Systems: Supporting Global-to-Local Programming

A generic model in as far as possible mathematical closed-form was developed that predicts the behavior of large self-organizing robot groups (robot swarms) based on their control algorithm. In addition, an extensive subsumption of the relatively young and distinctive interdisciplinary research field of swarm robotics is emphasized. The connection to many related fields is highlighted and the concepts and methods borrowed from these fields are described shortly

Synthesis and Analysis of Minimalist Control Strategies for Swarm Robotic Systems

The field of swarm robotics studies bio-inspired cooperative control strategies for large groups of relatively simple robots. The robots are limited in their individual capabilities, however, by inducing cooperation amongst them, the limitations can be overcome. Local sensing and interactions within the robotic swarm promote scalable, robust, and flexible behaviours. This thesis focuses on synthesising and analysing minimalist control strategies for swarm robotic systems. Using a computation-free swarming framework, multiple decentralised control strategies are synthesised and analysed. The control strategies enable the robots—equipped with only discrete-valued sensors—to reactively respond to their environment. We present the simplest control solutions to date to four multi-agent problems: finding consensus, gathering on a grid, shepherding, and spatial coverage. The control solutions—obtained by employing an offline evolutionary robotics approach—are tested, either in computer simulation or by physical experiment. They are shown to be—up to a certain extent—scalable, robust against sensor noise, and flexible to the changes in their environment. The investigated gathering problem is proven to be unsolvable using the deterministic framework. The extended framework, using stochastic reactive controllers, is applied to obtain provably correct solutions. Using no run-time memory and only limited sensing make it possible to realise implementations that are arguably free of arithmetic computation. Due to the low computational demands, the control solutions may enable or inspire novel applications, for example, in nanomedicine

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Distributed agents for autonomous spacecraft

Space missions have evolved considerably in the last fifty years in both complexity and ambition. In order to enable this continued improvement in the scientific and commercial return of space missions new control systems are needed that can manage complex combinations of state of the art hardware with a minimum of human interaction. Distributed multi-agent systems are one approach to controlling complex multisatellite space missions. A distributed system is not enough on its own however,the spacecraft must be able to carry out complex tasks such as planning,negotiation and close proximity formation flying autonomously. It is the coupling of distributed control with autonomy that is the focus of this thesis. Three contributions to the state of the art are described herein. They all involve the innovative use of multi-agent systems in space missions. The first is the development of a multi-agent architecture, HASA, specifically for space missions. The second is to use embedded agents to autonomously control an interferometric type space telescope. The third is based on software agents that coordinate multiple Earth observation missions coupled with a global optimisation technique for data extraction. The HASA architecture was developed in reaction to the over generality of most multi-agent architectures in the computer science and robotics literature and the ad-hoc, case-by-case approach, to multi-agent architectures when developed and deployed for space missions. The HASA architecture has a recursive nature which allows for the multi-agent system to be completely described throughout its development process as the design evolves and more sub-systems are implemented. It also inherits a focus on the robust generation of a product and safe operation from architectures in use in the manufacturing industry. A multi-agent system was designed using the HASA architecture for an interferometric space telescope type mission. This type of mission puts high requirements on formation flying and cooperation between agents. The formation flying agents were then implemented using a Java framework and tested on a multi-platform distributed simulation suite developed especially for this thesis. Three different control methods were incorporated into the agents and the multi-agent system was shown to be able to acquire and change formation and avoid collisions autonomously. A second multi-agent system was designed for the GMES mission in collaboration with GMV, the industrial partner in this project. This basic MAS design was transferred to the HASA architecture. A novel image selection algorithm was developed to work alongside the GMES multi-agent system. This algorithm uses global optimisation techniques to suggest image parameters to users based on the output of the multi-agent system

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Recent Advances in Multi Robot Systems

To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems

Collective Construction by Termite-Inspired Robots

Construction usually involves careful preplanning and direct human operation of tools and material. Bringing automation to construction has the potential to improve its speed and efficiency, and to enable building in settings where it is difficult or dangerous for humans to work, e.g., in extraterrestrial environments or disaster areas. Nature provides us with impressive examples of animal construction: in particular, many species of termites build complex mounds several orders of magnitude larger than themselves. Inspired by termites and their building activities, our goal is to develop systems in which large numbers of robots collectively construct human-scale structures autonomously. In this thesis I present TERMES, a system comprised of (1) A high-level control algorithm for decentralized construction of 3D user-specified structures using stigmergy, exploiting implicit rather than explicit communication; and (2) A complete physical implementation where three robots reliably assemble such structures using only local sensing, limited locomotion, and simple control, exploiting embodied rather than explicit intelligence. A major contribution of this work is the translation from abstract models to a real robotic system. I achieved this through careful co-design of algorithms and physical systems and of robots and building material, allowing passive mechanical features to minimize control complexity. To attain reliable performance without relying on costly high-precision sensors and actuators, I put an emphasis on error-tolerant control, making robots able to autonomously detect and recover from small errors. This work advances the aim of engineering collectives of robots that achieve human-specified goals, using biologically-inspired principles for robustness and scalability. While our work is inspired by models of termite construction from the 1970s and 1980s, much is still unknown about how individual termites coordinate and respond to different environmental factors. To address this issue I developed methods and tools to enable high-resolution quantitative data collection on the behavior of individual termites engaged in collective construction in confined experimental arenas. This work advances our ability to study the termites which will hopefully lead to new insights on the design of robust autonomous systems for collective construction.Engineering and Applied Science