Search Results (2,281)

In this paper, the development and performance evaluation of a 4WD robot system designed to follow near-distance moving objects using a 2D LiDAR sensor are presented. The study incorporates identifier (ID) classification and a distance-based dynamic angle of perception model to enhance the tracking capabilities of the 2D LiDAR sensor. A particle filter algorithm was utilized to verify the accuracy of object tracking. Furthermore, a proportional–derivative (PD) controller was designed and implemented to ensure the stability of the robot during operation. The experimental results demonstrate the potential applicability of these approaches in various industrial applications. Full article

Manipulator systems are increasingly deployed across various industries to perform complex, repetitive, and hazardous tasks, necessitating high-precision control for optimal performance. However, the design of effective control algorithms is challenged by nonlinearities, uncertain dynamics, disturbances, and varying real-world conditions. To address these issues, this paper proposes an advanced orbit-tracking control approach for manipulators, leveraging advancements in Time-Delay Estimation (TDE) and Fixed-Time Sliding Mode Control techniques. The TDE approximates the robot’s unknown dynamics and uncertainties, while a novel nonsingular fast terminal sliding mode (NFTSM) surface and novel fixed-time reaching control law (FTRCL) are introduced to ensure faster convergence within a fixed time and improved accuracy without a singularity issue. Additionally, an innovative auxiliary system is designed to address input saturation effects, ensuring that system states converge to zero within a fixed time even when saturation occurs. The Lyapunov-based theory is employed to prove the fixed-time convergence of the overall system. The effectiveness of the proposed controller is validated through simulations on a 3-DOF SAMSUNG FARA AT2 robot manipulator. Comparative analyses against NTSMC, NFTSMC, and GNTSMC methods demonstrate superior performance, characterized by faster convergence, reduced chattering, higher tracking accuracy, and a model-free design. These results underscore the potential of the proposed control strategy to significantly enhance the robustness, precision, and applicability of robotic systems in industrial environments. Full article

In the domain of modern manufacturing digitalization, artificial intelligence tools are increasingly employed for condition monitoring and technical diagnostics. However, the majority of existing methodologies primarily concentrate on the technical diagnosis of rotating machines, with a noticeable lack of research addressing these issues in sequential machines. In this paper, we deal with the selection of suitable vibration signal characteristics for the detection of an industrial robot’s release from its base during a handling operation. Statistical methods, including one-way ANOVA and t-tests, were used to identify the most significant features, which allowed us to isolate vibration metrics with significant predictive potential. These selected features were then used as inputs to various machine learning models to evaluate the hypothesis that these parameters can reliably indicate fastening releasing events. The results show that the optimized parameters significantly improve the detection accuracy, thus providing a reliable basis for future applications in predictive maintenance and monitoring. The findings represent an advance in robotic condition monitoring, providing a structured approach to feature selection that improves the reliability of disconnection detection in automated systems with potential applicability in various industrial environments. Full article

Robotic construction using three-dimensional (3D) concrete printing (3DCP) offers significant potential to transform Kuwait’s construction industry, particularly in reducing waste. This study explores the feasibility of integrating 3DCP into Kuwait’s construction waste management practices by examining the perspectives of key stakeholders. Through a mixed method approach of a comprehensive literature review, a survey of 87 industry professionals, and 33 in-depth interviews with representatives from the Public Authority for Housing Welfare (PAHW), Municipality, private sector, and the general public, the study identifies both the benefits and challenges of 3DCP adoption. The findings highlight key advantages of 3DCP, including increased construction efficiency, cost savings, enhanced design flexibility, and reduced material waste. However, several barriers, such as regulatory limitations, technical challenges in adapting 3DCP to local project scales, and cultural resistance, must be addressed. Results also indicate varying levels of stakeholder familiarity with 3DCP and existing waste management practices, underscoring the need for awareness and educational initiatives. This study makes two significant contributions: first, by providing a detailed analysis of the technical and regulatory challenges specific to Kuwait’s construction sector, and second, by offering a strategic roadmap for 3DCP integration, including regulatory reform, research into sustainable materials, and cross-sector collaboration. These recommendations aim to enhance waste management practices by promoting more sustainable and efficient construction methods by achieving SDGs 9, 11, 12, and 13. The study concludes that government support and policy development will be essential in driving the adoption of 3DCP and achieving long-term environmental benefits in Kuwait’s construction industry. Full article

The demand for flexible automation in manufacturing has increased, incorporating vision-guided systems for object grasping. However, a key challenge is in-hand error, where discrepancies between the actual and estimated positions of an object in the robot’s gripper impact not only the grasp but also subsequent assembly stages. Corrective strategies used to compensate for misalignment can increase cycle times or rely on pre-labeled datasets, offline training, and validation processes, delaying deployment and limiting adaptability in dynamic industrial environments. Our main contribution is an online self-supervised learning method that automates data collection, training, and evaluation in real time, eliminating the need for offline processes. Building on this, our system collects real-time data during each assembly cycle, using corrective strategies to adjust the data and autonomously labeling them via a self-supervised approach. It then builds and evaluates multiple regression models through an auto machine learning implementation. The system selects the best-performing model to correct the misalignment and dynamically chooses between corrective strategies and the learned model, optimizing the cycle times and improving the performance during the cycle, without halting the production process. Our experiments show a significant reduction in the cycle time while maintaining the performance. Full article

Industrial robots are widely used in welding operations because of their high production efficiency. The structure of the robot and the complex stress conditions during welding operations lead to the vibration of the end of robot, which leads to welding defects. However, current vibration suppression techniques for welding robots usually only consider the robotic performance while overlooking their impact on the welding metal forming process. Therefore, based on the influence of robot vibration on welding pool stability during the welding process, a new welding robot vibration suppression method is proposed in this paper, along with the establishment of a welding pool stability assessment model. The proposed vibration suppression algorithm is based on the optimization of the welding trajectory. To enhance the performance of the method, the Particle Swarm Optimization (PSO) algorithm is applied to optimize the joint angular velocity and angular acceleration. Finally, robot welding experiments are designed and conducted. By comparing vibration measurement data and welding quality before and after the vibration suppression, the effectiveness and stability of the proposed method are validated. Full article

This study aims to provide a set of experimentally determined forces needed for gripping operations related to a robotically manipulated microliter manual pipette. The experiments are conducted within the scope of automated sample processing for polymerase chain reaction (PCR) analysis in small-sized to medium-sized laboratories where dedicated automated equipment is absent and where procedures are carried out manually. Automation is justified by the requirement for increased efficiency and to eliminate possible errors generated by lab technicians. The test system comprises an industrial robot; a dedicated custom gripper assembly necessary for the pipette; pipetting tips; and mechanical holders for tubes with chemical substances and genetic material. The selected approach is to measure forces using the robot’s built-in force–torque sensor while controlling and limiting the pipette’s gripping force and the robot’s pushing force. Because the manipulation of different materials requires the attachment and discarding of tips to and from the pipette, the operator’s perceived tip release force is also considered. Full article

Current industrial robotic manipulators have made their lack of flexibility evident. The systems must know beforehand the piece and its position. To address this issue, contemporary approaches typically employ learning-based techniques, which rely on extensive amounts of data. To obtain vast data, an often sought tool is an extensive grasp dataset. This work introduces our Physics-Based Self-Supervised Grasp Pose Detection (PBSS-GPD) pipeline for model-based grasping point detection, which is useful for generating grasp pose datasets. Given a gripper-object pair, it samples grasping pose candidates using a modified version of GPD (implementing inner-grasps, CAD support…) and quantifies their quality using the MuJoCo physics engine and a grasp quality metric that takes into account the pose of the object over time. The system is optimized to run on CPU in headless-parallelized mode, with the option of running in a graphical interface or headless and storing videos of the process. The system has been validated obtaining grasping poses for a subset of Egad! objects using the Franka Panda two-finger gripper, compared with state-of-the-art grasp generation pipelines and tested in a real scenario. While our system achieves similar accuracy compared to a contemporary approach, 84% on the real-world validation, it has proven to be effective at generating grasps with good centering 18 times faster than the compared system. Full article

The integration of 3D concrete printing (3DCP) into architectural design and production offers a solution to challenges in the construction industry. This technology presents benefits such as mass customization, waste reduction, and support for complex designs. However, its adoption in construction faces various limitations, including technical, logistical, and legal barriers. This study provides insights relevant to architecture, engineering, and construction practices, guiding future developments in the field. The methodology involves fabricating closed architectural units using 3DCP, emphasizing space-filling geometries and ensuring structural strength. Across three production trials, iterative improvements were made, revealing challenges and insights into design optimization and fabrication techniques. Prioritizing controlled filling of the unit’s internal volume ensures portability and ease of assembly. Leveraging 3D robotic concrete printing technology enables precise fabrication of closed units with controlled voids, enhancing speed and accuracy in production. Experimentation with varying unit sizes and internal support mechanisms, such as sand infill and central supports, enhances performance and viability, addressing geometric capabilities and fabrication efficiency. Among these strategies, sand filling has emerged as an effective solution for internal support as it reduces unit weight, simplifies fabrication, and maintains structural integrity. This approach highlights the potential of lightweight and adaptable modular constructions in the use of 3DCP technologies for architectural applications. Full article

This article presents the fundamental principles of robot accuracy. It characterizes a modular robot, describes the measurement setup, and outlines the methodology for evaluating positioning accuracy across different configurations of the modular robot (four, five, and six modules) under varying loads of 6, 10, and 16 kg. An analysis was conducted on the impact of load changes on four- and five-module configurations, as well as the effect of configuration changes on the robot’s performance with 6 and 10 kg loads. The findings indicate that both the number of modules and the load affect positioning accuracy. This article highlights the importance of selecting the optimal configuration based on planned industrial tasks to ensure the highest precision and operational efficiency. Full article

The rapid advancement of tactile electronic skin (E-skin) has highlighted the effectiveness of incorporating bionic, force-sensitive microstructures in order to enhance sensing performance. Among these, cilia-like microstructures with high aspect ratios, whose inspiration is mammalian hair and the lateral line system of fish, have attracted significant attention for their unique ability to enable E-skin to detect weak signals, even in extreme conditions. Herein, this review critically examines recent progress in the development of cilia-inspired bionic tactile E-skin, with a focus on columnar, conical and filiform microstructures, as well as their fabrication strategies, including template-based and template-free methods. The relationship between sensing performance and fabrication approaches is thoroughly analyzed, offering a framework for optimizing sensitivity and resilience. We also explore the applications of these systems across various fields, such as medical diagnostics, motion detection, human–machine interfaces, dexterous robotics, near-field communication, and perceptual decoupling systems. Finally, we provide insights into the pathways toward industrializing cilia-inspired bionic tactile E-skin, aiming to drive innovation and unlock the technology’s potential for future applications. Full article

This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. The proposed AI model has the capacity to make highly accurate predictions and detect faults in DC motor drives, thus helping to ensure timely maintenance and reduce operational breakdowns. As a result, comparative analysis reveals that the proposed framework can achieve higher accuracy than the current existing method of combining CNN with Long Short-Term Memory networks (CNN-LSTM) as well as other CNNs, LSTMs, and traditional methods. The proposed CNN-RNN model can provide early fault detection for motor drives of industrial robots with a simpler architecture and lower complexity of the model compared to CNN-LSTM methods, which can enable the model to process faster than CNN-LSTM. It effectively extracts dynamic features and processes sequential data, achieving superior accuracy and precision in fault diagnosis, which can make it a practical and efficient solution for real-time fault detection in motor drive control systems of industrial robots. Full article

Construction robots are pivotal in advancing the construction industry towards intelligent upgrades. To further explore the current research landscape in this domain, the CNKI Chinese database and the Web of Science core database were employed as data sources. CiteSpace software (version 6.2R4) was utilized to visualize and the analyze relevant literature on construction robots from 2007 to 2024, generating pertinent maps. The findings reveal an annual increase in the number of publications concerning construction robots. An analysis of institutions and authors indicates closer collaboration among English institutions, while Chinese authors exhibit stronger cooperation. However, overall institutional and author collaboration remains limited and fragmented, with no prominent core group of authors emerging. Research hotspots in both the Chinese and English literature are largely aligned, focusing on intelligent construction, human-robot collaboration, and path planning. Notably, the Chinese literature emphasizes technical aspects, whereas the English literature is more application-oriented. Future trends in the field are likely to include human-robot collaboration, intelligent construction, robot vision technology, and the cultivation of specialized talent. Full article

The trajectory planning of manipulators plays a crucial role in industrial applications. This importance is particularly pronounced when manipulators operate in environments filled with obstacles, where devising paths to navigate around obstacles becomes a pressing concern. This study focuses on the environment of frame obstacles in industrial scenes. At present, many obstacle avoidance trajectory planning algorithms struggle to strike a balance among trajectory length, generation time, and algorithm complexity. This study aims to generate path points for manipulators in an environment with obstacles, and the trajectory for these manipulators is planned. The search direction adaptive RRT*Connect (SDA-RRT*Connect) method is proposed to address this problem, which adaptively adjusts the search direction during the search process of RRT*Connect. In addition, we design a path process method to reduce the length of the path and increase its smoothness. As shown in experiments, the proposed method shows improved performances with respect to path length, algorithm complexity, and generation time, compared to traditional path planning methods. On average, the configuration space’s path length and the time of generation are reduced by 38.7% and 57.4%, respectively. Furthermore, the polynomial curve trajectory of the manipulator was planned via a PSO algorithm, which optimized the running time of the manipulator. According to the experimental results, the proposed method costs less time during the manipulator’s traveling process with respect to other comparative methods. The average reduction in running time is 45.2%. Full article

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences. Full article