A Goal-Oriented Autonomous Controller for Space Exploration

The Goal-Oriented Autonomous Controller (GOAC) is the envisaged result of a multi-institutional effort within the on-going Autonomous Controller R&D activity funded by ESA ESTEC. The objective of this effort is to design, build and test a viable on-board controller to demonstrate key concepts in fully autonomous operations for ESA missions. This three-layer architecture is an integrative effort to bring together four mature technologies; for a functional layer, a verification and validation system, a planning engine and a controller framework for planning and execution which uses the sense-plan-act paradigm for goal oriented autonomy. GOAC as a result will generate plans in situ, deterministically dispatch activities for execution, and recover from off-nominal conditions

Meeting the challenges of decentralized embedded applications using multi-agent systems

International audienceToday embedded applications become large scale andstrongly constrained. They require a decentralized embedded intelligencegenerating challenges for embedded systems. A multi-agent approach iswell suited to model and design decentralized embedded applications.It is naturally able to take up some of these challenges. But somespecific points have to be introduced, enforced or improved in multiagentapproaches to reach all features and all requirements. In thisarticle, we present a study of specific activities that can complementmulti-agent paradigm in the ”embedded” context.We use our experiencewith the DIAMOND method to introduce and illustrate these featuresand activities

Learning the selection of actions for an autonomous social robot by reinforcement learning based on motivations

Autonomy is a prime issue on robotics field and it is closely related to decision making. Last researches on decision making for social robots are focused on biologically inspired mechanisms for taking decisions. Following this approach, we propose a motivational system for decision making, using internal (drives) and external stimuli for learning to choose the right action. Actions are selected from a finite set of skills in order to keep robot's needs within an acceptable range. The robot uses reinforcement learning in order to calculate the suitability of every action in each state. The state of the robot is determined by the dominant motivation and its relation to the objects presents in its environment. The used reinforcement learning method exploits a new algorithm called Object Q-Learning. The proposed reduction of the state space and the new algorithm considering the collateral effects (relationship between different objects) results in a suitable algorithm to be applied to robots living in real environments. In this paper, a first implementation of the decision making system and the learning process is implemented on a social robot showing an improvement in robot's performance. The quality of its performance will be determined by observing the evolution of the robot's wellbeing.The funds provided by the Spanish Government through the project called “Peer to Peer Robot-Human Interaction” (R2H), of MEC (Ministry of Science and Education), the project “A new approach to social robotics” (AROS), of MICINN (Ministry of Science and Innovation), and the RoboCity2030-II-CM project (S2009/DPI-1559), funded by Programas de Actividades I+D en la Comunidad de Madrid and cofunded by Structural Funds of the EU

Classifying the Capabilities of Robotic Systems. What is a robot?

There are various types of robots, yet there are no defined characteristics that relate them to each other. In order to compare robots, a detailed cross-domain classification system is required. The classification needs to be simple enough to be applicable to all robotic fields, yet comprehensive enough to capture robots accurately. The aim of the research reported in this thesis is to develop a novel classification scheme, subsequently named ‘ToRCH’ (Toward Robot CHaracterization), that categorizes robots according to their characteristics via a hierarchical structure. The layers of the hierarchy capture robot capabilities, sub-categorizes them and provides appropriate measurement levels. Some capabilities were adopted from the Multi-Annual Road map (MAR), that was developed to shape the European research development and innovation program, and the research reported in this thesis first extends MAR in a number of important dimensions. Then the study utilizes the extensive capability layers in ToRCH to characterize a robot’s performance in a form defined as the ‘Robot Capability Profile’ (RCP). The RCP helps in designing, developing, deploying and testing a robot for specific applications. It also facilitates the assessment of the best application that matches the specification of any particular robot. Finally, several aspects of ToRCH are evaluated including its structure, its usability and its generated RCPs. The results confirm that ToRCH is able to capture the capabilities of different robots in a way that could answer the question ‘what is a robot?’

An Overview of Verification and Validation Challenges for Inspection Robots

The advent of sophisticated robotics and AI technology makes sending humans into hazardous and distant environments to carry out inspections increasingly avoidable. Being able to send a robot, rather than a human, into a nuclear facility or deep space is very appealing. However, building these robotic systems is just the start and we still need to carry out a range of verification and validation tasks to ensure that the systems to be deployed are as safe and reliable as possible. Based on our experience across three research and innovation hubs within the UK’s “Robots for a Safer World” programme, we present an overview of the relevant techniques and challenges in this area. As the hubs are active across nuclear, offshore, and space environments, this gives a breadth of issues common to many inspection robot

De-Individuation of the Modern Subject in the Age of Artificial Intelligence. The Case of Self-Driving Cars and Algorithms for Decision Making.

L'abstract è presente nell'allegato / the abstract is in the attachmen

Percepción basada en visión estereoscópica, planificación de trayectorias y estrategias de navegación para exploración robótica autónoma

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia artificial, leída el 13-05-2015En esta tesis se trata el desarrollo de una estrategia de navegación autónoma basada en visión artificial para exploración robótica autónoma de superficies planetarias. Se han desarrollado una serie de subsistemas, módulos y software específicos para la investigación desarrollada en este trabajo, ya que la mayoría de las herramientas existentes para este dominio son propiedad de agencias espaciales nacionales, no accesibles a la comunidad científica. Se ha diseñado una arquitectura software modular multi-capa con varios niveles jerárquicos para albergar el conjunto de algoritmos que implementan la estrategia de navegación autónoma y garantizar la portabilidad del software, su reutilización e independencia del hardware. Se incluye también el diseño de un entorno de trabajo destinado a dar soporte al desarrollo de las estrategias de navegación. Éste se basa parcialmente en herramientas de código abierto al alcance de cualquier investigador o institución, con las necesarias adaptaciones y extensiones, e incluye capacidades de simulación 3D, modelos de vehículos robóticos, sensores, y entornos operacionales, emulando superficies planetarias como Marte, para el análisis y validación a nivel funcional de las estrategias de navegación desarrolladas. Este entorno también ofrece capacidades de depuración y monitorización.La presente tesis se compone de dos partes principales. En la primera se aborda el diseño y desarrollo de las capacidades de autonomía de alto nivel de un rover, centrándose en la navegación autónoma, con el soporte de las capacidades de simulación y monitorización del entorno de trabajo previo. Se han llevado a cabo un conjunto de experimentos de campo, con un robot y hardware real, detallándose resultados, tiempo de procesamiento de algoritmos, así como el comportamiento y rendimiento del sistema en general. Como resultado, se ha identificado al sistema de percepción como un componente crucial dentro de la estrategia de navegación y, por tanto, el foco principal de potenciales optimizaciones y mejoras del sistema. Como consecuencia, en la segunda parte de este trabajo, se afronta el problema de la correspondencia en imágenes estéreo y reconstrucción 3D de entornos naturales no estructurados. Se han analizado una serie de algoritmos de correspondencia, procesos de imagen y filtros. Generalmente se asume que las intensidades de puntos correspondientes en imágenes del mismo par estéreo es la misma. Sin embargo, se ha comprobado que esta suposición es a menudo falsa, a pesar de que ambas se adquieren con un sistema de visión compuesto de dos cámaras idénticas. En consecuencia, se propone un sistema experto para la corrección automática de intensidades en pares de imágenes estéreo y reconstrucción 3D del entorno basado en procesos de imagen no aplicados hasta ahora en el campo de la visión estéreo. Éstos son el filtrado homomórfico y la correspondencia de histogramas, que han sido diseñados para corregir intensidades coordinadamente, ajustando una imagen en función de la otra. Los resultados se han podido optimizar adicionalmente gracias al diseño de un proceso de agrupación basado en el principio de continuidad espacial para eliminar falsos positivos y correspondencias erróneas. Se han estudiado los efectos de la aplicación de dichos filtros, en etapas previas y posteriores al proceso de correspondencia, con eficiencia verificada favorablemente. Su aplicación ha permitido la obtención de un mayor número de correspondencias válidas en comparación con los resultados obtenidos sin la aplicación de los mismos, consiguiendo mejoras significativas en los mapas de disparidad y, por lo tanto, en los procesos globales de percepción y reconstrucción 3D.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu