Search Results (354)

This paper presents a novel interface that leverages a smartwatch for controlling industrial robotic arms. By harnessing the gyroscope and advanced gesture recognition capabilities of the smartwatch, our solution facilitates intuitive, real-time manipulation that caters to users ranging from novices to seasoned professionals. A dedicated application is implemented to aggregate sensor data via an open-source library, providing a streamlined alternative to conventional control systems. The experimental setup consists of a smartwatch equipped with a data collection application, a robotic arm, and a communication module programmed in Python. Our aim is to evaluate the practicality and effectiveness of smartwatch-based control in a real-world industrial context. The experimental results indicate that this approach significantly enhances accessibility while concurrently minimizing the complexity typically associated with automation systems. Full article

Accurate segmentation of densely stacked and weakly textured objects remains a core challenge in robotic depalletizing for industrial applications. To address this, we propose MaskNet, an instance segmentation network tailored for RGB-D input, designed to enhance recognition performance under occlusion and low-texture conditions. Built upon a Vision Transformer backbone, MaskNet adopts a dual-branch architecture for RGB and depth modalities and integrates multi-modal features using an attention-based fusion module. Further, spatial and channel attention mechanisms are employed to refine feature representation and improve instance-level discrimination. The segmentation outputs are used in conjunction with regional depth to optimize the grasping sequence. Experimental evaluations on camshaft depalletizing tasks demonstrate that MaskNet achieves a precision of 0.980, a recall of 0.971, and an F1-score of 0.975, outperforming a YOLO11-based baseline. In an actual scenario, with a self-designed flexible magnetic gripper, the system maintains a maximum grasping error of 9.85 mm and a 98% task success rate across multiple camshaft types. These results validate the effectiveness of MaskNet in enabling fine-grained perception for robotic manipulation in cluttered, real-world scenarios. Full article

The use of aerial robots for inspection and maintenance in industrial settings demands high maneuverability, precise control, and reliable measurements. This study explores the development of a fully customized unmanned aerial manipulator (UAM), composed of a tilting drone and an articulated robotic arm, designed to perform non-destructive in-contact inspections of iron structures. The system is intended to operate in complex and potentially hazardous environments, where autonomous execution is supported by shared-control strategies that include human supervision. A parallel force–impedance control framework is implemented to enable smooth and repeatable contact between a sensor for ultrasonic testing (UT) and the inspected surface. During interaction, the arm applies a controlled push to create a vacuum seal, allowing accurate thickness measurements. The control strategy is validated through repeated trials in both indoor and outdoor scenarios, demonstrating consistency and robustness. The paper also addresses the mechanical and control integration of the complex robotic system, highlighting the challenges and solutions in achieving a responsive and reliable aerial platform. The combination of semi-autonomous control and human-in-the-loop operation significantly improves the effectiveness of inspection tasks in hard-to-reach environments, enhancing both human safety and task performance. Full article

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null residual (and yet practical low) vibration in underactuated systems. The trajectory planning problem is cast as a constrained optimal control problem (OCP) for a two-degree-of-freedom revolute–revolute planar manipulator. The proposed method produces energy-efficient motion while limiting residual vibrations under motor torque limitations. Experiments compare the proposed trajectories to input shaping techniques (ZV, ZVD, NZV, NZVD). Results show energy savings that range from 12% to 69% with comparable and negligible residual oscillations. Full article

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article

The ability to autonomously generate up-to-date 3D models of robotic workcells is critical for advancing smart manufacturing, yet existing Next-Best-View (NBV) methods often rely on paradigms ill-suited for the fixed-base manipulators found in dynamic industrial environments. To address this gap, this paper proposes a novel NBV method for the complete exploration of a 6-DOF robotic arm’s workspace. Our approach integrates collision-based information gain metric, a potential field technique to generate candidate views from exploration frontiers, and a tunable fitness function to balance information gain with motion cost. The method was rigorously tested in three simulated scenarios and validated on a physical industrial robot. Results demonstrate that our approach successfully maps the majority of the workspace in all setups, with a balanced weighting strategy proving most effective for combining exploration speed and path efficiency, a finding confirmed in the real-world experiment. We conclude that our method provides a practical and robust solution for autonomous workspace mapping, offering a flexible, training-free approach that advances the state-of-the-art for on-demand 3D model generation in industrial robotics. Full article

Camera-centric perception has matured into a cornerstone of modern autonomy, from self-driving cars and factory cobots to underwater and planetary exploration. This review synthesizes more than a decade of progress in vision-based robotic navigation through an engineering lens, charting the full pipeline from sensing to deployment. We first examine the expanding sensor palette—monocular and multi-camera rigs, stereo and RGB-D devices, LiDAR–camera hybrids, event cameras, and infrared systems—highlighting the complementary operating envelopes and the rise of learning-based depth inference. The advances in visual localization and mapping are then analyzed, contrasting sparse and dense SLAM approaches, as well as monocular, stereo, and visual–inertial formulations. Additional topics include loop closure, semantic mapping, and LiDAR–visual–inertial fusion, which enables drift-free operation in dynamic environments. Building on these foundations, we review the navigation and control strategies, spanning classical planning, reinforcement and imitation learning, hybrid topological–metric memories, and emerging visual language guidance. Application case studies—autonomous driving, industrial manipulation, autonomous underwater vehicles, planetary rovers, aerial drones, and humanoids—demonstrate how tailored sensor suites and algorithms meet domain-specific constraints. Finally, the future research trajectories are distilled: generative AI for synthetic training data and scene completion; high-density 3D perception with solid-state LiDAR and neural implicit representations; event-based vision for ultra-fast control; and human-centric autonomy in next-generation robots. By providing a unified taxonomy, a comparative analysis, and engineering guidelines, this review aims to inform researchers and practitioners designing robust, scalable, vision-driven robotic systems. Full article

This paper presents the design and implementation of an autonomous robotic facility for textile sorting and recycling, leveraging advanced computer vision and machine learning technologies. The system enables real-time textile classification, localization, and sorting on a dynamically moving conveyor belt. A custom-designed pneumatic gripper is developed for versatile textile handling, optimizing autonomous picking and placing operations. Additionally, digital simulation techniques are utilized to refine robotic motion and enhance overall system reliability before real-world deployment. The multi-threaded architecture facilitates the concurrent and efficient execution of textile classification, robotic manipulation, and conveyor belt operations. Key contributions include (a) dynamic and real-time textile detection and localization, (b) the development and integration of a specialized robotic gripper, (c) real-time autonomous robotic picking from a moving conveyor, and (d) scalability in sorting operations for recycling automation across various industry scales. The system progressively incorporates enhancements, such as queuing management for continuous operation and multi-thread optimization. Advanced material detection techniques are also integrated to ensure compliance with the stringent performance requirements of industrial recycling applications. Full article

Bilateral teleoperation systems have been widely used in many fields of robotics, such as industrial manipulation, medical treatment, space exploration, and deep-sea operation. Delays in communication, known as an inevitable issues in practical implementation, especially for long-distance operations and challenging communication situations, can destroy system passivity and potentially lead to system failure. In this work, we address the time-delayed three-channel teleoperation design problem to guarantee system passivity and achieve high transparency simultaneously. To realize this, the three-channel teleoperation structure is first reformulated to form a two-channel-like architecture. Then, the wave variable technique is used to handle the communication delay and guarantee system passivity. Two novel wave variable compensators are proposed to achieve delay-minimized system transparency, and energy reservoirs are employed to monitor and regulate the energy introduced via these compensators to preserve overall system passivity. Numerical studies confirm that the proposed method significantly improves both kinematic and force tracking performance, achieving near-perfect correspondence with only a single-trip delay. Quantitative analyses using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Dynamic Time Warping (DTW) metrics show substantial error reductions compared to conventional wave variable and direct transmission-based three-channel teleoperation approaches. Moreover, statistical validation via the Mann–Whitney U test further confirms the significance of these improvements in system performance. The proposed design guarantees passivity with any passive human operator and environment without requiring restrictive assumptions, offering a robust and generalizable solution for teleoperation tasks with communication time delay. Full article

This study presents a novel investigation into immersive teleoperation systems using collaborative, device-agnostic interfaces for advancing smart haptics in industrial assistive applications. The research focuses on evaluating the quality of experience (QoE) of users interacting with a teleoperation system comprising a local robotic arm, a robot gripper, and heterogeneous remote tracking and haptic feedback devices. By employing a modular device-agnostic framework, the system supports flexible configurations, including one-user-one-equipment (1U-1E), one-user-multiple-equipment (1U-ME), and multiple-users-multiple-equipment (MU-ME) scenarios. The experimental set-up involves participants manipulating predefined objects and placing them into designated baskets by following specified 3D trajectories. Performance is measured using objective QoE metrics, including temporal efficiency (time required to complete the task) and spatial accuracy (trajectory similarity to the predefined path). In addition, subjective QoE metrics are assessed through detailed surveys, capturing user perceptions of presence, engagement, control, sensory integration, and cognitive load. To ensure flexibility and scalability, the system integrates various haptic configurations, including (1) a Touch kinaesthetic device for precision tracking and grounded haptic feedback, (2) a DualSense tactile joystick as both a tracker and mobile haptic device, (3) a bHaptics DK2 vibrotactile glove with a camera tracker, and (4) a SenseGlove Nova force-feedback glove with VIVE trackers. The modular approach enables comparative analysis of how different device configurations influence user performance and experience. The results indicate that the objective QoE metrics varied significantly across device configurations, with the Touch and SenseGlove Nova set-ups providing the highest trajectory similarity and temporal efficiency. Subjective assessments revealed a strong correlation between presence and sensory integration, with users reporting higher engagement and control in scenarios utilizing force feedback mechanisms. Cognitive load varied across the set-ups, with more complex configurations (e.g., 1U-ME) requiring longer adaptation periods. This study contributes to the field by demonstrating the feasibility of a device-agnostic teleoperation framework for immersive industrial applications. It underscores the critical interplay between objective task performance and subjective user experience, providing actionable insights into the design of next-generation teleoperation systems. Full article

This article presents a series of innovative systems developed through student laboratory projects, comprising two autonomous vehicles, a quadrupedal walking robot, an active ankle-foot orthosis, a ball-on-beam balancing mechanism, a ball-on-plate system, and a manipulator arm, all actuated by pneumatic artificial muscles (PAMs). Due to their flexibility, low weight, and compliance, fluidic muscles demonstrate substantial potential for integration into various mechatronic systems, robotic platforms, and manipulators. Their capacity to generate smooth and adaptive motion is particularly advantageous in applications requiring natural and human-like movements, such as rehabilitation technologies and assistive devices. Despite the inherent challenges associated with nonlinear behavior in PAM-actuated control systems, their biologically inspired design remains promising for a wide range of future applications. Potential domains include industrial automation, the automotive and aerospace sectors, as well as sports equipment, medical assistive devices, entertainment systems, and animatronics. The integration of self-constructed laboratory systems powered by PAMs into control systems education provides a comprehensive pedagogical framework that merges theoretical instruction with practical implementation. This methodology enhances the skillset of future engineers by deepening their understanding of core technical principles and equipping them to address emerging challenges in engineering practice. Full article

This research article tackles the control problem of an inverted pendulum, also known as the Furuta pendulum, mounted on a velocity-controlled robot manipulator in two configurations: the rotary pendulum and the translational pendulum. Differently from most of the existing control architectures where the motor actuating the pendulum motion is torque-controlled, the proposed control architecture exploits the inner velocity loop usually available on industrial robots, thus easing the implementation of an inverted pendulum. Another aspect investigated in this paper and mostly overlooked in the literature is the digital implementation of the control and, specifically, the latency introduced by the digital controller. The proposed control solution explicitly models such effects in the control design phase, improving the closed-loop performance. The additional novelty introduced by this paper is the friction compensation that is essential in the swing-up phase of the inverted pendulum, whereas classical control strategies for the nonlinear swing-up usually neglect this effect, and their solutions lead to control failures in practical systems. This paper presents detailed modeling and experimental identification phases followed by the control design of both the nonlinear swing-up algorithm and the linear stabilization controller, both experimentally validated on a Meca500 robotic arm controlled via an EtherCAT communication protocol by a mini PC featuring a Xenomai real-time operating system. The overall system showcases the potential of high-performance digital control systems in industrial robotic applications. Full article

At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable Brasenia schreberi was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness. Full article

This study develops an applied optimization method to address practical challenges in Laser Radar station planning for automotive Body-In-White (BIW) manufacturing inspection. Focusing on the spatially constrained industrial environments and complex measurement specifications, the work reformulates Laser Radar inspection planning as a multi-constrained optimization problem challenge. Firstly, a parametric geometric modeling approach is developed to define measurement spaces for individual features, accompanied by an innovative maximal complete subgraph mining algorithm to intelligently identify shared feasible measurement regions among multiple features. Secondly, kinematic equations are formulated using Denavit–Hartenberg (D-H) parameters, while a hierarchical bounding volume collision detection mechanism is integrated to establish a comprehensive constraint. Therefore, unified optimization method synergizing measurement coverage, robotic manipulator reachability, and operational safety requirements are proposed. Through experimental validations utilizing BIW (BIW) component inspection, the research has demonstrated its industrial applicability and has achieved a 92% measurement coverage with robot trajectories free of collisions. Compared with traditional manual planning methods, the proposed approach reduces the number of required inspection stations by 35% and improves the computational efficiency to meet industrial real-time deployment requirements. Experimental validation demonstrates the method’s effectiveness in measurement accuracy, operational safety, and equipment utilization for advanced manufacturing quality control systems. Full article