Search Results (57)

Force sensing on hands can provide an understanding of interaction forces during manipulation, with applications in different fields, including robotics and medicine. While several approaches to accomplish this have been proposed, they often require relatively complex and/or expensive fabrication techniques and materials. On the other hand, less complex and expensive approaches often suffer from poor accuracy of measurements. An example of this is provided by sensors built with Velostat, a polyethylene–carbon composite material that exhibits resistance changes when force is applied. This material is both cheap and easy to work with, but sensors made from Velostat have been shown to suffer from low accuracy, limiting its usefulness. This work explores the effect of stacking multiple layers of 0.1 mm Velostat sheets on accuracy, using no additional fabrication techniques or other material aside from electrode connections, with the rationale that this is both economical and can be accomplished easily. We evaluate measurement error for designs with different numbers of layers (1, 3, 4, 5, 10, 20, and 30) against a load cell, and also compare this with the error for a USD 10 commercial force sensing resistor designed for measurement of hand forces (FSR 402) in three evaluations (static, cyclic, and finger base interactions). Our results show that layered sensors outperform both the one-layer design and the commercial FSR sensor consistently under all conditions considered, with the best performing sensors reducing measurement errors by at least 27% and as much as 60% when compared against the one-layer design. Full article

The automotive industry is one of the most automatized industries, employing more than one million robots worldwide. Although several steps in car production are completely automated, many steps are still carried out by operators, especially in tasks requiring high dexterity. Additionally, customization and deployability are still pending issues in this industry, where a real collaboration between robots and operators would increase the reconfigurability of the assembly lines. This paper presents an innovative robotic cell focused on the motor and gearbox assembly, including collaborative industrial robots and autonomous mobile manipulators along the different assembly stations. The design also incorporates a human-centered approach, with an enhanced human interface to facilitate the interaction with operators with the complete robotic cell. The proposed approach has been deployed and validated on a real automotive industrial scenario, obtaining promising metrics and results. 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

The main task of the research involves creating a Digital Twin (DT) application serving as a framework for Virtual Commissioning (VC) with Supervisory Control and Data Acquisition (SCADA) and Cloud storage solutions. An Internet of Things (IoT) integrated automation system with Virtual Private Network (VPN) remote control for assembly and disassembly robotic cell (A/DRC) equipped with a six-Degree of Freedom (6-DOF) ABB 120 industrial robotic manipulator (IRM) is presented in this paper. A three-dimensional (3D) virtual model is developed using Siemens NX Mechatronics Concept Designer (MCD), while the Programmable Logic Controller (PLC) is programmed in the Siemens Totally Integrated Automation (TIA) Portal. A Hardware-in-the-Loop (HIL) simulation strategy is primarily used. This concept is implemented and executed as part of a VC approach, where the designed PLC programs are integrated and tested against the physical controller. Closed loop control and RM inverse kinematics model are validated and tested in PLC, following HIL strategy by integrating Industry 4.0/5.0 concepts. A SCADA application is also deployed, serving as a DT operator panel for process monitoring and simulation. Cloud data collection, analysis, supervising, and synchronizing DT tasks are also integrated and explored. Additionally, it provides communication interfaces via PROFINET IO to SCADA and Human Machine Interface (HMI), and through Open Platform Communication—Unified Architecture (OPC-UA) for Siemens NX-MCD with DT virtual model. Virtual A/DRC simulations are performed using the Synchronized Timed Petri Nets (STPN) model for control strategy validation based on task planning integration and synchronization with other IoT devices. The objective is to obtain a clear and understandable representation layout of the A/DRC and to validate the DT model by comparing process dynamics and robot motion kinematics between physical and virtual replicas. Thus, following the results of the current research work, integrating digital technologies in manufacturing, like VC, IoT, and Cloud, is useful for validating and optimizing manufacturing processes, error detection, and reducing the risks before the actual physical system is built or deployed. Full article

Background/Objectives: Lactic acidosis is one of the most common causes of metabolic acidosis in hospitalized patients. It happens when lactic acid production exceeds lactic acid clearance. The elevation of lactate was commonly improved after the restoration of tissue perfusion. However, there are rare cases of severe lactate elevation (greater than 8 mmol/L) in the intraoperative period of thoracoscopic surgery. A poor prognosis with high morbidity and mortality characterizes these cases. Case Description: A 72-year-old man was admitted to the Soroka University Medical Center for thoracoscopic robotic left upper lobe lobectomy due to squamous cell carcinoma. At the end of surgery (overall, 8.5 h), the lactate level reached 10.2 mmol/L with the development of severe lactic metabolic acidosis. Thiamine was successfully given to patients to stimulate lactate clearance towards the cycle of tricarboxylic acids via pyruvate. Conclusions: Though the pathogenesis of this state in our case is not fully clear, it may have been induced by chemotherapy and during tumor manipulation by a surgeon. The successful recovery of blood lactic levels after thiamine treatment is suggestive of thiamine deficiency as a possible cause of lactic acidosis in our patient. Although we do not have data on the plasma thiamine level, we suggest that its determination in the perioperative period would be beneficial for excluding a probable thiamine deficiency in the case of severe lactic acidosis. Full article

In recent years, technologies for human–robot interaction (HRI) have undergone substantial advancements, facilitating more intuitive, secure, and efficient collaborations between humans and machines. This paper presents a decentralized HRI platform, specifically designed for printed circuit board manufacturing. The proposal incorporates many input devices, including gesture recognition via Leap Motion and Tap Strap, and speech recognition. The gesture recognition system achieved an average accuracy of 95.42% and 97.58% for each device, respectively. The speech control system, called Cellya, exhibited a markedly reduced Word Error Rate of 22.22% and a Character Error Rate of 11.90%. Furthermore, a scalable user management framework, the decentralized multimodal control server, employs biometric security to facilitate the efficient handling of multiple users, regulating permissions and control privileges. The platform’s flexibility and real-time responsiveness are achieved through advanced sensor integration and signal processing techniques, which facilitate intelligent decision-making and enable accurate manipulation of manufacturing cells. The results demonstrate the system’s potential to improve operational efficiency and adaptability in smart manufacturing environments. Full article

In this work, we present the construction and control of a four-degrees-of-freedom (DOF) manipulator aimed at addressing one of the key challenges faced by the Academy-Industry Cooperation Center (CCAI): the need for mechatronic equipment to support and facilitate the development of advanced robotic cells. We begin by designing the robot’s structure and components using SolidWorks software for computer-aided design (CAD) modeling. This ensures that all the links and parts fit together properly without collisions. The robot links are then manufactured using 3D printing. Additionally, we performed kinematic modeling, dynamic analysis, and PI-V control, along with control using a trigonometric function (hyperbolic tangent). To evaluate the robot’s movement, we simulate these processes using Matlab R2019a/Simulink software, focusing on key parameters such as position, velocity, and acceleration, which inform the design of PI-V control for each link. We also present the electrical and electronic designs, followed by system implementation. The kinematics of the robot play a crucial role in the dynamics and controller design. We validate the kinematics using Peter Corke’s libraries based on the Denavit–Hartenberg parameters. The results show that the controller based on the trigonometric function improves the response time, particularly enhancing the performance of axes 2 and 3. Full article

The monitoring and control of an assembly/disassembly/replacement (A/D/R) multifunctional robotic cell (MRC) with the ABB 120 Industrial Robotic Manipulator (IRM), based on IoT (Internet of Things)-cloud, VPN (Virtual Private Network), and digital twin (DT) technology, are presented in this paper. The approach integrates modern principles of smart manufacturing as outlined in Industry/Education 4.0 (automation, data exchange, smart systems, machine learning, and predictive maintenance) and Industry/Education 5.0 (human–robot collaboration, customization, robustness, and sustainability). Artificial intelligence (AI), based on machine learning (ML), enhances system flexibility, productivity, and user-centered collaboration. Several IoT edge devices are engaged, connected to local networks, LAN-Profinet, and LAN-Ethernet and to the Internet via WAN-Ethernet and OPC-UA, for remote and local processing and data acquisition. The system is connected to the Internet via Wireless Area Network (WAN) and allows remote control via the cloud and VPN. IoT dashboards, as human–machine interfaces (HMIs), SCADA (Supervisory Control and Data Acquisition), and OPC-UA (Open Platform Communication-Unified Architecture), facilitate remote monitoring and control of the MRC, as well as the planning and management of A/D/R tasks. The assignment, planning, and execution of A/D/R tasks were carried out using an augmented reality (AR) tool. Synchronized timed Petri nets (STPN) were used as a digital twin akin to a virtual reality (VR) representation of A/D/R MRC operations. This integration of advanced technology into a laboratory mechatronic system, where the devices are organized in a decentralized, multilevel architecture, creates a smart, flexible, and scalable environment that caters to both industrial applications and educational frameworks. Full article

The use of computer vision in the industry has become fundamental, playing an essential role in areas such as quality control and inspection, object recognition/tracking, and automation. Despite this constant growth, robotic cell systems employing computer vision encounter significant challenges, such as a lack of flexibility to adapt to different tasks or types of objects, necessitating extensive adjustments each time a change is required. This highlights the importance of developing a system that can be easily reused and reconfigured to address these challenges. This paper introduces a versatile and adaptable framework that exploits Computer Vision and the Robot Operating System (ROS) to facilitate pick-and-place operations within robotic cells, offering a comprehensive solution for handling and sorting random-flow objects on conveyor belts. Designed to be easily configured and reconfigured, it accommodates ROS-compatible robotic arms and 3D vision systems, ensuring adaptability to different technological requirements and reducing deployment costs. Experimental results demonstrate the framework’s high precision and accuracy in manipulating and sorting tested objects. Thus, this framework enhances the efficiency and flexibility of industrial robotic systems, making object manipulation more adaptable for unpredictable manufacturing environments. Full article

The design of the work area of a robotic cell is currently an iterative process of trial and improvement, where, in the best cases, the user places the workstations and robotic manipulators in a 3D virtual environment to then semi-automatically verify variables such as the robot’s reach, cycle time, geometric interferences, and collisions. This article suggests using an evolutionary computation algorithm (genetic algorithm) as a tool to solve this optimization problem. Using information about the work areas and the robot’s reach, the algorithm generates an equipment configuration that minimizes the cell area without interference between the stations and, therefore, reduces the distances the robotic manipulator must travel. The objective is to obtain an optimized layout of the workstations and to validate this optimization by comparing the transfer times between stations with the actual times of an existing screwdriving cell. As a result, the transfer time was reduced by 9%. It is concluded that the algorithm can optimize the layout of a robotic cell, which can lead to significant improvements in efficiency, quality, and flexibility. Full article

The removal of cumulus cells from cumulus–oocyte complexes is a critical step in clinical in vitro fertilization. Since the oocyte is partially occluded by the surrounding cumulus cells and individual cumulus cells are small in size, it is difficult for embryologists to assess the oocyte's maturity before cumulus cell removal and to completely remove all the cumulus cells manually . Furthermore, it is easy for the oocyte to become lost inside the micropipette during aspiration due to the inaccuracy of manual control. To deal with these difficulties, a robotic system was developed to completely remove cumulus cells from mature oocytes. In this study, an EPSANet50 network was developed to accurately assess the maturity of oocytes, avoiding the removal of cumulus cells around the immature oocyte. An adaptive controller was designed to accurately position oocytes at the target position, reducing the loss of oocytes inside the micropipette. An improved Yolov5s network was proposed to quantify the number and size of cumulus cells and assess the completeness of cumulus cell removal. The experimental results on mouse cumulus–oocyte complexes showed that the robotic system had a higher success rate (98.0 ± 1.8% vs. 85.3 ± 2.4%) and lower discard rate (4.1 ± 2.7% vs. 19.6 ± 3.5%) than the manual operation. Moreover, a higher amplification rate and lower non-specific rate were also achieved by the robotic system in the subsequent genetic testing procedure, indicating reduced genetic contamination from the cumulus cells. Full article

Magnetic microgrippers, with their miniaturized size, flexible movement, untethered actuation, and programmable deformation, can perform tasks such as cell manipulation, targeted drug delivery, biopsy, and minimally invasive surgery in hard-to-reach regions. However, common external magnetic-field-driving devices suffer from low efficiency and utilization due to the significant size disparity with magnetic microgrippers. Here, we introduce a microgripper robot (MGR) driven by end electromagnetic and permanent magnet collaboration. The magnetic field generated by the microcoils can be amplified by the permanent magnets and the direction can be controlled by changing the current, allowing for precise control over the opening and closing of the magnetic microgripper and enhancing its operational range. Experimental results demonstrate that the MGR can be flexibly controlled in complex constrained environments and is highly adaptable for manipulating objects. Furthermore, the MGR can achieve planar and antigravity object grasping and transportation within complex simulated human cavity pathways. The MGR’s grasping capabilities can also be extended to specialized tasks, such as circuit connection in confined spaces. The MGR combines the required safety and controllability for in vivo operations, making it suitable for potential clinical applications such as tumor or abnormal tissue sampling and surgical assistance. Full article

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields. Full article

We present a thin and elastic tactile sensor glove for teaching dexterous manipulation tasks to robots through human demonstration. The entire glove, including the sensor cells, base layer, and electrical connections, is made from soft and stretchable silicone rubber, adapting to deformations under bending and contact while preserving human dexterity. We develop a glove design with five fingers and a palm sensor, revise material formulations for reduced thickness, faster processing and lower cost, adapt manufacturing processes for reduced layer thickness, and design readout electronics for improved sensitivity and battery operation. We further address integration with a multi-camera system and motion reconstruction, wireless communication, and data processing to obtain multimodal reconstructions of human manipulation skills. Full article

Controlling the collective behavior of micro/nanomotors with ultrasound may enable new functionality in robotics, medicine, and other engineering disciplines. Currently, various collective behaviors of nanomotors, such as assembly, reconfiguration, and disassembly, have been explored by using acoustic fields with a fixed frequency, while regulating their collective behaviors by varying the ultrasound frequency still remains challenging. In this work, we designed an ultrasound manipulation methodology that allows nanomotors to exhibit different collective behaviors by regulating the applied ultrasound frequency. The experimental results and FEM simulations demonstrate that the secondary ultrasonic waves produced from the edge of the sample cell lead to the formation of complex acoustic pressure fields and microfluidic patterns, which causes these collective behaviors. This work has important implications for the design of artificial actuated nanomotors and optimize their performances. Full article