SUMMARYIn this paper, we present the work related to the application of a visual odometry approach to estimate the location of mobile robots operating in off-road conditions. The visual odometry approach is based on template matching, which deals with estimating the robot displacement through a matching process between two consecutive images. Standard visual odometry has been improved using visual compass method for orientation estimation. For this purpose, two consumer-grade monocular cameras have been employed. One camera is pointing at the ground under the robot, and the other is looking at the surrounding environment. Comparisons with popular localization approaches, through physical experiments in off-road conditions, have shown the satisfactory behavior of the proposed strategy.
This paper analyzes the impact of photovoltaic (PV) systems on storage and electric vehicles in micro-grids. As these kinds of systems are becoming increasingly popular in the residential sector, the development of a new generation of equipment, such as more efficient batteries or solar panels, makes further study necessary. These systems are especially interesting in commercial or office buildings, since they have a more repetitive daily pattern of electricity consumption, which usually occurs within the maximum solar radiation hours. Based on this need, a novel control strategy aimed at efficiently managing this kind of micro-grid is proposed. The core of this strategy is a rule-based controller managing the power flows between the grid and the batteries of both the PV system and the electric vehicle. Through experimental data and simulations, this strategy was tested under different scenarios. The selected testbed consisted of the laboratory of a research center, which could be easily scalable to the entire building. Results showed the benefits of using an electric vehicle as an active agent in energy balance, leading to a reduction of the energetic costs of a micro-grid.Energies 2018, 11, 522 2 of 13 residential buildings is presented in [2], where energy storage and load management were presented as the main strategies for increasing self-consumption. The latest developments in battery technology have enabled their use in residential houses. In [3], self-consumption was quantified for a PV system with a home battery. Due to different electricity taxes, market tariffs, and stochastic consumption and production in Europe, that paper did not ensure that the battery was to be profitable in today's market. However, increasing electricity prices and the expected decrease in battery prices suggest that energy storage could be a profitable option in the near future, even in Northern Europe. A methodology for sizing residential PV battery systems was proposed in [4], and a control algorithm for these kinds of systems were proposed in [5,6]. Both works dealt with the limitations in the power flows between the PV system, the public electrical gird, and batteries. In combination with energy storage, intelligent load management may address important improvements in the self-consumption of buildings [7,8]. This strategy basically consists of scheduling the controllable loads to match the hours with the maximum production of solar energy. In commercial and office buildings where the loads and solar energy production occur at the same time, it is likely to reach high self-consumption [9]. However, in houses whose occupants spend the central part of the day outside, a mismatch between production and consumption occurs. In this scenario, active demand-side management (DSM) allows the improvement of self-consumption by selecting when a water heater, a washing machine, or other programmable appliances should start working.Taking into account the concepts of energy storage and DSM, the plug-in electric vehicle (EV) arises as a c...
In this paper we present a review of forest models developed in Spain in recent years for both timber and non timber production and forest dynamics (regeneration, mortality). Models developed are whole stand, size (diameter) class and individual-tree. The models developed to date have been developed using data from permanent plots, experimental sites and the National Forest Inventory. In this paper we show the different sub-models developed so far and the friendly use software. Main perspectives of forest modeling in Spain are presented.Key words: timber production; non-wood production; recruitment; modeling; forest. Resumen Modelos de crecimiento y producción en España: historia, ejemplos contemporáneos y perspectivasEn el presente trabajo se presenta una revisión sobre los modelos forestales desarrollados en España durante los úl-timos años, tanto para la producción maderable como no maderable y, para la dinámica de los bosques (regeneración, mortalidad). Se presentan modelos tanto de rodal completo como de clases diamétricas y de árbol individual. Los modelos desarrollados hasta la fecha se han desarrollado a partir de datos procedentes de parcelas permanentes, ensayos y el Inventario Forestal Nacional. En el trabajo se muestran los diferentes submodelos desarrollados hasta la fecha,
Keeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼100,000 m 2 ), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software.
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