Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S8, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S8, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-07-09 and 2014-01-05 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2013S7, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81)

Snow height was measured by the Snow Depth Buoy 2013S7, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXIX/6 (PS81). The resulting time series describes the evolution of snow height as a function of place and time between 2013-07-06 and 2013-09-13 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow height, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow height occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow height may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S15, deployed during POLARSTERN cruise ARK-XXVIII/4 (PS87)

Snow height was measured by the Snow Depth Buoy 2014S15, an autonomous platform, drifting on Arctic sea ice, deployed during POLARSTERN cruise ARK-XXVIII/4 (PS87). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-08-29 and 2014-12-31 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). The buoy was installed on multi year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Records without any snow depth may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow depth, because no initial readings of absolute snow depth are available.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S17, deployed during POLARSTERN cruise ANT-XXX/2 (PS89)

Snow height was measured by the Snow Depth Buoy 2014S17, an autonomous platform, drifting on Antarctic sea ice, deployed during POLARSTERN cruise ANT-XXX/2 (PS89). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-12-20 and 2015-02-01 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on first year ice. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). In this data set, diurnal variations occur in the data set, although the sonic readings were compensated for temperature changes. Records without any snow depth may still be used for sea ice drift analyses.

Snow height on sea ice and sea ice drift from autonomous measurements from buoy 2014S24, deployed during Antarctic Fast Ice Network 2014 (AFIN 2014)

Snow height was measured by the Snow Depth Buoy 2014S24, an autonomous platform, installed close to Neumayer III Base, Antarctic during Antarctic Fast Ice Network 2014 (AFIN 2014). The resulting time series describes the evolution of snow depth as a function of place and time between 2014-03-07 and 2014-05-16 in sample intervals of 1 hour. The Snow Depth Buoy consists of four independent sonar measurements representing the area (approx. 10 m**2) around the buoy. The buoy was installed on the ice shelf. In addition to snow depth, geographic position (GPS), barometric pressure, air temperature, and ice surface temperature were measured. Negative values of snow depth occur if surface ablation continues into the sea ice. Thus, these measurements describe the position of the sea ice surface relative to the original snow-ice interface. Differences between single sensors indicate small-scale variability of the snow pack around the buoy. The data set has been processed, including the removal of obvious inconsistencies (missing values). Records without any snow depth may still be used for sea ice drift analyses. Note: This data set contains only relative changes in snow depth, because no initial readings of absolute snow depth are available.

Arquitectura cognitiva basada en el gradiente sensorial y su aplicación a la robótica móvil

El objetivo de esta tesis es el desarrollo de un sistema completo de navegación, aprendizaje y planificación para un robot móvil. Dentro de los innumerables problemas que este gran objetivo plantea, hemos dedicado especial atención al problema del conocimiento autónomo del mundo. Nuestra mayor preocupación ha sido la de establecer mecanismos que permitan, a partir de información sensorial cruda, el desarrollo incremental de un modelo topológico del entorno en el que se mueve el robot. Estos mecanismos se apoyan invariablemente en un nuevo concepto propuesto en esta tesis: el gradiente sensorial. El gradiente sensorial es un dispositivo matemático que funciona como un detector de sucesos interesantes para el sistema. Una vez detectado uno de estos sucesos, el robot puede identificar su situación en un mapa topológico y actuar en consecuencia. Hemos denominado a estas situaciones especiales lugares sensorialmente relevantes, ya que (a) captan la atención del sistema y (b) pueden ser identificadas utilizando la información sensorial. Para explotar convenientemente los modelos construidos, hemos desarrollado un algoritmo capaz de elaborar planes internalizados, estableciendo una red de sugerencias en los lugares sensorialmente relevantes, de modo que el robot encuentra en estos puntos una dirección recomendada de navegación. Finalmente, hemos implementado un sistema de navegación robusto con habilidades para interpretar y adecuar los planes internalizados a las circunstancias concretas del momento. Nuestro sistema de navegación está basado en la teoría de campos de potencial artificial, a la que hemos incorporado la posibilidad de añadir cargas ficticias como ayuda a la evitación de mínimos locales. Como aportación adicional de esta tesis al campo genérico de la ciencia cognitiva, todos estos elementos se integran en una arquitectura centrada en la memoria, lo que pretende resaltar la importancia de ésta en los procesos cognitivos de los seres vivos y aporta un giro conceptual al punto de vista tradicional, centrado en los procesos. The general objective of this thesis is the development of a global navigation system endowed with planning and learning features for a mobile robot. Within this general objective we have devoted a special effort to the autonomous learning problem. Our main concern has been to establish the necessary mechanisms for the incremental development of a topological model of the robot’s environment using the sensory information. These mechanisms are based on a new concept proposed in the thesis: the sensory gradient. The sensory gradient is a mathematical device which works like a detector of “interesting” environment’s events. Once a particular event has been detected the robot can identify its situation in the topological map and to react accordingly. We have called these special situations relevant sensory places because (a) they capture the system’s attention and (b) they can be identified using the sensory information. To conveniently exploit the built-in models we have developed an algorithm able to make internalized plans, establishing a suggestion network in the sensory relevant places in such way that the robot can find at those places a recommended navigation direction. It has been also developed a robust navigation system able to navigate by means of interpreting and adapting the internalized plans to the concrete circumstances at each instant, i.e. a reactive navigation system. This reactive system is based on the artificial potential field approach with the additional feature introduced in the thesis of what we call fictitious charges as an aid to avoid local minima. As a general contribution of the thesis to the cognitive science field all the above described elements are integrated in a memory-based architecture, emphasizing the important role played by the memory in the cognitive processes of living beings and giving a conceptual turn in the usual process-based approach.

An Autonomous Engine for Services Configuration and Deployment.

The runtime management of the infrastructure providing service-based systems is a complex task, up to the point where manual operation struggles to be cost effective. As the functionality is provided by a set of dynamically composed distributed services, in order to achieve a management objective multiple operations have to be applied over the distributed elements of the managed infrastructure. Moreover, the manager must cope with the highly heterogeneous characteristics and management interfaces of the runtime resources. With this in mind, this paper proposes to support the configuration and deployment of services with an automated closed control loop. The automation is enabled by the definition of a generic information model, which captures all the information relevant to the management of the services with the same abstractions, describing the runtime elements, service dependencies, and business objectives. On top of that, a technique based on satisfiability is described which automatically diagnoses the state of the managed environment and obtains the required changes for correcting it (e.g., installation, service binding, update, or configuration). The results from a set of case studies extracted from the banking domain are provided to validate the feasibility of this proposa

Engineering an Ontology for Autonomous Systems - The OASys Ontology

This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programe on autonomous systems’ engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems’ stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system’s conceptual models. The objective is to obtain and to use these models as main input for the autonomous system’s model-based control system.