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The use of renewable energy, especially wind power, is the most practical way to mitigate the environmental effects that various countries around the world are suffering from. To meet the growing need for electricity, wind energy is, nevertheless, being used more and more. Researchers have come to understand that a near-perfect output power estimate must be sacrificed. Variations in the weather influence wind energy, including wind speed, surface temperature, and pressure. In this study, the wind turbine output power was estimated using three approaches of artificial neural networks (ANNs). The multilayer feed-forward neural network (MLFFNN), cascaded forward neural network (CFNN), and recurrent neural network (RNN) were employed for estimating the entire output power of wind turbine farms in Egypt. Therefore, each built NN made use of wind speed, surface temperature, and pressure as inputs, while the wind turbine's output power served as its output. The data of 62 days were gathered from wind turbine farm for the training and efficiency examination techniques of every implemented ANN. The first 50 days' worth of data were utilized to train the three created NNs, and the last 12 days' worth of data were employed to assess the efficiency and generalization capacity of the trained NNs. The outcomes showed that the trained NNs were operating successfully and effectively estimated power. When analyzed alongside the other NNs, the RNN produced the best main square error (MSE) of 0.00012638, while the CFNN had the worst MSE of 0.00050805. A comparison between the other relevant research studies and our suggested approach was created. This comparison led us to the conclusion that the recommended method was simpler and had a lower MSE than the others. Additionally, the generalization ability was assessed and validated using the approved methodology. [ABSTRACT FROM AUTHOR]

Robotic manipulators have become very necessary in industrial applications all over the world. In this paper, a 5-DOF robotic manipulator is designed and manufactured to simulate a real industrial task. The manipulator is intended to transfer an object with a weight of 30 grams from a known place to another known one, which is a pick and place task. Firstly, all parts of the manipulator are designed using SolidWorks software. During the design, all parts' dimensions are considered. The end-effector of the manipulator is designed based on gear system. Secondly, 3D printing technology is used to manufacture these designed parts. The manufacturing process is very accurate and efficient. Servo motors are considered to do the motion of the manipulator, which are easily and directly connected to the control circuit. As, 5-DOF manipulator is manufactured, five servo motors are used: one motor for every joint. The motion of the motors is controlled by Arduino Uno unit which is a cheap and easy programming unit. Experiments are executed with the developed robot to show its effectiveness and success by preparing three boxes which the robot effectively transfers from one place to another. Eventually, the challenges during the design and manufacturing of this robot are mentioned in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the aim is to classify torque signals that are received from a 3-DOF manipulator using a pattern recognition neural network (PR-NN). The output signals of the proposed PR-NN classifier model are classified into four indicators. The first predicts that no collisions occur. The other three indicators predict collisions on the three links of the manipulator. The input data to train the PR-NN model are the values of torque exerted by the joints. The output of the model predicts and identifies the link on which the collision occurs. In our previous work, the position data for a 3-DOF robot were used to estimate the external collision torques exerted by the joints when applying collisions on each link, based on a recurrent neural network (RNN). The estimated external torques were used to design the current PR-NN model. In this work, the PR-NN model, while training, could successfully classify 56,592 samples out of 56,619 samples. Thus, the model achieved overall effectiveness (accuracy) in classifying collisions on the robot of 99.95%, which is almost 100%. The sensitivity of the model in detecting collisions on the links "Link 1, Link 2, and Link 3" was 97.9%, 99.7%, and 99.9%, respectively. The overall effectiveness of the trained model is presented and compared with other previous entries from the literature. [ABSTRACT FROM AUTHOR]

In this paper, force sensor signals are classified using a pattern recognition neural network (PRNN). The signals are classified to show if there is a collision or not. In our previous work, the joints positions of a 2-DOF robot were used to estimate the external force sensor signal, which was attached at the robot end-effector, and the external joint torques of this robot based on a multilayer feedforward NN (MLFFNN). In the current work, the estimated force sensor signal and the external joints' torques from the previous work are used as the inputs to the proposed designed PRNN, and its output is whether a collision is found or not. The designed PRNN is trained using a scaled conjugate gradient backpropagation algorithm and tested and validated using different data from the training one. The results prove that the PRNN is effective in classifying the force signals. Its effectiveness for classifying the collision cases is 92.8%, and for the non-collisions cases is 99.4%. Therefore, the overall efficiency is 99.2%. The same methodology and work are repeated using a PRNN trained using another algorithm, which is the Levenberg–Marquardt (PRNN-LM). The results using this structure prove that the PRNN-LM is also effective in classifying the force signals, and its overall effectiveness is 99.3%, which is slightly higher than the first PRNN. Finally, a comparison of the effectiveness of the proposed PRNN and PRNN-LM with other previous different classifiers is included. This comparison shows the effectiveness of the proposed PRNN and PRNN-LM. [ABSTRACT FROM AUTHOR]

This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this closed kinematic chain. The robot end-effector is guided by the human operator via an admittance controller to complete a straight-line segment motion, which is the desired task. The best location of the selected task is determined by maximizing the minimum of the condition number along the path. The performance of the proposed approach is evaluated using a criterion related to ergonomics. The experiments are executed with several subjects using a KUKA LWR robot to repeat the specified motion to evaluate the introduced approach. A comparison is presented between the current proposed approach and our previously implemented approach where the task performance criterion was based on the manipulability index of the closed kinematic chain. The results reveal that the condition number-based approach improves the human–robot cooperation in terms of the achieved accuracy, stability, and human comfort, but at the expense of task speed and completion time. On the other hand, the manipulability-index-based approach improves the human–robot cooperation in terms of task speed and human comfort, but at the cost of the achieved accuracy. [ABSTRACT FROM AUTHOR]

In this research paper, the multilayer feedforward neural network (MLFFNN) is architected and described for solving the forward and inverse kinematics of the 3-DOF articulated robot. When designing the MLFFNN network for forward kinematics, the joints' variables are used as inputs to the network, and the positions and orientations of the robot endeffector are used as outputs. In the case of inverse kinematics, the MLFFNN network is designed using only the positions of the robot endeffector as the inputs, whereas the joints' variables are the outputs. For both cases, the training of the proposed multilayer network is accomplished by Levenberg Marquardt (LM) method. A sinusoidal type of motion using variable frequencies is commanded to the three joints of the articulated manipulator, and then the data is collected for the training, testing, and validation processes. The experimental simulation results demonstrate that the proposed artificial neural network that is inspired by biological processes is trained very effectively, as indicated by the calculated mean squared error (MSE), which is approximately equal to zero. The resulted in smallest MSE in the case of the forward kinematics is 4.592 × 10-8 in the case of the inverse kinematics, is 9.071 × 10-7. This proves that the proposed MLFFNN artificial network is highly reliable and robust in minimizing error. The proposed method is applied to a 3-DOF manipulator and could be used in more complex types of robots like 6-DOF or 7-DOF robots. [ABSTRACT FROM AUTHOR]

The ideal smart home could be automatically controlled using a variety of electronic tools and devices to perform everyday tasks. Smart home automation is crucially beneficial for human life, particularly when considering those with disabilities, inpatients, and elderly populations. In this paper, applications and systems for smart homes are investigated. During experimentation they were controlled via an Android mobile phone and the Arduino platform. Bluetooth Module HC-06 was used to connect the Arduino Uno R3 with the mobile phone. Five smart home applications were developed to control the lighting and electrical sockets, fan speed, temperature- and humidity-meter display/controls, as well as the fire-alarm and toxic-gas alarm systems. Herein, the definition, the graphical user interface, the required main components, and the control circuit connections are prepared and presented for each application. The graphical user interface was created using the RemoteXY website, which is a reliable website for this purpose. The developed applications were tested, and they were found to work efficiently and correctly. Additionally, this innovative system is both cost-effective and affordable (total cost at the time of development was 110 USD). [ABSTRACT FROM AUTHOR]

Background: Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods: In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator's joints and considers only the signals of the position sensors that are intrinsic to the manipulator's joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. Results: The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. Conclusions: Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work. [ABSTRACT FROM AUTHOR]

Solar photovoltaics (PV) is considered an auspicious key to dealing with energy catastrophes and ecological contamination. This type of renewable energy is based on climatic conditions to produce electrical power. In this article, a multilayer feedforward neural network (MLFFNN) is implemented to predict and forecast the output power for a solar PV power station. The MLFFNN is designed using the module temperature and the solar radiation as the two main only inputs, whereas the expected power is its output. Data of approximately one week (6-days) are obtained from a real PV power station in Egypt. The data of the first five days are used to train the MLFFNN. The training of the designed MLFFNN is executed using two types of learning algorithms: Levenberg-Marquardt (LM) and error backpropagation (EBP). The data of the sixth day, which are not used for the training, are used to check the efficiency and the generalization capability of the trained MLFFNN by both algorithms. The results provide evidence that the trained MLFFNN is running very well and efficiently to predict the power correctly. The results obtained from the trained MLFFNN by LM (MLFFNN-LM) are compared with the corresponding ones obtained by the MLFFNN trained by EBP (MLFFNN-EBP). From this comparison, the MLFFNN-LM has slightly lower performance in the training stage and slightly better performance in the stage of effectiveness investigation compared with the MLFFNN-EBP. Finally, a comparison with other previously published approaches is presented. Indeed, predicting the power correctly using the artificial NN is useful to avoid the fall of the power that maybe happen at any time. [ABSTRACT FROM AUTHOR]

Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first part of this paper reviews the variable admittance (VA) control for human–robot co-manipulation tasks, where the virtual damping, inertia, or both are adjusted. An overview of the published research for the VA control approaches, their methods, the accomplished collaborative co-manipulation tasks and applications, and the criteria for evaluating them are presented and compared. Then, the performance of various VA controllers is compared and investigated. In the second part, the safety of HRI systems is discussed. The various methods for detection of human–robot collisions (model-based and data-based) are investigated and compared. Furthermore, the criteria, the main aspects, and the requirements for the determination of the collision and their thresholds are discussed. The performance measure and the effectiveness of each method are analyzed and compared. The third and final part of the paper discusses the perspectives, necessity, influences, and expectations of the HRI for future robotic systems. [ABSTRACT FROM AUTHOR]

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SUMMARY: In this paper, a multilayer feedforward neural network (NN) is designed and trained, for human–robot collisions detection, using only the intrinsic joint position sensors of a manipulator. The topology of one NN is designed considering the coupled dynamics of the robot and trained, with and without external contacts, by Levenberg–Marquardt algorithm to detect unwanted collisions of the human operator with the manipulator and the link that is collided. The proposed approach could be applied to any industrial robot, where only the joint position signals are available. The designed NN is compared quantitatively and qualitatively with an NN, where both the intrinsic joint position and the torque sensors of the manipulator are used. The proposed method is evaluated experimentally with the KUKA LWR manipulator, which is considered as an example of the collaborative robots, using two of its joints in a planar horizontal motion. The results illustrate that the developed system is efficient and fast to detect the collisions and identify the collided link. [ABSTRACT FROM AUTHOR]