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Since titanium alloy parts produced by the additive manufacturing (AM) method often have rough and complex curved surfaces, surface polishing is relatively difficult and time-consuming. Therefore, this study integrates a robotic arm into the electrochemical mechanical polishing (ECMP) process of AM titanium alloys to improve its surface quality and corrosion resistance, and to meet the needs of automated polishing. The parameters and process for ECMP with a multi-axis robotic arm were determined, resulting in an automated ECMP system that could effectively and rapidly reduce the roughness of a curved AM surface. The performance of system was tested. With a novel bias grinding brush head and jet electrolyte flow, the surface roughness of the AM titanium can be greatly improved from Ra 16 µm to Ra 0.227 µm. Moreover, microstructural observation revealed that a dense passivation film approximately 10 nm in thickness was formed on the surface after polishing, improving corrosion resistance of the material. Electrochemical impedance spectroscopy investigation also revealed that the absolute impedance (|Z|f=0.01 Hz) of the material was promoted from 9788 Ω*cm2 to 27292 Ω*cm2 after ECMP. [ABSTRACT FROM AUTHOR]

Robotic grasping techniques for regular targets with known shapes are now well established. However, unknown shaped objects have complex features such as texture, shape, and appearance, which leads to inaccurate recognition and localization of shaped objects during grasp detection. To improve the generalization ability of the grasping detection network for unfamiliar shaped objects, we propose a lightweight shaped object grasping detection network (LSOGD) based on feature fusion, which solves the problem that the network repeatedly extracts features from images and ultimately improves the accuracy of model detection by combining different features. The effectiveness of LSOGD is confirmed by performance evaluation on the Cornell dataset and Jacquard dataset, where the detection accuracy reaches 97.9% and 96.7% for unknown objects, respectively. In addition, due to the small proportion of shaped objects in the current publicly available dataset, we added a portion of industrial-shaped pieces based on the selection of some shaped objects in the Cornell grasping dataset to build a shaped object dataset named X-Cornell on which the accuracy of our proposed model for grasping and detecting the unknown shaped objects is 94.6%. Finally, an actual robot grasping experiment was conducted using a Realsense d435i camera and a Kinova robotic arm, and the success rate of grasping shaped objects was 94%. [ABSTRACT FROM AUTHOR]

The robotic arm is a complicated system with multiple inputs and outputs, strong coupling, containing uncertainties and nonlinearities. This study proposes a new practical robust control method based on the dynamics model and tracking error, including a model- and error-based proportional-differential feedback term and an error-based robust term. Specifically, the dynamics of the system are modeled using the Lagrangian method. Uncertainties are presumed to be time-varying but limited. Based on the Lyapunov method, the proposed controller has theoretically demonstrated the controlled system with uniform boundedness (UB) and uniform ultimate boundedness (UUB). Furthermore, the radius of the ultimately bounded hypersphere is arbitrarily small based on selecting appropriate design parameters. Based on the two-degree-of-freedom (2-DOF) planar robotic arm experimental platform, the self-developed rapid controller prototype CSPACE-RT is intended to eliminate tedious programming or debugging, significantly simplifying the experimental process. Finally, numerical simulation and experiment results verified the excellent control performance of the suggested controller. [ABSTRACT FROM AUTHOR]

Robotic spine surgery, utilizing 3D imaging and robotic arms, has been shown to improve the accuracy of pedicle screw placement compared to conventional methods, although its superiority remains under debate. There are few studies evaluating the accuracy of 3D navigated versus robotic-guided screw placement across lumbar levels, addressing anatomical challenges to refine surgical strategies and patient safety. This study aims to investigate the pedicle screw placement accuracy between 3D navigation and robotic arm-guided systems across distinct lumbar levels. A retrospective review of a prospectively collected registry. Patients undergoing fusion surgery with pedicle screw placement in the prone position, using either via 3D image navigation only or robotic arm guidance. Radiographical screw accuracy was assessed by the postoperative computed tomography (CT) according to the Gertzbein-Robbins classification, particularly focused on accuracy at different lumbar levels. Accuracy of screw placement in the 3D navigation (Nav group) and robotic arm guidance (Robo group) was compared using Chi-squared test/Fisher's exact test with effect size measured by Cramer's V, both overall and at each specific lumbosacral spinal level. A total of 321 patients were included (Nav, 157; Robo, 189) and evaluated 1210 screws (Nav, 651; Robo 559). The Robo group demonstrated significantly higher overall accuracy (98.6 vs 93.9%; p<.001, V=0.25). This difference of no breach screw rate was signified the most at the L3 level (No breach screw: Robo 91.3 vs 57.8%, p<.001, V=0.35) followed by L4 (89.6 vs 64.7%, p<.001, V=0.28), and L5 (92.0 vs 74.5%, p<.001, V=0.22). However, screw accuracy at S1 was not significant between the groups (81.1 vs 72.0%, V=0.10). This study highlights the enhanced accuracy of robotic arm-guided systems compared to 3D navigation for pedicle screw placement in lumbar fusion surgeries, especially at the L3, L4, and L5 levels. However, at the S1 level, both systems exhibit similar effectiveness, underscoring the importance of understanding each system's specific advantages for optimization of surgical complications. [ABSTRACT FROM AUTHOR]

The increasing space debris population in critical orbits due to spacecraft failure dictates the need for action. On-Orbit Servicing (OOS) has been proposed as a method for mitigating this trend by repairing existing space assets. The development of large servicing spacecraft has been given significant attention. In this paper, we are approaching the problem with a new paradigm for a servicing spacecraft by proposing a CubeSat class servicer equipped with a one-degree-of-freedom (DoF) robotic arm. Considering the OOS context, we choose the Orbital Replacement Unit (ORU) installation on a target spacecraft as a challenging benchmark task for the proposed servicer configuration. To carry out this task, we formulate and compare performance of four controllers to achieve coordinated control of both the CubeSat and the robotic arm for the installation task. We start with a basic PD controller and progress to the more advanced impedance controller, Model Predictive Control (MPC) and Model Predictive Impedance Control (MPIC) designs. The controllers are evaluated and compared across several relevant metrics for the ORU installation and their merits for practical implementation are discussed. • Comparative analysis of model-based controllers against traditional control methods. • Monte Carlo simulations used to test robustness of the controllers. • Novel, mechanically simple CubeSat servicer for simulated ORU installation. • MPC, PD controllers show similar success rates, MPC requires higher control efforts. [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]

Background: In modern Hybrid ORs, the synergies of navigation and robotics are assumed to contribute to the optimisation of the treatment in trauma, orthopaedic and spine surgery. Despite promising evidence in the area of navigation and robotics, previous publications have not definitively proven the potential benefits. Therefore, the aim of this retrospective study was to evaluate the potential benefit and clinical outcome of patients treated in a fully equipped 3D-Navigation Hybrid OR. Methods: Prospective data was collected (March 2022- March 2024) after implementation of a fully equipped 3D-Navigation Hybrid OR ("Robotic Suite") in the authors level 1 trauma centre. The OR includes a navigation unit, a cone beam CT (CBCT), a robotic arm and mixed reality glasses. Surgeries with different indications of the spine, the pelvis (pelvic ring and acetabulum) and the extremities were performed. Spinal and non-spinal screws were inserted. The collected data was analysed retrospectively. Pedicle screw accuracy was graded according to the Gertzbein and Robbins (GR) classification. Results: A total of n = 210 patients (118 m:92f) were treated in our 3D-Navigation Hybrid OR, with 1171 screws inserted. Among these patients, 23 patients (11.0%) arrived at the hospital via the trauma room with an average Injury Severity Score (ISS) of 25.7. There were 1035 (88.4%) spinal screws inserted at an accuracy rate of 98.7% (CI95%: 98.1-99.4%; 911 GR-A & 111 GR-B screws). The number of non-spinal screws were 136 (11.6%) with an accuracy rate of 99.3% (CI95%: 97.8-100.0%; 135 correctly placed screws). This resulted in an overall accuracy rate of 98.8% (CI95%: 98.2-99.4%). The robotic arm was used in 152 cases (72.4%), minimally invasive surgery (MIS) was performed in 139 cases (66.2%) and wound infection occurred in 4 cases (1,9%). Overall, no revisions were needed. Conclusion: By extending the scope of application, this study showed that interventions in a fully equipped 3D-Navigation Hybrid OR can be successfully performed not only on the spine, but also on the pelvis and extremities. In trauma, orthopaedics and spinal surgery, navigation and robotics can be used to perform operations with a high degree of precision, increased safety, reduced radiation exposure for the OR-team and a very low complication rate. [ABSTRACT FROM AUTHOR]

AbstractThis study investigates the integration of modal analysis and topology optimization in the design of a robotic arm to enhance both its reliability and efficiency. The primary objectives are to reduce the weight and minimize the vibration of the robotic arm. Initially, the kinematics and dynamics of the robotic arm were examined to identify the joint experiencing the highest torque. Finite element analyses (FEA) were then conducted on this critical joint to assess its vibration characteristics and redesign the joint for improved performance through topology optimization. Comparative analysis of the initial and optimized designs has highlighted significant improvements in weight reduction and vibration control. The selected robot arm component was manufactured using fused deposition modeling (FDM). Experimental modal analysis validated the theoretical predictions, demonstrating the effectiveness of the optimized design. The selected component of the robotic arm was redesigned using three different topology geometries and two different unit cell sizes for each, resulting in a maximum weight reduction of 29.37%. Stresses were reduced by 41% under critical operating conditions, which contributed significantly to the system’s reliability. The improvements in efficiency were measured through reductions in weight and vibration, demonstrating the enhanced dynamic performance of the robotic arm. The optimized design was validated through experimental modal analysis, confirming the effectiveness of the redesign. This study underscores the synergy of modal analysis and topology optimization in advancing robotic arm technology, providing a comprehensive approach to design optimization for enhanced reliability. [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]

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]

The study targets uncertain coupling faults in robotic arm actuators and proposes a new fault-tolerant visual servo control strategy. Specifically, it considers both multiplicative and additive actuator faults within the dynamic of the robotic arm, treating the coupling faults and time-varying disturbances as an aggregate of concentrated uncertainties. A radial basis function neural network-based state observer is introduced to online approximate these concentrated uncertainties, which include fault information, eliminating the need for prior knowledge of faults. Furthermore, a fault-tolerant controller based on a non-singular fast terminal sliding mode is proposed, which separately decouples the nominal quantities and concentrated uncertainties and develops individual adaptive control laws for each. This effectively reduces the detrimental impact of coupled faults and disturbances on the system's performance, facilitating image feature trajectory tracking control with minimal jitter, high precision, and strong transient response capabilities. The stability of the state observer and the fault-tolerant controller has been substantiated through Lyapunov's theory. Lastly, numerical simulations validate the efficacy and robustness of the proposed fault-tolerant visual servo control approach. [ABSTRACT FROM AUTHOR]

Robotic arms have become increasingly popular and widely used in various industrial applications. However, conventional control methods are not capable of adequately controlling a robotic arm in tasks that require contact with a surface. To address this issue, this study proposes a Cartesian impedance control method to control a 3-DOF robotic arm in real-time during contact with a surface. The proposed controller consists of two control loops: an inner loop and an outer loop. The inner loop utilizes a motion control method in the joint space, with the parameters of the controller being calculated through system identification. The outer loop implements Cartesian impedance control in the Cartesian space using a mass-spring-damper model. The coefficients of the Cartesian impedance control were determined based on the over-damped response with real-time applications. By selecting the inner loop in the joint space and the outer loop in the Cartesian space, the control of the robotic arm is guaranteed. The proposed method was tested in real-time, and its performance was compared with the PID with gravity compensation control in the Cartesian space. The results indicated that the proposed method was able to successfully follow reference trajectories and reduce the contact force. [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]

Abstract Background In modern Hybrid ORs, the synergies of navigation and robotics are assumed to contribute to the optimisation of the treatment in trauma, orthopaedic and spine surgery. Despite promising evidence in the area of navigation and robotics, previous publications have not definitively proven the potential benefits. Therefore, the aim of this retrospective study was to evaluate the potential benefit and clinical outcome of patients treated in a fully equipped 3D-Navigation Hybrid OR. Methods Prospective data was collected (March 2022- March 2024) after implementation of a fully equipped 3D-Navigation Hybrid OR (“Robotic Suite”) in the authors level 1 trauma centre. The OR includes a navigation unit, a cone beam CT (CBCT), a robotic arm and mixed reality glasses. Surgeries with different indications of the spine, the pelvis (pelvic ring and acetabulum) and the extremities were performed. Spinal and non-spinal screws were inserted. The collected data was analysed retrospectively. Pedicle screw accuracy was graded according to the Gertzbein and Robbins (GR) classification. Results A total of n = 210 patients (118 m:92f) were treated in our 3D-Navigation Hybrid OR, with 1171 screws inserted. Among these patients, 23 patients (11.0%) arrived at the hospital via the trauma room with an average Injury Severity Score (ISS) of 25.7. There were 1035 (88.4%) spinal screws inserted at an accuracy rate of 98.7% (CI95%: 98.1-99.4%; 911 GR-A & 111 GR-B screws). The number of non-spinal screws were 136 (11.6%) with an accuracy rate of 99.3% (CI95%: 97.8-100.0%; 135 correctly placed screws). This resulted in an overall accuracy rate of 98.8% (CI95%: 98.2-99.4%). The robotic arm was used in 152 cases (72.4%), minimally invasive surgery (MIS) was performed in 139 cases (66.2%) and wound infection occurred in 4 cases (1,9%). Overall, no revisions were needed. Conclusion By extending the scope of application, this study showed that interventions in a fully equipped 3D-Navigation Hybrid OR can be successfully performed not only on the spine, but also on the pelvis and extremities. In trauma, orthopaedics and spinal surgery, navigation and robotics can be used to perform operations with a high degree of precision, increased safety, reduced radiation exposure for the OR-team and a very low complication rate.

The paper proposes a command filtering adaptive output feedback control scheme with preset performance for a Robotic Arm Model (RAM) designed specifically for steel structures. Initially, a neural network observer is employed to approximate the nonlinear functions within the model and to estimate the unmeasured states of the system. Subsequently, within the backstepping framework, the integration of preset performance theory and command filtering technology addresses the differential complexity challenges commonly encountered in traditional backstepping methods. This approach ensures rapid convergence of the system’s tracking error within predetermined boundaries. The efficacy of this strategy is demonstrated through simulation instances.

Aims: Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods: This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results: We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion: This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680.

Abstract Background In the last decade, notable progress in mechatronics paved the way for a new generation of arm prostheses, expanding motor capabilities thanks to their multiple active joints. Yet, the design of control schemes for these advanced devices still poses a challenge, especially with the limited availability of command signals for higher levels of arm impairment. When addressing this challenge, current commercial devices lack versatility and customizing options to be employed as test-beds for developing novel control schemes. As a consequence, researchers resort to using lab-specific experimental apparatuses on which to deploy their innovations, such as virtual reality setups or mock prosthetic devices worn by unimpaired participants. Methods To meet this need for a test-bed, we developed the Smart Arm platform, a human-like, multi-articulated robotic arm that can be worn as a trans-humeral arm prosthesis. The design process followed three principles: provide a reprogrammable embedded system allowing in-depth customization of control schemes, favor easy-to-buy parts rather than custom-made components, and guarantee compatibility with industrial standards in prosthetics. Results The Smart ArM platform includes motorized elbow and wrist joints while being compatible with commercial prosthetic hands. Its software and electronic architecture can be easily adapted to build devices with a wide variety of sensors and actuators. This platform was employed in several experiments studying arm prosthesis control and sensory feedback. We also report our participation in Cybathlon, where our pilot with forearm agenesia successfully drives the Smart Arm prosthesis to perform activities of daily living requiring both strength and dexterity. Conclusion These application scenarios illustrate the versatility and adaptability of the proposed platform, for research purposes as well as outside the lab. The Smart Arm platform offers a test-bed for experimenting with prosthetic control laws and command signals, suitable for running tests in lifelike settings where impaired participants wear it as a prosthetic device. In this way, we aim at bridging a critical gap in the field of upper limb prosthetics: the need for realistic, ecological test conditions to assess the actual benefit of a technological innovation for the end-users.

Addressing the issues of slow speed and poor performance in multi-object grasping detection in unstructured environments, a method of performing object detection before grasping detection is pro- posed. In object detection, to accelerate the network's running speed, this paper improved the YOLOv5 network by employing depth wise separable convolutions and coordinate attention mechanisms. For the grasping task.a single-stage grasping pose detection algorithm was designed. Firstly, considering the in- terference present in unstructured environments, RGB-D images were selected as the input data for the grasping network, and GG-CNN was chosen as the backbone network. Secondly, to enhance the feature extraction capabilities of the grasping network, the parallel use of different size convolutional kernels in the Inception-ResNet module was utilized to broaden the network's receptive field. Additionally, the inte gration of a parameter-free three-dimensional attention mechanism enabled the network to focus more on grasping information features and suppress background noise. Finally, a grasping quality evaluation was employed to refine the grasping boxes, and the grasping box with the highest confidence score was out- put. The experimental results indicate that the improved object detection network has a parameter count of 2 776 708 and achieves 102 frames per second (FPS). On the public Cornell dataset, the improved grasping detection network achieves an accuracy of 96. 57% with a FPS of 54, 17. The combination of the two improved networks can be deployed on robotic arms and effectively accomplish grasping tasks in multi-object scenarios, making them suitable for practical industrial applications. [ABSTRACT FROM AUTHOR]

3D printing offers a cost‐effective solution for rapidly prototyping and customizing composite products. The integration of multi‐axis robotic systems with the printing process significantly enhances motion control, design flexibility, and manufacturing scalability. In this study, a robot‐assisted manufacturing platform and the associated digital workflow for the 3D printing of UV‐curable continuous fiber‐reinforced polymer composites (CFRPCs) is introduced. Specifically, a transferable protocol is established for robotic 3D printing of CFRPCs, which involves coordinate calculation, trajectory generation, and validation checks. This protocol enables the printing of composite samples or large‐scale structures on both planar substrates and curved 3D substrates. Additionally, composite printing on substrates with unknown profiles using laser‐based 3D scanning is demonstrated. Overall, the developed printing method and workflow are applicable to a broader range of feedstock materials and robotic manipulators, which makes this study a valuable resource for future developments in 3D‐printed CFRPCs. [ABSTRACT FROM AUTHOR]

The autonomous rock collector using robotic arm for exploration of unknown territories (ARCAxUT) is introduced as an innovative solution for the efficient retrieval of rock samples in unexplored space regions. Traditional, human-reliant methods are costly and hazardous, prompting the development of ARCAxUT. Equipped with a smart robotic arm, an RGB-D camera, and NUC computer, the system autonomously detects and estimates the mass of various rock samples. Validated in simulated and real-world environments, the algorithm ensures precise gripper control, achieving an impressive 95.4% accuracy in rock size estimation. This breakthrough offers transformative capabilities for space missions, revolutionizing celestial body sample collection and advancing broader societal implications in space exploration technologies. [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 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]

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. [ABSTRACT FROM AUTHOR]

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system's design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17. [ABSTRACT FROM AUTHOR]

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This paper proposes a method for quick recognition of jigsaw puzzles using a small number of samples, solely relying on image processing and template matching techniques. In the preliminary stage, this paper proposes a segmentation approach to divide the jigsaw puzzle into individual pieces with partial features, obtaining template data for each puzzle piece. Subsequently, scattered puzzle pieces are captured using a camera, and their contours are extracted to acquire puzzle image data. The puzzle images are then matched with the template data, employing polar coordinate transformation during the matching process to reduce computational time. The matching process also identifies the puzzle piece's number and orientation. Finally, the matching results are integrated with a robotic arm to complete the task of puzzle assembly. This paper not only outlines the generation of puzzle image data and template data through image processing but also elaborates on the determination of corresponding numbers and orientations during image matching. The feasibility of this method is validated through different types of puzzles, and its applicability to industrial parts is also examined for practicality. [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]

The robotic arm is one of the vital components of robot assembly. The purpose of the robotic arm is to transmit power and conduct the desired motion, i.e., translation or rotation. Robotic limbs are designed and constructed to execute certain tasks with a high degree of speed, accuracy, and efficiency. This research focuses on to enhancing the strength-to-weight ratio of robotic arm using certain techniques of additive manufacturing, i.e., topology optimization and lattice structure. Employing the finite element analysis, the impact of weight reduction optimization on structural parameters such as stress and deformation in the current design is assessed using ANSYS R18.1 for FE analysis and Creo parametric 7.0 design software for computer-aided design modeling. Observations reveal that the 0.5 and .4 scale lattice structure designs have deformation of 0.01453mm and 0.01453mm respectively though the generic design has 0.01043mm deformation. Notably, the 0.5 scale lattice of the robotic arm exhibits a 31.08% higher equivalent stress than the generic design with 29.3%. reduction in mass of the robotic arm. These findings highlight the efficacy of lattice structures for optimizing the robotic arm's performance, contributing to advancements in power-efficient robot assembly processes. [ABSTRACT FROM AUTHOR]

A multi-modal deep neural network grasping detection network was proposed to address the issue of low accuracy in robot grasping detection tasks for unknown objects. Firstly, residual modules were introduced in both RGB and depth channels to further enhance the feature extraction capability of the network. Secondly, a multimodal feature fusion module was introduced for feature fusion. Finally, the best grasping detection result was obtained by fusing features through fully connected layer regression. The experimental results demonstrate that the algorithm proposed achieves a precision rate of 95. 7% for grasping and 94. 6% for object segmentation on the Cornell dataset. In addition, it has been demonstrated through ablation experiments that introducing residual modules can improve the performance of network crawling detection. [ABSTRACT FROM AUTHOR]

This paper proposed a particle swarm algorithm based on pyramid layer topology to optimize energy consumption trajectories of industrial robots. The algorithm introduced a pyramid layer topology to sort and stratify the particles thus improving the competitive strategy of the algorithm and increasing the population diversity. The algorithm introduced a new cooperation strategy to update the speed and position of the particles. It introduced a victory percentage to adaptively adjust the weight coefficients of the particle swarm algorithm and improved the search efficiency of the particles. In order to verify the effectiveness of the algorithm, this paper tested it on the set of test functions and compared it with other eight variants of particle swarm algorithms. The results show that the performance of the proposed algorithm has significant advantages. Finally, it applied the algorithm to industrial robot trajectory planning. Simulation experiments show that the algorithm can effectively solve the optimal trajectory of the robot's energy consumption, and the energy consumption of the robot is significantly reduced, which meets the kinematics and dynamics constraints of industrial robots. [ABSTRACT FROM AUTHOR]

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]

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]

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]

The recent surge in biosignal-based control signifies a profound paradigm shift in biomedical engineering. This innovative approach has injected new life into control theory, ushering in advancements in human-body interaction and control. Surface Electromyography (sEMG) emerges as a pivotal biosignal, attracting considerable attention for its wide-ranging applications across medicine, science, and engineering, particularly in the domain of functional rehabilitation. This study delves into the use of sEMG signals for controlling a robotic arm, with the overarching aim of improving the quality of life for people with disabilities in Vietnam. Raw sEMG signals are acquired via appropriate sensors and subjected to a robust processing methodology involving analog-to-digital conversion, band-pass and low-pass filtering, and envelope detection. To demonstrate the efficacy of the processed sEMG signals, this study introduces a robotic arm model capable of mimicking intricate human finger movements. Employing a fuzzy logic control strategy, the robotic arm demonstrates successful operation in experimental trials, characterized by swift response times, thereby positioning it as a valuable assistive device for people with disabilities. This investigation not only validates the feasibility of sEMG-based control for robotic arms, but also underscores its potential to significantly improve the lives of individuals with disabilities, a demographic that represents a substantial portion (approximately 8%) of the Vietnamese population. [ABSTRACT FROM AUTHOR]

Background: Robotic systems for total knee arthroplasty (TKA) may utilize computed tomography three-dimensional modeling and intraoperative ligamentous balancing data to assist surgeons with implant size and position. This study evaluated the effect of such robotic systems on implant selection. Methods: We reviewed 645 TKAs performed with a single prosthetic design at 2 academic medical centers between 2016 and 2022. A robotic system was utilized in 304 TKAs, 341 were conventionally instrumented. Implant sizing was compared between cohorts. Multivariate analyses assessed for confounding and effect modification on the basis of demographics. Results: The 2 cohorts exhibited no significant differences in age (P = .33), weight (P = .29), or race (P = .24). The robotic-arm cohort had fewer women (58.9% vs 66.7% P = .04) and was taller on average (66.3 in vs 65.0 in P < .001). Mean polyethylene liner thickness was larger in the manual cohort (10.3 robotic and 10.6 manual; P < .00). On multivariate analysis, robotic-arm TKAs had larger tibial components (P < .001) and smaller femoral components (P = .017). Conclusions: Robotic-arm assisted TKA with computed tomography–based three-dimensional planning was associated with a larger mean tibial component size and a smaller mean femoral component size when compared to conventionally instrumented TKAs. Observed differences likely reflect differences in the data informing implant size selection; effects on clinical outcomes warrant further study.