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The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data. [ABSTRACT FROM AUTHOR]

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications. [ABSTRACT FROM AUTHOR]

In this study, crack-free TiC/Ti composites with TiC content ranging from 0 to 15 wt.% were successfully fabricated using Direct Energy Deposition with a dual-feeder system that concomitantly delivered different amounts of both constituents into a high-power laser beam. The samples were investigated to evaluate the morphologies and distribution behavior of TiC. The microhardness values of the samples obtained under optimal processing conditions increased from 192 ± 5.3 HV0.2 (pure Ti) to 300 ± 14.2 HV0.2 (Ti + wt.% 15 TiC). Also, TiC has a significant impact on the Ti matrix, increasing the strength of TMCs up to 725 ± 5.4 MPa, while the elongation drastically decreased to 0.62 ± 0.04%. The wear rate is not proportionally affected by the rise content of TiC reinforcement; the hypoeutectic region of TMCs exhibited a wear rate of 2.45 mm3/N·m (Ti + wt.% 3 TiC) and a friction coefficient of 0.48 compared to the ones from the hypereutectic region, which measured a wear rate of 3.02 mm3/N·m (Ti + wt.% 15 TiC) and a friction coefficient of 0.63. The improved values of mechanical properties in the case of TMCs as compared to pure Ti are provided due to the solid solution strengthening of carbon and the fine grain strengthening. This work outlines a method for changing TiC morphologies to improve the hardness and tensile strength of TMCs fabricated starting from micro-scale powder. [ABSTRACT FROM AUTHOR]

Freedom of movement of the hands is the most desired hope of stroke patients. However, stroke recovery is a long, long road for many patients. If artificial intelligence can assist human arm movement, the possibility of stroke patients returning to normal hand movement might be significantly increased. This study uses the artificial neuromolecular system (ANM system) developed in our laboratory as the core of motion control, in an attempt to learn to control the mechanical arm to produce actions similar to human rehabilitation training and the transition between different activities. This research adopts two methods. The first is hypothetical exploration, the so-called "artificial world" simulation method. The detailed approach uses the V-REP (Virtual Robot Experimentation Platform) to conduct different experimental runs to capture relevant data. Our policy is to establish an action database systematically to a certain extent. From these data, we use the ANM system with self-organization and learning capabilities to develop the relationship between these actions and establish the possibility of conversion between different activities. The second method of this study is to use the data from a hospital in Toronto, Canada. Our experimental results show that the ANM system can continuously learn for problem-solving. In addition, our three experimental results of adaptive learning, transfer learning, and cross-task learning further confirm that the ANM system can use previously learned systems to complete the delivered tasks through autonomous learning (instead of learning from scratch). [ABSTRACT FROM AUTHOR]

Amidst escalating global populations and dwindling arable lands, enhancing agricultural productivity and sustainability is imperative. Addressing the inefficiencies of traditional agriculture, which struggles to meet the demands of large-scale production, this paper introduces a highly configurable smart agricultural robotic arm system (CARA), engineered using convolutional neural networks and multilayer perceptron. CARA integrates a highly configurable robotic arm, an image acquisition module, and a deep processing center, embodying the convergence of advanced robotics and artificial intelligence to facilitate precise and efficient agricultural tasks including harvesting, pesticide application, and crop inspection. Rigorous experimental validations confirm that the system significantly enhances operational efficiency, adapts seamlessly to diverse agricultural contexts, and bolsters the precision and sustainability of farming practices. This study not only underscores the vital role of intelligent automation in modern agriculture but also sets a precedent for future agricultural innovations. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive incremental nonlinear dynamic inversion (INDI) controller for unmanned aerial manipulators (UAMs). A novel adaptive law is employed to enable aerial manipulators to manage the inertia parameter changes that occur when the manipulator moves or picks up unknown objects during any phase of the UAM's flight maneuver. The adaptive law utilizes a Kalman filter to estimate a set of weighting factors employed to adjust the control gain matrix of a previously developed INDI control law formulated for the corresponding UAV (no manipulator included). The proposed adaptive control scheme uses acceleration and actuator input measurements of the UAV without necessitating any knowledge about the manipulator, its movements, or the objects being grasped, thus enabling the use of previously developed INDI UAV controllers for UAMs. The algorithm is validated through simulations demonstrating that the adaptive control gain matrix used in the UAV's INDI controller is promptly updated based on the UAM maneuvers, resulting in effective UAV and robot arm control. [ABSTRACT FROM AUTHOR]

The current building industry is facing challenges of labor shortages and labor-intensive practices. Effectively collaborating with robots will be crucial for industry upgrading. This research introduces a MR iterative design and robot-assisted construction mode based on human–robot collaboration, facilitating an integrated process innovation from design to construction. The development of the ROCOS (Robot Collaboration System) comprises three key aspects: (1) Layout Stage: using MR technology to layout the site, forming a full-scale integrated virtual and physical digital twin design environment. (2) Design Stage: conducting virtual iterative design in the digital twin environment and automatically simulating assembly processes. (3) Assembly Stage: translating simulated results into assembly path commands and driving a robotic arm to perform actual assembly. In the end, this research setup two experiments to examine the feasibility of this iterative design–construction loop script. The results showed that although the presence of obstacles reduced the designer's freedom and increased the number of steps, the designer could still finish both tasks. This means that the ROCOS has value in the prototype of human–robot collaboration. In addition, some valuable findings from users' feedback showed that potential improvements can be addressed in operability, customization, and real construction scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a gesture-controlled robotic arm system designed for agricultural harvesting, utilizing a data glove equipped with bending sensors and OptiTrack systems. The system aims to address the challenges of labor-intensive fruit harvesting by providing a user-friendly and efficient solution. The data glove captures hand gestures and movements using bending sensors and reflective markers, while the OptiTrack system ensures high-precision spatial tracking. Machine learning algorithms, specifically a CNN+BiLSTM model, are employed to accurately recognize hand gestures and control the robotic arm. Experimental results demonstrate the system's high precision in replicating hand movements, with a Euclidean Distance of 0.0131 m and a Root Mean Square Error (RMSE) of 0.0095 m, in addition to robust gesture recognition accuracy, with an overall accuracy of 96.43%. This hybrid approach combines the adaptability and speed of semi-automated systems with the precision and usability of fully automated systems, offering a promising solution for sustainable and labor-efficient agricultural practices. [ABSTRACT FROM AUTHOR]

This study presents a novel image-based visual servoing fault-tolerant control strategy aimed at ensuring the successful completion of visual servoing tasks despite the presence of robotic arm actuator faults. Initially, a depth-independent image-based visual servoing model is established to mitigate the effects of inaccurate camera parameters and missing depth information on the system. Additionally, a robotic arm dynamic model is constructed, which simultaneously considers both multiplicative and additive actuator faults. Subsequently, model uncertainties, unknown disturbances, and coupled actuator faults are consolidated as centralized uncertainties, and an iterative learning fault observer is designed to estimate them. Based on this, suitable sliding surfaces and control laws are developed within the super-twisting sliding mode visual servo controller to rapidly reduce control deviation to near zero and circumvent the chattering phenomenon typically observed in traditional sliding mode control. Finally, through comparative simulation between different control strategies, the proposed method is shown to effectively counteract the effect of actuator faults and exhibit robust performance. [ABSTRACT FROM AUTHOR]

In the fresh tomato market, it is crucial to sort and sell tomatoes based on their quality. This is important to enhance the competitiveness and profitability of the market. However, the manual sorting process is subjective and inefficient. To address this issue, we have developed an automatic tomato sorting system that uses the Raspberry PI 4B as the control platform for the robot arm. This system has been integrated with a human–computer interaction interface sorting system. Our experimental results indicate that this sorting method has an accuracy rate of 99.1% and an efficiency of 1350 tomatoes per hour. This development is in line with modern agricultural mechanization and intelligence needs. [ABSTRACT FROM AUTHOR]

Featured Application: This research delves into the cutting-edge domain of robotic automation, specifically focusing on the development and application of a six-degree-of-freedom refueling robotic arm guided by RGB-D (Red, Green, Blue—Depth) visual technology. The study explores the intricate processes involved in the accurate positioning and docking of a robotic arm in refueling tasks, leveraging the advanced capabilities of RGB-D sensors for enhanced spatial awareness and precise maneuvering. The current application areas of this technology predominantly reside in industrial automation, particularly in sectors requiring precise and repetitive tasks such as automotive manufacturing, aerospace, and logistics. The potential applications extend further into fields like unmanned service stations, military logistics, and remote operation environments where human intervention is limited or hazardous. This research contributes to the evolving landscape of robotic automation, offering insights into more efficient, more accurate, and safer automated refueling processes, potentially revolutionizing how these tasks are approached in various industrial and commercial sectors. The main contribution of this paper is the proposal of a six-degree-of-freedom (6-DoF) refueling robotic arm positioning and docking technology guided by RGB-D camera visual guidance, as well as conducting in-depth research and experimental validation on the technology. We have integrated the YOLOv8 algorithm with the Perspective-n-Point (PnP) algorithm to achieve precise detection and pose estimation of the target refueling interface. The focus is on resolving the recognition and positioning challenges of a specialized refueling interface by the 6-DoF robotic arm during the automated refueling process. To capture the unique characteristics of the refueling interface, we developed a dedicated dataset for the specialized refueling connectors, ensuring the YOLO algorithm's accurate identification of the target interfaces. Subsequently, the detected interface information is converted into precise 6-DoF pose data using the PnP algorithm. These data are used to determine the desired end-effector pose of the robotic arm. The robotic arm's movements are controlled through a trajectory planning algorithm to complete the refueling gun docking process. An experimental setup was established in the laboratory to validate the accuracy of the visual recognition and the applicability of the robotic arm's docking posture. The experimental results demonstrate that under general lighting conditions, the recognition accuracy of this docking interface method meets the docking requirements. Compared to traditional vision-guided methods based on OpenCV, this visual guidance algorithm exhibits better adaptability and effectively provides pose information for the robotic arm. [ABSTRACT FROM AUTHOR]

Sensors are a key component in industrial automation systems. A fault or malfunction in sensors may degrade control system performance. An engineering system model is usually disturbed by input uncertainties, which brings a challenge for monitoring, diagnosis, and control. In this study, a novel estimation technique, called adaptive unknown-input observer, is proposed to simultaneously reconstruct sensor faults as well as system states. Specifically, the unknown input observer is used to decouple partial disturbances, the un-decoupled disturbances are attenuated by the optimization using linear matrix inequalities, and the adaptive technique is explored to track sensor faults. As a result, a robust reconstruction of the sensor fault as well as system states is then achieved. Furthermore, the proposed robustly adaptive fault reconstruction technique is extended to Lipschitz nonlinear systems subjected to sensor faults and unknown input uncertainties. Finally, the effectiveness of the algorithms is demonstrated using an aircraft system model and robotic arm and comparison studies. [ABSTRACT FROM AUTHOR]

Given the difficulty in manually adjusting the position and posture of the pile body during the pile driving process, the improved Denavit-Hartenberg (D-H) parameter method is used to establish the kinematics equation of the mechanical arm, based on the motion characteristics of each mechanism of the mechanical arm of the pile driver, and forward and inverse kinematics analysis is carried out to solve the equation. The mechanical arm of the pile driver is modeled and simulated using the Robotics Toolbox of MATLAB to verify the proposed kinematics model of the mechanical arm of the pile driver. The Monte Carlo method is used to investigate the working space of the mechanical arm of the pile driver, revealing that the arm can extend from the nearest point by 900 mm to the furthest extension of 1800 mm. The actuator's lowest point allows for a descent of 1000 mm and an ascent of up to 1500 mm. A novel multi-strategy grey wolf optimizer (GWO) algorithm is proposed for robotic arm three-dimensional (3D) path planning, successfully outperforming the basic GWO, ant colony algorithm (ACA), genetic algorithm (GA), and artificial fish swarm algorithm (AFSA) in simulation experiments. Comparative results show that the proposed algorithm efficiently searches for optimal paths, avoiding obstacles with shorter lengths. In robotic arm simulations, the multi-strategy GWO reduces path length by 16.575% and running time by 9.452% compared to the basic GWO algorithm. [ABSTRACT FROM AUTHOR]

In the process of industrial production, manual assembly of workpieces exists with low efficiency and high intensity, and some of the assembly process of the human body has a certain degree of danger. At the same time, traditional machine learning algorithms are difficult to adapt to the complexity of the current industrial field environment; the change in the environment will greatly affect the accuracy of the robot's work. Therefore, this paper proposes a method based on the combination of machine vision and the YOLOv5 deep learning model to obtain the disk porous localization information, after coordinate mapping by the ROS communication control robotic arm work, in order to improve the anti-interference ability of the environment and work efficiency but also reduce the danger to the human body. The system utilizes a camera to collect real-time images of targets in complex environments and, then, trains and processes them for recognition such that coordinate localization information can be obtained. This information is converted into coordinates under the robot coordinate system through hand–eye calibration, and the robot is then controlled to complete multi-hole localization and tracking by means of communication between the upper and lower computers. The results show that there is a high accuracy in the training and testing of the target object, and the control accuracy of the robotic arm is also relatively high. The method has strong anti-interference to the complex environment of industry and exhibits a certain feasibility and effectiveness. It lays a foundation for achieving the automated installation of docking disk workpieces in industrial production and also provides a more favorable choice for the production and installation of the process of screw positioning needs. [ABSTRACT FROM AUTHOR]

Since robotic arms operating close to people are becoming increasingly common, there is a need to better understand how they can be made safe when unintended contact occurs, while still providing the required performance. Several actuators and methods for improving robot safety are studied and compared in this paper. A robotic arm moving its end effector horizontally and colliding with a person's head is simulated. The use of a conventional electric actuator (CEA), series elastic actuator (SEA), pneumatic actuator (PA) and hybrid pneumatic electric actuator (HPEA) with model-based controllers are studied. The addition of a compliant covering to the arm and the use of collision detection and reaction strategies are also studied. The simulations include sensor noise and modeling error to improve their realism. A systematic method for tuning the controllers fairly is proposed. The motion control performance and safety of the robot are quantified using root mean square error (RMSE) between the desired and actual joint angle trajectories and maximum impact force (MIF), respectively. The results show that the RMSE values are similar when the CEA, SEA, and HPEA drive the robot's first joint. Regarding safety, using the PA or HPEA with a compliant covering can reduce the MIF below the safety limit established by the International Organization for Standardization (ISO). To satisfy this ISO safety limit with the CEA and SEA, a collision detection and reaction strategy must be used in addition to the compliant covering. The influences of the compliant covering's stiffness and the detection delay are also studied. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0, optimizing the trajectory of intelligent textile robotic arms within cluttered configuration spaces for enhanced operational safety and efficiency has emerged as a pivotal area of research. Traditional path-planning methodologies predominantly employ inverse kinematics. However, the inherent non-uniqueness of these solutions often leads to varied motion patterns in identical settings, potentially leading to convergence issues and hazardous collisions. A further complication arises from an overemphasis on the tool center point, which can cause algorithms to settle into suboptimal solutions. To address these intricacies, our study introduces an innovative path-planning optimization strategy utilizing a model-free, deep reinforcement learning framework guided by inverse kinematics experience. We developed a deep reinforcement learning algorithm for path planning, amalgamating environmental enhancement strategies with multi-information entropy-based geometric optimization. This approach specifically targets the challenges outlined. Extensive experimental analyses affirm the enhanced optimality and robustness of our method in robotic arm path planning, especially when integrated with inverse kinematics, outperforming existing algorithms in terms of safety. This advancement notably elevates the operational efficiency and safety of intelligent textile robotic arms, offering a groundbreaking and pragmatic solution for path planning in real-world intelligent knitting applications. [ABSTRACT FROM AUTHOR]

Modeling errors and external disturbances have significant impacts on the control accuracy of robotic arm trajectory tracking. To address this issue, this paper proposes a novel method, the neural network terminal sliding mode control (ALSSA-RBFTSM), which combines fast nonsingular terminal sliding mode (FNTSM) control, radial basis function (RBF) neural network, and an improved salp swarm algorithm (ALSSA). This method effectively enhances the trajectory tracking accuracy of robotic arms under the influence of uncertain factors. Firstly, the fast nonsingular terminal sliding surface is utilized to enhance the convergence speed of the system and achieve finite-time convergence. Building upon this, a novel multi-power reaching law is proposed to reduce system chattering. Secondly, the RBF neural network is utilized to estimate and compensate for modeling errors and external disturbances. Then, an improved salp swarm algorithm is proposed to optimize the parameters of the controller. Finally, the stability of the control system is demonstrated using the Lyapunov theorem. Simulation and experimental results demonstrate that the proposed ALSSA-RBFTSM algorithm exhibits superior robustness and trajectory tracking performance compared to the global fast terminal sliding mode (GFTSM) algorithm and the RBF neural network fast nonsingular terminal sliding mode (RBF-FNTSM) algorithm. [ABSTRACT FROM AUTHOR]

This paper shows the implementation and performance analysis of motion control algorithms for a 3D Concrete Printing system based on an industrial robotic arm. This work is part of the project of digital fabrication of low-cost housing. Regarding the technological architecture integrated in the present work, the hardware devices used are the EPSON C12 industrial robot arm and the RC700-A controller, and the software tools are Fusion 360 and RC+. To evaluate the point extraction and sequencing algorithms of a 3D structure and the motion control algorithms of the robot arm, three test wall models were designed: a semi-circle with horizontal undulations, a semi-circle with vertical undulations and an orthohedron without undulations. For the performance analysis, 140 trajectory times were extracted for each test model. The extracted values are the trajectory time intervals of the wall model envelope for each layer and of the internal trajectory of the wall model infill. Due to the increasing and decreasing trends of time for specific cases, it was concluded that in the working area of the robot arm, there are parts in which the robot is more efficient and therefore the joints offer less inertia for certain types of movements, for example straight or curved movements and short or long movements. [ABSTRACT FROM AUTHOR]

The main goal of this research is to develop a highly advanced anthropomorphic control system utilizing multiple sensor technologies to achieve precise control of a robotic arm. Combining Kinect and IMU sensors, together with a data glove, we aim to create a multimodal sensor system for capturing rich information of human upper body movements. Specifically, the four angles of upper limb joints are collected using the Kinect sensor and IMU sensor. In order to improve the accuracy and stability of motion tracking, we use the Kalman filter method to fuse the Kinect and IMU data. In addition, we introduce data glove technology to collect the angle information of the wrist and fingers in seven different directions. The integration and fusion of multiple sensors provides us with full control over the robotic arm, giving it flexibility with 11 degrees of freedom. We successfully achieved a variety of anthropomorphic movements, including shoulder flexion, abduction, rotation, elbow flexion, and fine movements of the wrist and fingers. Most importantly, our experimental results demonstrate that the anthropomorphic control system we developed is highly accurate, real-time, and operable. In summary, the contribution of this study lies in the creation of a multimodal sensor system capable of capturing and precisely controlling human upper limb movements, which provides a solid foundation for the future development of anthropomorphic control technologies. This technology has a wide range of application prospects and can be used for rehabilitation in the medical field, robot collaboration in industrial automation, and immersive experience in virtual reality environments. [ABSTRACT FROM AUTHOR]

A personal-computer-based and a Raspberry Pi single-board computer-based virtual force sensor with EtherCAT communication for a six-axis robotic arm are proposed in this paper. Both traditional mathematical modeling and machine learning techniques are used in the establishment of the dynamic model of the robotic arm. Thanks to the high updating rate of EtherCAT, the machine learning-based dynamic model on a personal computer achieved an average correlation coefficient between the estimated torque and the actual torque feedback from the motor driver of about 0.99. The dynamic model created using traditional mathematical modeling and the Raspberry Pi single-board computer demonstrates an approximate correlation coefficient of 0.988 between the estimated torque and the actual torque. The external torque observer is established by calculating the difference between the actual torque and the estimated torque, and the virtual force sensor converts the externally applied torques calculated for each axis to the end effector of the robotic arm. When detecting external forces applied to the end effector, the virtual force sensor demonstrates a correlation coefficient of 0.75 and a Root Mean Square Error of 12.93 N, proving its fundamental competence for force measurement. In this paper, both the external torque observer and the virtual force control are applied to applications related to sensing external forces of the robotic arm. The external torque observer is utilized in the safety collision detection mechanism. Based on experimental results, the system can halt the motion of the robotic arm using the minimum external force that the human body can endure, thereby ensuring the operator's safety. The virtual force control is utilized to implement a position and force hybrid controller. The experimental results demonstrate that, under identical control conditions, the position and force hybrid controller established by the Raspberry Pi single-board computer achieves superior control outcomes in a constant force control scenario with a pressure of 40 N. The average absolute error is 9.62 N, and the root mean square error is 11.16 N when compared to the target pressure. From the analysis of the results, it can be concluded that the Raspberry Pi system implemented in this paper can achieve a higher control command update rate compared to personal computers. As a result, it can provide greater control benefits in position and force hybrid control. [ABSTRACT FROM AUTHOR]

In this article, we propose an effective grasp detection network based on an improved deformable convolution and spatial feature center mechanism (DCSFC-Grasp) to precisely grasp unidentified objects. DCSFC-Grasp includes three key procedures as follows. First, improved deformable convolution is introduced to adaptively adjust receptive fields for multiscale feature information extraction. Then, an efficient spatial feature center (SFC) layer is explored to capture the global remote dependencies through a lightweight multilayer perceptron (MLP) architecture. Furthermore, a learnable feature center (LFC) mechanism is reported to gather local regional features and preserve the local corner region. Finally, a lightweight CARAFE operator is developed to upsample the features. Experimental results show that DCSFC-Grasp achieves a high accuracy (99.3% and 96.1% for the Cornell and Jacquard grasp datasets, respectively) and even outperforms the existing state-of-the-art grasp detection models. The results of real-world experiments on the six-DoF Realman RM65 robotic arm further demonstrate that our DCSFC-Grasp is effective and robust for the grasping of unknown targets. [ABSTRACT FROM AUTHOR]

Many approaches inspired by brain science have been proposed for robotic control, specifically targeting situations where knowledge of the dynamic model is unavailable. This is crucial because dynamic model inaccuracies and variations can occur during the robot's operation. In this paper, inspired by the central nervous system (CNS), we present a CNS-based Biomimetic Motor Control (CBMC) approach consisting of four modules. The first module consists of a cerebellum-like spiking neural network that employs spiking timing-dependent plasticity to learn the dynamics mechanisms and adjust the synapses connecting the spiking neurons. The second module constructed using an artificial neural network, mimicking the regulation ability of the cerebral cortex to the cerebellum in the CNS, learns by reinforcement learning to supervise the cerebellum module with instructive input. The third and last modules are the cerebral sensory module and the spinal cord module, which deal with sensory input and provide modulation to torque commands, respectively. To validate our method, CBMC was applied to the trajectory tracking control of a 7-DoF robotic arm in simulation. Finally, experiments are conducted on the robotic arm using various payloads, and the results of these experiments clearly demonstrate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Stroke patients cannot use their hands as freely as usual. However, recovery after a stroke is a long road for many patients. If artificial intelligence can assist human arm movement, it is believed that the possibility of stroke patients returning to normal hand movement can be significantly increased. In this study, the artificial neuromolecular system (ANM system) developed by our laboratory is used as the core motion control system to learn to control the mechanical arm, produce similar human rehabilitation actions, and assist patients in transiting between different activities. The strength of the ANM system lies in its ability to capture and process spatiotemporal information by exploiting the dynamic information processing inside neurons. Five experiments are conducted in this research: continuous learning, dimensionality reduction, moving problem domains, transfer learning, and fault tolerance. The results show that the ANM system can find out the arm movement trajectory when people perform different rehabilitation actions through the ability of continuous learning and reduce the activation of multiple muscle groups in stroke patients through the learning method of reducing dimensions. Finally, using the ANM system can reduce the learning time and performance required to switch between different actions through transfer learning. [ABSTRACT FROM AUTHOR]

This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images. [ABSTRACT FROM AUTHOR]

More daily interactions are happening in the digital world of the metaverse. Providing individuals with means to perform a handshake during these interactions can enhance the overall user experience. In this paper, we put forward the design and implementation of two right-handed underactuated Digital Twin robotic arms to mediate the physical handshake interaction between two individuals. This allows them to perform a handshake while they are in separate locations. The experimental findings are very promising as our evaluation shows that the participants were highly interested in using our system to shake hands with their loved ones when they are physically separated. With this Digital Twin robotic arms system, we also found a correlation between the handshake characteristics and personality traits of the participants from the handshake data collected during the experiment. [ABSTRACT FROM AUTHOR]

The artificial potential field method is a highly popular obstacle avoidance algorithm which is widely used in the field of industrial robotics due to its high efficiency. However, the traditional artificial potential field method has poor real-time performance, making it less suitable for modern factory work patterns, and it is difficult to handle situations when the robotic arm encounters singular configurations. In this paper, we propose an improved artificial potential field method in joint space, which effectively improves the real-time performance of the algorithm, and still performs well when the robotic arm falls into a singular configuration. This method solves the gradient of the repulsive potential field in advance by defining the shortest distance from each joint of the robotic arm to the obstacle, and only needs to calculate potential field function once per cycle, which significantly reduces the calculation time. In addition, when a robotic arm falls into a local minimum position in potential field, the algorithm adds a virtual obstacle to make it leave the position, while this virtual obstacle does not require additional input information. Experimental results show that the algorithm obtains short movement paths and requires very little computing time in the face of different obstacles. [ABSTRACT FROM AUTHOR]

With increasing demand for efficient archive management, robots have been employed in paper-based archive management for large, unmanned archives. However, the reliability requirements of such systems are high due to their unmanned nature. To address this, this study proposes a paper archive access system with adaptive recognition for handling complex archive box access scenarios. The system comprises a vision component that employs the YOLOV5 algorithm to identify feature regions, sort and filter data, and to estimate the target center position, as well as a servo control component. This study proposes a servo-controlled robotic arm system with adaptive recognition for efficient paper-based archive management in unmanned archives. The vision part of the system employs the YOLOV5 algorithm to identify feature regions and to estimate the target center position, while the servo control part uses closed-loop control to adjust posture. The proposed feature region-based sorting and matching algorithm enhances accuracy and reduces the probability of shaking by 1.27% in restricted viewing scenarios. The system is a reliable and cost-effective solution for paper archive access in complex scenarios, and the integration of the proposed system with a lifting device enables the effective storage and retrieval of archive boxes of varying heights. However, further research is necessary to evaluate its scalability and generalizability. The experimental results demonstrate the effectiveness of the proposed adaptive box access system for unmanned archival storage. The system exhibits a higher storage success rate than existing commercial archival management robotic systems. The integration of the proposed system with a lifting device provides a promising solution for efficient archive management in unmanned archival storage. Future research should focus on evaluating the system's performance and scalability. [ABSTRACT FROM AUTHOR]

In this paper, a kinematic separation calibration method of 6R series manipulator is proposed, and its absolute accuracy is improved by a binocular camera and standard sphere. First, a geometric error mapping model for the robotic arm was established, and the error parameters were divided into position parameters and attitude parameters for calibration purposes. Second, in the process of solving error parameters using numerical algorithms, it is easy to encounter matrix ill-conditioned problems. The spectral correction iteration method is introduced to improve the calculation accuracy. Third, three standard balls are installed at the end of the robotic arm as markers, and the center coordinates are measured using a binocular camera to obtain the actual end pose parameters. To verify the effectiveness of the proposed method, a simulation model verification was designed, and the results showed that the separation calibration method was the best. Finally, the IRB-1200 robot was successfully calibrated using the proposed method; the average robot position and angle error after calibration was significantly decreased. The position accuracy was improved by 66.9%, and the attitude accuracy was improved by 86.2%. [ABSTRACT FROM AUTHOR]

One of the tasks of robotics is to develop a robot's ability to perform specific actions for as long as possible without human assistance. One such step is to open different types of doors. This task is essential for any operation that involves moving a robot from one room to another. This paper proposes a versatile and computationally efficient algorithm for an autonomous mobile robot opening different types of doors, using machine learning methods. The latter include the YOLOv5 object detection model, the RANSAC iterative method for estimating the mathematical model parameters, and the DBSCAN clustering algorithm. Alternative clustering methods are also compared. The proposed algorithm was explored and tested in simulation and on a real robot manufactured by SOMATIC version Dalek. The percentage of successful doors opened out of the total number of attempts was used as an accuracy metric. The proposed algorithm reached an accuracy of 95% in 100 attempts. The result of testing the door-handle detection algorithm on simulated data was an error of 1.98 mm in 10,000 samples. That is, the average distance from the door handle found by the detector to the real one was 1.98 mm. The proposed algorithm has shown high accuracy and the ability to be applied in real time for opening different types of doors. [ABSTRACT FROM AUTHOR]

In mixed-reality (MR) telecollaboration, the local environment is remotely presented to a remote user wearing a virtual reality (VR) head-mounted display (HMD) via a video capture device. However, remote users frequently face challenges in naturally and actively manipulating their viewpoints. In this paper, we propose a telepresence system with viewpoint control, which involves a robotic arm equipped with a stereo camera in the local environment. This system enables remote users to actively and flexibly observe the local environment by moving their heads to manipulate the robotic arm. Additionally, to solve the problem of the limited field of view of the stereo camera and limited movement range of the robotic arm, we propose a 3D reconstruction method combined with a stereo video field-of-view enhancement technique to guide remote users to move within the movement range of the robotic arm and provide them with a larger range of local environment perception. Finally, a mixed-reality telecollaboration prototype was built, and two user studies were conducted to evaluate the overall system. User study A evaluated the interaction efficiency, system usability, workload, copresence, and user satisfaction of our system from the remote user's perspective, and the results showed that our system can effectively improve the interaction efficiency while achieving a better user experience than two traditional view-sharing techniques based on 360 video and based on the local user's first-person view. User study B evaluated our MR telecollaboration system prototype from both the remote-user side and the local-user side as a whole, providing directions and suggestions for the subsequent design and improvement of our mixed-reality telecollaboration system. [ABSTRACT FROM AUTHOR]

The bat algorithm (BA) is a nature inspired algorithm which is mimicking the bio-sensing characteristics of bats, known as echolocation. This paper suggests a Bat-based meta-heuristic for the inverse kinematics problem of a robotic arm. An intrinsically modified BA is proposed to find an inverse kinematics (IK) solution, respecting a minimum variation of the joints' elongation from the initial configuration of the robot manipulator to the proposed new pause position. The proposed method is called IK-BA, it stands for a specific bat algorithm dedicated to robotic-arms' inverse geometric solution, and where the elongation control mechanism is embedded in bat agents update equations. Performances analysis and comparatives to related state of art meta-heuristics solvers showed the effectiveness of the proposed IK bat solver for single point IK planning as well as for geometric path planning, which may have several industrial applications. IK-BA was also applied to a real robotic arm with a spherical wrist as a proof of concept and pertinence of the proposed approach. [ABSTRACT FROM AUTHOR]

For the trajectory optimization of the time–jerk of robotic arms with a chicken swarm optimization algorithm, using five-order B-spline interpolation can ensure smooth and continuous acceleration, but, due to the performance problems of the algorithm, the low solution accuracy and the slow convergence speed, the ideal trajectory curve cannot be obtained. To address these problems, an improved chicken swarm algorithm based on a parallel strategy and dynamic constraints (PDCSO) is proposed, where the rooster update method is employed with a parallel strategy using X-best guidance and a Levy flight step. Dynamic constraints for the rooster are given, followed by the hens, and the optimal rooster position that improved the convergence accuracy while preventing the local optimum was determined. Simulation experiments using 18 classical test functions showed that the PDCSO algorithm outperformed other comparative algorithms in terms of convergence speed, solution accuracy and solution stability. Simulation validation in ADAMS and real machine tests proved that PDCSO can effectively reduce the running time and motion shock for robotic arms and improve the execution efficiency of such arms. [ABSTRACT FROM AUTHOR]

From traditionally handmade items to the ability of people to use machines to process and even to human-robot collaboration, there are many risks. Traditional manual lathes and milling machines, sophisticated robotic arms, and computer numerical control (CNC) operations are quite dangerous. To ensure the safety of workers in automated factories, a novel and efficient warning-range algorithm is proposed to determine whether a person is in the warning range, introducing YOLOv4 tiny-object detection algorithms to improve the accuracy of determining objects. The results are displayed on a stack light and sent through an M-JPEG streaming server so that the detected image can be displayed through the browser. According to the experimental results of this system installed on a robotic arm workstation, it is proved that it can ensure recognition reaches 97%. When a person enters the dangerous range of the working robotic arm, the arm can be stopped within about 50 ms, which will effectively improve the safety of its use. [ABSTRACT FROM AUTHOR]

This paper presents a method on how to compute the position of a robotic arm in the 2D and 3D spaces. This method is slightly different from the well-known methods, such as forward or inverse kinematics. The method presented in this paper is an optical method, which uses two video cameras in stereo vision configuration to locate and compute the next move of a robotic arm in space. The method recognizes the coordinates of the markers placed at the joints of the robotic arm using the two video cameras. The coordinate points of these markers are connected with straight lines. Around certain points, circles are drawn. From the tangent to the circles, a non-Cartesian (orthogonal) coordinate system is drawn, which is enough to compute the target position of the robotic arm. All of these drawings are overlaid on the live video feed. This paper also presents another method for calculating the stereo distance using the triangulation method. An alternative method is also presented when a non-Cartesian (orthogonal) 3D coordinate system is created, which is used to compute the target position of the robotic arm in the 3D space. Because the system is in a loop, it can make micro-adjustments of the robotic arm, in order to be exactly in the desired position. In this way, there is no need to make calibrations for the robotic arm. In an industrial system, there is no need to stop the production line, which can be a really big cost saver. [ABSTRACT FROM AUTHOR]

Recent technological advances enable gripper-equipped robots to perform many tasks traditionally associated with the human hand, allowing the use of grippers in a wide range of applications. Depending on the application, an ideal gripper design should be affordable, energy-efficient, and adaptable to many situations. However, regardless of the number of grippers available on the market, there are still many tasks that are difficult for grippers to perform, which indicates the demand and room for new designs to compete with the human hand. Thus, this paper provides a comprehensive review of robotic arm grippers to identify the benefits and drawbacks of various gripper designs. The research compares gripper designs by considering the actuation mechanism, degrees of freedom, grasping capabilities with multiple objects, and applications, concluding which should be the gripper design with the broader set of capabilities. [ABSTRACT FROM AUTHOR]

Robotic arms are widely used in sectors such as automotive or assembly logistics due to their flexibility and cost. Other manufacturing sectors would like to take advantage of this technology, however, higher accuracy is required for their purposes. This paper integrated a multi-camera system to achieve the requirements for milling and drilling tasks in aeronautic parts. A closed-loop framework allows the position of the robot's end-effector to be corrected with respect to a static reference. This is due to the multi-camera system tracking the position of both elements due to the passive targets on their surface. The challenge is to find an auxiliary system to measure these targets with an uncertainty that allows the desired accuracy to be achieved in high volumes (>3 m3). Firstly, in a reduced scenario, a coordinate measuring machine (CMM), a laser tracker (LT), and portable photogrammetry (PP) have been compared following the guidelines from VDI/VDE 2634-part 1. The conclusions allowed us to jump into an industrial scenario and run a similar test with a higher payload than in the laboratory. The article ends with an application example demonstrating the suitability of the solution. [ABSTRACT FROM AUTHOR]

Modern deep learning systems make it possible to develop increasingly intelligent solutions in various fields of science and technology. The electronics of single board computers facilitate the control of various robotic solutions. At the same time, the implementation of such tasks does not require a large amount of resources. However, deep learning models still require a high level of computing power. Thus, the effective control of an intelligent robot manipulator is possible when a computationally complex deep learning model on GPU graphics devices and a mechanics control unit on a single-board computer work together. In this regard, the study is devoted to the development of a computer vision model for estimation of the coordinates of objects of interest, as well as the subsequent recalculation of coordinates relative to the control of the manipulator to form a control action. In addition, in the simulation environment, a reinforcement learning model was developed to determine the optimal path for picking apples from 2D images. The detection efficiency on the test images was 92%, and in the laboratory it was possible to achieve 100% detection of apples. In addition, an algorithm has been trained that provides adequate guidance to apples located at a distance of 1 m along the Z axis. Thus, the original neural network used to recognize apples was trained using a big image dataset, algorithms for estimating the coordinates of apples were developed and investigated, and the use of reinforcement learning was suggested to optimize the picking policy. [ABSTRACT FROM AUTHOR]

The current development of a robotic arm solution for the manufacturing industry requires performing pick-and-place operations for work pieces varying in size, shape, and color across different stages of manufacturing processes. It aims to reduce or eliminate the human error and human intervention in order to save manpower costs and enhance safety at the workplace. Machine learning has become more and more prominent for object recognition in these pick-and-place applications with the aid of imaging devices and advances in the image processing hardware. One of the key tasks in object recognition is feature extraction and object classification based on convolutional neural network (CNN) models, which are generally computationally intensive. In this paper, an intelligent object detection and picking system based on MobileNet is developed and integrated into an educational six-axis robotic arm, which requires less computation resources. An experimental test is conducted on six-axis robotic arm called Niryo One to train the model and identify three objects with difference shapes and colors. It is shown by the confusion matrix that the MobileNet model achieves an accuracy of 91%, a dramatic improvement compared to 65% of the Niryo One's original sequential model. The statistical study also shows the MobileNet can achieve a higher precision with more clustered spread of accuracy. [ABSTRACT FROM AUTHOR]

Magnetic navigation systems (MNSs) have been developed to use in the diagnosis of gastrointestinal problems. However, most conventional magnetic navigation systems are expensive and have structural problems because of their large weights and volumes. Therefore, this paper proposes C-Mag, a novel compact MNS composed of two electromagnets and a robotic arm. The two electromagnets generate a planar magnetic field, and the robotic arm rotates and translates the electromagnets to manipulate the magnetic capsule in a large 3-dimensional (3-D) space. The C-Mag design considers the payload of the robotic arm and the capacity of the power supply unit. Under these limited conditions, the C-Mag was optimized to generate the maximum magnetic field considering several major factors. Finally, the C-Mag was constructed, and the maximum magnetic field that could be generated in one direction was 18.65 mT in the downward direction. Additionally, the maximum rotating magnetic field was 13.21 mT, which was used to manipulate the capsule. The performance was verified by measuring the generated magnetic field, and it matched well with the simulated result. Additionally, the path-following experiment of the magnetic capsule showed that the proposed C-Mag can effectively manipulate the magnetic capsule in 3-D space using the robotic arm. This study is expected to contribute to the further development of magnetic navigation systems to treat gastrointestinal problems. [ABSTRACT FROM AUTHOR]

Featured Application: The method studied in this paper is applied to the control calculation of manipulator, especially the matrix chain multiplication in the calculation of forward and inverse kinematics solutions of manipulator. Intelligence development has put forward increasing requirements of real-time planning and dynamic feedback in controlling robotic arms. It has become essential in engineering applications to complete the kinematics calculation of complex manipulators in real time. This paper proposes a matrix cascading multiplication equivalent reduced-order parallel computing method for the multiplication of homogeneous matrices in the process of forward and inverse kinematics solutions, which reduces the order of the matrix according to the distribution of zero vectors in the homogeneous matrix calculations. The method removes the unnecessary multiplication in joint homogeneous matrixes containing zero vectors. It obtains the optimal calculation order of the cascade matrix multiplication through dynamic planning searches, improving the efficiency of effective dot product calculation by parallel computation. Calculation processes and specific examples are presented in this paper. Compared with the previous algorithms, the proposed algorithm reduces the calculation cycle by 90%, effectively improving the real-time calculation efficiency in the complex control process. [ABSTRACT FROM AUTHOR]

Grafting is one of the main techniques used in intensive tomato cultivation to address abnormal weather, diseases, insect pests, and continuous cropping problems. In recent years, due to agricultural labor constraints, the industry has needed to make use of more machinery. In this study, a clamping and cutting arrangement device for Solanaceae scion and stock seedlings was developed; it included a UR5 robotic arm to realize the automatic clamping and cutting of multiple seedlings, to facilitate the mechanized grafting of seedlings. The clamping success rate of eggplant and tomato seedlings using this device was 99%. When working with four seedlings in a single operation, the oblique-cutting success rates for eggplant and tomato seedlings were 97.76% and 94.55%, respectively. Moreover, 404 eggplant seedlings and 384 tomato seedlings could be worked with in 1 h. After the splicing and grafting process had been completed, the survival rate of the grafted seedlings was 85%. The simple device structure could be used by the industry to assist operators in clipping and cutting seedlings, reduce the complexity of grafting operations, shorten training times, address the problem of labor constraints in Taiwan's agricultural industry, and improve product quality and efficiency. [ABSTRACT FROM AUTHOR]

The game of Jenga is a benchmark used for developing innovative manipulation solutions for complex tasks. Indeed, it encourages the study of novel robotics methods to successfully extract blocks from a tower. A Jenga game involves many traits of complex industrial and surgical manipulation tasks, requiring a multi-step strategy, the combination of visual and tactile data, and the highly precise motion of a robotic arm to perform a single block extraction. In this work, we propose a novel, cost-effective architecture for playing Jenga with e.Do, a 6DOF anthropomorphic manipulator manufactured by Comau, a standard depth camera, and an inexpensive monodirectional force sensor. Our solution focuses on a visual-based control strategy to accurately align the end-effector with the desired block, enabling block extraction by pushing. To this aim, we trained an instance segmentation deep learning model on a synthetic custom dataset to segment each piece of the Jenga tower, allowing for visual tracking of the desired block's pose during the motion of the manipulator. We integrated the visual-based strategy with a 1D force sensor to detect whether the block could be safely removed by identifying a force threshold value. Our experimentation shows that our low-cost solution allows e.DO to precisely reach removable blocks and perform up to 14 consecutive extractions in a row. [ABSTRACT FROM AUTHOR]

This paper presents a vibration control method for the vibration suppression of a robotic arm. To understand the causes of the vibration in the six-axis manipulator and then eliminate the vibration through an effective control method, the vibration control structure can be divided into two parts. First, variational mode decomposition (VMD) and the Hilbert–Huang transform (HHT) algorithm are integrated to analyze the vibration signal and extract the vibration characteristics. Second, a smooth trajectory planning method is used to eliminate the residual vibration. Then, a feedback controller and iterative learning controller are used for controlling the vibration to stabilize the system while compensating for the torque of the elastic-joint model and repetitive errors caused by the vibration through the repeated operation of the arm. In addition, to improve the iterative learning control (ILC) performance, the integration of VMD and the HHT is used to distinguish useless information. The ILC is able to learn more quickly and compensate for the error caused by the vibration. The proposed VMD-HHT iterative learning control for the elastic joint (VH-ILC-EJ) method, along with a laboratory-developed six-axis degree-of-freedom (6-DOF) robotic arm, has been numerically and experimentally validated for vibration suppression. [ABSTRACT FROM AUTHOR]

Intelligent robotics has drawn a great deal of attention due to its high precision, stability, and reliability, which are the basic key factors for industrial automation. This paper proposes an iterative learning control (ILC) technique with predefined-time convergence as a solution to an applied engineering problem, namely, that local time cannot be preset when a second-order nonlinear system undertakes control of the accurate tracking of local time under any initial iterative value. A time-varying sliding surface with an initial value of zero was designed, and it was theoretically proven that the trajectory tracking error in the sliding surface could converge to zero within a predefined time. The iterative control problem of trajectory tracking was thus changed to an iterative control problem of time-varying sliding-mode surface tracing with a starting value of zero. A PD-type closed-loop ILC with a time-varying sliding mode surface was designed such that the trajectory tracking error converged and stabilized on the sliding mode surface after a finite number of learning iterations. The control goal for the system's output was the ability to track the desired trajectory accurately within a predefined time interval, and it was achieved by combining this with the predefined time convergence characteristics of the time-varying sliding mode surface. Numerical simulation of trajectory tracking control of a repetitive motion manipulator was used to verify the effectiveness of the proposed controller and its robustness in the face of external disturbances. [ABSTRACT FROM AUTHOR]

The development of specialised equipment for three-dimensional printing of cement mortar requires the deployment of advanced design methods. The accuracy of printing robotic arms is influenced by the change in the position of the end effector, which is influenced by the stiffness of the arm, or deformation of parts of the arm and yielding in the place of rotation axes. Determining the actual change in the end effector position is often a difficult challenge. In this paper, we analysed the displacement on a large robotic arm by the non-contact optical photogrammetry method. We applied this method to a specialised 2.8 m long robotic arm SCARA with an added rotational axis. We compared the results from photogrammetry with the results from measurements with a mechanical deflection meter, and with the predicted displacement values from the FEM simulation. The results from both measurement methods showed maximum deviations of hundredths of a mm. The findings of the analysis thus indicate that photogrammetry meets the strict requirements for displacement measurement on a robotic arm for the 3D printing of cement mortar. A significant advantage of the method is the possibility of measuring almost all attainable arm positions and achieving results in hundreds of places. [ABSTRACT FROM AUTHOR]

Space is the driving force of the world's sustainable development, and ensuring the sustainability of human activity in space is also necessary. Robotic arm active debris capture removal (RA-ADCR) is a noteworthy technology for containing the dramatic increase in space debris and maintaining orbital safety. This review divides the RA-ADCR technology progress history into three periods and presents the status of related research. Two major development trends are summarized and subdivided through the analysis and collation of research achievements over the past three years. Taking the treatment of parameter uncertainties as the entry point, researchers would like to improve the discrimination accuracy and scope to reduce uncertainties. On the other hand, researchers accept such uncertainties and would like to offset and avoid the impact of uncertainties by extending the error margins. Subsequently, the challenges of RA-ADCR are analyzed in line with the task execution flow, which mainly focuses on the conflict between on-satellite computing power and the performance of task execution. In addition, feasible solutions for the current phase are discussed. Finally, future outlooks are evaluated and discussed. [ABSTRACT FROM AUTHOR]

The Tactile Internet enables physical touch to be transmitted over the Internet. In the context of electronic medicine, an authenticated key agreement for the Tactile Internet allows surgeons to perform operations via robotic systems and receive tactile feedback from remote patients. The fifth generation of networks has completely changed the network space and has increased the efficiency of the Tactile Internet with its ultra-low latency, high data rates, and reliable connectivity. However, inappropriate and insecure authentication key agreements for the Tactile Internet may cause misjudgment and improper operation by medical staff, endangering the life of patients. In 2021, Kamil et al. developed a novel and lightweight authenticated key agreement scheme that is suitable for remote surgery applications in the Tactile Internet environment. However, their scheme directly encrypts communication messages with constant secret keys and directly stores secret keys in the verifier table, making the scheme vulnerable to possible attacks. Therefore, in this investigation, we discuss the limitations of the scheme proposed by Kamil scheme and present an enhanced scheme. The enhanced scheme is developed using a one-time key to protect communication messages, whereas the verifier table is protected with a secret gateway key to mitigate the mentioned limitations. The enhanced scheme is proven secure against possible attacks, providing more security functionalities than similar schemes and retaining a lightweight computational cost. [ABSTRACT FROM AUTHOR]

Wire rope manufacturing is an old industry that maintains its place in the market due to the need for products with specific characteristics in different sectors. The necessity for modernization and performance improvement in this industry, where there is still a high amount of labor dedicated to internal logistics operations, led to the development of a new technology method, to overcome uncertainties related to human behaviour and fatigue. The removal of successive yarn coils from a twisting and winding machine, as well as cutting the yarn and connecting the other end to the shaft in order to proceed with the process, constitutes the main problem. As such, a mobile automatic system was created for this process, due to its automation potential, with a project considering the design of a 3D model. This novel robotic manipulator increased the useful production time and decreased the winding coil removal cycle time, resulting in a more competitive, fully automated product with the same quality. This system has led to better productivity and reliability of the manufacturing process, eliminating manual labor and its cost, as in previously developed works in other industries. [ABSTRACT FROM AUTHOR]

This document presents an assisted operation system of a robotic arm for positioning near an explosive device selected by the user through the visualization of the cameras on the screen. Two non-converging cameras mounted on the robotic arm in camera-in-hand configuration provide the three-dimensional (3D) coordinates of the object being tracked, using a 3D reconstruction technique with the help of the continuously adaptive mean shift (CAMSHIFT) algorithm for object tracking and feature matching. The inverse kinematics of the robot is implemented to locate the end effector close to the explosive so that the operator can perform the operation of grabbing the grenade more easily. Inverse kinematics is implemented in its geometric form, thus reducing the computational load. Tests conducted with various explosive devices verified the effectiveness of the system in locating the robotic arm in the desired position. [ABSTRACT FROM AUTHOR]

Typhoons in summer and cold snaps during winter in Taiwan often cause huge aquaculture losses. Simultaneously, the lack of human resources is a problem. Therefore, we used wireless transmission technology with various sensors to transmit the temperature, pH value, dissolved oxygen, water level, and life expectancy of the sensor in the fish farm to the server. The integrated data are transmitted to mobile devices through the Internet of Things, enabling administrators to monitor the water quality in a fish farm through mobile devices. Because the current pH sensors cannot be submerged in the liquid for a long time for measurements, human resources and time are required to take the instrument to each fish farm for testing at a fixed time. Therefore, a robotic arm was developed to complete automatic measurement and maintenance actions. We designed this arm with a programmable logic controller, a single chip combined with a wireless transmission module, and an embedded system. This system is divided into control, measurement, server, and mobility. The intelligent measurement equipment designed in this study can work 24 h per day, which effectively reduces the losses caused by personnel, material resources, and data errors. [ABSTRACT FROM AUTHOR]