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A developing country that does not have adequate energy solutions in non-interconnected zones (ZNIs) undoubtedly experiences adverse effects in terms of its competitiveness, as well as economic and social development. The primary sources of electrical energy in these communities are diesel generators, which are expensive and not environment-friendly. This paper seeks to describe how to reduce the consumption of this fossil fuel in ZNIs. The technical and economical implementation of a small-scale photovoltaic system with and without a hybrid energy storage system connected to the grid is studied, based on the incentive policy of the Colombian regulation. A mixed-integer nonlinear programming optimization model was developed, which includes the storage costs of a lead-acid battery and supercapacitor, to analyze the benefits of the electricity tariff and to maximize the revenues of the photovoltaic generation system. The conclusions of this study offer policy suggestions and economic incentives to enhance the use of renewable energy sources in the ZNIs of Colombia.

This paper presents a robotic navigation system that uses mixed reality concepts to develop sensing and communication virtual devices, based on the visual localization of the robot in the environment. The main objective of the navigation system is to provide conditions for the use of very simple robots with severe limitations on the mentioned peripheral devices for simulation, analysis and test of multi-robot applications. In an experiment with real robots, each one receives its virtual navigation skills in an independent way from the tool that emulates the function of such peripherals. Thus, the behavior of a group of robots, independently commanded, is implemented in the virtual environment and accomplished in the real world. An experiment composed by real multiple Sphero robots executing an exploratory task within an unknown dynamic environment is carried out to validate the proposed navigation system. The use of mixed reality concepts allows an easy implementation of cooperation mechanisms based on indirect communication skill and fuzzy controllers for the robots’ movement. The results confirm the feasibility of the proposed autonomous navigation system.

This paper proposes a path planner for multi-robot systems based on the solution of the multiple traveling salesman problem by genetic algorithm. The main planning goal is to build an efficient path to each robot of the system which jointly ensures that several a priori known points (ways-points) in the environment can be attained by at least one of the robots. During navigation, each robot try to follow its planned path while performing tasks and deviating from unknown static and dynamic obstacles. As the robots have limited sensing and communication skills, their positions can easily become uncertain and the robots will be unable to follow their planned paths. To circumvent this problem, each robot has a local planner, able to recalculate its position and then to resume its planned path. This computation is based on the knowledge about way-points positions, information exchanged with other robots and a self localization algorithm by triangulation. The proposed global/local path planner is firstly validated by computer simulations. An experimental study is also carried out with a small system with very simple mobile robots with differential drive and Bluetooth communication. The obtained results confirm the efficacy of the proposed path planner.

Demand response (DR) is an important factor contributing to achieve a balance between energy production and demand in Smart Grids. DR plays a key role in the use of residential energy allowing to improve the load management, electrical grid reliability, to reduce energy demand during peak hours and to minimize the use of energy in face of increasing energy prices. This article proposes a Model Predictive Control (MPC) strategy to manage the energy resources of a residential microgrid combined with DR techniques, such as load curtailment, that promotes short term reduction of electricity demand in pre-defined hours. In particular, the presented approach encompasses degradation issues of the Energy Storage System (ESS), the cost of the electricity, renewable energy generation, and other operational constraints. The developed control strategy is able to maximize microgrid economical benefit, while minimizing the degradation of the ESS, reducing electricity consumption during the day, and fulfilling the different operational constraints. The proposed strategy is validated in an experimental renewable-energy based microgrid platform for different climatic conditions. The obtained results demonstrate and verify the effectiveness of the proposed control and management strategy.

This paper aims to present a cooperative and distributed navigation strategy, that is an on-line path planner, for an autonomous multi-robot system. The robots are intended to navigate and explore an unknown environment in order to find and reach obligatory passage points or way-points (goals), and then achieve a known final position. All robots in the team are homogeneous, independent and have limited communication skills. However they interact among them and with the environment to autonomously decide about their paths and tasks: if they should explore the environment, or avoid visiting a previously explored region, or to reach a discovered goal. Information sharing is directly carried out when the robots are into a communication area and/or indirectly by stigmergy. In this case, artificial pheromone, as a repulsive field, is used to mark regions that have already been explored by other members of the team, therefore avoiding redundant exploration and time waste. Fuzzy controllers are used for robots’ motion. The proposed on line path planner performance is evaluated in different simulated environment scenarios and the main results are presented.

The tactile sensing system contains six in-series sensors distributed in order to monitor pressures applied in areas that allow the foot type identification. Principal component analysis was used to infer the system performance.

This paper discusses a spatial sensor to identify and track objects in the environment. The sensor is composed of an RGB-D camera that provides point cloud and RGB images and an egomotion sensor able to identify its displacement in the environment. The proposed sensor also incorporates a data processing strategy developed by the authors to conferring to the sensor different skills. The adopted approach is based on four analysis steps: egomotive, lexical, syntax, and prediction analysis. As a result, the proposed sensor can identify objects in the environment, track these objects, calculate their direction, speed, and acceleration, and also predict their future positions. The on-line detector YOLO is used as a tool to identify objects, and its output is combined with the point cloud information to obtain the spatial location of each identified object. The sensor can operate with higher precision and a lower update rate, using YOLOv2, or with a higher update rate, and a smaller accuracy using YOLOv3-tiny. The object tracking, egomotion, and collision prediction skills are tested and validated using a mobile robot having a precise speed control. The presented results show that the proposed sensor (hardware + software) achieves a satisfactory accuracy and usage rate, powering its use to mobile robotic. This paper's contribution is developing an algorithm for identifying, tracking, and predicting the future position of objects embedded in a compact hardware. Thus, the contribution of this paper is to convert raw data from traditional sensors into useful information.

Climbing robots are characterized by a secure surface coupling that is designed to prevent falling. The robot coupling ability is assured by an adhesion method leading to nonlinear dynamic models with time-varying parameters that affect the robot&rsquo, s mobility. Additionally, the wheel friction and the force of gravity force are also relevant issues that can compromise the climbing ability if they are not well modeled. This work presents a model-based torque controller for velocity tracking in a four-wheeled climbing robot specially designed to inspect storage tanks. The model-based controller (MPC) compensates for the effects of nonlinearities due to the forces of gravity, friction, and adhesion through the dynamic and kinematic modeling of the climbing robot. Dynamic modeling is based on the Lagrange-Euler approach, which allows a better understanding of how forces and torques affect the robot&rsquo, s movement. Besides, an analysis of the interaction force between the robot and the contact surface is proposed, since this force affects the motion of the climbing robot according to spatial orientation. Finally, simulations are carried out to examine the robot&rsquo, s dynamics during the climbing movement, and the MPC is validated through the redrobot simulator V-REP and practical experiments. The presented results highlight the compensation of the nonlinear effects due to the robot&rsquo, s climbing motion by the proposed MPC controller.

Ideally, a Energy Management System (EMS) should keep the system in a safe and operational scenario even if a fault occurs. To help achieve this goal, this paper presents the development of a Model Predictive Control (MPC) strategy for energy management in microgrids combined with a fault detection and isolation system. The control strategy is based on an MPC that allows operational recovering if it identifies a fault in a system control input. In this study, adjustments occur by reduction of electricity consumption using Demand Response (DR) concepts such as load curtailment and reconfiguration of parameters that adapt the controller to the new power supply scenario. The proposed control strategy validation is carried out through simulation on a model of a real microgrid composed by renewable energy sources, local electrical grid and Energy Storage Systems (ESSs). Simulations show satisfactory preliminary results.

This paper addresses the control of a fleet of unmanned aerial systems (UAVs), termed as drones, for flight formation problems. Getting drones to fly in formation is a relevant problem to be solved when cooperative cargo transportation is desired. A general approach for this problem considers the coordination of a fleet of UAVs, by fusing all information coming from several individual sensors posed on each UAVs. However, this approach induces a high cost as every UAV should have its advanced perception system. As an alternative, this paper proposes the use of a single perception system by a fleet composed of several elementary drones (workers) with primitive low-cost sensors and a leader drone carrying a 3D perception source. We propose a Quadral-Fuzzy approach to ensure that all drones fly in formation and will not collide with each other or with environment obstacles. We also develop a new way to compute potential fields based on possibility fuzzy (fuzziness) measure with the focus of avoiding collisions between the drones. The proposed approach encompasses four high-coupled intelligent controllers that respectively control the leader and worker drones' motion and implement obstacle and collision avoidance procedures. Simulation results using a fleet of four aerial drones are presented, showing the potential for solving usual problems to flights in formation, such as dodging obstacles, avoiding collisions between the drones, among others., This work was supported in part by the National Counsel of Technological and Scientific Development of Brazil (CNPq), in part by the Coordination for the Improvement of Higher Level People (CAPES), in part by the Brazilian Ministry of Science, Technology, Innovation and Communication (MCTIC), and in part by the Robotics and Internet-of-Things Lab in Prince Sultan University.

Integration and coordinated operation of renewable energy sources (RESs) offer to conventional consumers the possibility to control energy production and consumption in order to obtain technical and economic benefits. In this work, a mixed-integer linear programming approach is proposed to model and optimize DC-coupled grid-tied microgrids. The objective is to reduce industrial consumer electricity bill and energy storage systems degradation, considering operational constraints and rules defined by a complex binomial differentiated tariff and net metering policies to exchange energy. A novel model is developed to DC-coupling integration considering the efficiency of power transformation process and elements with two possible power flow conditions. An original formulation based on logical constraints is also proposed to model the net metering policies and to find the best compensation energy strategy for solving the optimization problem. A case study of a Brazilian industrial consumer, owner of a real-world DC grid-tied microgrid, is presented. The proposed approach involves low-cost procedures that can be integrated into a commercial solution to optimize microgrids in similar net metering contexts, encouraging the use of RESs in developing countries.

In this work, a new approach to integrate Model Predictive Control (MPC) and Real-Time Optimization (RTO) layers within an hierarchic control structure is presented. When compared to the standard hierarchic structure, the proposed approach improves the tracking of the optimum operating point and minimizes inconsistencies that might occur due to different calculations made in each layer, each one based on a different process model. The strategy consists of the introduction of a sub-layer steady-state target optimization (SSTO) into the MPC layer. This sub-layer will reevaluate the targets from the RTO to setpoints attainable by the MPC. Additionally, the SSTO–MPC augmented layer uses identification techniques to help keep its models continuously updated, improving predictions and mitigating unexpected behavior. The proposed approach is validated through simulated example and the obtained results are validated in a benchmark model also used in a similar work done on the Economic Model Predictive Control approach.

An improvement on the scheduling of pumping and delivery operations in an installed pipeline network can lead to considerable profits to the using companies, such as oil companies. This paper proposes a decomposition approach that integrates heuristic procedures and mixed integer linear programming (MILP) models, a matheuristic, to solve the long-term scheduling of a pipeline system, which connects a single-source to multiple distribution centers. The approach provides a rigorous inventory management and flow rate control taking into account several operational aspects, such as simultaneous deliveries, and prespecified periods of tank maintenance and pipeline maintenance. To validate the developed approach, two case studies were devised. In case study 1, several instances of an illustrative network were solved and case study 2 addressed three examples of a real-world network: base instance; extended instance with maintenance periods; and model performance tests. Valid solutions that can be operationally implemented were obtained for all executions in a reasonable computational time. Detailed discussions of the obtained solutions are presented and indicate an inventory control in accordance with operational requirements.

This paper presents an artificial life environment based on dynamic fuzzy cognitive maps (DFCMs) and inspired by multiagent systems, machine learning, and concepts from classical fuzzy cognitive map theory. The proposed architecture includes features such as a reinforcement learning algorithm to dynamically fine-tune the weights of the DFCM, a finite states machine, governing the behavior of the creatures by adding/removing concepts into the DFCM, and others. These features are used to add adaptability to the artificial creatures (agents) in a simulated hunter-prey environment with synthetic data. Some experiments carried out in a simulated virtual environment have shown promising results for further research in the subject of this paper.