NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

Engineering framework for service-oriented automation systems

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201

Fourth Conference on Artificial Intelligence for Space Applications

Proceedings of a conference held in Huntsville, Alabama, on November 15-16, 1988. The Fourth Conference on Artificial Intelligence for Space Applications brings together diverse technical and scientific work in order to help those who employ AI methods in space applications to identify common goals and to address issues of general interest in the AI community. Topics include the following: space applications of expert systems in fault diagnostics, in telemetry monitoring and data collection, in design and systems integration; and in planning and scheduling; knowledge representation, capture, verification, and management; robotics and vision; adaptive learning; and automatic programming

PROPOSED MIDDLEWARE SOLUTION FOR RESOURCE-CONSTRAINED DISTRIBUTED EMBEDDED NETWORKS

The explosion in processing power of embedded systems has enabled distributed embedded networks to perform more complicated tasks. Middleware are sets of encapsulations of common and network/operating system-specific functionality into generic, reusable frameworks to manage such distributed networks. This thesis will survey and categorize popular middleware implementations into three adapted layers: host-infrastructure, distribution, and common services. This thesis will then apply a quantitative approach to grading and proposing a single middleware solution from all layers for two target platforms: CubeSats and autonomous unmanned aerial vehicles (UAVs). CubeSats are 10x10x10cm nanosatellites that are popular university-level space missions, and impose power and volume constraints. Autonomous UAVs are similarly-popular hobbyist-level vehicles that exhibit similar power and volume constraints. The MAVLink middleware from the host-infrastructure layer is proposed as the middleware to manage the distributed embedded networks powering these platforms in future projects. Finally, this thesis presents a performance analysis on MAVLink managing the ARM Cortex-M 32-bit processors that power the target platforms

Longterm Generalized Actions for Smart, Autonomous Robot Agents

Creating intelligent artificial systems, and in particular robots, that improve themselves just like humans do is one of the most ambitious goals in robotics and machine learning. The concept of robot experience exists for some time now, but has up to now not fully found its way into autonomous robots. This thesis is devoted to both, analyzing the underlying requirements for enabling robot learning from experience and actually implementing it on real robot hardware. For effective robot learning from experience I present and discuss three main requirements: (a ) Clearly expressing what a robot should do, on a vague, abstract level I introduce Generalized Plans as a means to express the intention rather than the actual action sequence of a task, removing as much task specific knowledge as possible. (a ) Defining, collecting, and analyzing robot experiences to enable robots to improve I present Episodic Memories as a container for all collected robot experiences for any arbitrary task and create sophisticated action (effect) prediction models from them, allowing robots to make better decisions. (a ) Properly abstracting from reality and dealing with failures in the domain they occurred in I propose failure handling strategies, a failure taxonomy extensible through experience, and discuss the relationship between symbolic/discrete and subsymbolic/continuous systems in terms of robot plans interacting with real world sensors and actuators. I concentrate on the domain of human-scale robot activities, specifically on doing household chores. Tasks in this domain offer many repeating patterns and are ideal candidates for abstracting, encapsulating, and modularizing robot plans into a more general form. This way, very similar plan structures are transformed into parameters that change the behavior of the robot while performing the task, making the plans more flexible. While performing tasks, robots encounter the same or similar situations over and over again. Albeit humans are able to benefit from this and improve at what they do, robots in general lack this ability. This thesis presents techniques for collecting and making robot experiences accessible to robots and outside observers alike, answering high level questions such as What are good spots to stand at for grasping objects from the fridge? or Which objects are especially difficult to grasp with two hands while they are in the oven? . By structuring and tapping into a robot's memory, it can make more informed decisions that are not based on manually encoded information, but self-improved behavior. To this end, I present several experience-based approaches to improve a robot's autonomous decisions, such as parameter choices, during execution time. Robots that interact with the real world are bound to deal with unexpected events and must properly react to failures of any kind of action. I present an extensible failure model that suits the structure of Generalized Plans and Episodic Memories and make clear how each module should deal with their own failures rather than directly handing them up to a governing cognitive architecture. In addition, I make a distinction between discrete parametrizations of Generalized Plans and continuous low level components, and how to translate between the two

Adaptive Computing Systems for Aerospace

RÉSUMÉ En raison de leur complexité croissante, les systèmes informatiques modernes nécessitent de nouvelles méthodologies permettant d’automatiser leur conception et d’améliorer leurs performances. L’espace, en particulier, constitue un environnement très défavorable au maintien de la performance de ces systèmes : sans protection des rayonnements ionisants et des particules, l’électronique basée sur CMOS peut subir des erreurs transitoires, une dégradation des performances et une usure accélérée causant ultimement une défaillance du système. Les approches traditionnellement adoptees pour garantir la fiabilité du système et prolonger sa durée de vie sont basées sur la redondance, généralement établie durant la conception. En revanche, ces solutions sont coûteuses et parfois inefficaces, puisqu'elles augmentent la taille et la complexité du système, l'exposant à des risques plus élevés de surchauffe et d'erreurs. Les conséquences de ces limites sont d'autant plus importantes lorsqu'elles s’appliquent aux systèmes critiques (e.g., contraintes par le temps ou dont l’accès est limité) qui doivent être en mesure de prendre des décisions sans intervention humaine. Sur la base de ces besoins et limites, le développement en aérospatial de systèmes informatiques avec capacités adaptatives peut être considéré comme la solution la plus appropriée pour les dispositifs intégrés à haute performance. L’informatique auto-adaptative offre un potentiel sans égal pour assurer la création d’une génération d’ordinateurs plus intelligents et fiables. Qui plus est, elle répond aux besoins modernes de concevoir et programmer des systèmes informatiques capables de répondre à des objectifs en conflit. En nous inspirant des domaines de l’intelligence artificielle et des systèmes reconfigurables, nous aspirons à développer des systèmes informatiques auto-adaptatifs pour l’aérospatiale qui répondent aux enjeux et besoins actuels. Notre objectif est d’améliorer l’efficacité de ces systèmes, leur tolerance aux pannes et leur capacité de calcul. Afin d’atteindre cet objectif, une analyse expérimentale et comparative des algorithmes les plus populaires pour l’exploration multi-objectifs de l’espace de conception est d’abord effectuée. Les algorithmes ont été recueillis suite à une revue de la plus récente littérature et comprennent des méthodes heuristiques, évolutives et statistiques. L’analyse et la comparaison de ceux-ci permettent de cerner les forces et limites de chacun et d'ainsi définir des lignes directrices favorisant un choix optimal d’algorithmes d’exploration. Pour la création d’un système d’optimisation autonome—permettant le compromis entre plusieurs objectifs—nous exploitons les capacités des modèles graphiques probabilistes. Nous introduisons une méthodologie basée sur les modèles de Markov cachés dynamiques, laquelle permet d’équilibrer la disponibilité et la durée de vie d’un système multiprocesseur. Ceci est obtenu en estimant l'occurrence des erreurs permanentes parmi les erreurs transitoires et en migrant dynamiquement le calcul sur les ressources supplémentaires en cas de défaillance. La nature dynamique du modèle rend celui-ci adaptable à différents profils de mission et taux d’erreur. Les résultats montrent que nous sommes en mesure de prolonger la durée de vie du système tout en conservant une disponibilité proche du cas idéal. En raison des contraintes de temps rigoureuses imposées par les systèmes aérospatiaux, nous étudions aussi l’optimisation de la tolérance aux pannes en présence d'exigences d’exécution en temps réel. Nous proposons une méthodologie pour améliorer la fiabilité du calcul en présence d’erreurs transitoires pour les tâches en temps réel d’un système multiprocesseur homogène avec des capacités de réglage de tension et de fréquence. Dans ce cadre, nous définissons un nouveau compromis probabiliste entre la consommation d’énergie et la tolérance aux erreurs. Comme nous reconnaissons que la résilience est une propriété d’intérêt omniprésente (par exemple, pour la conception et l’analyse de systems complexes génériques), nous adaptons une définition formelle de celle-ci à un cadre probabiliste dérivé à nouveau de modèles de Markov cachés. Ce cadre nous permet de modéliser de façon réaliste l’évolution stochastique et l’observabilité partielle des phénomènes du monde réel. Nous proposons un algorithme permettant le calcul exact efficace de l’étape essentielle d’inférence laquelle est requise pour vérifier des propriétés génériques. Pour démontrer la flexibilité de cette approche, nous la validons, entre autres, dans le contexte d’un système informatisé reconfigurable pour l’aérospatiale. Enfin, nous étendons la portée de nos recherches vers la robotique et les systèmes multi-agents, deux sujets dont la popularité est croissante en exploration spatiale. Nous abordons le problème de l’évaluation et de l’entretien de la connectivité dans le context distribué et auto-adaptatif de la robotique en essaim. Nous examinons les limites des solutions existantes et proposons une nouvelle méthodologie pour créer des géométries complexes connectées gérant plusieurs tâches simultanément. Des contributions additionnelles dans plusieurs domaines sont résumés dans les annexes, nommément : (i) la conception de CubeSats, (ii) la modélisation des rayonnements spatiaux pour l’injection d’erreur dans FPGA et (iii) l’analyse temporelle probabiliste pour les systèmes en temps réel. À notre avis, cette recherche constitue un tremplin utile vers la création d’une nouvelle génération de systèmes informatiques qui exécutent leurs tâches d’une façon autonome et fiable, favorisant une exploration spatiale plus simple et moins coûteuse.----------ABSTRACT Today's computer systems are growing more and more complex at a pace that requires the development of novel and more effective methodologies to automate their design. Space, in particular, represents a challenging environment: without protection from ionizing and particle radiation, CMOS-based electronics are subject to transients faults, performance degradation, accelerated wear, and, ultimately, system failure. Traditional approaches adopted to guarantee reliability and extended lifetime are based on redundancy that is established at design-time. These solutions are expensive and sometimes inefficient, as they increase the complexity and size of a system, exposing it to higher risks of overheating and incurring in radiation-induced errors. Moreover, critical systems---e.g., time-constrained ones and those where access is limited---must be able to cope with pivotal situations without relying on human intervention. Hence, the emerging interest in computer systems with adaptive capabilities as the most suitable solution for novel high-performance embedded devices for aerospace. Self-adaptive computing carries unmatched potential and great promises for the creation of a new generation of smart, more reliable computers, and it addresses the challenge of designing and programming modern and future computer systems that must meet conflicting goals. Drawing from the fields of artificial intelligence and reconfigurable systems, we aim at developing self-adaptive computer systems for aerospace. Our goal is to improve their efficiency, fault-tolerance, and computational capabilities. The first step in this research is the experimental analysis of the most popular multi-objective design-space exploration algorithms for high-level design. These algorithms were collected from the recent literature and include heuristic, evolutionary, and statistical methods. Their comparison provides insights that we use to define guidelines for the choice of the most appropriate optimization algorithms, given the features of the design space. For the creation of a self-managing optimization framework---enabling the adaptive trade-off of multiple objectives---we leverage the tools of probabilistic graphical models. We introduce a mechanism based on dynamic hidden Markov models that balances the availability and lifetime of multiprocessor systems. This is achieved by estimating the occurrence of permanent faults amid transient faults, and by dynamically migrating the computation on excess resources, when failure occurs. The dynamic nature of the model makes it adjustable to different mission profiles and fault rates. The results show that we are able to lead systems to extended lifetimes, while keeping their availability close to ideal. On account of the stringent timing constraints imposed by aerospace systems, we then investigate the optimization of fault-tolerance under real-time requirements. We propose a methodology to improve the reliability of computation in the presence of transient errors when considering the mapping of real-time tasks on a homogeneous multiprocessor system with voltage and frequency scaling capabilities. In this framework, we take advantage of probability theory to define a novel trade-off between power consumption and fault-tolerance. As we recognize that resilience is a pervasive property of interest (e.g., for the design and analysis of generic complex systems), we adapt a formal definition of it to one more probabilistic framework derived from hidden Markov models. This allows us to realistically model the stochastic evolution and partial observability of complex real-world environments. Within this framework, we propose an efficient algorithm for the exact computation of the essential inference step required to construct generic property checking. To demonstrate the flexibility of this approach, we validate it in the context, among others, of a self-aware, reconfigurable computing system for aerospace. Finally, we move the scope of our research towards robotics and multi-agent systems: a topic of thriving popularity for space exploration. We tackle the problem of connectivity assessment and maintenance in the distributed and self-adaptive context of swarm robotics. We review the limitations of existing solutions and propose a novel methodology to create connected complex geometries for multiple task coverage. Additional contributions in the areas of (i) CubeSat design, (ii) the modelling of space radiation for FPGA fault-injection, and (iii) probabilistic timing analysis for real-time systems are summarized in the appendices. In the author's opinion, this research provides a number of useful stepping stones for the creation of a new generation of computing systems that autonomously---and reliably---perform their tasks for longer periods of time, fostering simpler and cheaper space exploration

Distributing intelligence in the wireless control of a mobile robot using a personal digital assistant

Personal Digital Assistants (PDAs) have recently become a popular component in mobile robots. This compact processing device with its touch screen, variety of built-in features, wireless technologies and affordability can perform various roles within a robotic system. Applications include low-cost prototype development, rapid prototyping, low-cost humanoid robots, robot control, robot vision systems, algorithm development, human-robot interaction, mobile user interfaces as well as wireless robot communication schemes. Limits on processing power, memory, battery life and screen size impact the usefulness of a PDA in some applications. In addition various implementation strategies exist, each with its own strengths and weaknesses. No comparison of the advantages and disadvantages of the different strategies and resulting architectures exist. This makes it difficult for designers to decide on the best use of a PDA within their mobile robot system. This dissertation examines and compares the available mobile robot architectures. A thorough literature study identifies robot projects using a PDA and examines how the designs incorporate a PDA and what purpose it fulfils within the system it forms part of. The dissertation categorises the architectures according to the role of the PDA within the robot system. The hypothesis is made that using a distributed control system architecture makes optimal use of the rich feature set gained from including a PDA in a robot system’s design and simultaneously overcomes the device’s inherent shortcomings. This architecture is developed into a novel distributed intelligence framework that is supported by a hybrid communications architecture, using two wireless connection schemes. A prototype implementation illustrates the framework and communications architecture in action. Various performance measurements are taken in a test scenario for an office robot. The results indicate that the proposed framework does deliver performance gains and is a viable alternative for future projects in this area

Reconfigurable Computing Systems for Robotics using a Component-Oriented Approach

Robotic platforms are becoming more complex due to the wide range of modern applications, including multiple heterogeneous sensors and actuators. In order to comply with real-time and power-consumption constraints, these systems need to process a large amount of heterogeneous data from multiple sensors and take action (via actuators), which represents a problem as the resources of these systems have limitations in memory storage, bandwidth, and computational power. Field Programmable Gate Arrays (FPGAs) are programmable logic devices that offer high-speed parallel processing. FPGAs are particularly well-suited for applications that require real-time processing, high bandwidth, and low latency. One of the fundamental advantages of FPGAs is their flexibility in designing hardware tailored to specific needs, making them adaptable to a wide range of applications. They can be programmed to pre-process data close to sensors, which reduces the amount of data that needs to be transferred to other computing resources, improving overall system efficiency. Additionally, the reprogrammability of FPGAs enables them to be repurposed for different applications, providing a cost-effective solution that needs to adapt quickly to changing demands. FPGAs' performance per watt is close to that of Application-Specific Integrated Circuits (ASICs), with the added advantage of being reprogrammable. Despite all the advantages of FPGAs (e.g., energy efficiency, computing capabilities), the robotics community has not fully included them so far as part of their systems for several reasons. First, designing FPGA-based solutions requires hardware knowledge and longer development times as their programmability is more challenging than Central Processing Units (CPUs) or Graphics Processing Units (GPUs). Second, porting a robotics application (or parts of it) from software to an accelerator requires adequate interfaces between software and FPGAs. Third, the robotics workflow is already complex on its own, combining several fields such as mechanics, electronics, and software. There have been partial contributions in the state-of-the-art for FPGAs as part of robotics systems. However, a study of FPGAs as a whole for robotics systems is missing in the literature, which is the primary goal of this dissertation. Three main objectives have been established to accomplish this. (1) Define all components required for an FPGAs-based system for robotics applications as a whole. (2) Establish how all the defined components are related. (3) With the help of Model-Driven Engineering (MDE) techniques, generate these components, deploy them, and integrate them into existing solutions. The component-oriented approach proposed in this dissertation provides a proper solution for designing and implementing FPGA-based designs for robotics applications. The modular architecture, the tool 'FPGA Interfaces for Robotics Middlewares' (FIRM), and the toolchain 'FPGA Architectures for Robotics' (FAR) provide a set of tools and a comprehensive design process that enables the development of complex FPGA-based designs more straightforwardly and efficiently. The component-oriented approach contributed to the state-of-the-art in FPGA-based designs significantly for robotics applications and helps to promote their wider adoption and use by specialists with little FPGA knowledge