Abstract
Featured Application The robotic bin picking solution presented in this work serves as a stepping stone towards the development of cost-effective, scalable systems for handling entangled objects. This study and its experiments focused on tube-shaped objects, which have a widespread presence in the industry. Abstract Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.

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The advantages of wind power integration over other renewable resources are well-known information and the natural results are the massive worldwide integration. Such massive integration, without the correct management together with conventional generation leads to an augmented complexity and the inflexibility of conventional power systems. For several reasons, forecasting tools are one of the most valuable tools in the power systems field, because they helps to decide in advance the way to manage correctly and with profits the electrical mix production. In this work, an extended hybrid wind power forecasting approach, with probabilistic features, is proposed to forecast twenty-four hours-ahead, considering only real historical wind power data. To validate the proposed forecasting approach, a comparison with other validated models is performed to offer a fair and proportional analysis. The outcomes show that the suggested forecasting approach performs adequately even considering the reduced data available as input.

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A viable option for electrification of remote areas far from power grids is to set up microgrids and feed them with local generation. Such microgrids are referred to as isolated microgrids and due to the lack of possibility of power exchange with the grid, their operation is different from grid-connected microgrids. Isolated microgrids, similar to grid-connected microgrids are equipped with energy management systems including unit commitment and economic dispatch modules. In this paper, the aim is to formulate the dynamic economic load dispatch (DELD) in isolated microgrids, while curtailment of responsive loads and curtailment of renewable power is allowed and load shedding is used as the last resort for balancing generation and demand. The generated power of dispatchable distributed generators (DGs), curtailed power of renewable DGs, curtailed demand and shed power are determined for each time period. The formulated DELD problem is solved with the well-established particle swarm optimisation (PSO) algorithm. The results for a microgrid with four dispatchable DGs and two renewable DGs show the performance of PSO over grey wolf optimisation (GWO) and also indicate the significant effect of demand response in reducing the operation cost of isolated microgrids.

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Counting subgraph occurrences in complex networks is an important analytical task with applicability in a multitude of domains such as sociology, biology and medicine. This task is a fundamental primitive for concepts such as motifs and graphlet degree distributions. However, there is a lack of online algorithms for computing and updating subgraph counts in dynamic networks. Some of these networks exist as a streaming of edge additions and deletions that are registered as they occur in the real world. In this paper we introduce StreamFaSE, an efficient online algorithm for keeping track of exact subgraph counts in dynamic networks, and we explain in detail our approach, showcasing its general applicability in different network scenarios. We tested our method on a set of diverse real directed and undirected network streams, showing that we are always faster than the current existing methods for this task, achieving several orders of magnitude speedup when compared to a state-of-art baseline. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract