Precise Handling Research Articles

QLTI-09. CLINICAL TRIALS IN NEURO-ONCOLOGY: DESIGN, LOGISTICS, CHALLENGES, AND PRACTICAL CONSIDERATIONS

Abstract Timely, secure and reliable transportation, precise handling and storage of biological specimens are critical for the success of clinical research. Research in neuro-oncology often involves complex assays such as next-generation sequencing where the integrityof brain tumor specimens is paramount. These challenges are pronounced in most Lower Middle-Income Countries (LMIC), due to climatic factors and infrastructure limitations, necessitating stringent measures to preserve sample quality and their genetic data integrity for downstream analysis. In this study, we conducted extensive cohort analysis on 80 tissues randomly picked samples of the 3600 samples collected across India to assess the impact of collection protocols, transportation logistics, and long-term storage on the genetic material of tissues. The quality and quantity of the isolated nucleic acids was determined by BioAnalyzer and Qubit parameters. Tissue samples were collected and stored at -80°C in RNAstable® within 4-12 hrs of collection. The DNA and RNA obtained from tissue samples stored for over one year was found to be of good quality with corresponding DNA Integrity Number (DIN) and RNA Integrity Number (RIN)between 7-9. Also, optimum quantity of DNA and RNA was obtained for whole exome and transcriptome sequencing. Upon sequencing, these samples showed good read coverage of 100X or more. These findings provide critical insights into the measures necessary for optimizing sample preservation and transportation to ensure accurate research outcomes. The results indicate that appropriate sample collection, transportation, and long-term storage of bio-specimens can be used effectively for future research. Additionally, the outcome underscores the potential for long-term studies in neuro-oncology using archived biological samples without compromising data integrity.

Application of multi-modal temporal neural network based on enhanced sparrow optimization in lithium battery life prediction

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model’s efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model’s robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model’s exceptional generalizability and potential for real-world applications in electric vehicle battery management.

Towards full autonomy in mobile robot navigation and manipulation

AbstractRobot autonomy is a key for automatizing industrial production plants, facilitating responsive search and rescue (SAR), and for interacting in urban settings. This work presents mobile systems capable of carrying out these tasks and summarises experience from practical deployments in three different domains for navigation. For industry, a mobile manipulator is presented, which is used for dynamic precise docking and object handling. For SAR, a semi-autonomous system is presented which is used for sampling hazardous materials, manipulating tools, and for localizing nuclear radiation sources. To improve urban logistics, a mobile robot autonomously navigates complex outdoor environments using semantic mapping techniques. Future work will explore further advancements that strive toward increased flexibility, reasoning and universal applicability of mobile robots. The goal is to reduce training, onboarding and tool changing times to reduce manual interfacing times.

High-precision silver electrode based on PEN substrate with robust mechanical performance

Wearable electronics are becoming more and more popular, and in order to ensure the safety and accuracy of wearable electronics, the requirements for the refinement and stability of flexible electrodes are getting higher and higher. The reduction of the amount of ink jet and the reduction of surface charge density and electrostatic stress near the nozzle hole can significantly improve the jetting state of the ink. And nozzle voltage is a key factor that affects them. The spreading of ink droplets on the substrate and the formation of a specific pattern is a dynamic process, which is closely related to the properties of the substrate and the state of the ink droplets. In this paper, PEN is used as the substrate to print silver electrode, with its good thermal stability and good mechanical properties. In order to solve these problems and improve the accuracy of the electrode pattern on PEN substrate, a joint control method of ink drop spacing and nozzle voltage is proposed. It is found that the accuracy of the pattern increases with the increase of the drop spacing. At 45 μm, 90.91 % of the pattern accuracy with the ideal pattern can be achieved. As the nozzle voltage decreases, so does the satellite spot. And at 25 V, it can achieve satellite-free spot printing. Because this method avoids the adjustment of ink properties and the regulation of voltage waveform, this advancement provides a simpler solution for patterned high-precision printed silver electrodes based on flexible substrates. In addition, a silver electrode with a resistivity of 3.43 μΩ·cm was obtained at an annealing temperature of 160 °C. The resistivity change of the electrode only 7.58 % under 5000 times of bending. This is very rare in flexible electrodes.The silver film on the PEN substrate exhibited excellent adhesion under the tape test. XPS depth profiling was used to characterize the elemental states of the films at different depths. The results indicated that the coordination bond between the PEN substrate and the Ag film resulted in good bending resistance, which gives a persuasive explanation for the first time. This result is also due to the precise handling of the film, which also results in a homogeneous distribution of the film. It solves the problem of poor adhesion of silver electrodes based on other substrates or other preparation methods. Therefore, silver electrodes based on PEN substrates have a broad application prospect in the field of flexible wearable electronics.

Alarm management approach for supervision of Green Hydrogen Plants

Amid the increasing significance of renewable energy sources, Green Hydrogen Plants (GHPs) have emerged as pivotal contributors to sustainable energy solutions. However, efficient and reliable alarm management in such complex systems remains a significant challenge. This paper presents a novel methodology called V-nets-based alarm management (VBAM), designed to improve supervision by addressing the intricacies of discrete event management in industrial applications such as GHPs. VBAM offers a powerful formalism that integrates visual modeling and temporal patterns, enabling the precise detection and handling of alarms. The proposed methodology is applied to a case study in a GHP, where critical operational parameters are continuously monitored. By analyzing discrete events and their temporal patterns, V-nets facilitate early fault detection, minimize false positives, and optimize alarm response. The theoretical application of VBAM demonstrates its efficacy in improving system safety, reducing downtime, and enhancing overall operational efficiency within GHPs. The contributions of this work to the state of the art include the development of a comprehensive VBAM methodology tailored to GHPs, as well as the theoretical and practical demonstration of its potential impact on alarm management within GHP operations. The outcomes from the experiments showcase the adaptability and effectiveness of VBAM in addressing the complexities of alarm management in GHPs, thereby contributing to enhanced safety, efficiency, and resilience in the operation of GHPs.

Ultrasonic Levitation as a Handling Tool for In-Space Manufacturing Processes

Abstract 3D printing is one of the key technologies in space exploration. The disparity in gravitational forces between Earth and space presents both challenges and opportunities with regard to material handling. This article examines the potential of employing ultrasonic levitation as a handling tool for substrate-free additive manufacturing processes in microgravity environments. Through preliminary experiments, we demonstrate the feasibility of manipulating polymer powders using acoustic fields while concurrently melting the levitated material. Subsequent experiments conducted in our drop tower facility confirm our ability to manipulate particles with acoustic traps under microgravity conditions. Building upon these findings, we outline plans to further advance our research using an expanded acoustic levitation system capable of three-dimensional object manipulation. Our objectives include moving and orienting large components beyond the wavelength limit in microgravity, manipulating granular raw material while melting it in proximity to the print part, and achieving a semi-continuous fusion of print material with the print part. Therefore, we present an intelligent control strategy based on the results of a digital twin simulation. Furthermore, we utilize a stereo camera combined with computer vision as feedback for the control system to ensure precise handling of the manipulated objects and particles. This study represents a significant advance toward the realization of efficient substrate-free additive manufacturing processes in microgravity environments, with potential applications for in-space manufacturing. Ultimately, this could result in long-term space missions becoming less reliant on supply deliveries, thus reducing cost and additionally enabling faster response to unforeseen issues.

Two-stage spatiotemporal cooperative reentry guidance strategy using transformer and improved beluga whale optimization

This research addresses the challenge of insufficient control margin caused by the coupling of multiple constraints in the cooperative precise reentry guidance of hypersonic vehicles. Drawing inspiration from the concept of spatiotemporal decoupling control, a rapid guidance strategy is developed to ensure precise handling of all constraints, including attack time, attack angle, and trajectory constraints. Initially, during the early phase of gliding flight, the adjustment of the heading angle is conceptualized as a single variable root-solving problem, in relation to the entrance width of the lateral azimuth error corridor. Subsequently, a lateral azimuth error corridor with adaptively narrowing entrance width, coupled with a Transformer network-based bank angle predictor, is incorporated to achieve precise fine-tuning of the heading angle under the soft constraint of velocity. In the later phase of gliding flight, the design of a cooperative guidance law under complex multiple constraints is transformed into a nonlinear rapid optimization problem of control commands. An enhanced beluga whale optimization suited to this guidance task is proposed. Finally, numerical simulations are carried out to validate the effectiveness of the proposed strategy under both nominal and uncertain conditions.

Flexible droplet microfluidic devices for tuneable droplet generation

Droplet microfluidics is a promising technology for applications that require precise handling of minute fluid volumes. This technology has broad applications in the pharmaceutical industry, food and beverage, and material synthesis. Droplet size and frequency are the two key parameters in these applications. Current droplet microfluidic devices are rigid platforms where the geometry and dimensions of microchannels are fixed after the device fabrication. This poses a significant limitation when adjusting the droplet generation characteristics. Repetitive design, fabrication, and testing are often needed to achieve the optimal microchannel dimensions. To overcome this bottleneck, we develop a proof-of-concept stretchable microfluidic device that can control droplet size and generation frequency by precisely controlling the microchannel dimensions in real-time. Theoretical analysis, numerical modelling, and experimental characterisation were conducted to study the influence of device lateral stretching on these characteristics. We found that the lateral stretching of the device increased the droplet diameter and spacing but reduced the droplet generation frequency. Droplet diameter and spacing increased by ∼20 %, and droplet frequency decreased by ∼45 % when the device was strained up to 25 %. We believe this innovative, flexible droplet microfluidic platform will provide an alternative way to precisely control the droplet formation by modifying the channel dimensions on-site and in real-time.

The automatization of press statistics as one of the ways to improve the service of information users

The Department of Press Statistics at the Book Palace of Lithuania is the first in the Soviet Union to apply computers for preparing quick accounts of gathered information. This significantly helps to regulate book production and its thematic planning. The Department of Press Statistics functions independently as a constituent part of the automatization of press statistics in the Book Palace. The rapid and precise handling of statistical tasks, which reflects the basic tendencies of press production activities, enables the improvement of the press production process. The grouping and gathering of the main features of the press and the analysis of statistical feedback help to use computers in the most rational way to increase the capacity for solving statistical tasks.

Soft Robotic System with Continuum Manipulator and Compliant Gripper: Design, Fabrication, and Implementation

This paper presents the design, construction, and implementation of a soft robotic system comprising a continuum manipulator arm equipped with a compliant gripper. Three main objectives were pursued: (1) developing a soft silicone gripper as an alternative to expensive and rigid steel grippers, enabling safe and precise handling of delicate or irregular objects such as fruits, glassware, and irregular shapes; (2) fabricating a continuum manipulator arm with robotic joints inspired by vertebrae, allowing for smooth, non-linear motion and more excellent maneuverability compared to traditional rigid arms, enabling access to hard-to-reach areas; and (3) integrating the compliant gripper with the continuum manipulator and implementing a control system for the soft gripper and remote bending arm using a microcontroller. The soft gripper, manipulator arm vertebrae, and other components were fabricated using 3D printing with PLA material for the molds. The gripper construct used hyperelastic silicone (Ecoflex 00.30). The continuum manipulator achieved a higher degree of freedom and mobility, while simulations and experiments validated the design’s effectiveness. The comparison shows that the close agreements differ by only 2.5%. In practical experiments involving lifting objects, the gripper was able to carry items with a greater mass. The proposed soft, integrated robotic system outperformed traditional rigid approaches, offering safe and flexible handling capabilities in unstructured environments. The nature-inspired design enabled a compliant grip and enhanced maneuverability, making it suitable for various applications requiring dexterous manipulation of delicate or irregularly shaped objects.

Heat and mass transfer analysis for bi-dimensional bioconvective MHD nanofluid with varying thermal traits

ABSTRACT Bioconvection phenomena offer practical applications in biomicrosystems, fuel cells, biosensors, biocomputing, electronic thermal management, and renewable energy systems, showcasing their relevance and potential impact across various engineering sectors. Consequently, this study delves into the examination of a bi-dimensional magnetohydrodynamic nanofluid flow model encompassing the intricate dynamics of gyrotactic microorganisms, subject to the effects of suction and thermal radiation. The primary objective of the investigation is to optimize the thermal energy transfer efficiency of the fluid while concurrently minimizing associated costs, with the secondary aim of diminishing the frictional resistance at the fluid-solid interface. The basis for energy and momentum equations incorporating radiation using Rosseland’s approximation is established by the Buongiorno nanofluid model. The foundational PDEs along with their BCs, are reformulated into ODEs utilizing similarity variables, then transformed into a set of first-order ODEs ensuring compatibility with MATLAB’s bvp4c solver, which offers high convergence rates and accuracy due to its efficient algorithms and precision handling. Heat transfer rate boosts up by increasing suction and radiation parameters, while it dwindles due to an increase in the magnetic field. Enhancing suction and the magnetic field lowers skin friction. Microbial concentration surges by increasing the magnetic field, while suction minimizes it. Intensifying the thermophoresis parameter increases the concentration of nanoparticles.

Performance of different microfluidic devices in continuous liquid-liquid separation

Droplet-based microfluidics exhibit numerous benefits leading to relevant innovations and many applications in various fields. The precise handling of droplets in capillaries, including droplet formation, manipulation, and separation, is essential for successful operation. Only a few reports are known concerning the separation of segmented flows, particularly the continuous separation of droplets, which is of high interest regarding the control of biochemical and chemical reactions or other applications where the contact time of the involved phases is crucial. Here, the separation must be flexible and adjusted to different flow parameters, such as the surface tension, the volumetric flow rates, and their ratios. This contribution presents two novel open-source approaches based on additive manufacturing and mechanical deforming for continuous liquid–liquid separation under various flow conditions. The Laplace pressure is the driving force for the separation, which is adjusted to the flow conditions by adapting the distance of pinning points provided by the design of the devices. Details of the device design and experimental setup are shown along with limitations to promote further development and to increase availability for researchers. With the right parameters, sophisticated separations can be realized by inexpensive laboratory equipment and simple control of them. It was found that the distance between the pinning points needs to enlarged for increasing volumetric flow rates and reduced for higher viscosities of the continuous phase respectively higher amounts of the dispersed phase. The open source approach of this article expands the exploration space in addition to commercially available phase separators only available to a selected group of people.Graphical

Design Optimization of the control arms of a Double Wishbone Suspension System using Topological Approach

This paper focuses on design optimization of an existing double wishbone type suspension system from a consumer vehicle. The suspension system acts as a key element in between the vehicle and the road, and has a significant impact on the handling and feel of the vehicle. The suspension system is a critical system that directly influences vehicle dynamics, ride comfort and overall driving experience. Optimizing core suspension factors such as weight, strength and geometry are a key to achieving optimal performance, handling, ride quality and stability. Known for its precise handling characteristics and superior ride comfort, the double wishbone suspension is an ideal candidate for improving its design to a new ceiling. By using FEM with the principles of topology optimization, we aim to create an optimized control arm design that is lighter in weight as compared to its original design, without significantly affecting its strength characteristics. Key Words: control arms, double wishbone suspension, topology optimization, design, efficiency, lightweight design.

Čimbenici rizika i učestalost mišićno-koštanih tegoba radnika pri radu u tehnici batika uzrokovanih položajem tijela i rada mišića ruku

The batik industry as one of Malaysian cultural heritage is mainly located on the East Coast of Malaysia, particularly in the states of Kelantan and Terengganu. Most of the industry is home-based. Hence, the traditional batik work environment and tasks impose threats to the well-being of the artisans. This study investigates ergonomic issues and the prevalence of musculoskeletal disorders in two of Kelantan’s batik industries. The artisans’ natural working postures and the environments of the two places were observed and video recorded. Muscle activations on the wrist of the artisan doing the paint (canting) were imitated. Surface electromyography (sEMG) investigations were performed in the laboratory. A questionnaire survey was also distributed among craftsmen and artisans involved in the batik industry in Kelantan. Results illustrate evidence of the need to educate batik industries on ergonomic risk factors. According to the survey of 33 respondents, the most affected body parts are the waist (60%), back and feet (57%). The walkthroughs and site visits showed that batik making process demands awkward postures which include standing, precision handling of hand tools, trunk bending & rotating and overreaching repetitively between 4 to 8 hours. The Rapid Upper Limb Assessment (RULA) index obtained from the assessments ranges from 7-8. It was also shown from the sEMG data that trunk bending affects muscle contraction of the wrist during painting movement. Therefore, immediate interventions are required to improve batik artisans’ postural working conditions, especially on the neck, trunk, legs, wrists and arms.

All-In-One OsciDrop Digital PCR System for Automated and Highly Multiplexed Molecular Diagnostics.

Digital PCR (dPCR) holds immense potential for precisely detecting nucleic acid markers essential for personalized medicine. However, its broader application is hindered by high consumable costs, complex procedures, and restricted multiplexing capabilities. To address these challenges, an all-in-one dPCR system is introduced that eliminates the need for microfabricated chips, offering fully automated operations and enhanced multiplexing capabilities. Using this innovative oscillation-induced droplet generation technique, OsciDrop, this system supports a comprehensive dPCR workflow, including precise liquid handling, pipette-based droplet printing, in situ thermocycling, multicolor fluorescence imaging, and machine learning-driven analysis. The system's reliability is demonstrated by quantifying reference materials and evaluating HER2 copy number variation in breast cancer. Its multiplexing capability is showcased with a quadruplex dPCR assay that detects key EGFR mutations, including 19Del, L858R, and T790M in lung cancer. Moreover, the digital stepwise melting analysis (dSMA) technique is introduced, enabling high-multiplex profiling of seven major EGFR variants spanning 35 subtypes. This innovative dPCR system presents a cost-effective and versatile alternative, overcoming existing limitations and paving the way for transformative advances in precision diagnostics.

Bionic Surgery Meets Bionic Reconstruction - First In-human use of Robotic Microsurgery in Targeted Muscle Reinnervation

Robotic microsurgery is an emerging field in reconstructive surgery, which provides benefits such as improved precision, optimal ergonomics, and reduced tremors. However, only a few robotic platforms are available for performing microsurgical procedures, and successful nerve coaptation is still a challenge. Targeted muscle reinnervation (TMR) is an innovative reconstructive procedure that rewires multiple nerves to remnant stump muscles, thereby reducing neuroma and phantom limb pain and improving the control of bionic prostheses. The precision of surgical techniques is critical in reducing axonal sprouting around the coaptation site to minimise the potential for neuroma formation. This study reports the first use of a microsurgical robotic platform for multiple nerve transfers in a patient undergoing TMR for bionic extremity reconstruction. The Symani robotic platform, combined with external microscope magnification, was successfully used, and precise handling of nerve tissue and coaptation was easily feasible even in anatomically challenging environments. While the precision and stability offered by robotic assistance may be especially useful for nerve surgery, the high economic costs of robotic microsurgery remain a major challenge for current healthcare systems. In conclusion, this study demonstrated the feasibility of using a robotic microsurgical platform for nerve surgery and transfers, where precise handling of tissue is crucial and limited space is available. Future studies will explore the full potential of robotic microsurgery in the future.

AI-driven warehouse automation: A comprehensive review of systems

This comprehensive review explores the profound impact of artificial intelligence (AI) on warehouse automation, providing an in-depth examination of various AI-driven systems. As industries increasingly embrace automation to enhance efficiency and streamline operations, the integration of AI technologies into warehouse management systems has become pivotal, reshaping the landscape of logistics and supply chain management. AI-driven warehouse automation systems leverage advanced algorithms to optimize various aspects of warehouse operations, from inventory management to order fulfillment. Machine learning algorithms play a key role in demand forecasting, allowing warehouses to predict and adapt to changing customer needs. Computer vision technologies enhance robotic vision, facilitating tasks such as item recognition, pick-and-place operations, and quality control. These advancements significantly contribute to increased accuracy, speed, and cost-effectiveness in warehouse processes. The review provides a detailed examination of the applications of AI in warehouse automation, encompassing autonomous mobile robots (AMRs), robotic arms, and automated guided vehicles (AGVs). AMRs equipped with AI algorithms navigate warehouse environments autonomously, optimizing pick routes and adapting to changes in the warehouse layout. Robotic arms, enhanced by AI, enable precise and adaptable material handling, contributing to the efficiency of tasks like packing and palletizing. AGVs, guided by AI, ensure seamless material transport within warehouses, enhancing overall operational agility. Recent trends in AI-driven warehouse automation systems underscore the dynamic evolution of this field. Edge computing solutions empower these systems to process data locally, reducing latency and enhancing real-time decision-making. Reinforcement learning algorithms enable robotic systems to learn and adapt their behavior based on changing environmental conditions, contributing to continuous improvement and efficiency gains. In conclusion, this review illuminates the pivotal role of AI in transforming warehouse automation systems, revolutionizing the way logistics and supply chain operations are conducted. The collaborative synergy between AI and warehouse automation promises to drive unprecedented advancements in efficiency, accuracy, and adaptability within the evolving landscape of modern warehouses.

Analytical Method for Quantifying Monoterpenoids (p-Cymene and Myrcene) in Nanoemulsions Using High-Performance Liquid Chromatography.

Myrcene and cymene, aromatic monoterpenes found in plants and essential oils, possess distinctive aromatic qualities. However, their volatility and limited solubility pose challenges in precise handling and formulation. Meanwhile, nanoemulsions emerge as promising drug delivery systems, improving the bioavailability and stability of these active ingredients. This article aimed to develop an HPLC method for the quantification of two monoterpenoids, p-cymene and myrcene, in nanoemulsions. The method used a Phenomenex® Synergi™ Fusion-RP column (150 mm × 4.6 mm id, 4 μm particle size) on an HPLC system with isocratic elution. The mobile phase was composed of acetonitrile and water (60:40, v/v) and was validated in terms of specificity, linearity, accuracy, precision, robustness, and selectivity. The method provided accurate and precise results with a correlation coefficient of 0.999 and RSD values of less than 2%. The method can be used for quality control of nanoemulsions containing these monoterpenoids and as a reference for future studies on their efficacy and stability. The study demonstrates the feasibility of using HPLC for the quantification of monoterpenoids in nanoemulsions and its potential as a quality control tool for nanoemulsion-based drug delivery systems. The method's accuracy, precision, and reliability, as evidenced by high correlation coefficients and low RSD values, underscore its suitability for ensuring the consistent formulation of these monoterpenoid-containing nanoemulsions, while also serving as a reference point for future research endeavors in this field.

Automation and Computerization of (Bio)sensing Systems.

Sensing systems necessitate automation to reduce human effort, increase reproducibility, and enable remote sensing. In this perspective, we highlight different types of sensing systems with elements of automation, which are based on flow injection and sequential injection analysis, microfluidics, robotics, and other prototypes addressing specific real-world problems. Finally, we discuss the role of computer technology in sensing systems. Automated flow injection and sequential injection techniques offer precise and efficient sample handling and dependable outcomes. They enable continuous analysis of numerous samples, boosting throughput, and saving time and resources. They enhance safety by minimizing contact with hazardous chemicals. Microfluidic systems are enhanced by automation to enable precise control of parameters and increase of analysis speed. Robotic sampling and sample preparation platforms excel in precise execution of intricate, repetitive tasks such as sample handling, dilution, and transfer. These platforms enhance efficiency by multitasking, use minimal sample volumes, and they seamlessly integrate with analytical instruments. Other sensor prototypes utilize mechanical devices and computer technology to address real-world issues, offering efficient, accurate, and economical real-time solutions for analyte identification and quantification in remote areas. Computer technology is crucial in modern sensing systems, enabling data acquisition, signal processing, real-time analysis, and data storage. Machine learning and artificial intelligence enhance predictions from the sensor data, supporting the Internet of Things with efficient data management.

Towards utilising Artificial Intelligence for advanced reasoning and adaptability in human-robot collaborative workstations

Significant effort has been allocated recently by the research community for the seamless integration of Artificial Intelligence (AI) in modern human-robot collaborative (HRC) systems. Mainly in the context of human operator support and robotic object manipulation, the adoption of AI could become the main enabler towards the realisation of seamless HRC. This work presents the vision of the European funded project MASTERLY for an AI-based framework to enhance operator assistance and optimize robotic object manipulation. The proposed framework aims to contribute towards the realisation of intelligent, human-centric collaborative workstations focusing on two principal areas. The first area is human operator action recognition, which involves interpreting human actions to provide instructions and notifications to the operator only when it is considered necessary via Augmented Reality (AR) interfaces. The second area is the generation of a strategy for robotic manipulation of unknown objects. The proposed approach utilizes AI methods to identify optimal grasping points on unknown objects for efficient and precise robotic handling. The performance of the proposed framework is tested and validated in a case study from the elevators’ manufacturing industry, and more specifically, the assembly of the elevator's electrical panel.