Search Results (2,919)

The article presents the analytic method for real-time detection of the stationary state of a vehicle based on information retrieved from 6 DoF IMU sensor. Reliable detection of stillness is essential for the application of resetting the inertial sensor’s output bias, called Zero Velocity Update method. It is obvious that the signal from the strapped on inertial sensor differs while the vehicle is stationary or moving. Effort was then made to find a computational method that would automatically discriminate between both states with possibly small impact on the vehicle embedded controller. An algorithmic step-by-step method for building, optimizing, and implementing a diagnostic system that detects the vehicle’s stationary state was developed. The proposed method adopts the “Mahalanobis Distance” quantity widely used in industrial quality assurance systems. The method transforms (fuses) information from multiple diagnostic variables (including linear accelerations and angular velocities) into one scalar variable, expressing the degree of deviation in the robot’s current state from the stationary state. Then, the method was implemented and tested in the dead reckoning navigation system of an autonomous wheeled mobile robot. The method correctly classified nearly 93% of all stationary states of the robot and obtained only less than 0.3% wrong states. Full article

Mobile manipulator robots have an increasing number of applications in industry because they extend the workspace of a fixed base manipulator mounted on a mobile platform, making it important to further investigate their control and optimization. This paper presents an implementation proposal for a coupled base–arm dynamics computed torque controller (CTC) for trajectory tracking of a differential-drive mobile manipulator, which considers the dynamics of the fixed base manipulator and the mobile base in a coupled way and compares its performance with that of a Proportional Derivative (PD) controller. Both controllers are tuned using Particle Swarm Optimization (PSO) with a cost function that aims to simultaneously reduce the control energy and the end-effector tracking error for different types of trajectories, and they operate in discrete time, thus accounting for inherent process delays. Simulation and laboratory implementation results show the superior performance of the CTC in both cases: in simulation, the average end-effector positioning error is reduced by 51.55% and the average RMS power by 46.44%; in the laboratory experiments, the average end-effector positioning error is reduced by 43.29% and the average RMS power by 53.49%, even in the presence of possible model uncertainties and system disturbances. Full article

Industrial robotic workstations contribute substantially to the total energy demand of modern manufacturing, yet most existing energy-saving approaches focus on modifying robot trajectories, motion parameters, or the position of the robot’s base. This paper proposes a novel methodology for the automatic optimization of the spatial placement of a fixed technological trajectory within the robot workspace, without altering the task itself. The method combines pre-simulation filtering of infeasible configurations, large-scale energy simulation in ABB RobotStudio, and real measurement using a dual acquisition system consisting of the robot’s controller and an external power meter. A digital twin of the workstation is used to systematically evaluate thousands of candidate positions of a standardized trajectory. Experimental validation on an ABB IRB 1600–10/1.2 confirms a 23.4% difference in total energy consumption between two workspace configurations selected from the simulation study. The non-optimal configuration exhibits higher current draw, greater power variability, and a more intensive warm-up phase, indicating increased mechanical loading arising purely from geometric placement. By providing a scalable, trajectory-preserving approach grounded in digital-twin analysis and IoT-based measurement, this work establishes a data foundation for future AI-driven predictive and adaptive energy optimization in smart manufacturing environments. Full article

To address the problems of low success rate, excessively long obstacle avoidance paths, and a large number of invalid nodes in path planning for robotic arms in complex environments, this paper proposes an obstacle avoidance path planning method based on the Cauchy Chaotic African Vulture Optimization Algorithm (CC-AVOA). By introducing a Cauchy perturbation term, the algorithm retains a certain degree of randomness in the later stages of the search, which helps to escape local optima. Furthermore, the introduction of a logical chaotic mapping increases the diversity of the initial vulture population, thereby improving the overall search efficiency of the algorithm. This paper compares the performance of the CC-AVOA algorithm with the standard African Vulture Optimization Algorithm (AVOA), the Rapid Exploratory Random Tree (RRT) algorithm, and the A* algorithm through simulation experiments in MATLAB R2024a under two-dimensional, three-dimensional, and robotic arm space environments. The results show that the CC-AVOA algorithm can generate paths with fewer nodes and shorter paths. Finally, the CC-AVOA algorithm is validated on both the RoboGuide industrial simulation platform and a physical FANUC robotic arm. The planned trajectories can be accurately executed without collisions, further confirming the feasibility and reliability of the proposed method in real industrial scenarios. Full article

Industrial Internet of Things (IIoT) networks, Microsoft Azure Internet of Things (IoT) Hub, and device twins (DvT) are increasingly recognized as core enablers of adaptive, data-driven manufacturing. This paper proposes a hierarchical IIoT framework that integrates industrial IoT networking, DvT for asset-level virtualisation, system-level digital twins (DT) for cell orchestration, and cloud-native services to support the digital transformation of brownfield, programmable logic controller (PLC)-centric modular automation (MA) environments. Traditional PLC/supervisory control and data acquisition (SCADA) paradigms struggle to meet interoperability, observability, and adaptability requirements at scale, motivating architectures in which DvT and IoT Hub underpin real-time orchestration, virtualisation, and predictive-maintenance workflows. Building on and extending a previously introduced conceptual model, the present work instantiates a multilayered, end-to-end design that combines a federated Message Queuing Telemetry Transport (MQTT) mesh on the on-premises side, a ZigBee-based backup mesh, and a secure bridge to Azure IoT Hub, together with a systematic DvT modelling and orchestration strategy. The methodology is supported by a structured analysis of relevant IIoT and DvT design choices and by a concrete implementation in a nine-cell MA laboratory featuring a robotic arm predictive-maintenance scenario. The resulting framework sustains closed-loop monitoring, anomaly detection, and control under realistic workloads, while providing explicit envelopes for telemetry volume, buffering depth, and latency budgets in edge-cloud integration. Overall, the proposed architecture offers a transferable blueprint for evolving PLC-centric automation toward more adaptive, secure, and scalable IIoT systems and establishes a foundation for future extensions toward full DvT ecosystems, tighter artificial intelligence/machine learning (AI/ML) integration, and fifth/sixth generation (5G/6G) and time-sensitive networking (TSN) support in industrial networks. Full article

This work proposes a general methodology for the design and experimental validation of dual-arm robotic platforms intended for intelligent manipulation tasks in real-world environments. The proposed framework formalizes the complete engineering process, from the definition of functional requirements to the structural validation of the final prototype, ensuring reproducibility and adaptability across different applications. The methodology is organized into five main stages: (i) requirement analysis and context characterization; (ii) conceptual architecture definition; (iii) detailed mechanical design and structural analysis; (iv) prototype construction and integration; and (v) experimental validation and iterative refinement. Each stage defines its expected deliverables, evaluation metrics, and decision criteria to support systematic design progression. The approach is demonstrated through its implementation within the European project MANiBOT, where the framework guided the development of a modular bimanual robotic platform capable of integrating collaborative manipulators and conveyor subsystems for dual-arm manipulation. Structural testing, deflection measurements, and stability analyses confirmed the robustness and safety of the resulting design. Beyond this specific case, the proposed methodology provides a replicable and extensible design reference for research and industrial teams developing modular robotic structures, supporting the standardization of engineering practices in bimanual mobile robotics. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

Artificial intelligence (AI) technology, serving as an indispensable component within intelligent construction systems, has become a cornerstone for driving the digital and intelligent transformation of the construction industry. Although AI demonstrates autonomous decision-making capabilities in specific operational contexts, because of the dynamic and often unforeseeable nature of construction workflows, human–AI collaboration (HAIC) still dominates the operational paradigm. This study undertakes a systematic review of the prior research on human–AI collaboration in intelligent construction. Through a bibliometric search, scientometric analysis, and in-depth literature classification, 191 highly cited articles in the past five years, which are in the top 10% by citation count within the dataset (as of May 2025, based on Scopus, Google Scholar, and WOS), were screened, and four research streams were formed based on a co-citation analysis and clustering, namely, construction robotics, productivity and safety, intelligent algorithms and modelling, and factors related to construction workers. Finally, a three-dimensional knowledge framework covering the technical layer, application layer, and management layer was constructed. Through this comprehensive synthesis, the study developed a human–AI collaboration knowledge framework in the field of construction science that integrates technology, scenarios, and management dimensions, revealing the co-evolutionary path of artificial intelligence technology and industry digital transformation. Full article

Using Machine Learning (ML) in educational management transforms higher education strategy. This study examines students’ views on machine learning (ML) technologies and how they might be used to plan, monitor, and predict student performance. The Faculty of Industrial Engineering and Robotics surveyed 118 third-year undergraduates. It featured closed- and open-ended questions to collect quantitative and qualitative data. Descriptive statistics showed broad patterns, inferential tests (Chi-square, t-test, ANOVA) showed group differences, regression models predicted school outcomes, and exploratory factor analysis (EFA) and clustering found hidden attitudes and student profiles. A multi-method quantitative approach combining descriptive statistics, inferential tests, regression modeling, and exploratory techniques (EFA and clustering) was employed. The findings show that most students realize that ML may help them be more productive, adapt their study pathways, and learn about the future. Concerns remain regarding its accuracy, overreliance, and morality. The findings indicate that ML can both support and challenge educational management, depending on how responsibly it is implemented. Results show that institutions may utilize ML as a strategic tool to boost academic progress and make better judgments, provided they incorporate it responsibly and follow ethical rules and training. Full article

This study presents a magnetically actuated soft robot inspired by the peristaltic locomotion of flatworms, designed to replicate the biological locomotion of worms to achieve robust maneuverability. Fabricated entirely from photocurable soft resin, the robot features a flexible elastomeric body and two webbed fins with embedded soft magnets. By applying a vertically oscillating magnetic field, the robot achieves forward crawling through the coordinated bending and lifting of fins, converting oscillating magnetic fields into continuous undulatory motion that mimics the gait of flatworms. The experimental results demonstrate that the system maintains consistent bidirectional velocities in the range of 4–7 mm/s on flat surfaces. Beyond linear locomotion, the robot demonstrates effective terrain adaptability, navigating complex topographies, including curved obstacles up to 16 times its body thickness, by autonomously adopting a high-lifting kinematic strategy to overcome gravitational resistance. Furthermore, load-carrying tests reveal that the robot can transport a 6 g payload without velocity degradation. These findings underscore the robot’s efficacy in overcoming mobility constraints, highlighting promising applications in fields requiring non-invasive intervention, such as biomedical capsule endoscopy and industrial pipeline inspection. Full article

Gas leak detection in industrial environments poses critical safety challenges that require algorithms capable of balancing rapid source identification with comprehensive spatial coverage. Conventional approaches using fixed sensor networks provide limited coverage, while manual inspection methods expose personnel to hazardous conditions. This paper presents a novel Gradient-Guided Depth-First Search (GG-DFS) algorithm designed for autonomous mobile robots, which integrates gradient-following behavior with systematic exploration guarantees. The algorithm utilizes local concentration gradient estimation to direct movement toward leak sources while implementing depth-first search with backtracking to ensure complete environmental coverage. We assess the performance of GG-DFS through extensive simulations comprising 160 independent runs with varying leak configurations (1–4 sources) and starting positions. Experimental results show that GG-DFS achieves rapid initial source detection $(9.3\pm 7.3\mathrm{steps};\mathrm{mean}\pm \mathrm{SD})$, maintains 100% coverage completeness with 100% detection reliability, and achieves 50% exploration efficiency. In multi-source conditions, GG-DFS requires 70% fewer detection steps in four-leak scenarios compared to single-leak environments due to gradient amplification effects. Comparative evaluation demonstrates a substantial improvement in detection speed and efficiency over standard DFS, with GG-DFS achieving a composite performance score of 0.98, compared to 0.65 for standard DFS, 0.64 for the lawnmower pattern, and 0.53 for gradient ascent. These findings establish GG-DFS as a robust and reliable framework for safety-critical autonomous environmental monitoring applications. Full article

The use of recycled materials and locally sourced alternative binders in 3D concrete printing (3DCP) has significant potential to reduce carbon emissions in concrete construction. This study examines the effect of sugarcane bagasse ash (SCBA), a byproducts from the sugarcane industry, as a sustainable binder in 3DCP. SCBA was oven-dried at 105 °C, sieved to 250 µm, and used to replace up to 25% of the total binder by weight in a supplementary cementitious material (SCM) blended system. The impact of polypropylene (PP) and steel (ST) microfibres on SCBA-based mixes was also investigated. The fresh properties of the mortar were evaluated using the flow table, Vicat needle, shape retention, buildability, and rheometer tests. The mortar was 3D printed using a small-scale robotic setup with a RAM extruder. Mechanical properties were then tested, including compressive and flexural strengths, and interlayer bonding, along with microstructure analysis. The results showed that increasing the SCBA content led to greater slump and improved flowability, as well as a slower rate of static yield stress development, with up to a 90 percent reduction compared to the control mix. The addition of PP fibres doubled the static yield stress in the mixes containing 20 percent SCBA. The 10 percent SCBA mix achieved the highest mechanical strength, both in compression and flexure, due to its denser microstructure and enhanced pozzolanic reaction. Full article

In modern shipbuilding, the structural complexity of ship components and the constrained workspace make robotic grinding prone to collisions. To improve safety and stability, this paper proposes a collision-free posture optimization method for ship-component operations. First, forward and inverse kinematic models are established, and postures along the path are organized into a directed graph. Feasible postures are then identified under joint-limit and singularity constraints. Directed bounding boxes and the GJK collision detection algorithm are applied to construct a collision-free posture set. An improved A* algorithm is then introduced. It incorporates a multi-source heuristic based on joint-space geometric distance and a safety-distance penalty to compute an optimal posture sequence with minimal joint deviation. This design promotes smooth transitions between consecutive postures. Simulation results show that the proposed method avoids robot–workpiece interference in constrained environments and improves obstacle avoidance and motion smoothness. Compared with the standard A* algorithm, the proposed approach reduces search time by 15.8% and increases the minimum safety distance by nearly fivefold. Compared with a non-optimized posture sequence, cumulative joint variation is reduced by up to 92.5%. The joint amplitude range decreases by an average of 41.2%, and the standard deviation of joint fluctuations decreases by 37.8%. The proposed method provides a generalizable solution for robotic measurement, assembly, and machining in complex and confined environments. Full article

Amid the dual-driven trends of Industry 5.0 and smart manufacturing integration, as well as the global imperative for manufacturing sustainability to address resource constraints, carbon neutrality goals, and circular economy demands, human–robot collaborative (HRC) manufacturing has emerged as a core direction for reshaping manufacturing production modes while aligning with sustainable development principles. This paper comprehensively reviews HRC manufacturing systems, summarizing their technical framework, practical applications, and development trends with a focus on the synergistic realization of operational efficiency and sustainability. Addressing the rigidity of traditional automated lines, inefficiency of manual production, and the unsustainable drawbacks of high energy consumption and resource waste in conventional manufacturing, HRC integrates humans’ flexible decision-making and environmental adaptability with robots’ high-precision and continuous operation, not only improving production efficiency, quality, and safety but also optimizing resource allocation, reducing energy consumption, and minimizing production waste to bolster manufacturing sustainability. Its core technologies include task allocation, multimodal perception, augmented interaction (AR/VR/MR), digital twin-driven integration, adaptive motion control, and real-time decision-making, all of which can be tailored to support sustainable production scenarios such as energy-efficient process scheduling and circular material utilization. These technologies have been applied in automotive, aeronautical, astronautical, and shipping industries, boosting high-end equipment manufacturing innovation while advancing the sector’s sustainability performance. Finally, challenges and future directions of HRC are discussed, emphasizing its pivotal role in driving manufacturing toward a balanced development of efficiency, intelligence, flexibility, and sustainability. Full article

Robotics is increasingly used in higher education laboratories, but most commercial robots are costly and designed for industrial use. This paper presents the design, modeling, and experimental evaluation of a low-cost four-degree-of-freedom (DOF) SCARA robot for educational and research purposes. The robot supports pick-and-place and laser engraving tasks. Direct and inverse kinematics were developed using Denavit–Hartenberg parameters, and the mechanical structure was validated through the dynamic analyses. A new machine learning (ML) framework integrating Support Vector Machine (SVM) and Random Forest (RF) models was implemented to enhance motion precision, predict task success, and compensate positioning errors in real time. Experimental tests over 360 cyles under varying speeds, payloads, and object types show that the SVM predicts grasp success with 94.4% accuracy, while the RF model estimates XY positioning error with an RMSE of 1.84 mm and cycle time error with an RMSE of 0.41 s. Moreover, a novel approach in this work that combines it with a laser engraving machine has been suggested. Repeatability experiments report 0.97 mm ISO-standard repeatability, and laser engraving trials yield mean positional errors of 0.45 mm, with maximum deviation of 0.90 mm. Compared to a baseline PID controller, the ML-enhanced strategy reduces RMS positioning error from 3.30 mm to 1.83 mm and improves repeatability by 36.5%, while slightly decreasing cycle time. These results demonstrate that the proposed SCARA robot achieves high-precision, consistent, and flexible operation suitable for both academic and light-duty practical applications. Full article