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Recently, continuous- and discrete-time models of a zeroing neural network (ZNN) have been developed to provide online solutions for the time-dependent linear equation (TDLE) with boundary constraint. This paper presents a novel approach to address the bound-constrained TDLE (BCTDLE) problem by proposing a new discrete-time ZNN (DTZNN) model. The proposed DTZNN model is designed using the Taylor difference formula to discretize the previous continuous-time ZNNN (CTZNN) model. Theoretical analysis indicates the computational property of the proposed DTZNN model, and numerical results further demonstrate its validity. The applicability of the proposed DTZNN model is finally confirmed via its application to the motion planning of a PUMA560 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]

Many challenges and obstacles prevail in the agricultural fields, such as weed removal, horticulture, and spraying herbicides and fertilizers. Individual robots for all these tasks cost a lot, which ordinary farmers cannot afford. The delta arm has to perform versatile functions like spraying and weed removal with great accuracy and reduced environmental impact. The primary objective of this research is to design a delta arm that can move in all directions efficiently. The driving and driven arm in the delta arm design should be correlated, which helps with the proper positioning of the end effector carrier. Similarly, the moving platform and the fixed platform are designed in a circular form. These measurements give a proper design and make the robotic arm work properly as it can move in all three degrees of freedom in XYZ directions. The NVIDIA jetson orin nano operates as the controlling unit of the delta arm. The study proved that the delta arm had achieved an accuracy rate of 98% at a speed of 107.5 mm/s, making it well-suited for agricultural field operations. In addition, the delta arm exhibits the capability to handle an end effector weighing up to 300 g. Therefore, introducing the delta arm agribot in the row-based agricultural field can make a revolution in the upcoming years as it can perform versatile tasks at a high speed. [ABSTRACT FROM AUTHOR]

Introduction: The use of a robotic system for the placement of pedicle screws in spine surgeries is well documented in the literature. However, there is only a single report in the United States describing the use of a robotic system to place two screws in osseous fixation pathways (OFPs) commonly used in the treatment of pelvic and acetabular fractures in a simulated bone model. The purpose of this study was to demonstrate the use of a robotic system to place screws in multiple, clinically relevant OFPs in a cadaveric model and to quantitatively measure accuracy of screw placement relative to the preoperative plan. Methods: A single cadaveric specimen was obtained for the purpose of this study. All surrounding soft tissues were left intact. Screws were placed in OFPs, namely iliosacral (IS), trans-sacral (TS), Lateral Compression-II (LC-II), antegrade anterior column (AC) and antegrade posterior column (PC) of the right hemipelvis using standard, fluoroscopically assisted percutaneous or mini-open technique. Following the placement of screws into the right hemipelvis using standard techniques, screws were planned and placed in the same OFPs of the contralateral hemipelvis using the commercially available ExcelsiusGPS® robotic system (Globus Medical Inc., Audubon, PA). After robotic-assisted screw placement, a post-procedure CT scan was obtained to evaluate actual screw placement against the pre-procedure plan. A custom-made image analysis program was devised to measure screw tip/tail offset and angular offset on axial and sagittal planes. Results: For different OFPs, the mean tip offset, tail offset and angular offsets were 1.6 ± 0.9 mm (Range 0.0–3.6 mm), 1.4 ± 0.4 mm (Range 0.3–2.5 mm) and 1.1 ± 0.4° (Range 0.5–2.1), respectively. Conclusion: In this feasibility study, surgeons were able to place screws into the clinically relevant fracture pathways of the pelvis using ExcelsiusGPS® for robotic-assisted surgery. The measured accuracy was encouraging; however, further investigation is needed to demonstrate that robotic-assisted surgery can be used to successfully place the screws in the bony corridors of the pelvis to treat traumatic pelvic injuries. [ABSTRACT FROM AUTHOR]

Copyright of Fly Ash Comprehensive Utilization is the property of Hebei Fly Ash Comprehensive Utilization Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Xinyang Normal University Natural Science Edition is the property of Journal of Xinyang Normal University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract With the development and the application popularization of artificial intelligence robot technology and 5G technology, a robotic arm is designed and developed for rinsing porcelain bushing in high voltage substation in this paper. Firstly, the components and implementation of robotic arm are presented, subsequently, a circular cleaning structure with a 120-degree split is proposed to rinse the porcelain bushing. Secondly, a two-stage simple and effective method to realize automatic orientation is proposed utilizing photoelectric switches. Moreover, a prototype of robotic arm with control system is developed based on the regime switching function, and the result of edge computing is transmitted by 5G technology. Finally, feasibility and effectiveness of the robotic arm are verified in the Nanjing power grid. The case study manifests that the robotic arm developed by the proposed method in the paper can achieve efficient rinsing and all the corresponding information can be transmitted preciously. The proposed method lays a foundation for wide application of cleaning robot in high voltage substation.

For traditional machine learning algorithms, visual recognition algorithms have low recognition accuracy and slow running time. This research studies the scene of the robot doing housework in the family scene, and uses the RGB image information as input to complete the grasping pose estimation of the target object. Based on the object detection model YOLOv5s, the network architecture is built by combining data enhancement and transfer learning with its advantages of lightweight and fast speed. After building a family scene data set to enhance the data of a small number of training samples, the model is trained on the target data set using transfer learning, and the parameters are fine-tuned at the same time. The positioning information of the target object is transformed into the grasping pose of the robotic arm through coordinate transformation, and the robotic arm is controlled to finally complete the grasping task with a fixed grasping posture. Finally, the effectiveness of the algorithm is verified by building an experimental platform and manipulating the UR5 robotic arm to carry out actual grasping experiments. The proposed method based on target detection is fast, has high real-time performance, and has a false/missed recognition rate of less than 2%. The application in the robotic arm can efficiently complete the task.

Robotic manipulators are typically engineered with high-power systems to handle payloads and counteract gravity, owing to their robust and weighty structures. However, there is now a growing need for eco-friendly and safe human-robot collaboration solutions that require power-efficient innovations. Currently, gravity compensation techniques employ various counterbalancing mechanisms that are specific and challenging to expand for systems with higher degrees of freedom (DOF). In response, this research introduces a novel gravity compensator system capable of effectively counterbalancing manipulators with multiple degrees of freedom (DOF) using only one spring. The proposed system uses parallelogram mechanisms, cam follower systems, and a square-root mechanism to solve potential equations mechanically. The proposed system can generalize the design of a gravity compensator for a multi-DOF manipulator using only one linear spring. This paper outlines the design and development of prototypes, focusing on one- and three-degree-of-freedom manipulators, which serve as experimental testbeds. The experiment shows that our gravity compensator system significantly reduces the required torques, power, and overall energy consumption of the robotic system. This innovative approach addresses existing challenges and paves the way for sustainable and power-efficient robotic manipulator design.

As the key equipment of data acquisition in oil and gas exploration, the node can be replaced by a mechanical arm because of its long-term continuous work and high efficiency, but the structure rigidity is weak and the motion ability is poor, so the structure optimization and lightweight design are carried out to improve the structure rigidity and motion ability. Based on the screw theory, the forward kinematics equation of the manipulator with node placement is established, the global static stiffness index and the global velocity index considering the direction of the task are proposed, and the mathematical model of the size optimization of the node-placing manipulator is established. Based on the multi-objective particle swarm algorithm and the variable density method, the size optimization and lightweight design of the node-distributed manipulator arm are carried out. The results show that the overall static stiffness index and the overall velocity performance in the mission direction are improved by 19.8% and 8.8% respectively, and the lightweight design of the upper arm reduces the mass by 14.7%, which is beneficial to improve the response speed of the movement ability. The research results are of great significance for improving the accuracy and efficiency of oil and gas exploration.

This paper proposes a fixed-time fuzzy adaptive output feedback control scheme for the robotic arm model (RAM) of steel structures. Firstly, the process of transforming RAM into a nonlinear system is elaborated. Secondly, a fuzzy observer is designed to approximate the nonlinear function and estimate the observed state of the system. Subsequently, a fixed-time adaptive controller is constructed, ensuring the system’s stability within a fixed time, with the convergence time unaffected by the initial state. Finally, the effectiveness of the strategy is verified through simulation examples.

The main challenges in the field of fault diagnosis of robot arms lie in the difficulties of acquiring fault data and ensuring model applicability. For a fault robot arm, the trained models typically only perform well on test data and cannot be effectively applied in practical scenarios. As a result, the time cost is very much to construct adequate fault datasets. The paper proposes a dynamic simulation method for obtaining fault data to address these issues. The motion feature of the arm with joint faults is replicated by the simulation software, thereby obtaining vibration signals in the fault mode as samples. Additionally, under the main framework of Deep Learning (DL) with an end-to-end feature extraction capability, a Stacked Continuous Wavelet Transform (SCWT) method is designed to visualize timing signals graphically based on the traditional wavelet transform. Furthermore, to enhance traditional DL performance, a dual-channel architecture for data fusion within DL is designed to enrich the feature space and improve fault-distinguishing ability. Lastly, a Domain Discriminator $G_{d}$ is designed to identify the upper bounds for differences between spatial distributions of simulated and actual fault data. By the domain discriminator, the feature distribution of target and source data is aligned, facilitating the transfer application of the simulation-trained diagnostic model on the actual fault. The proposed method is tested and evaluated using a self-constructed experimental data set. The results substantiate its effectiveness and superiority.

The state-of-the-art deep reinforcement learning (DRL) methods, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), among others, demonstrate significant capability in solving the optimal static state feedback control (SSFC) problem. This problem can be modeled as a fully observed Markov decision process (MDP). However, the optimal static output feedback control (SOFC) problem with measurement noise is a typical partially observable MDP (POMDP), which is difficult to solve, especially for the continuous state-action-observation space with high dimensions. This paper proposes a two-stage framework to address this challenge. In the laboratory stage, both the states and the noisy outputs are observable; the SOFC policy is converted to a constrained stochastic SSFC policy, of which the probability density function is generally not analytical. To this end, a density estimation based SAC algorithm is proposed to explore the optimal SOFC policy by learning the optimal constrained stochastic SSFC. Consequently, in the real-world stage, only the noisy outputs and the learned SOFC policy are required to solve the optimal SOFC problem. Numerical simulations and the corresponding experiments with robotic arms are provided to illustrate the effectiveness of our method. The code is available at . [ABSTRACT FROM AUTHOR]

The robotic manipulators are highly complex coupling dynamic systems, which require a mathematical model for planning and controlling the robotic motions. It is imperative to calculate the kinematic parameters such as rotational matrix, joint angles, angular velocity, and angular acceleration, which determines the control performance of the models. For this purpose, a multiple-sensor-based Mathematical approach that utilizes inertial measurement unit (IMU) and triple-axis accelerometer is presented in this paper. A combination of one IMU and three triple-axis accelerometers is affixed to each of the two rigid bodies for real-time determination of parameters and the robotic arm orientation. Additionally, the model incorporates an Extended Kalman filter (EKF) fusion technique to combine data from various sensors, mitigate measurement noise, and adapt in real-time to changing environments. To implement this approach, a MATLAB code is developed to read, preprocess sensors data, and simulation of the proposed model. All the results are presented graphically and indicate that the motion parameters and pose measurements are calculated accurately and effectively. [ABSTRACT FROM AUTHOR]

The robotic arm has the characteristics of multi-degree-of-freedom motion and can perform complex tasks, making it the first choice to replace manual operations in space environment. However, traditional robotic arms still face many challenges in achieving both lightweight and high precision. To overcome this, we present an integrated stator-rotor piezoelectric actuator that integrates structural and functional design, enabling substantial weight reduction and high motion accuracy. The proposed design meets the requirements of the aerospace field, making it an ideal replacement for manual operations in space. The mechanism is composed of two sets of orthogonally linked rotary joints with driven arm joints enabling rotation in two vertical directions. To reduce the impact of clamping on vibration characteristics, the vibration modes and structural parameters are optimized through simulation. The size of the fabricated prototype is 160 × 60 × 60 mm, and its weight is only 24 g. The experimental results show that the maximum motion speed of the mechanism is 620 deg/s and the stalling torque is 20 mN m at 300 Vpp. The minimum resolution can reach 25 μrad in pulse mode, and a startup and shutdown response time of 45 ms and 31 ms at 200 Vpp, respectively. These characteristics verify the correctness of the design and show that the actuator has tremendous application potential. • The actuator has the characteristics of lightweight and high precision. • Using modal optimization to reduce the impact of clamping. • The rotary joint has the characteristics of structural and functional integration. • The proposed actuator has application potential in the field of precision drive. [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]

Propellers comprise fan-shaped structures with spiral blades. The real-time measurement of cutting and grinding forces during the milling and grinding of propellers is difficult to achieve. The spindle motor current is a common parameter used to measure cutting force. However, data related to this parameter are not accessible during the use of DC motors for cutting processes and can be obtained only from servo motor drive systems. Given these considerations, in this study, we adopted LabVIEW software to develop a program for use in combination with an inexpensive sensor to achieve the simultaneous measurement of the cutting force and motor current. The program was verified to accurately depict the relationship between the cutting force and the motor current of reconfigurable precision five-axis machine tools, rendering it a viable alternative to measurement schemes that rely on expensive force sensors. Subsequently, the developed program was further tested by measuring changes in spindle motor current while cutting three blades to produce a 3D-printed polylactic acid propeller. [ABSTRACT FROM AUTHOR]

Robotic arms are widely used in the automation industry to package and deliver classified objects. When the products are small objects with very similar shapes, such as screwdriver bits with slightly different threads, pointed tips, and thicknesses, object selection and assembly often lead to misjudgment. We have developed a practical robotic arm control system based on vision detection techniques for screwdriver bits' placement. In addition to effectiveness, easy deployment and high flexibility in the field are also taken into account. The vision-based system consists of four processing stages in the following order: world coordinate conversion from image pixel coordinates, object detection, edge detection, and object orientation. In the first stage, a manual two-point marking method is proposed to easily configure the coordinate conversion for robot operating system (ROS)-based manipulators. For the following stages, we focus on the fine integration of state-of-the-art methods for the technical feasibility of the screwdriver bit placement. Such integration includes the selection between object detection methods and the data flow control among system stages. The experimental results show that (1) in detecting screwdriver bits, You Only Look Once (YOLO) v4 outperforms YOLOv7 and Single Shot MultiBox Detector at an accuracy rate of 99.51%; (2) in the edge detection, the object detection output can better illustrate the object contour than a whole image, achieving a mean absolute error of 0.86% in estimating the object angle; and (3) a successful real-time replacement rate of 96% is achieved for 12 screwdriver bits randomly scattered on a conveyor belt. [ABSTRACT FROM AUTHOR]

The positioning accuracy of medical bone drilling has a great impact on the success rate of orthopedic surgery. Due to the need for high accuracy of medical bone drilling, with use of any 6-DoF-type robotic arm to assist in drilling, a novel six-degree-of-freedom compensation positioning algorithm of robotic arm-assisted medical bone drilling is proposed, in which only position but also orientation errors of robotic end are considered, increasing positioning capability of drilling instrument end effector with position and orientation correction for medical drilling motion accurate controlling. For the different position and orientation characteristics of the robot arm controlling and measurement point/zone, position and orientation transformation from measurement to controlling point/zone is established through position and angle calibration experiments, realizing position and orientation error measured by optical tracking system from measurement point/zone to controlling point/zone. Based on 6-DoF parameter and principle of robotic arm, a positioning compensation model based on the 6-DoF position and orientation errors of robotic arm end is proposed, and iteration cycle is used for higher accuracy, realizing double correction of position and orientation of robotic arm end. The accuracy verification experiments measured by the optical tracking system shows that the average position error before and after the 6-DoF compensation positioning algorithm reduces from 1.42 mm (before compensation with DH-based inverse kinematic model) to 0.20 mm (after compensation model) and the average angle error from 0.470° to 0.046°. A bovine spine drilling experiment is carried out based on the 6-DoF compensation algorithm, and the average position error of the hole in the specific direction measured by the contour projector is 0.221 mm, achieving high positioning accuracy of bone drilling, demonstrating reliability and practical application value of medical bone drilling with the compensation of positioning algorithm. [ABSTRACT FROM AUTHOR]

For the problem that the RRT (rapidly-exploring random tree) path planning algorithm generates a huge number of nodes when planning the obstacle avoidance path of robotic arm in high-dimensional space, resulting in a large burden of algorithm operation, poor obstacle avoidance performance, and easy to fall into local extremes, an improved RRT algorithm combining A* judgment function is proposed. The sampling method of RRT is changed to generate a sequence of multiple randomly sampled points each time, and the improved A* judgment function is used for sorting. Distance judgment is performed on each generated node to prevent it from falling into local search. Finally, a repetitive greedy strategy is used to remove redundant nodes, and cubic B-spline is used to make path smooth. The performance of the algorithm is analyzed in 2D and 3D maps and robot arm simulations and prototype experiments. The improved RRT algorithm can effectively reduce the number of nodes for the robotic arm to reach the target poses, alleviate the local extremes, and avoid obstacles to reach the target poses quickly and stably, which proves the effectiveness and superiority of the improved RRT 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]

In order to reduce the weight of the metal robot, the big arm of the 6-DOF robot is taken as the research object, and combined the forming process of the three-dimensional braided carbon fiber composite material, a lightweight design method of the manipulator based on the CFRP/QT (carbon fiber reinforced polymer/castiron QT 500-7) hybrid structure is proposed to design the structure of the manipulator. Through the static analysis of the hybrid structure manipulator by the finite element analysis method, the final scheme of the hybrid structure of the mechanical arm is obtained under the requirements of strength and stiffness. At the same time, the dynamic simulation results show that the robot equipped with a 24% weight reduction hybrid structure manipulator has good motion performance, and the driving torque of the hybrid structure manipulator robot at the J1 joint after weight reduction is smaller. [ABSTRACT FROM AUTHOR]

Robotic arms have been widely used in industrial fields. However, researchers have seldom considered the factors affecting the actual factory environment. For example, when objects are conveyed in a factory, conveyor belts are often used to dynamically plan the overall production line. In addition, each object requires multiple checkpoints for repeated audits and inspections to ensure its quality. In this study, a vision-based robotic arm system equipped with multiple functionalities was developed. The development process consisted of three steps: detecting multiple dynamic objects, determining the size of each object, and identifying object defects. In the first step, You Only Look Once was used to detect multiple dynamic objects on a conveyor belt in real time. In the second step, the original image of the object was converted into a grayscale image, and the edge contour of the object was drawn using a Canny edge detection algorithm. Objects in the image were then rotated for vertical and horizontal projections, and then an artificial neural network (ANN) was used to calculate the size of each object. In the third step, a convolutional fuzzy neural network (CFNN) was used to identify object defects. This network was divided into an input layer, a convolution pooling layer, a feature fusion layer, a fuzzy layer, a regular layer, and a defuzzification layer. According to the experimental results, the standard error of the mean between the object size obtained by the ANN and the actual size was 0.009. In addition, the accuracy, recall, precision, and F1-score obtained by the CFNN in object defect detection were 0.9580, 0.9535, 0.9535, and 0.9535, respectively. Compared with other deep neural network models, such as AlexNet and LeNet, the proposed CFNN has fewer parameters and higher performance. [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]

Copyright of Journal of Polytechnic is the property of Journal of Polytechnic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Fish-exporting countries must meet international standards and customer expectations to avoid trade disruption and economic repercussions. Ensuring the quality of fish products is therefore not just a matter of reputation but also of economic stability. Bangladesh, a major fish exporter, maintaining the quality of exported fish products is crucial. A single faulty product can have serious consequences, potentially causing harm. For this reason, we develop a deep learning-based approach capable of detecting and dividing rotten fish. We have used the Mask R–CNN method for our model. A device captures images of fish eyes and sends them to a computer system. The condition of a fish, whether fresh or rotten, can be determined by its eyes. A collection of 5000 image datasets is developed in this research work. The input images start matching the dataset through the Mask R–CNN and give us the result. Based on the result, if the fish is found fresh, it will proceed through the conveyor system; however, if it is identified as rotten, a robotic arm will separate it. To test its efficiency and reliability, we have tested it with 5000 images. And the test satisfied us with the results of 96.5% accuracy. With the assistance of our model, fish-exporting nations can efficiently distinguish between fresh and rotten fish, enhancing their ability to export more significant quantities of high-quality fish.

Waste segregation is an essential aspect of a smoothly functioning waste management system. Usually, various recyclable waste types are disposed of together at the source, and this brings in the necessity to segregate them into their categories. Dry waste needs to be separated into its own categories to ensure that the proper procedures are implemented to treat and process it, which leads to an overall increased recycling rate and reduced landfill impact. Paper, plastics, metals, and glass are just a few examples of the many dry waste materials that can be recycled or recovered to create new goods or energy. Over the past years, much research has been conducted to devise effective and productive ways to achieve proper segregation for the waste that is being produced at an ever-increasing rate. This article introduces a multi-class garbage segregation system employing the YOLOv5 object detection model. Our final prototype demonstrates the capability of classifying dry waste categories and segregating them into their respective bins using a 3D-printed robotic arm. Within our controlled test environment, the system correctly segregated waste classes, mainly paper, plastic, metal, and glass, eight out of 10 times successfully. By integrating the principles of artificial intelligence and robotics, our approach simplifies and optimizes the traditional waste segregation process.

Thin-walled structures, including the hollow tubes and curvature shells, are integral to a number of engineering applications. Manufacturing those structures with the recently emerged additive manufacturing scheme, such as the screw-extrusion additive manufacturing (SEAM), can greatly improve the manufacturing efficiency and reduce the overall manufacturing cost. Yet, the support structure is often required to print such structures, adding undesired weight to the otherwise light-weight structures. In this work, the SEAM is integrated with a 6-axis industrial robot to enable the dynamic reorientation of the nozzle, which stays perpendicular to the deposition surface. As a result, the geometrically-complex hollow tubes and arc-like thin shells can be directly printed without any support structure. In addition, by selectively designate the infill directions and starting points, a light-weight honeycomb-infilled curved shell can be directly printed without any additional processing. Mechanical tests have also shown the conformally-printed curved parts exhibit higher strength than the conventionally-printed counterparts. The result from this work can hopefully incentive new insights in developing novel strategies for 3D-printing functional thin-walled structures.

Aims: To perform an incremental cost-utility analysis and assess the impact of differential costs and case volume on the cost-effectiveness of robotic arm-assisted unicompartmental knee arthroplasty (rUKA) compared to manual (mUKA). Methods: This was a five-year follow-up study of patients who were randomized to rUKA (n = 64) or mUKA (n = 65). Patients completed the EuroQol five-dimension questionnaire (EQ-5D) preoperatively, and at three months and one, two, and five years postoperatively, which was used to calculate quality-adjusted life years (QALYs) gained. Costs for the primary and additional surgery and healthcare costs were calculated. Results: rUKA was associated with a relative 0.012 QALY gain at five years, which was associated with an incremental cost per QALY of £13,078 for a unit undertaking 400 cases per year. A cost per QALY of less than £20,000 was achieved when ≥ 300 cases were performed per year. However, on removal of the cost for a revision for presumed infection (mUKA group, n = 1) the cost per QALY was greater than £38,000, which was in part due to the increased intraoperative consumable costs associated with rUKA (£626 per patient). When the absolute cost difference (operative and revision costs) was less than £240, a cost per QALY of less than £20,000 was achieved. On removing the cost of the revision for infection, rUKA was cost-neutral when more than 900 cases per year were undertaken and when the consumable costs were zero. Conclusion: rUKA was a cost-effective intervention with an incremental cost per QALY of £13,078 at five years, however when removing the revision for presumed infection, which was arguably a random event, this was no longer the case. The absolute cost difference had to be less than £240 to be cost-effective, which could be achieved by reducing the perioperative costs of rUKA or if there were increased revision costs associated with mUKA with longer follow-up. Cite this article: Bone Jt Open 2023;4(11):889–899.

Aims: In-hospital length of stay (LOS) and discharge dispositions following arthroplasty could act as surrogate measures for improvement in patient pathways, and have major cost saving implications for healthcare providers. With the ever-growing adoption of robotic technology in arthroplasty, it is imperative to evaluate its impact on LOS. The objectives of this study were to compare LOS and discharge dispositions following robotic arm-assisted total knee arthroplasty (RO TKA) and unicompartmental arthroplasty (RO UKA) versus conventional technique (CO TKA and UKA). Methods: This large-scale, single-institution study included patients of any age undergoing primary TKA (n = 1,375) or UKA (n = 337) for any cause between May 2019 and January 2023. Data extracted included patient demographics, LOS, need for post anaesthesia care unit (PACU) admission, anaesthesia type, readmission within 30 days, and discharge dispositions. Univariate and multivariate logistic regression models were also employed to identify factors and patient characteristics related to delayed discharge. Results: The median LOS in the RO TKA group was 76 hours (interquartile range (IQR) 54 to 104) versus 82.5 (IQR 58 to 127) in the CO TKA group (p < 0.001) and 54 hours (IQR 34 to 77) in the RO UKA versus 58 (IQR 35 to 81) in the CO UKA (p = 0.031). Discharge dispositions were comparable between the two groups. A higher percentage of patients undergoing CO TKA required PACU admission (8% vs 5.2%; p = 0.040). Conclusion: Our study showed that robotic arm assistance was associated with a shorter LOS in patients undergoing primary UKA and TKA, and no difference in the discharge destinations. Our results suggest that robotic arm assistance could be advantageous in partly addressing the upsurge of knee arthroplasty procedures and the concomitant healthcare burden; however, this needs to be corroborated by long-term cost-effectiveness analyses and data from randomized controlled studies. Cite this article: Bone Jt Open 2023;4(10):791–800.

Electromyography (EMG) signals hold critical significance in biomedical research, capturing muscle electrical activity during rest and contraction in the upper limb. Their versatility in applications, particularly in human-assisting robotic tools, drives ongoing exploration and research. This paper presents an original study focusing on leveraging machine learning techniques to classify EMG datasets and efficiently control a robotic arm based on predicted gestures. Data acquisition involves strategically placing an EMG muscle sensor on the forearm to ensure precise measurement of signals associated with hand gestures and movements. Diverse classifiers including random forest, support vector machine (SVM), K-nearest neighbors (KNN), Gaussian naïve Bayes, gated recurrent unit (GRU), long short-term memory (LSTM), artificial neural network (ANN), recurrent neural network (RNN), convolutional neural network (CNN), and vision transformer (ViT) are employed. Performance results are meticulously analyzed and presented in tabular format, showcasing the ViT classifier as the most successful, achieving an impressive 97.7% accuracy in robotic arm control. Notably, ANN, RNN, and CNN also exhibit high accuracy exceeding 90%. Furthermore, this work is comprehensively compared with existing literature, laying the groundwork for future advancements in human-robot interaction and cutting-edge assistive technologies that markedly enhance the quality of life for individuals with motor impairments or disabilities. The findings carry significant implications for designing and implementing intuitive, responsive robotic systems based on EMG signals. [ABSTRACT FROM AUTHOR]

This paper presents a fractional order system modeling of a robotic arm and the development of a Fractional Order PID (FOPID) controller applied to the system. The controller technique originated from non-integer calculus, which improves the robotic arm's overall stability and positioning. The robotic arm system is modeled using the non-integer order technique in order to improve system accuracy. Thus, a non-integer order Proportional Integral Derivative (PID) control method is implemented to stabilize the plant positioning. Using MATLAB/Simulink the FOPID controller simulations were confirmed and compared to the Integer Order PID (IOPID) controller for tracking the robotic arm positioning. Simulation outcomes imply that the proposed non-integer controller increases the system stability and position with/without external disturbances being present in the environment. [ABSTRACT FROM AUTHOR]

To reduce the complexity of monitoring and management of robots in service, a six-axis robot control system based on digital twin is proposed. Based on 3D printing technology, a six-axis robot is developed. At the same time, the kinematics of the robot is analyzed, and its kinematics model is built using the D-H rule. The forward and reverse kinematics of the robot are solved. Through the two-way data interaction between the model layer and the entity layer, the simulation operation of the robot and the twin synchronous operation of the virtual real robot are realized. Based on the real-time data drive, the key parameters of the robot are monitored, and the health parameter table of the current, voltage, joint vibration, and other parameters of the robot system is established. Based on the idea of comparison of the same kind, the abnormal state detection of the robot is realized through quantitative analysis. Finally, the feasibility of the proposed system is verified by experiments. [ABSTRACT FROM AUTHOR]

Digital fabrication technologies are revealing new ways of dealing with design processes. Robotic fabrication technologies are generally dismissed at the undergraduate level, especially in first-year design education. This is due to the common belief that novice design students have insufficient skills for designing with a robotic arm. This paper presents an experiment that took place in a first-year basic design studio at a faculty of architecture. The studio investigates the utilization of a robotic arm as a design and production tool. Students without robot programming and operation skills were encouraged to transfer a priori skills of hand tools and techniques learned in successive assignments to utilize the robotic arm. This experiment revealed the educational potential strategies for the integration of robot technology in first-year design studios. [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]