Robotic Equipment Research Articles

Barriers and recommendations for the implementation of robot-assisted minimally invasive surgery in Africa.

Robotic surgery offers several advantages to the African setting, including shorter hospital stays, faster return to work, and increased overall productivity. However, its adoption has been limited by several factors. This review aims to present the barriers to implementation, and recommendations for integrating robotic surgery into the African healthcare system. Use of robotic surgery in Africa is primarily limited to Egypt and South Africa. Barriers faced by other countries were categorized into economic, infrastructural, systemic, and training-related. They include limited healthcare budgets, initial costs of robotic systems, patients' inability to afford robotic procedures, out-of-pocket healthcare financing, inadequate power supply, limited internet connectivity, poor healthcare leadership, and insufficient surgeon training facilities. Public-private partnerships, provision of loans and subsidies, introduction of cheaper robotic systems, and local manufacturing of robotic equipment will serve as cost-effective innovations. It is also important to improve healthcare financing and strengthen healthcare leadership across Africa. To address the lack of surgeon training facilities, remote assistance for surgeon training can be used to create a mentor-mentee relationship between robotic surgeons in any part of the world and surgical trainees in Africa to facilitate knowledge transfer. Prior investment in electricity and network infrastructure is however necessary. Establishment of fellowships to provide early exposure to robotic surgery should also be explored. AI-integrated robotic surgery can also enhance precision and safety, and provide tailored training tools for surgeons. Similar barriers to the adoption of surgical robotics are faced across Africa. By implementing the provided recommendations, robotic surgery can still be widely adopted in African settings, despite the delay.

Improving On-farm Energy Use Efficiency by Optimizing Machinery Operations and Management: A Review

AbstractThe energy use and emissions from direct fossil fuel combustion on-farms to power farm machinery was critically reviewed. Approximately, 15% of agricultural production costs on-farm are energy-related. A potential solution to more sustainable energy use is a shift toward biofuels from renewable resources. The reduction of greenhouse gas emissions through the substitution of diesel oil with biodiesel depends on the feedstock, the inter-esterification process, the storage period, and ambient conditions. In modern tractors, increased fuel use efficiency (or reduced fuel consumption) has been achieved by power/load matching and the use of variable transmission. Engine management systems that are capable of continuously communicating with the engine and transmission to make appropriate adjustments based on inputs received from the tractor allow for quick and precise responses to changing conditions. As a result, maximum efficiency and productivity can be obtained from the tractor operating similarly to the traditional ‘gear-up and throttle-back’ methods of a proficient operator. The future for autonomous tractors is promising, though not new. Electric-powered tractors are near to commercialization or are already commercially available. Hybrid electric driven tractors present some advantages in terms of increased energy use efficiency and functionalities. Increased efficiency can lead to a reduction in diesel fuel consumption and hence, a concurrent decrease in CO2 emission. Where the local electricity supply has a low-carbon emission factor, this can also result in significant emission reductions. Small light-weight robotic equipment can potentially perform functions currently undertaken by tractor-drawn and other heavy equipment with high-fuel consumption, provided field operating capacity was not compromised. However, the size and weight limitations inherent in current harvesting and transport technology mean that soil compaction will still be a problem with robotic units. The robotic operation of medium-scale equipment within a precision-controlled traffic farming environment should offer more feasible and energy-efficient alternatives.

Design of Connector Assembly Equipment for the Automotive Industry

The automotive industry is one of the most demanding sectors of all manufacturing industries due to its competitiveness. It is necessary to innovate through the implementation of automated and robotic equipment, leading to cycle time and labor cost reduction. This work aims to design semi-automatic equipment to assemble cabling connectors used in the automotive sector, replacing a manual process currently taking place in an automotive components company. In the proposed equipment, the operator places a connector in the equipment, and the components (pins and seals) are automatically inserted. A vision sensor with artificial intelligence then confirms the correct application. The equipment operation defined as Finite Element Method (FEM) was applied for structural verification; the materials and fabrication processes were detailed; the associated costs were calculated, and the equipment subsets were validated. The design was successfully accomplished, and the imposed requirements were fulfilled, with significant advantages over the current process, providing new knowledge on how semi-automatic systems can be deployed to enhance the productivity and quality of manufacturing processes. The design principles and insights gained from this work can be applied to other automation challenges, particularly where manual processes need to be replaced by more efficient semi-automatic or automatic systems. The modularity of the overall solution and the design concepts of the component inserter, component feeder, and assembly process allow for its use in different assembly scenarios beyond the automotive sector, such as electronics or aerospace, providing a contribution to increased competitiveness and survival in the global market.

Liquid Handling Technologies: A Study through Major Discoveries and Advancements

Liquid handling is an essential process in laboratories involving automatically transferring accurate amounts of liquids for various task completions such as sample preparation, pipetting, dilution, and mixing. Automated liquid handling systems (ALHS) help to minimize the errors, complications, and difficulties that are faced with manual methods of liquid handling by efficiently enhancing the accuracy, precision, and reproducibility of experimental workflows and have transformed life sciences aiding research in various fields. Processes like Nuclear Magnetic Resonance (NMR) spectrometry, mass spectrometry, and acoustic droplet ejection with ALHS showcase tremendous potential. Outdated manual machinery is being replaced rapidly with automated tools. We have observed that these laboratory setups are sophisticated and costly. It is challenging to access such technologies, especially in poor or developing countries. At the nanoscale level, handling and evaporation of biomaterials becomes a great issue. Multitudinous devices can combat the cost issue along with utilization of single equipment without the enhancement of add-on modules. Liquid handling devices enhance the quality of experiments contributing to efficient dealing of circumstances like COVID-19. They aid in genomics and proteomics and play a pivotal role in liquid manipulation, drug screening, quantifications, forensic studies and many more. Liquid handling devices with robotic equipment can perform multiple tasks and can assist in a wide range of fields. We anticipate that in the coming years, our laboratories will be furnished with advanced, robotic technologies and cause a revolution in the scientific scenery.

Разработка трудноизвлекаемых запасов угля мобильными горнодобывающими комплексами без постоянного присутствия людей в зоне ведения горных работ

Today, millions of tons of coil remain in the subsoil and are written off as losses. These include hard-to-recover reserves unfit for mining with traditional high-performance means of integrated mechanization. One of the all-purpose technical solutions to the problem of developing such reserves can be technologies using robotic mobile mining equipment based on a heading-and-winning machine and a self-propelled car. Flowcharts for coal deposit development have been designed for seams unfit for mining with integrated mechanization means (medium thick seams, thick seams, flat and steeply inclined seams). These include open-pit and underground mining of marginal reserves from the side of the pit wall and limited reserves from the daytime surface performed by mobile mechanization means. During the operation of these means, the following process operations are repeated: coal breaking, loading into a self-propelled car, transportation along a preset trajectory, unloading into a reloader or other transport vehicle, or a warehouse on the bench. These operations can be robotized, which will increase both the safety and efficiency of coal mining. In order to perform coal-mining operations without constant attendance of maintenance staff, the existing mechanization means must be retrofitted with machine vision and a remote control system. Moreover, a robotized control system for the set of equipment must be developed to resolve the following problems. For the heading-and-winning machine: transportation (feed) along a straight line; transportation along the preset curve (formation of a diagonal entry); coal breaking at the preset thickness; coal breaking within coal-rock boundaries or along a preset cross-sectional contour; loading of broken coal into a self-propelled car; and control of the car loading level. For a self-propelled car: transportation along a preset trajectory from the loading point to the unloading point; control and management of the completeness of loading (unloading).

Voice Control System for Robotics in a Noisy Environment

This paper analyzes the effectiveness of the developed voice control system for robotics based on MFCC and GMM-SVM under the influence of interference in the communication channel. The system allows characterizing individual features of speech signals with their subsequent classification and making a reliable decision on the interpretation and execution of voice commands by robotic equipment. The proposed voice control system for robotics based on MFCC and GMM-SVM is implemented using the following technologies: 1) selection of active speech areas by calculating the short-term energy and the number of zero crossings between adjacent frames of the speech signal; 2) adaptive wavelet filtering of the speech signal, where it is necessary to generate threshold values, which will reduce the impact of additive noise; 3) selection of recognition features, which are used as mel-frequency cepstral coefficients; 4) classification of recognition features based on mixtures of Gaussian distributions and the support vector method using the linear Campbell kernel and the principal component method with a projection on latent structures, which will reduce errors of the 1st and 2nd kind.

Heterogeneous Organohydrogel Toward Automated and Interference-Free Gradient Feeding of Drugs in Cell Screening.

Cell-based microarrays are widely used in the fields of drug discovery and toxicology. Precise gradient generation and automated drug feeding are essential for high-throughput screening of live cells in tiny droplets. However, most existing technologies either require sophisticated robotic equipment or cause mechanical/physiological interference with cells. Here, a heterogeneous organohydrogel is presented for automated gradient drug feeding, while ensuring minimal interference with cells. The heterogeneous organohydrogel comprises three crucial components. The bottom surface can automatically generate gradients functioning as a gradient generator, the organohydrogel bulk allows unidirectional transport of drugs without backflow, and the top surface with hydrophilic arrays can firmly anchor the cell-based droplet array to evaluate the concentration-dependent bioeffects of drugs accurately. Such a unique structure enables universal screening of different cell types and drugs dissolved in different solvents, requiring neither additional accessories nor arduous drug functionalization. The heterogeneous organohydrogel with unprecedented automation and non-interference possesses the enormous potential to be a next-generation platform for drug screening.

Comparative analysis of semantic segmentation and multi-boosted faster R-CNN for robotic tool detection in diabetes patient care

Diabetes, a common chronic illness, need meticulous patient care and treatment, which frequently necessitates advanced robotic help. The identification of robotic tools is critical in guaranteeing precise and safe medical procedures. With the growing use of robots in healthcare, reliable identification of instruments inside a patient care setting is critical for allowing smooth and efficient medical procedures. We used a Multi-Boosted Faster-Region-based Convolutional Neural Network (MBF-RCNN) to estimate Robotic Tool Detection in Diabetes Patient Care in the present research. Semantic Segmentation is a machine learning methodology centered on classifying pictures pixel-by-pixel, whereas Multi-Boosted Faster R-CNN recognizes objects using a variety of boosting strategies. Decimal Scaling Normalization is used during the preprocessing step to optimize the input data for both procedures. The effectiveness of both approaches is improved by doing extraction of features using Linear Discriminant Analysis (LDA) to find key trends in the information that is provided.The Multi-Boosted Faster-Region-based Convolutional Neural Network (MBF-RCNN) is an improved variation of the Faster R-CNN design that uses the boosting approach to improve object identification performance.Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic (ROC) curve among the assessment metrics used. The findings show that the suggested approaches are effective in identifying and localizing robotic equipment, which is critical for effective treatments in controlling diabetes.This research promotes computer vision applications in healthcare, particularly in the care of diabetic patients. It assesses the efficiency and dependability of Semantic Segmentation and MBF-RCNN for robotic tool identification, opening the way for more efficient and dependable automation for healthcare contexts.

Preliminary architecture design for human-in-the-loop control of robotic equipment in remote handling tasks: Case study on the NEFERTARI project

In this work, we present a general control architecture for robotic systems dedicated to the remote handling of in-vessel components in fusion machines. This control architecture will be tested for the inspection and maintenance of the first wall components of the RFX-mod2 experiment, in the scope of the New Equipment For the Experimental Research and Technological Advancement for the Rfx Infrastructure (NEFERTARI) project. The architecture is split into low-level and high-level layers. The former is used for the low-level control of the robotics systems and to manage safety; the latter is used for the high-level planning and implements several sub-modules, such as a Virtual Reality (VR) environment for the visualisation of the robot’s digital twin. Moreover, an Application Programming Interface (API) is intended to connect the two layers, and the communication between modules and layers is provided by the ROS2 framework. A typical usage of such a framework involves a human operator who is teleoperating the real robot, data from motor encoders are used as inputs for the dynamic model module. This module is used to compute the forward dynamics in real-time, providing an accurate simulation of the robot (digital twin) in the virtual environment.

Communication and Contextual Factors in Robotic-Assisted Surgical Teams: Protocol for Developing a Taxonomy.

Robotic-assisted surgery (RAS) has been rapidly integrated into surgical practice in the past few decades. The setup in the operating theater for RAS differs from that for open or laparoscopic surgery such that the operating surgeon sits at a console separate from the rest of the surgical team and the patient. Communication and team dynamics are altered due to this physical separation and visual barriers imposed by the robotic equipment. However, the factors that might comprise effective communication among members of RAS teams and the contextual factors that facilitate or inhibit effective communication in RAS remain unknown. We aim to develop a taxonomy of communication behaviors and contextual factors that influence communication in RAS teams. We also aim to examine the patterns of communication behaviors based on gender. We will first perform a scoping review on communication in RAS to develop a preliminary taxonomy of communication based on the existing literature. We will then conduct semistructured interviews with RAS team members, including the surgeon, assisting surgeon or trainee, bedside or first assistant, nurses, and anesthetists. Participants will represent different disciplines, including urology, general surgery, and gynecology, and have a range of experiences in RAS. We will use a reflexive thematic analysis to analyze the data and further refine the taxonomy. We will also observe live robotic surgeries at Royal College of Surgeons in Ireland (RCSI)-affiliated hospitals. We will observe varying lengths and conditions of RAS procedures to a capture a wide range of communication behaviors and contextual factors to help finalize the taxonomy. Although we anticipate conducting 30 interviews and 30 observations, we will collect data until we achieve data sufficiency. We will conduct data collection in parallel with data analysis such that if we identify a new behavior in an interview, we will follow up with questions related to that behavior in additional interviews and/or observations. The taxonomy from this project will include a list of actionable communication behaviors, contextual factors, their descriptions, and examples. As of May 2024, this project has been approved by the RCSI Research and Ethics Committee. Data collection started in June 2024 and will continue throughout the year. We plan to publish the findings as meaningful results emerge in our data analysis in 2024 and 2025. The results from this project will be used to observe and train surgical teams in a simulated environment to effectively communicate with each other and prevent communication breakdowns. The developed taxonomy will also add to the knowledge base on the role of gender in communication in RAS and produce recommendations that can be incorporated into training. Overall, this project will contribute to the improvement of communication skills of surgical teams and the quality and safety of patient care. PRR1-10.2196/54910.

Research on Automatic Unattended Bill Collection, Paste and Verifiction Integrated Robot Equipment and Control Platform based on Deep Convolutional Neural Network

A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm , detection of invoice pasting and repeating based on ultrasonic sensors, and tidal temporary storage of paper invoices, and so on, the automatic high-speed identification and inspection of bills in the supplier service hall are realized, and the efficiency and accuracy of bill processing in the supplier hall are improved. Experiments show that this research method reinforces ability of identification calibration and order correlation, and improves the efficiency of Invoice filing.

Virtual robotic telepresence early childhood mental health consultation to childcare centers in the aftermath of COVID-19: training approaches and perceived acceptability and usefulness.

Childcare center closures during COVID-19 impacted education for approximately 40 million children nationwide. Unfortunately, COVID-19 restrictions significantly limited the extent that outside personnel could provide in-person support to educators, resulting in the need for innovative approaches to meet childcare centers' needs. A virtual robotic telepresence approach was applied to early childhood consultation models to promote child resilience while mitigating COVID-19 risks. The goal of this study was to examine how training influenced consultants' and childcare staff uptake of the virtual robotic telepresence consultation approach and their acceptance of this technology. Ten early childhood consultants received multimedia/simulation training and weekly communities of practice related to virtual telepresence robotic consultation. Telepresence robotic consultation equipment was deployed to 16 childcare centers in a diverse multilingual metropolitan area as a part of a larger randomized controlled trial. Consultants trained childcare staff (14 center directors and 58 teachers) on how to receive virtual telepresence robotic consultation. Demographic information and measures of technology acceptability and uptake were collected from childcare staff and consultants. A mixed methods approach was used including multilevel modeling and focus groups to examine consultation uptake, acceptability, barriers, and facilitators of virtual telepresence robotic consultation implementation. Consultants and childcare staff generally perceived the virtual telepresence consultation approach to be useful and easy to use. Consultant perceptions of the acceptability of technology did not change over time. Childcare staff, center, and consultant factors impacted the uptake of the virtual robotic telepresence consultation approach and childcare staff acceptance of the technology. Focus groups revealed that consultants believed that additional hands-on training with childcare staff would have benefited implementation and expressed a desire for a hybrid approach for consultation. Perceptions of telepresence robotic consultation acceptability are discussed, including future recommendations for training.

The pivotal role of the robotic nurse in the management of the robotic urologic surgical environment

AbstractIntroduction and ObjectivesRobotic surgery has revolutionized urology. However, upfront costs, time investments and knowledge required to proficiently operate and maintain robotic equipment and supplies often make it advantageous to implement the role of dedicated robotic nurse (DN) teams. Herein, we aimed to investigate the impact of DN on operative times during robotic urological procedures.Materials and MethodsWe retrospectively identified consecutive robotic uro‐oncological procedures performed at our institution from Jan to Oct 2023. The cohort was stratified based on the presence or absence of a DN team (DN vs no‐DN). The DN team was defined as having independently managed ≥100 robotic urological procedures. Endpoints were preoperative nursing time (pre‐NT), postoperative nursing time (post‐NT), surgeon operative time (surg‐OT), and total operative time (t‐OT). Continuous variables were compared using Student's t‐test. Subgroup analyses were conducted for specific procedures: robot‐assisted radical cystectomy (RARC), radical prostatectomy (RARP), partial nephrectomy (RAPN), and radical nephrectomy (RARN).ResultsOverall, 421 procedures were included. The DN group exhibited shorter pre‐NT (13.5 vs 23.7, p < 0.001), post‐NT (12.2 vs. 15, p < 0.001), surg‐OT (117 vs. 136, p < 0.001), and t‐OT (143.2 vs. 174.9, p < 0.001) compared to the no‐DN group. Subgroup analysis revealed shorter pre‐NT in the DN group for all procedures: RARP (13.2 vs. 23.5, p < 0.001), RARC (16.4 vs. 29.2, p < 0.001), RARN (12.6 vs. 18.9, p < 0.001) and RAPN (12.7 vs. 20.8, p < 0.001). Conversely, DN group exhibited shorter post‐NT in RARP (12 vs. 15.8, p < 0.001) and RARN (12.4 vs. 15.3, p: 0.01), but not in RARC (13 vs. 15, p: 0.06) and RAPN (12.2 vs. 12.7, p: 0.4) subgroup. Similarly, DN group demonstrated shorter t‐OT in RARP (162.7 vs. 185.4, p < 0.001), but not in RARC (232.8 vs. 245, p: 0.44), RAPN (91.2 vs. 107.4, p: 0.35) and RARN (118.5 vs. 127.2, p: 0.4) subgroups. Finally, no differences in surg‐OT were recorded between the DN and non‐DN groups for any of the individual procedures (Table ; Figure ). Comparison of time per procedures between dedicated and non‐dedicated teams. Variables mean (SD) RAPN group Variables mean (SD) RARP group Variables mean (SD) Overall procedures Dedicated nurse team (86) No dedicated nurse team (35) p‐value Dedicated nurse team (86) No dedicated nurse team (35) p‐value Dedicated nurse team (86) No dedicated nurse team (35) p‐value Preoperative nursing time 12.7 (±2.7) 20.8 (±5.9) <0.001 Preoperative nursing time 13.2 (±3.3) 23.5 (±9.8) <0.001 Preoperative nursing time 13.5 (±3.6) 23.7 (±9.7) <0.001 Postoperative nursing time 12.2 (±3.7) 12.7 (±3.4) 0.4 Postoperative nursing time 12 (±4.1) 15.8 (±6.3) <0.001 Postoperative nursing time 12.2 (±3.9) 15 (±5.6) <0.001 Operative time 66.2 (±29.1) 73.8 (±44.4) 0.3 Operative time 137.5 (±37.2) 146.1 (±41.1) 0.18 Operative time 117 (±68) 136 (±68.8) <0.001 Total operation time 91.2 (±30.2) 107.4 (±46.1) 0.35 Total operation time 162.7 (±37.8) 185.4 (±39.6) <0.001 Total operation time 143.2 (±69) 174.9 (±72,1) <0.001 Variables mean (SD) RARC group Variables mean (SD) RARN group Dedicated nurse team (86) No dedicated nurse team (35) p‐value Dedicated nurse team (86) No dedicated nurse team (35) p‐value Preoperative nursing time 16.4 (±4.8) 29.2 (±8.9) <0.001 Preoperative nursing time 12.6 (±2.8) 18.9 (±4.7) <0.001 Postoperative nursing time 13 (±3.5) 15 (±4.5) 0.06 Postoperative nursing time 12.4 (±3.9) 15.3 (±5.7) 0.01 Operative time 203.4 (±68.5) 200.8 (±69.7) 0.86 Operative time 93.5 (±38.7) 93 (±42.2) 0.9 Total operation time 232.8 (±66.4) 245 (±72.6) 0.44 Total operation time 118.5 (±38.9) 127.2 (±43.1) 0.4 Note: Bold indicates significant value. Comparison of preoperative nursing time DN Team versus NoDN Team.imageConclusionsOur study suggests that the implementation of a dedicated robotic nursing team can lead to substantial reductions in overall operative time. This benefit is primarily attributed to shorter preoperative nursing times, highlighting the crucial role of the robotic nurse in optimizing the robotic surgical environment.

The Role of Robotics in Meeting Institutional Goals: A Unified Strategy to Facilitate Program Excellence.

The rapid expansion of robotic surgical equipment necessitates a review of the needs and challenges faced by hospitals introducing robots for the first time to compete with experienced institutions. The aim of this study was to analyze the impact of robotic surgery on our hospital compared to open and laparoscopic surgery, examine internal transformations, and assess regional, domestic, and international implications. A retrospective review was conducted of electronic medical records (EMRs) from 2019 to 2022 at Inha University Hospital, including patients who underwent common robotic procedures and equivalent open and laparoscopic operations. The study investigated clinical and operational performance changes in the hospital after the introduction of robotic technology. It also evaluated the operational effectiveness of robot implementation in local, national, and international contexts. To facilitate comparison with other hospitals, the data were transmitted to Intuitive Surgical, Inc. for analysis. The study was conducted in compliance with domestic personal information regulations and received approval from our Institutional Review Board. We analyzed EMR data from 3,147 patients who underwent surgical treatment. Over a period of 3.5 years, the adoption of robotic technology in a hospital setting significantly enhanced the technical skills of all professors involved. The introduction of robotic systems led to increased patient utilization of conventional surgical techniques, as well as a rise in the number of patients choosing robotic surgery. This collective trend contributed to an overall increase in patient numbers. This favorable evaluation of the operational effectiveness of our hospital's robot implementation in the context of local, national, and global factors is expected to positively influence policy changes. Stakeholders should embrace data science and evidence-based techniques to generate valuable insights from objective data, assess the health of robot-assisted surgery programs, and identify opportunities for improvement and excellence.

Virtual and Remote Robotic Laboratory

The Virtual Remote Robotic Laboratory (VRRL) represents a paradigm shift in scientific experimentation, offering researchers unprecedented access to remote laboratory facilities and robotic systems. This paper explores the design, implementation, and benefits of VRRL systems in enhancing access and collaboration in scientific research. VRRL leverages cloud computing, IoT technologies, and advanced user interfaces to enable researchers to remotely control and interact with robotic systems and laboratory equipment from anywhere with an internet connection. Through intuitive user interfaces, researchers can seamlessly navigate virtual environments, manipulate robots, monitor experiments in real- time, and analyze data, all without physical presence in the laboratory. Key features of VRRL include robust security protocols to protect sensitive data, flexible experimentation capabilities to accommodate diverse research needs, and collaboration tools to facilitate knowledge sharing and teamwork among researchers worldwide. By breaking geographical barriers and providing on-demand access to laboratory resources, VRRL empowers researchers to conduct experiments more efficiently, accelerate scientific discovery, and foster interdisciplinary collaboration

CrowdBot: An Open-Environment Robot Management System for On-Campus Services

In contemporary campus environments, the provision of timely and efficient services is increasingly challenging due to limitations in accessibility and the complexity and openness of the environment. Existing service robots, while operational, often struggle with adaptability and dynamic task management, leading to inefficiencies. To overcome these limitations, we introduce CrowdBot, a robot management system that enhances service in campus environments. Our system leverages a hierarchical reinforcement learning-based cloud-edge hybrid scheduling framework (REDIS), for efficient online streaming task assignment and dynamic action scheduling. To verify the REDIS framework, we have developed a digital twin simulation platform, which integrates large language models and hot-swapping technology. This facilitates seamless human-robot interaction, efficient task allocation, and cost-effective execution through the reuse of robot equipment. Our comprehensive simulations corroborate the system's remarkable efficacy, demonstrating significant improvements with a 24.46% reduction in task completion times, a 9.37% decrease in travel distances, and up to a 3% savings in power usage. Additionally, the system achieves a 7.95% increase in the number of tasks completed and a 9.49% reduction in response time. Real-world case studies further affirm CrowdBot's capability to adeptly execute tasks and judiciously recycle resources, thereby offering a smart and viable solution for the streamlined management of campus services.

View for a future of UAVs in military applications

One of the most stable trends in recent years is the increase in the number of robotic equipment and complexes, primarily complexes with unmanned aerial vehicles (UAVs). The purpose of the work is to conduct a study of the prospects for military use of UAVs. The directions of the use of UAVs in military affairs are presented, as well as a list of scientific studies that need to be carried out for more effective use of UAVs. The presented material can be used to form programs for the development of military UAVs.

Comprehensive Review of Noninvasive Brain-Computer Interfaces for Controlling Robotic Arms

A robotic arm is a mechanical device with a given number of Degrees of Freedom (DoFs) that mimics the functions of a human arm and performs any desired task, such as grasping and moving objects. Current research is directed toward the design of robots and artificial human body parts controlled by brain signals, translating human thoughts into actions. Brain-Computer Interface (BCI) systems have been used to enable people with motor disabilities to control assistive robotic equipment that replaces the lost functions. This paper presents a review of the state-of-the-art of the latest papers dealing with the control of a robotic arm based on Electroencephalogram (EEG). A comparative study of the different methods and techniques used in different blocks of the robotic arm’s noninvasive BCI controlling system is conducted. These blocks include signal acquisition using noninvasive electrodes, signal preprocessing, feature extraction, classification, and command. Additionally, this paper presents a performance comparison of the reviewed controlling systems of robotic arms using EEG signals.

НАПРАВЛЕНИЯ РАЦИОНАЛЬНОЙ ЛОКАЛИЗАЦИИ ОБОРУДОВАНИЯ ДЛЯ ПРОИЗВОДСТВА ПЕЛЬМЕНЕЙ

The scientific article contains a theoretical analysis of scientific literature on the problems of ensuring the economic efficiency of automation and robotization of technological processes in the industrial production sector, their impact on the socio-economic situation and the economic system as a whole. Directions for the effective organization of dumpling production are presented, the most important of which is the rational localization of equipment. Attention is drawn to the need to invest in the process of robotization or automation of the production of dumplings, and robotization is several times more expensive than conventional automatic equipment. The technological processes for the production of dumplings are considered and equipment capable of robotizing or automating this process is shown. At the technological stage of meat chopping, the importance of using universal equipment for chopping meat and vegetables such as a cutter is shown. The dough preparation step is shown using a robotic system capable of preparing the dough or starter and then the dough, equipped with a robotic arm for mixing the bowl. For comparison, a more budget-friendly option is presented - a traditional automatic dough kneading machine. Options for localizing equipment for preparing dumplings are considered: robotic equipment for the production of dumplings developed by the Japanese Institute of Technology AICHI and an automatic dumpling production machine that does not require a dough sheeter. It is indicated that the final technological process is shock freezing, for which a special cabinet is used. It was noted that after shock freezing the product is ready for sale, but it is more efficient to package and store it in a freezer before transferring it to the retail chain. The effectiveness of investments is confirmed by the payback period, the calculation of which takes into account the volume of investments and the income expected to be received from the use of the equipment.

Use of an Impact Recording Device to Determine the Risk of Bruising in Packaged Potatoes

Handling potatoes individually or collectively in packages can create opportunities for potatoes to develop quality defects including blackspot and shatter bruise. Three trials were conducted to examine how handling packaged potatoes can influence the risk for physical damage including shatter and blackspot bruise. An impact recording device was used to record peak acceleration (max g-force) in common fresh market packaging options (boxes or bales) at four drop heights (15 to 91 cm) on to three different surface types. When boxed potatoes were dropped onto concrete or a plastic slip, the potatoes on the bottom of the box had the highest risk of damage (greater than 100 g-force). When drop heights were lowered, or when cushioning material was added to hard surfaces (e.g., wooden pallet on top of concrete floor), the risk for impact damage was decreased throughout the box. When palletizing boxed potatoes, the risk of bruise decreased after the first layer was stacked on the pallet. Drop heights need to be below 15 cm, especially when making the first layer in a palletized stack of packaged potatoes to reduce potential bruising. The risk of high peak accelerations was not seen in the dropped or stationary bales for any of the drop heights examined. This study provided information for educating personnel on handling packaged potatoes and determining situations in which robotic stacking equipment needs to be adjusted to lower drop heights of packaged potatoes.