Search Results (294)

Manual assembly remains essential in modern manufacturing, yet the increasing complexity of customised production imposes significant cognitive burdens and error rates on workers. Existing Spatial Augmented Reality (SAR) systems often operate passively, lacking adaptive interaction, real-time feedback and a control system with gesture. In response, we present Gest-SAR, a SAR framework that integrates a custom MediaPipe-based gesture classification model to deliver adaptive light-guided pick-to-place assembly instructions and real-time error feedback within a closed-loop interaction instance. In a within-subject study, ten participants completed standardised Duplo-based assembly tasks using Gest-SAR, paper-based manuals, and tablet-based instructions; performance was evaluated via assembly cycle time, selection and placement error rates, cognitive workload assessed by NASA-TLX, and usability test by post-experimental questionnaires. Quantitative results demonstrate that Gest-SAR significantly reduces cycle times with an average of 3.95 min compared to Paper (Mean = 7.89 min, p < 0.01) and Tablet (Mean = 6.99 min, p < 0.01). It also achieved 7 times less average error rates while lowering perceived cognitive workload (p < 0.05 for mental demand) compared to conventional modalities. In total, 90% of the users agreed to prefer SAR over paper and tablet modalities. These outcomes indicate that natural hand-gesture interaction coupled with real-time visual feedback enhances both the efficiency and accuracy of manual assembly. By embedding AI-driven gesture recognition and AR projection into a human-centric assistance system, Gest-SAR advances the collaborative interplay between humans and machines, aligning with Industry 5.0 objectives of resilient, sustainable, and intelligent manufacturing. 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

This research study investigates the impact of a virtual dashboard on the quality of task execution in robotic teleoperation. More specifically, this study investigates how a virtual dashboard improves user awareness and grasp precision in a teleoperated pick-and-place task by providing users with critical information in real-time. An experiment was conducted with 30 participants in a robotic teleoperated task to measure their task performance in two different experimental conditions: a control group used conventional interfaces, and an experimental group utilized the virtual dashboard with additional information. Research findings indicate that integrating a virtual dashboard improves grasping accuracy, reduces user fatigue, and speeds up task completion, thereby improving task effectiveness and the quality of the experience. Full article

Several pressing issues have been revealed by deep learning-based tomato ripening detection technology in intricate environmental applications: The ripening transition stage distinction is not accurate enough, small target tomato detection is likely to miss, and the detection technology is more susceptible to variations in light. Based on the YOLOv11 model, a YOLOv11-SLBA tomato ripeness detection model was presented in this study. First, SPPF-LSKA is used in place of SPPF in the backbone section, greatly improving the model’s feature discrimination performance in challenging scenarios including dense occlusion and uneven illumination. Second, a new BiAttFPN hierarchical progressive fusion is added in the neck area to increase the feature retention of small targets during occlusion. Lastly, the feature separability of comparable categories is significantly enhanced by the addition of the auxiliary detection head DetectAux. In this study, comparative experiments are carried out to confirm the model performance. Under identical settings, the YOLOv11-SLBA model is compared to other target detection networks, including Faster R-CNN, SSD, RT-DETR, YOLOv7, YOLOv8, and YOLOv11. With 2.7 million parameters and 10.9 MB of model memory, the YOLOv11-SLBA model achieves 92% P, 83.5% R, 91.3% mAP50, 64.6% mAP50-95, and 87.5% F1-score. This is a 3.4% improvement in accuracy, a 1.5% improvement in average precision, and a 1.6% improvement in F1-score when compared to the baseline model YOLOv11. It outperformed the other comparison models in every indication and saw a 1.6% improvement in score. Furthermore, the tomato-ripeness1public dataset was used to test the YOLOv11-SLBA model, yielding model p values of 78.6%, R values of 91.5%, mAP50 values of 93.7%, and F1-scores of 84.6%. This demonstrates that the model can perform well across a variety of datasets, greatly enhances the detection generalization capability in intricate settings, and serves as a guide for the algorithm design of the picking robot vision system. Full article

Manipulators performing pick-and-place tasks with objects of complex shapes must consider not only how to grasp the objects but also how to maneuver them out of a bin. In this paper, we explore the industrial challenge of picking hook-shaped components, whose interlocking nature often leads to failed attempts at safely retrieving a single component at a time. Rather than explicitly modeling contact-rich interactions within optimization-based motion planners, we tackle this challenge by leveraging recent advances in sampling-based optimization and parallelizable physics simulators to predict the impact of motion on the separating subgoal, aimed at resolving interlocking. The proposed framework generates candidate trajectories initialized from a user-provided demonstration, which are then simulated and evaluated in a physics simulator to optimize robot trajectories in joint space while considering the entire planning horizon. We validate our approach through real-world experiments on a manipulator, demonstrating improved success rates in terms of separating interlocked objects compared to the industrial baseline. Full article

Recent industrial production paradigms have seen the promotion of the outsourcing of low-value-added operations to robotic cells as a service, particularly end-of-line packaging. As a result, various types of research have emerged, offering different approaches to the trajectory design optimization of robotic manipulators and their applications. Over time, numerous improvements and updates have been made to the proposed methodologies, addressing the limitations and restrictions of earlier work. This survey-type article compiles research articles published in recent years that focus on the main algorithms proposed for addressing placement and minimum-time path planning for a manipulator responsible for performing pick-and-place tasks. Specifically, the research examines the construction of an automated robotic cell for the palletizing of regular heterogeneous boxes on a collision-free mixed pallet. By reviewing and synthesizing the most recent research, this article sheds light on the state-of-the-art manipulator planning algorithms for pick-and-place tasks in palletizing applications. Full article

With the increasing demand for efficiency and profitability in industrial applications, modularity offers significant advantages such as system reconfiguration, reduced acquisition costs, and enhanced versatility. However, achieving compatibility across multi-vendor modular systems remains a challenge, particularly in motion control. This study focuses on improving motion control and sensing compatibility and performance, partly using open-source tools to enhance performance in modular systems. In such systems, effective motion coordination between modules is crucial; without it, operations are constrained to sequential execution, limiting efficiency. This paper quantifies the performance benefits of collaborative motion compared to sequential motion in modular mechatronic systems for pick-and-place operations. The experimental validation, conducted on a robotic manipulator mounted on a linearly sliding platform, demonstrates a substantial improvement. The results show time savings of 36% to 52% and an approximate 35% reduction in energy consumption, highlighting the potential for improved productivity and sustainability in modular automation solutions. Full article

Road safety signboards are now difficult to see due to pollution and harsh weather elements such as snow and fog, which has resulted in more accidents. The problem is especially common in Western countries where snow can block these critical signs. An approach addressing this issue involves a system that uses Radio Frequency Identification (RFID) and Internet of Things (IoT). The real-time alerts that this system sends to drivers improve driver safety in complex environments. For this purpose, an RFID reader is placed in the vehicle, and passive RFID tags are attached to road safety signboards. The reader picks up the signal as a vehicle comes within range, and the warning for the vehicle is sent to the driver. It helps to reduce the number of accidents resulting from poor visibility. In addition, because its multi-lingual audio alerts the drive through speakers and visual warnings displayed on a display screen, the system is accessible to drivers from various regions. To make the system more sustainable, we added some solar panels to the system to cut costs as far as energy efficiency is concerned. The system combines GPS and GSM modules to provide the vehicle position in real time in the cloud. It gives better warnings and helps avoid accidents. In addition to improving road safety, the system offers support for the environment, by limiting emissions and waste of resources caused by accidents. Traffic patterns can thus be studied with the data, creating more efficient and ecofriendly transportation systems. This solution enables a smarter vehicle network that is safer and more sustainable with quick, accurate alerts. Full article

A data-driven 3D simulation for the robotic automation of the most labor-intensive packaging process in meal kit production was developed using Tecnomatix plant simulation software. The workflow and environments of the existing manual process were analyzed. An existing production site was scanned using a 3D Lidar scanner to create 3D models and design the initial assembly layout. Two types of 3D simulation models, implemented with a single or double delta robot, were designed to determine the optimal robot-automated packaging process. Key performance indicators for simulation models of a manual and two robot automation systems were analyzed. The throughputs of the manual, single delta robot and double delta robot models were 2112, 1510, and 2568 ea/h, respectively. The single robot system achieved only 68.3% of the throughput of the manual process, which is attributed to a cycle time of 2.36 s for picking and placing all components. On the other hand, the cycle time of the double robot system was 1.66 times faster, and the throughput was 1.7 times greater compared to the single robot system. The developed 3D simulation model for the meal kit packaging system demonstrates the potential of robotic automation in addressing the labor shortage issue as well as improving production efficiency. Full article

Background: Minimally invasive surgical techniques, including laparoscopic and robotic surgery, have profoundly impacted surgical practice by improving precision, reducing recovery times, and minimizing complications. However, these modalities differ in their cognitive demands and skill acquisition requirements, which can influence the learning curve and operative performance. To assess and evaluate this variability across these modalities, a functional near-infrared spectroscopy (fNIRS) system is used as an objective method for monitoring cognitive activity in surgical trainees. fNIRS can provide insights and further our understanding of the mental demands of different surgical techniques and their association with varying task complexity. Objective: This study seeks to assess the influence of surgical modality (laparoscopy vs. robotic surgery) and task complexity (pick and place (PP) vs. knot tying (KT)) on cognitive workload through fNIRS. We compare real-world and simulation-based training environments to determine changes in brain activation patterns and task performance. Methods: A total of twenty-six surgical trainees (general and gynecologic surgery residents and specialists) participated in this study. Participants completed standardized laparoscopic and robotic surgical tasks at varying levels of complexity while their cognitive workload was measured using fNIRS. This study included both simulation-based training and real-world surgical environments. Hemodynamic responses in the prefrontal cortex (PFC), task completion times, and performance metrics were analyzed. Results: Laparoscopic surgery elicited higher activity changes in the prefrontal cortex, indicating increased cognitive demand compared with robotic surgery, particularly for complex tasks like knot tying. Task complexity significantly influenced mental load, with more intricate procedures eliciting greater neural activation. Real-world training resulted in higher cognitive engagement than simulation, emphasizing the gap between simulated and actual surgical performance. Conclusions: Cognitive workload was lower and significantly different during robotic surgery than during laparoscopy, potentially due to its ergonomic advantages and enhanced motor control. Simulation-based training effectively prepares surgeons, but the cognitive workload results indicate that it may not fully replicate real-world surgical environments. These findings reveal the importance of cognitive workload assessment in surgical education and suggest that incorporating neuroimaging techniques such as fNIRS into training programs could enhance skill acquisition and performance. Full article

The teleoperation of robot arms can prevent users from working in hazardous environments, but current teleoperation uses a 2D display and controls the end effector of robot arms, which introduces the problem of a limited view and complex operations. In this study, a teleoperation method for robot arms is proposed, which can control the robot arm by interacting with the digital twins of objects. Based on the objects in the workspace, this method generates a virtual scene containing digital twins. Users can observe the virtual scene from any direction and move the digital twins of the objects at will to control the robot arm. This study compared the proposed method and the traditional method, which uses a 2D display and a game controller, through a pick-and-place task. The proposed method achieved 45% lower scores in NASA-TLX and 31% higher scores in SUS than traditional teleoperation methods. The results indicate that the proposed method can reduce the workload and improve the usability of teleoperation. Full article

Robotic harvesters and grippers have been widely developed for fruit-picking tasks. However, existing approaches often fail to account for the fruit’s post-harvest condition, leading to premature decay due to excessive grasping forces. This study addresses this gap by designing and evaluating passive soft grasping interfaces for rigid robotic grippers, aiming to handle delicate fruits and vegetables while minimizing bruising. Using hyperelastic materials and 3D printing, four different interface designs, including Gyroid, Grid, Cubic, and Cross 3D patterns, were developed and tested. Experimental evaluations assessed surface adaptability, grasping force distribution, and post-harvest bruising effects. Results indicate that collapsible interface patterns greatly reduce grasping forces and offer lower bruising severity when compared to traditional rigid grippers. These findings suggest that hybrid soft-rigid grasping strategies offer a promising solution for improving fruit-handling efficiency in autonomous harvesting and pick-and-place operations. Full article

Manual material handling, a common practice in various industries, often involves moving or lifting heavy objects, placing significant physical strain on workers, especially in the lower back. A prime example is manhole cover removal, which typically requires handling heavy weights, potentially leading to lower back muscle strain. This study investigates the effectiveness of a passive exoskeleton in reducing ergonomic risks during manhole cover removal. Twenty able-bodied workers participated, performing the task with and without extractor tools in the field. Techniques such as surface electromyography and inertial measurement units were employed to measure muscle activity and body posture using the Rapid Entire Body Assessment (REBA). This study compared muscle activities and REBA scores under different conditions: manually lifting covers, using an in-house lever tool, and using a sledgehammer and a pick bar tool named Jake, both with and without an exoskeleton. Results revealed that the in-house Lever tool was the safest and most efficient method, resulting in the lowest muscle activities and REBA scores, regardless of exoskeleton use. Interestingly, the exoskeleton significantly reduced muscle strain when using the Jake tool. These findings indicate that while the Lever tool is optimal for this task, passive exoskeletons can effectively lower ergonomic risks associated with more physically demanding tools. Full article

Background: In today’s business world, where competition lies between supply chains, customer expectations are changing dynamically. Effective order picking in warehouses has become a top concern given expectations for rapid delivery, a larger product range, and continuous support. Methods: In this study, it is aimed to find a simultaneous solution to the problems of picker routing and order batching, which have an important place in order picking. A genetic algorithm-based solution with group-based coding is proposed to minimize the travel time of pickers. Results: A new set of equations for rectangular warehousing systems with three or more blocks (multi-blocks) is presented to directly determine the shortest distances between order points. It is found that the proposed solution methodology gives better results than traditional approaches. Conclusions: The study is expected to contribute to the improvement of order picking, which is the most costly and repetitive activity in warehouses, within the scope of practical and academic applications. Full article

Robots are essential for carrying out tasks, for example, in a nuclear industry, where direct human involvement is limited. However, present-day nuclear robots are not versatile due to limited autonomy and higher costs. This research presents a merely teleoperated Dexter^TM nuclear robot’s transformation into an autonomous manipulator for nuclear sort and segregation tasks. The Dexter^TM system comprises a arm client manipulator designed to operate in extreme radiation environments and a similar single/dual-arm local manipulator. In this paper, initially, a kinematic model and convex optimization-based dynamic model identification of a single-arm Dexter^TM manipulator is presented. This model is used for autonomous Dexter^TM control through Robot Operating System (ROS). A new integration framework incorporating vision, AI-based grasp generation and an intelligent radiological surveying method for enhancing the performance of autonomous Dexter^TM is presented. The efficacy of the framework is demonstrated on a mock-up nuclear waste test-bed using similar waste materials found in the nuclear industry. The experiments performed show potency, generality and applicability of the proposed framework in overcoming the entry barriers for autonomous systems in regulated domains like the nuclear industry. Full article