Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Quadcopter Attitude Control Optimization and Multi-Agent Coordination

This thesis presents a method of automated control gain tuning for a Quadcopter Unmanned Aerial Vehicle and proposes a method of coordination multiple autonomous robotic agents capable for formation aggregation. Sliding Mode Control for Quadcopter altitude and attitude stabilization is presented and tuned using Particle Swarm Optimization. Different configurations for the optimization process are compared to determine an effective and time-efficient setup to complete the control gain tuning. The multi-agent coordination scheme expands upon an existing adjustable swarm framework based on an Artificial Potential Field Sliding Mode Controller. The original leader-follower scheme is modified with the goal of producing a leaderless swarm where agents move towards specific locations to aggregate a desired formation. Analysis of the swarm control scheme pays particular attention to maintaining proper distance between agents. Using Lyapunov methods following that of the original controller analysis, stability under first order and general higher order dynamics is analyzed. Numerical simulations of the swarm controller using agents with nonlinear Quadcopter or second order point mass dynamics are presented to illustrate the capabilities of this algorithm. The automatically tuned Quadcopter controller is used in simulations when applicable. The development of an experimental test platform is discussed with the intention of validating the simulation results on physical Quadcopters

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

Towards Reliable Robotics: from Navigation to Coordination

Les robots autonomes et les systèmes multi-robots ont connu un intérêt sans cesse croissant par les scientifiques et l’industrie. Plusieurs applications telles que les robots assistants, les robots gestionnaires de stock ainsi que les véhicules autonomes nécessitent des algorithmes de navigation et de coordination fiables pour permettre leur déploiement dans des environnements dynamiques et relativement méconnus. Ainsi, la capacité d’adaptation est une caractéristique fondamentale permettant une utilisation accrue et une intégration plus facile des systèmes multi-robots. Afin de posséder cette agilité d’adaptation, les robots devraient opter vers un comportement assez robuste avec une aptitude à réajuster leurs actions selon la cinématique de l’environnement. Ce mémoire de thèse, s’interesse aux problèmes de fiabilité lors du déploiement des systèmes multi-robots dans des environnements dynamiques et inconnus. Il s’articule autour de deux contributions majeures, à savoir : Un mécanisme de planification et de réajustement de mouvement quasi optimal qui roule à une fréquence allant jusqu’à 200 Hz. Ainsi qu’un framework de vérification de la robustesse des comportements coopératifs des systèmes multi-robots. La première contribution a été inspirée de l’habilité de quelques animaux à naviguer en se fiant au champ magnétique terrestre. En effet, nous avons constaté que le champ magnétique n’admet pas de maxima locaux, ce qui permet aux animaux de suivre son gradient. Par conséquent, un robot est capable de parcourir tout type d’environnements en faisant propager un champ magnétique virtuel et en suivant son gradient. Toutefois, la résolution des équations de Maxwell, qui décrivent la physique des champs magnétiques, est complexe et nécessitent des simulations numériques couteuses en termes de ressources et temps de calcul. Pour pallier cette difficulté, nous proposons un approximateur de la solution des équations de Maxwell basé sur un réseau de neurones profond entrainé exclusivement sur des solutions provenant de simulations numériques avancées. L’environnement est représenté par une carte de conductivité. Nous affectons une conductivité maximale à la destination du robot et une conductivité nulle aux obstacles. Le calcul de la distribution du champ magnétique virtuel permettra au robot de suivre le gradient qui le mènera vers sa destination selon un chemin quasi optimal.----------ABSTRACT: Autonomous robots and multi-robot systems are of growing interest for industry and academia. Many real-world applications such as assistive robotics, inventory management, and autonomous driving require reliable navigation and coordination algorithms that can be deployed in a partially unknown, dynamic environment. The ability to adapt is a key feature for the widespread use and societal integration of multi-robot systems. To achieve this adaptation ability, robots must implement inherently robust behaviors and must be sufficiently fast to re-plan their actions when their environment changes. This dissertation deals with the problem of reliably deploying a group of robots in a dynamic, unknown environment, and provides two key contributions: a mechanism for robots to plan and re-plan their motion near optimally up to 200 times per second; and a framework to verify the robustness of multi-robot cooperative behaviors. For the first contribution, observing how some animals are able to navigate using the Earth’s magnetic field, we realize that this is possible because the magnetic field has no local maxima, and animals can follow its gradient. This means that a robot can navigate any kind of environment by propagating a known virtual magnetic field and following its gradient. However, solving Maxwell’s equations–which govern the physics of magnetic fields– is complex and demands computationally costly numerical simulations. To overcome this problem, we propose a deep neural network as an approximator for Maxwell’s equations, exclusively trained on high-quality numerical simulations. We model the environment as a conductivity map with its maximum in a goal location and zero for obstacles. After computing the virtual field propagation, a robot can follow the virtual magnetic gradient to optimally reach the goal