Search Results (104)

This study tries to make some improvements in the order picking operations by offering a novel mathematical model and efficient solution algorithm. Accordingly, the order picking policies are examined to allow for picking more orders by reducing the collection time/distance of order pickers. Batching orders for the pick are included in the order picking process as it could enable the order picker to collect more orders. Since the most labor-intensive movement in the order picking function in a high-level shelf layout is the retrieval of products from upper shelves and placing them onto the collection vehicle in the picker-to-part system, the use of drones is preferred to eliminate this costly movement. Drones assist humans in the order picking process by retrieving products from upper levels, thus reducing the order picking time. Here, a Vehicle Routing Problem (VRP) is formulated to deal with drone routing which is then solved based on the Order Picking Problem (OPP) framework. Consequently, an integrated OPP involving both order pickers and drones is addressed and formulated using a Mixed-Integer Linear Programming (MILP) model. To cope with the complexity of the problem, an Adaptive Genetic Algorithm (AGA) is designed which is able to yield superior results compared to the classical Genetic Algorithm (GA). Finally, a sensitivity analysis is performed to assess the behavior of the model against real-world fluctuations. The main reason for this research is to speed up the order picking process in warehouses by taking advantage of the tools brought by the technology age. According to the research results, when the results of the drone-assisted order picking process are compared to the order picking process without drone support, an improvement of 29.68% is observed. The theoretical contribution of this work is that it initially mathematically defines the drone-aided OPP in the literature and proposes a solution with the help of the AGA. As a practical contribution, it provides a solution with the capacity to reduce operational costs by accelerating the order picking operation in warehouses and a practical optimization framework for logistics managers. In addition, warehouse managers, senior company managers, and researchers working on order picking processes can benefit from this study. Full article

High-precision 6D pose estimation for pick-and-place operations remains a critical problem for industrial robot arms in manufacturing. This study introduces an analytics-based solution for 6D pose estimation designed for a real-world industrial application: it enables the Staubli TX2-60L (manufactured by Stäubli International AG, Horgen, Switzerland) robot arm to pick up metal plates from various locations and place them into a precisely defined slot on a brake pad production line. The system uses a fixed eye-to-hand Intel RealSense D435 RGB-D camera (manufactured by Intel Corporation, Santa Clara, California, USA) to capture color and depth data. A robust software infrastructure developed in LabVIEW (ver.2019) integrated with the NI Vision (ver.2019) library processes the images through a series of steps, including particle filtering, equalization, and pattern matching, to determine the X-Y positions and Z-axis rotation of the object. The Z-position of the object is calculated from the camera’s intensity data, while the remaining X-Y rotation angles are determined using the angle-of-inclination analytics method. It is experimentally verified that the proposed analytical solution outperforms the hybrid-based method (YOLO-v8 combined with PnP/RANSAC algorithms). Experimental results across four distinct picking scenarios demonstrate the proposed solution’s superior accuracy, with position errors under 2 mm, orientation errors below 1°, and a perfect success rate in pick-and-place tasks. Full article

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

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

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

The transition towards a circular economy, as outlined in the European Union’s Green Deal, requires the development of industries dedicated to recycling and material recovery. Within this context, the recycling of plastic and packaging waste is critical in reducing greenhouse gas emissions. Traditional pick-and-place systems encounter significant challenges when applied to heterogeneous waste streams due to the variability in shape, weight, and material properties of the processed materials. To address these challenges, this research proposes a heuristic to optimize the use of multiple gripper systems within a multirobot multigripper sorting setup, with the goal of both maximizing sorting efficiency and recovery rates in PPW recycling. Therefore, the performance of grippers on specific PPW objects, materials and shapes is quantitatively assessed by measuring the grasp efficiency. This grasp efficiency is incorporated into the proposed scheduling heuristic and used to assign the PPW objects to the different available robots, taking into account the position of the object with respect to the robot and the gripper installed on the robot. This heuristic is then evaluated and benchmarked through simulations considering the sorting system design and the waste stream composition based on a real-world portable robotic material recycling facility. The findings demonstrate substantial improvements in picking efficiency of up to 3.6% and pick rates up to 37.5%, underscoring the potential of advanced heuristic algorithms in robotic waste sorting systems. Future work will focus on refining gripper designs and exploring predictive algorithms to further enhance grasp success rates. Full article

Parcel delivery networks have grown rapidly during the last few years due to the intensive evolution of online marketplaces. We address the issue of managing the operation of a network’s pick-up point, including the selection of the warehouse’s capacity and the policy for accepting orders for delivery. The existence of the time lag between order placing and delivery to the pick-up point is accounted for via modeling the order’s processing as the service in the dual tandem queueing system. Distinguishing features of this tandem queue are the account of possible irregularity in order generation via consideration of the versatile Markov arrival process and the possibilities of batch transfer of the orders to the pick-up point, group withdrawal of orders there, and client no-show. To reduce the probability of an order rejection at the pick-up point due to the overflow of the warehouse, a threshold strategy of order admission at the first stage on a tandem is proposed. Under the fixed value of the threshold, tandem operation is described by the continuous-time multidimensional Markov chain with a block lower Hessenberg structure for the generator. Stationary performance measures of the tandem system are calculated. Numerical results highlight the dependence of these measures on the capacity of the warehouse and the admission threshold. The possibility of the use of the results for managerial goals is demonstrated. In particular, the results can be used for the optimal selection of the capacity of a warehouse and the policy of suspending order admission. Full article

This study presents a fault-tolerant control system for Selective Compliance Assembly Robot Arm (SCARA) robots, ensuring operational continuity in cooperative tasks. It is evaluated in five scenarios: normal operation, failures without reconfiguration, and with active reconfiguration. The system employs redundant actuators, differential gears, torque limiters, and rapid detection and reconfiguration algorithms. Simulations in MATLAB R2024a demonstrated reconfiguration times of 0.5 s and reduced trajectory errors (0.0042 m on the X-axis for Robot 1), achieving efficiency above 99%. Nonlinear Model Predictive Controllers (NLMPCs) and Adaptive Sliding Mode Control (ASMC) were compared, with NLMPC excelling in stability and ASMC in precision. The system showcased high productivity in pick-and-place tasks, even under critical failures, establishing itself as a robust solution for industrial environments requiring high reliability and advanced automation. Full article

Transplanting is a critical step in vegetable production, and the application of automatic transplanters can significantly reduce labor intensity, improve production efficiency, and enhance the precision and consistency of operations. However, automatic transplanters are structurally complex, with diverse components, each design and function offering its own advantages and limitations. To assist industry professionals in quickly understanding and selecting transplanters suited to specific crops and environments, this paper reviews three key technologies in current vegetable transplanters: planting mechanisms, automated seedling picking and placing, and tray conveyance. Each technology is classified, compared, and analyzed to evaluate its applicability. Based on the current state of technology, the paper identifies major challenges in the development of vegetable transplanters in China, including insufficient integration of machinery and agronomy, high demands for equipment adaptability, lack of standardized systems, and delays in the development of core technologies for fully automated transplanting. Solutions are proposed for each of these issues. Finally, the paper discusses future directions for the development of automatic transplanters, including enhancing transplanting efficiency, achieving autonomous navigation, digitalizing operations, developing supporting systems for transplanting, and unmanned transplanting. Full article

Due to the complex growth positions of dragon fruit and the difficulty in robotic picking, this paper proposes a six degrees of freedom dragon fruit picking robot and investigates the manipulator’s motion characteristics to address the adaptive motion issues of the picking manipulator. Based on the agronomic characteristics of dragon fruit cultivation, the structural design of the robot and the dimensions of its manipulator were determined. A kinematic model of the dragon fruit picking robot based on screw theory was established, and the workspace of the manipulator was analyzed using the Monte Carlo method. Furthermore, a dynamic model of the manipulator based on the Kane equation was constructed. Performance experiments under trajectory and non-trajectory planning showed that trajectory planning significantly reduced power consumption and peak torque. Specifically, Joint 3’s power consumption decreased by 62.28%, and during the picking, placing, and resetting stages, the peak torque of Joint 4 under trajectory planning was 10.14 N·m, 12.57 N·m, and 16.85 N·m, respectively, compared to 12.31 N·m, 15.69 N·m, and 22.13 N·m under non-trajectory planning. This indicated that the manipulator operates with less impact and smoother motion under trajectory planning. Comparing the dynamic model simulation and actual testing, the maximum absolute error in the joint torques was −2.76 N·m, verifying the correctness of the dynamic equations. Through field picking experiments, it was verified that the machine’s picking success rate was 66.25%, with an average picking time of 42.4 s per dragon fruit. The manipulator operated smoothly during each picking process. In the study, the dragon fruit picking manipulator exhibited good stability, providing the theoretical foundation and technical support for intelligent dragon fruit picking. Full article

This paper outlines a conceptual design for a multi-level natural language-based planning system and describes a demonstrator. The main goal of the demonstrator is to serve as a proof-of-concept by accomplishing end-to-end execution in a real-world environment, and showing a novel way of interfacing an LLM-based planner with open-set semantic maps. The target use-case is executing sequences of tabletop pick-and-place operations using an industrial robot arm and RGB-D camera. The demonstrator processes unstructured user prompts, produces high-level action plans, queries a map for object positions and grasp poses using open-set semantics, then uses the resulting outputs to parametrize and execute a sequence of action primitives. In this paper, the overall system structure, high-level planning using language models, low-level planning through action and motion primitives, as well as the implementation of two different environment modeling schemes—2.5 or fully 3-dimensional—are described in detail. The impacts of quantizing image embeddings on object recall are assessed and high-level planner performance is evaluated using a small reference scene data set. We observe that, for the simple constrained test command data set, the high-level planner is able to achieve a total success rate of 96.40%, while the semantic maps exhibit maximum recall rates of 94.69% and 92.29% for the 2.5d and 3d versions, respectively. Full article