Search Results (151)

The integration of sustainability in higher education institutions (HEIs) is critical but often hindered by the limitations of existing sustainability assessment tools (SATs), which are complex, rigid, and not sufficiently adaptable to specific organizational and socio-economic or local contexts. This study presents the Sustainability Implementation Reflective Assessment Framework (SIRAF), a meta-framework designed to assist HEIs in developing their own reflective, flexible, and user-friendly tools. The SIRAF taxonomy was developed through the findings of: a. a systematic literature review retrieved in authors’ previous research, b. a comparative analysis and synthesis of 12 SATs, as well as c. a theory-building process. It features a taxonomy of six core indicators with multiple sub-indicators. Its “pick-and-mix” approach enables institutions to customize assessments to align with their distinct needs, objectives, and resources. The SIRAF model was assessed in eight Greek universities offering tourism studies programs. The assessment incorporated data from institutional websites and a qualitative analysis. An evaluation of three fundamental indicators—curriculum, research, and institutional identity—disclosed a paucity of sustainability integration in curricula and governance, notwithstanding the augmentation of sustainability-related research activity. The findings underscore the significance of meticulously designed yet user-centred tools that facilitate evaluation, organizational learning, and strategic planning. As SIRAF shifts its paradigm of sustainability reporting from external compliance to internal improvement, it concomitantly reduces technical barriers and fosters institutional change. Though initially implemented in tourism and higher education, its inherent flexibility suggests the potential for broader applications, while future enhancements could include weighted scoring and wider empirical validation. Full article

This paper presents the design of a high-efficiency apple harvesting robot based on a hybrid pneumatic-electric drive system, capable of operating around the clock. The robotic system comprises a mobile platform with two degrees of freedom (DOF) and a five-DOF PRRRP manipulator for fruit picking. To meet the harvesting requirements, a spoon-shaped end-effector with pneumatic control was developed, enabling precise manipulator control and flexible grasping. The robot’s vision system integrates machine vision and deep neural network approaches. Additionally, an industrial computer and AC servo drivers were employed to control the manipulator and end-effector. An integrated nighttime illumination system allowed for all-weather operation. Initial experiments were conducted in a controlled laboratory. Subsequently, comprehensive identification and harvesting tests were performed in both laboratory and field environments to validate system robustness. Experimental results validated the effectiveness of the proposed system, demonstrating an apple harvesting success rate of 81% and an average harvesting time of 7.81 s per apple. The system achieved a fruit damage rate of less than 5% during field experiments, demonstrating its potential for gentle handling. The primary innovation of this work lies in its hybrid drive architecture and adaptive vision strategy, which together offer a cost-effective and robust solution for all-weather automated harvesting, addressing key limitations of high cost and environmental sensitivity in existing robotic harvesters. Full article

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article

Clinical diagnostic laboratories continue to face growing pressure from rising test volumes, increasingly complex testing menus, significant workforce shortages, and expectations for faster turnaround times at sustainable cost. Total laboratory automation (TLA) has become a central strategy for improving efficiency in high-volume laboratories, where integrated systems from Abbott, Roche, Siemens Healthineers, and Beckman Coulter have demonstrated substantial reductions in turnaround time, error rates, and labor requirements. Evidence across multiple health systems shows that TLA improves performance and stabilizes laboratory operations even during workload peaks. Despite these gains, large segments of pre-analytical and post-analytical workflows remain manual, especially tasks related to specimen transportation, bench-level manipulation, instrument tending, and troubleshooting. Recent progress in collaborative robotics (cobots), autonomous mobile robots (AMRs), and hospital service robots demonstrates that these technologies can complement TLA by addressing not only the logistical and dexterous tasks that fixed automation lines cannot reach but also enabling robots that can work safely right alongside humans in a shared space. Cobots have shown sub-millimeter precision in colony picking and other fine-motor tasks, though typically at lower throughputs than dedicated track modules, and AMRs have demonstrated reliable transport of pathology carts and medical supplies through large clinical environments. Meanwhile, humanoid-capable mobile manipulators, like Moxi from Diligent Robotics, deployed in hospitals are already completing hundreds of thousands of supply deliveries, indicating real-world significance. Here, we integrate technical, regulatory, operational, and business perspectives on TLA, collaborative robotics, and mobile platforms. We discuss real-world efficiency gains, regulatory expectations under the CLIA and United States FDA, and the emerging case for hybrid automation ecosystems that combine TLA islands, cobotic workcells, AMRs, and AI-enabled orchestration. We argue that the next decade of laboratory automation will move beyond monolithic tracks with robots toward flexible, modular robotic systems designed to operate safely together with humans and to augment the increasingly strained laboratory workforce. This not only allows clinical staff to dedicate more time to patient care but also ensures greater reliability and scalability for essential services throughout demanding hospital environments. Full article

Robotic grippers play a crucial role in pick-and-place tasks, as their performance directly affects the robot’s operational efficiency, stability, and safety. In industrial applications, such as coal gangue sorting, the target objects have irregular shapes and sharp surfaces, which pose challenges to the gripper’s grasping ability. To solve these problems, an adaptive under-actuated gripper based on rope control is designed. The gripper is simple to control and combines the excellent features of both rigid and flexible grippers. To analyze the characteristics of the gripper, both mathematical analysis and holding force experiments are conducted. The results show that the gripper can generate a greater holding force when grasping larger objects with a constant input air pressure. Furthermore, irregularly shaped testing objects, including coal lumps and ores, are selected to conduct grasping experiments. The gripper achieves a 100% grasping success rate with a load of up to four times the object’s weight suspended beneath it and shows the ability to reliably grasp irregularly shaped objects in high-speed pick-and-place tasks with a payload of four times the object’s weight. Meanwhile, the gripper has a passive anti-collision ability due to the special outer contour of the distal finger when subjected to unexpected, sudden force. Full article

In response to the growing need for efficient, stable, and non-destructive gripping in apple harvesting robots, this study proposes a novel rigid–flexible coupled end-effector. The design integrates an underactuated mechanism with a real-time force feedback control system. First, compression tests on ‘Red Fuji’ apples determined the minimum damage threshold to be 24.33 N. A genetic algorithm (GA) was employed to optimize the geometric parameters of the finger mechanism for uniform force distribution. Subsequently, a rigid–flexible coupled multibody dynamics model was established to simulate the grasping of small (70 mm), medium (80 mm), and large (90 mm) apples. Additionally, a harvesting experimental platform was constructed to verify the performance. Results demonstrated that by limiting the contact force of the distal phalange region silicone (DPRS) to 24 N via active feedback, the peak contact forces on the proximal phalange region silicone (PPRS) and middle phalange region silicone (MPRS) were effectively maintained below the damage threshold across all three sizes. The maximum equivalent stress remained significantly below the fruit’s yield limit, ensuring no mechanical damage occurred, with an average enveloping time of approximately 1.30 s. The experimental data showed strong agreement with the simulation, with a mean absolute percentage error (MAPE) of 5.98% for contact force and 5.40% for enveloping time. These results confirm that the proposed end-effector successfully achieves high adaptability and reliability in non-destructive harvesting, offering a valuable reference for agricultural robotics. Full article

This study examines the logistical challenges arising in omni-channel retailing, where the interaction between traditional stores and online channels requires flexible and efficient transportation planning. In particular, the growth of Buy-Online-and-Pick-up-in-Store (BOPS) services has intensified the need to manage both forward deliveries and customer returns, the latter being a costly component of reverse logistics. To address these challenges, this study introduces the Shared Capacity Vehicle Routing Problem with Simultaneous Pickup and Delivery (SCVRP-SPD), which minimizes total operational cost by considering both transportation costs and the additional transfer costs incurred when reallocating store visits to more efficient delivery paths. In the SCVRP-SPD, stores are designed to serve a dual role as both pickup and return points, and a shared-capacity mechanism is incorporated to utilize leftover capacity in pre-planned trips, improving efficiency while reducing overall logistics cost. A mixed-integer programming model is developed for the problem, and solutions are obtained using GUROBI (version 11.0) and a newly designed Modified Differential Evolution (MDE) algorithm. Numerical experiments are conducted to evaluate the performance of the proposed MDE algorithm and to generate managerial insights, showing that the SCVRP-SPD is a promising strategy for omni-channel retailers seeking to reduce transportation costs, streamline reverse logistics, and better utilize resources. 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

To address the challenges of inefficient Camellia oleifera fruits harvesting in hilly and mountainous regions due to the difficulty of using large machinery, a handheld vibration harvesting device for Camellia oleifera fruits was designed. Based on the vibration-induced detachment process of Camellia oleifera fruits, a single-pendulum dynamic model of the “fruit-branch” system was established and solved to calculate the tangential acceleration required for fruit detachment. The key factors influencing harvesting efficiency were identified as vibration frequency, amplitude, height, and duration. Using ANSYS, modal response and harmonic response analyses were conducted on a 3D model of the Camellia oleifera tree to determine the operational parameters ensuring branch acceleration meets the fruit detachment. Furthermore, a rigid-flexible coupling simulation system integrating the harvesting device and Camellia oleifera tree was developed on the ADAMS. This analysis revealed the variation patterns of branch acceleration with respect to vibration frequency and clamping height, thereby validating the rationality of the dynamic model and the feasibility of the device. Finally, an orthogonal experiment was designed using Design-Expert 13, and multi-objective optimization analysis was performed on the device’s working parameters based on the experimental data. The aforementioned research identified the optimal working parameter combination and actual harvesting performance of the handheld vibration harvesting device: when the vibration frequency is 14 Hz, vibration height is 980 mm, and vibration duration is 13 s, the fruit picking rate reaches 95.22%. The harvesting efficiency of this device is significantly higher than manual picking methods, fully meeting the requirements for efficient Camellia oleifera fruit harvesting. Full article

To address the automation of table grape harvesting, a clamping and cutting integrated, four-point flexible end-effector is designed, based on the biological and mechanical characteristics of grapes. The clamping device is validated in regard to force closure requirements using a force spiral. On this basis, a finite element model of the grape pedicel–blade system is established, and dynamic simulations of pedicel cutting are conducted using ANSYS 2021/LS-DYNA. The simulation results indicate that when the pedicel diameter is 10 mm, the maximum shear stress is 1.515 MPa. A kinematic simulation of the clamping device is performed using ADAMS, producing a contact force curve between the end effector’s finger joints and the grape during the clamping process. The simulation results show that the peak contact force of 11 N is lower than the critical rupture force of the grape (24.79 N), satisfying the requirements for flexible, low-damage harvesting. Furthermore, to address the vulnerability of grapes, a contact-force control system is designed, employing a position–speed–torque three-loop control strategy. Pressure sensors integrated into the four clamping fingers provide real-time feedback to adjust the contact force, ensuring precise clamping control. Finally, a physical prototype of the end effector and controller is developed, and harvesting trials are conducted in a vineyard. The harvesting success rate reaches 96.7%, with an average harvesting time of 13.7 s per trial. The grape cluster damage and berry drop rates are 3.2% and 2.8%, respectively, meeting the expected design requirements. Full article

The collection and annotation of large-scale image datasets remains a significant challenge in training vision-based AI models, especially in domains such as industrial automation. In industrial settings, this limitation is especially critical for quality inspection tasks within Flexible Manufacturing Systems and Batch-Size-of-One production, where high variability in components restricts the availability of relevant datasets. This study presents a pipeline for generating photorealistic synthetic images to support automated visual inspection. Rendered images derived from geometric models of manufactured parts are enhanced using a Cycle-Consistent Adversarial Network (CycleGAN), which transfers pixel-level features from real camera images. The pipeline is applied in two scenarios: (1) domain transfer between similar objects for data augmentation, and (2) domain transfer between dissimilar objects to synthesize images before physical production. The generated images are evaluated using mean Average Precision (mAP) and the Turing test, respectively. The pipeline is further validated in two industrial setups: object detection for a pick-and-place task using a Niryo robot, and anomaly detection in products manufactured by a FESTO machine. The successful implementation of the pipeline demonstrates its potential to generate effective training data for vision-based AI in industrial applications and highlights the importance of enhancing domain quality in industrial synthetic data workflows. Full article

The joint optimization of storage location assignment and order picking efficiency for fresh products has become a vital challenge in intelligent warehousing because of the perishable nature of goods, strict temperature requirements, and the need to balance cost and efficiency. This study proposes a comprehensive mathematical model that integrates five critical cost components: picking path, storage layout deviation, First-In-First-Out (FIFO) penalty, energy consumption, and picker workload balance. To solve this NP-hard combinatorial optimization problem, we develop a Particle Swarm-guided hybrid Genetic-Simulated Annealing (PS-GSA) algorithm that synergistically combines global exploration by Particle Swarm Optimization (PSO), population evolution of Genetic Algorithm (GA), and the local refinement and probabilistic acceptance of Simulated Annealing (SA) enhanced with Variable Neighborhood Search (VNS). Computational experiments based on real enterprise data demonstrate the superiority of PS-GSA over benchmark algorithms (GA, SA, HPSO, and GSA) in terms of solution quality, convergence behavior, and stability, achieving 4.08–9.43% performance improvements in large-scale instances. The proposed method not only offers a robust theoretical contribution to combinatorial optimization but also provides a practical decision-support tool for fresh e-commerce warehousing, enabling managers to flexibly weigh efficiency, cost, and sustainability under different strategic priorities. Full article

A contactless passive magnetoelastic sensing setup, recently proposed for detecting pest/substance accumulation in confined spaces (labs, museum reserves), is optimized for enhanced low-frequency performance. The setup uses a short flexible polymer slab, clamped at one end. There, a short Metglas^® 2826MB magnetoelastic ribbon is fixed upon the slab’s surface. The opposite end receives excitation by a remotely controlled module of ultra-low amplitude vibration. When vibrating (with the slab), the ribbon generates magnetic flux, which depends on (and reflects) the slab’s dynamics. This changes when loads accumulate on its surface. The flux induces voltage in a contactless manner in a low-cost pick-up coil suspended above the ribbon. Voltage monitoring allows for evaluation of the vibrating slab’s real-time dynamics and, consequently, the detection of load-induced changes. This work innovates by introducing a low-cost passive circuit for real-time voltage processing, thus achieving an accurate representation of the low-frequency dynamics of the magnetic flux. Furthermore, it introduces an algorithm, which statistically detects load-induced changes using the voltage’s low-frequency power characteristics. Both additions enable load detection at relatively low frequencies, thus addressing a principal issue of passive contactless sensing setups. Extensive testing at different occasions demonstrates promising load detection performance under various conditions, especially given its cost-efficient hardware and operation. Full article

Electronic quick response (e-QR) codes provide access to real-time sensor data using smartphone readers and internet connectivity. Printed electronics and hybrid integration on flexible substrates is a promising solution for wide-scale and low-cost deployment of sensor systems. This paper presents a 21 × 21-pixel e-QR display implemented on black Kapton using hybrid additive and subtractive microfabrication techniques. The process flow for the double-sided circuit allows for layer alignment using multiple fiducial markers. The steps include inkjet printing of tracks on both sides of the substrate, laser-cut via holes, stencil-aided via filling, solder paste dispensing, and final integration of discrete surface-mount components by semi-automatic pick-and-place. Full article

Continuum robots with outstanding compliance, dexterity, and lean bodies are successfully applied in medicine, aerospace engineering, the nuclear industry, rescue operations, construction, service, and manipulation. However, the inherent low stiffness characteristics of continuum bodies make it challenging to develop ultra-long and small-diameter continuum robots. To address this size–scale challenge of continuum robots, we developed an 8 m long continuum robot with a diameter of 23 mm by a tip actuation and growth mechanism. Meanwhile, we also realized the untethered design of the continuum robot, which greatly increased its usable space range, portability, and mobility. Demonstration experiments prove that the developed growing continuum robot has good flexibility and manipulability, as well as the ability to cross obstacles and search for targets. Its continuum body can transport liquids over long distances, providing water, medicine, and other rescue items for trapped individuals. The functionality of an untethered growing continuum robot (UGCR) can be expanded by installing multiple tools, such as a grasping tool at its tip to pick up objects in deep wells, pits, and other scenarios. In addition, we established a static model to predict the deformation of UGCR, and the prediction error of its tip position was within 2.6% of its length. We verified the motion performance of the continuum robot through a series of tests involving workspace, disturbance resistance, collision with obstacles, and load performance, thus proving its good anti-interference ability and collision stability. The main contribution of this work is to provide a technical reference for the development of ultra-long continuum robots based on the tip actuation and steering principle. Full article