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This study analyzes future synergies between the Oil and Gas (O&G) and renewables sectors in a Danish context and explores how exploiting these synergies could lead to economic and environmental benefits. We review and highlight relevant technologies and related projects, and synthesize the state of the art in offshore energy system integration. All of these preliminary results serve as input data for a holistic energy system analysis in the Balmorel modeling framework. With a timeframe out to 2050 and model scope including all North Sea neighbouring countries, this analysis explores a total of nine future scenarios for the North Sea energy system. The main results include an immediate electrification of all operational Danish platforms by linking them to the shore and/or a planned Danish energy island. These measures result in cost and CO2 emissions savings compared to a BAU scenario of 72% and 85% respectively. When these platforms cease production, this is followed by the repurposing of the platforms into hydrogen generators with up to 3.6 GW of electrolysers and the development of up to 5.8 GW of floating wind. The generated hydrogen is assumed to power the future transport sector, and is delivered to shore in existing and/or new purpose-built pipelines. The contribution of the O&G sector to this hydrogen production amounts to around 19 TWh, which represents about 2% of total European hydrogen demand for transport in 2050. The levelized costs (LCOE) of producing this hydrogen in 2050 are around 4 €2020/kg H2, which is around twice those expected in similar studies. But this does not account for energy policies that may incentivize green hydrogen production in the future, which would serve to reduce this LCOE to a level that is more competitive with other sources.

The Spanish Ministry of Defense is currently attempting to reduce the amount of energy that is consumed by its military bases and has therefore raised concerns about how to make their facilities more energy efficient. To fulfill this objective, the Spanish army has developed various studies and projects, as well as a technical prescription sheet that defines the thermal transmittance values of the materials that are to be used to construct the different elements of the containers that make up the temporary housing units at Spanish military camps. Both governments and private entities have developed initiatives that are aimed at improving the energy efficiency of buildings, which are classified into two groups: those aimed at the development of mandatory building codes and those that are based on voluntary certification programs. The use of passive strategies is one of the key actions that is being implemented to achieve the NZEB category, as its first requirement is to be a “very low energy consumption building”. This paper compares the energy efficiency requirements of the tents and containers that are used in military camps and the energy-efficient design requirements that are demanded by the energy efficiency standards for buildings in the civil sector. Through this comparison, we determine how energy efficient the current living spaces in military camps are in order to define strategies that can be implemented to improve the design requirements of these living spaces so to reduce the consumption and operation logistics and to improve both operability and safety in military camp facilities.

One of the active challenges in multi-robot missions is related to managing operator workload and situational awareness. Currently, the operators are trained to use interfaces, but in the near future this can be turned inside out: the interfaces will adapt to operators so as to facilitate their tasks. To this end, the interfaces should manage models of operators and adapt the information to their states and preferences. This work proposes a videogame-based approach to classify operator behavior and predict their actions in order to improve teleoperated multi-robot missions. First, groups of operators are generated according to their strategies by means of clustering algorithms. Second, the operators’ strategies are predicted, taking into account their models. Multiple information sources and modeling methods are used to determine the approach that maximizes the mission goal. The results demonstrate that predictions based on previous data from single operators increase the probability of success in teleoperated multi-robot missions by 19%, whereas predictions based on operator clusters increase this probability of success by 28%.

Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.

The constant growth of the population with mobility impairments has led to the development of several gait assistance devices. Among these, smart walkers have emerged to provide physical and cognitive interactions during rehabilitation and assistance therapies, by means of robotic and electronic technologies. In this sense, this paper presents the development and implementation of a human−robot−environment interface on a robotic platform that emulates a smart walker, the AGoRA Walker. The interface includes modules such as a navigation system, a human detection system, a safety rules system, a user interaction system, a social interaction system and a set of autonomous and shared control strategies. The interface was validated through several tests on healthy volunteers with no gait impairments. The platform performance and usability was assessed, finding natural and intuitive interaction over the implemented control strategies.

This paper presents a novel tool capable of collecting thermal signatures inside a building by using low-cost IR temperature sensors mounted on-board an aerial platform. The proposed system aims to facilitate the detection of heat loss inside buildings, which is a key aspect for improving energy efficiency in large commercial or industrial buildings. Current detection systems usually require manual labor as well as the use of expensive instrumentation. The proposed system on the other hand, relies on the use of a small unmanned aerial vehicle carrying low-cost thermopile IR sensors. Moreover, the system delivers a fast temperature sensing scheme and it provides coverage to inaccessible areas, thus overcoming the limitations of current mobile platforms which use ground robots. Different experiments were carried out in order to assess the behavior of the sensors as well as to validate the full system. Moreover, the hypothesis that thermopile IR sensors can be used to track temperature signature on-the-fly is validated experimentally with the use of the proposed system over different targets.

This paper presents a human detection system that can be employed on board a mobile platform for use in autonomous surveillance of large outdoor infrastructures. The prediction is based on the fusion of two detection modules, one for the laser and another for the vision data. In the laser module, a novel feature set that better encapsulates variations due to noise, distance and human pose is proposed. This enhances the generalization of the system, while at the same time, increasing the outdoor performance in comparison with current methods. The vision module uses the combination of the histogram of oriented gradients descriptor and the linear support vector machine classifier. Current approaches use a fixed-size projection to define regions of interest on the image data using the range information from the laser range finder. When applied to small size unmanned ground vehicles, these techniques suffer from misalignment, due to platform vibrations and terrain irregularities. This is effectively addressed in this work by using a novel adaptive projection technique, which is based on a probabilistic formulation of the classifier performance. Finally, a probability calibration step is introduced in order to optimally fuse the information from both modules. Experiments in real world environments demonstrate the robustness of the proposed method.

There are many outdoor robotic applications where a robot must reach a goal position or explore an area without previous knowledge of the environment around it. Additionally, other applications (like path planning) require the use of known maps or previous information of the environment. This work presents a system composed by a terrestrial and an aerial robot that cooperate and share sensor information in order to address those requirements. The ground robot is able to navigate in an unknown large environment aided by visual feedback from a camera on board the aerial robot. At the same time, the obstacles are mapped in real-time by putting together the information from the camera and the positioning system of the ground robot. A set of experiments were carried out with the purpose of verifying the system applicability. The experiments were performed in a simulation environment and outdoor with a medium-sized ground robot and a mini quad-rotor. The proposed robotic system shows outstanding results in simultaneous navigation and mapping applications in large outdoor environments.

En el presente artículo se determina el comportamiento de una red de área metropolitana bajo el estándar IEEE 802.16 tanto para nodos fijos como móviles, utilizando NCTUns, considerando el comportamiento de los protocolos de enrutamiento. Mostrando en los resultados las velocidades de transmisión que se pueden obtener en redes Mesh y la distancia máxima a la cual pueden encontrarse los nodos y la estación radio base.

Esta publicación presenta tres grandes resultados de la aplicación del Modelo de Modernización para la Gestión de Organizaciones – MMGO- en 127 PyMEs colombianas: el análisis estadístico y psicométrico de los instrumentos utilizados en el MMGO, la evidencia empírica de la aplicación del Modelo en las 127 empresas y las recomendaciones para desarrollar una gestión empresarial de excelencia para la PyME colombiana.

The productivity of greenhouses highly depends on the environmental conditions of crops, such as temperature and humidity. The control and monitoring might need large sensor networks, and as a consequence, mobile sensory systems might be a more suitable solution. This paper describes the application of a heterogeneous robot team to monitor environmental variables of greenhouses. The multi-robot system includes both ground and aerial vehicles, looking to provide flexibility and improve performance. The multi-robot sensory system measures the temperature, humidity, luminosity and carbon dioxide concentration in the ground and at different heights. Nevertheless, these measurements can be complemented with other ones (e.g., the concentration of various gases or images of crops) without a considerable effort. Additionally, this work addresses some relevant challenges of multi-robot sensory systems, such as the mission planning and task allocation, the guidance, navigation and control of robots in greenhouses and the coordination among ground and aerial vehicles. This work has an eminently practical approach, and therefore, the system has been extensively tested both in simulations and field experiments.