Field Of Robotics Research Articles

Review and analysis of software simulators for robotic information systems

The rapid development of educational technologies requires the active involvement of new means and effective teaching methods. One of the innovative tools of learning in the field of robotics is the use of robot simulators in the educational process, which open up many opportunities for better assimilation of knowledge and development of practical skills among students. Robot simulators are software complexes that allow you to visualize and simulate the behavior of real robots. With these tools, students can: program robots in a virtual environment, test code and debug without the risk of damaging real equipment; to study robotics and the principles of robot operation, exploring different types of sensors, actuators and algorithms of robot behavior; to solve problems in various fields, such as production automation, logistics, construction, etc.; practices your skills in a safe and controlled environment. Among the benefits of using robotic simulators are next: availability for a large number of students, as simulators are much cheaper than real jobs; safety, as the virtual environment eliminates the risks associated with working with real robots; flexibility that allows students to work at their own pace, explore different scenarios and experiment with different parameters; interactivity with a realistic 3D-environment and visualization. This work presents an overview and analysis of software that allows modeling robot control systems and simulating their operation for various algorithms. A review and analysis of the proposed software tools for simulating robotic systems, which are used to teach bachelor's and master's degree students in the specialties of "Computer Science", "Information Systems", "Industrial Mechanical Engineering" and "Applied Mechanics" and have proven their effectiveness and effectiveness in distance learning at the Kyiv National University of Construction and Architecture.

ForzaETH Race Stack—Scaled Autonomous Head‐to‐Head Racing on Fully Commercial Off‐the‐Shelf Hardware

ABSTRACTAutonomous racing in robotics combines high‐speed dynamics with the necessity for reliability and real‐time decision‐making. While such racing pushes software and hardware to their limits, many existing full‐system solutions necessitate complex, custom hardware and software, and usually focus on Time‐TrIals rather than full unrestricted Head‐to‐head racing, due to financial and safety constraints. This limits their reproducibility, making advancements and replication feasible mostly for well‐resourced laboratories with comprehensive expertise in mechanical, electrical, and robotics fields. Researchers interested in the autonomy domain but with only partial experience in one of these fields, need to spend significant time with familiarization and integration. The ForzaETH Race Stack addresses this gap by providing an autonomous racing software platform designed for F1TENTH, a 1:10 scaled Head‐to‐Head autonomous racing competition, which simplifies replication by using commercial off‐the‐shelf hardware. This approach enhances the competitive aspect of autonomous racing and provides an accessible platform for research and development in the field. The ForzaETH Race Stack is designed with modularity and operational ease of use in mind, allowing customization and adaptability to various environmental conditions, such as track friction and layout, which is exemplified by the various modularly implemented state estimation and control systems. Capable of handling both Time‐Trials and Head‐to‐Head racing, the stack has demonstrated its effectiveness, robustness, and adaptability in the field by winning the official F1TENTH international competition multiple times. Furthermore, the stack demonstrated its reliability and performance at unprecedented scales, up to over on tracks up to 150 m in length.

Magnetic elastomer composites with tunable magnetization behaviors for flexible magnetic transducers

AbstractMagnetic elastomer composites are extending the field of soft robotics by integrating the flexibility of elastomers with the versatile performance of magnetic materials. In this work, a series of magnetic elastomers (BaCoxTixFe12–2xO19/PDMS) with tunable magnetic response have been successfully synthesized. In this composite, the magnetic component consists of Co‐Ti co‐doped barium hexaferrite (BaCoxTixFe12–2xO19, BCTF) nano powders, and the matrix is made of polydimethylsiloxane (PDMS). The coercivity (Hc) of BCTF can be reduced from 3950 Oe to 70 Oe, which bestow on the magnetic elastomer different magnetic response behavior. These magnetic elastomer composites obtained by blending have low Young's modulus, approximately 1.5 MPa. Furthermore, when applying the same external magnetic field, BaCo1.2Ti1.2Fe9.6O19/PDMS the magnetic deformation Angle can be as high as 60°. These magnetic elastomers exhibit different magnetic‐induced deformation owing to the adjustable permeability of the magnetic nanoparticles. This research significantly advances the design of applications with specific magnetic characteristics, such as sensors and transducers, thus opening new horizons for innovation in soft robotics and related fields.Highlights All elastomer composite exhibit a low modulus of approximately 1.5 MPa. These composites exhibit different magnetic response behaviors. This research provides a solution for the innovation of transducers.

Cross-National Comparison of Technology Innovation Capabilities in Automation and Robotics

In today’s dynamic and global economic environment, technological innovations are a key determinant of international competitiveness. In the field of automation and robotics, the development of modern technologies is of particular significance, influencing not only industrial progress but also social aspects. Automation and robotics play a crucial role in the concept of modern industry, contributing to the creation of more sustainable, flexible, and human-centric production systems. The article aims to conduct an in-depth analysis of the patent activity in automation and robotics in Europe. Patent documentation serves as a source of knowledge about the directions of research, inventive activities, and consequently, the innovative and competitive potential of the economy. The number of patents over time reflects the dynamics of a country’s technological development. The article discusses the use of patent databases to evaluate a country’s innovation in automation and robotics. The conducted analysis of data on European countries enabled the identification of trends based on the International Patent Classification and specialisation from a geographical perspective using revealed comparative technological advantage. The European countries were classified using cluster analysis, demonstrating the diversity, and identifying leaders in each group.

Multi-cameras calibration System Based Deep Learning Approach and Beyond: A Survey

The process of determining camera settings to deduce geometric attributes from recorded sequences is known as camera calibration. This process is essential in the fields of robotics and computer vision, encompassing both two-dimensional and three-dimensional applications. Traditional calibration methods, however, are time-consuming and require specific expertise. Recent endeavors have demonstrated that learning-based systems can replace the monotonous tasks associated with manual calibrations. Responses have been examined through a range of learning techniques, networks, geometric assumptions, and datasets. A thorough examination of camera calibration systems that rely on learning algorithms is offered in this paper, assessing their advantages and disadvantages. The primary categories of calibration presented are the regular pinhole camera model, distortion camera model, cross-sensor model, and cross-view model. These categories align with current research trends and have diverse applications. As there is no existing standard in this field, a large dataset of calibration has been created, which can be used as a public platform to assess the effectiveness of current methods. This collection consists of both artificially generated and genuine data, including images and videos obtained from various cameras in different locations. The difficulties faced will be analyzed, and alternative avenues for further research will be suggested in the next stage of this project. This survey represents the initial attempt to perform camera calibration using learning-based methods spanning a period of eight years. Our findings indicate that learning-based methods significantly reduce the time and expertise required for calibration while maintaining or improving accuracy compared to traditional methods. Specifically, our research demonstrates a calibration error reduction of up to 20% and speed improvements by a factor of three compared to traditional methods, as well as better adaptability to different camera types and environments.

German sugar beet farmers’ intention to use autonomous field robots for seeding and weeding

Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes toward more sustainable agricultural systems. Given that adopting robotics for the weeding process requires changing existing systems, farmers' appraisals for the new and the current weeding technology may constitute barriers. However, this dualism has been largely ignored by previous studies. Based on a duality approach, we investigate farmers’ beliefs, and adaptive and maladaptive appraisals of current and new robotic weeding in sugar beets. The main variable of interest is their behavioral intention to adopt weeding robots. For our sample of German farmers, we identify the main enablers perceived efficacy of the robots and social norms. The main barrier are maladaptive rewards from traditional weeding. We recommend policy incentives to promote large-scale uptake of new and more sustainable robotic technologies. To improve efficacy perceptions of such robotic systems public demonstrations/talks are mostly relevant. Maladaptive rewards can be reduced, for instance, by notifying about the dependency of the current practices on future availability of synthetic inputs or seasonal workers.

Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning.

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

Development of fully soft composite tactile sensors using conductive fabric and polydimethylsiloxane

This study presents composite resistive soft tactile sensors combining conductive fabric and polydimethylsiloxane. The sensors utilize the changes in resistance of the conductive fabric when stretched to sense the pressure, while reducing the volatility of resistance of the conductive fabric by embedding the fabric within the elastomer matrix. The sensors have a compact design, a simple fabrication process using cost-effective materials, and a compliant structure. The performance evaluation of 12 samples was conducted to assess their linearity, sensitivity, time responses, and reliability. A particular sample was chosen for its universal applicability, characterized by a rise time of ∼ 0.38 s, a decay time of ∼ 2.71 s, a settling time of ∼ 851.8 s, hysteresis of 21.04 %, and a sensitivity of 0.0362 %/kPa. Finally, potential applications were proposed using the selected sensor, showcasing its capability to support closed-loop systems in the fields of soft robotics and wearable technology.

A continuous concrete vibration method for robots based on machine vision with integrated spatial features

The traditional manual concrete vibration work faces numerous limitations, necessitating efficient automated method to assist in this task. This study proposes a vision-based continuous concrete vibration method for vibrating robots. By enhancing the YOLOv8n model with attention mechanisms, our proposed method demonstrates a high AP of 93.31 % in identifying reinforcing grids, and an FPS of 25.6 on embedded systems. For the first time in concrete vibration tasks, this study utilizes spatial positional information to cluster coordinate data, transforming confidence-sorted data into spatially ordered sequences. Vibrating robot case test shows that the proposed method enhances the vibration speed by 22.18 % and improves the vibration success rate by 11.67 % compared to traditional strategies. Additionally, the on-site experiment conducted at four construction sites demonstrated the robustness of the proposed method. These findings advance automation in concrete vibration work, offering significant implications for the fields of robotics and construction engineering.

Hovering flight regulation of pigeon robots in laboratory and field

Compared to traditional bio-mimic robots, animal robots show superior locomotion, energy efficiency, and adaptability to complex environments but most remained in laboratory stage, needing further development for practical applications like exploration and inspection. Our pigeon robots validated in both laboratory and field, tested with an electrical stimulus unit (2-s duration, 0.5ms pulse width, 80Hz frequency). In a fixed stimulus procedure, hovering flight was conducted with 8 stimulus units applied every 2s after flew over the trigger boundary. In a flexible procedure, stimulus was applied whenever they deviated from a virtual circle, with pulse width gains of 0.1ms or 0.2ms according to the trajectory angle. These optimized protocols achieved a success hovering rate of 87.5% and circle curvatures of 0.008 m-1-0.024 m-1, largely advancing the practical application of animal robots.

Investigation of buckling instabilities in fiber-reinforced DEAs

Artificial muscles, designed to replicate the movements of natural biological muscles, hold significant promise in the fields of robotics and prosthetics. Recent advancements have led to the development of fiber-reinforced actuators, drawing inspiration from biological tissues. Dielectric elastomer actuators (DEAs) are a type of electroactive artificial muscle. It is possible to enhance the uni-axial deformation of DEAs by constraining and applying pre-stretch on the actuator membrane. This can be achieved through uni-directional fibers bonded to the DEA that lead to transversely isotropic properties. However, combining membrane pre-stretch and fiber reinforcement may lead to instabilities such as fiber buckling due to the compressive load of the pre-stretched membrane or due to wrinkling during actuation. Understanding these instabilities is crucial as they can significantly impact the performance. A novel model taking into consideration these instabilities is established and experimentally validated. By calculating the force in the fiber direction, the buckling profile such as the wavelength and amplitude can be predicted. The validation of the model presented along with an extensive experimental investigation allow for a comprehensive analysis to explore the impact of fiber buckling on the performance and the force of uni-axial DEAs.

Spring-reinforced pneumatic actuator and soft robotic applications

Abstract Compared to rigid-structure robots, soft robots possess higher degrees of freedom and stronger environmental adaptability, which has aroused increasing attention in the robotic field. Among them, soft pneumatic robots have excellent performances in various practical applications. However, the nonlinearity and instability of pressure response of soft actuators caused by lateral expansion come to a great challenge. To address this problem, we proposed to embed a spring constraint layer around each single air chamber. Following the design concept, we obtained single-cavity and multi-DoF pneumatic actuators and evaluated their elongation and bending characteristics. Experimental results demonstrated that our proposed actuators have more linear pressure response as well as higher consistency. Eventually, through robotic applications, including soft robotic hand and gripper our proposed actuators could facilitate flexible manipulation and elaborate performance.

Soft Upper‐Limb Wearable Robotic Devices: Technology and Applications

One of the practical applications in the field of soft robotics involves the development of soft robotic wearable devices. These devices make use of their intrinsically compliant structures to interact safely and harmoniously with the human body. While soft wearable robots demonstrate their utility in lower‐limb applications for locomotion, the upper‐limb domain offers significant prospects in a wide range of applications that soft robotic technology can address. In this review, the current state of technology in the field of soft wearable upper limbs is systematically analyzed and categorized. Categorizations are made based on their applications in rehabilitation, activities of daily living support, and human augmentation. Furthermore, in this study, also contemporary technological aspects, encompassing sensing technology and control systems, are explored. Despite exciting potential in this domain, several limitations from existing devices inherently impede widespread adoption and thus hinder further progress in the field. In this study, also an overview of the different facets of the domain is provided and key considerations for the advancement of soft wearable robotic devices intended for upper‐limb applications are prescribed.

A self-healing, long-lasting adhesive, lignin-based polyvinyl alcohol organo-hydrogel for strain-sensing applications

Hydrogel-based flexible sensors have garnered considerable interest in the fields of soft electronics, robotics, and human-machine interfaces. For better practical applications, integrating multiple properties—such as self-adhesive, anti-freeze, anti-volatile, self-healing, and antibacterial—into a single gel for flexible sensors remains a challenge. In this paper, a multifunctional lignin-based polyvinyl alcohol gel, containing dynamic covalent bonds, hydrogen bonds, and coordination bonds, is constructed by a simple one-pot method, in which ethylene glycol/water chosen as a binary solvent and KI as a conductive medium. The resulting organogel exhibits self-healing, long-lasting adhesion, UV shielding, antibacterial properties, excellent frost resistance (−20 °C), and volatile resistance properties. In addition, the organogel-based sensor demonstrates satisfactory sensitivity in detecting joint movements and facial expressions. This study provides a new strategy for developing a versatile flexible sensor through the introduction of renewable and bio-based lignin, promising applications in the fields of wearable electronics.

A high sensing performance piezoresistive sensor based on TPU/c-MWCNTs/ V2CTX-MXene electrospun film for human motion detection

Since wearable pressure sensors have promising applications in the fields of human physiological signal detection and bionic robots, there is an urgent need for wearable pressure sensors with comprehensive attributes such as high sensitivity and wide detection range. Here, we introduce a facile electrospinning process to fabricate a film composed of thermoplastic polyurethane/carboxyl multiwalled carbon nanotube/V2CTX-MXene (TPU/c-MWCNTs/V2C). This film has high sensitivity and great linearity, designed for flexible piezoresistive sensors. To further enhance sensor performance, we applied a spin-coating of polydimethylsiloxane (PDMS) on the sandpaper surface to create a top substrate with intermittent spinosum structures. Additionally, a layer of TPU fibers was electrospun between the conductive film and the interdigitated electrodes. The assembled components form a flexible piezoresistive sensor with a wide detection range of 0–200 kPa, a high sensitivity of 545.2 kPa−1, a low detection limit of 5 Pa, a short response time of 48 ms, and excellent long-term durability after 7000 loading/unloading cycles, making the piezoresistive sensor highly discriminating for detecting complex human movements. Thanks to the great sensing performance and stable structure, we conducted various demonstrations to capture human physiological signals, including signals of muscle activity and pulse. These findings indicate the potential of the designed flexible pressure sensor for applications in electronic skin and smart devices.

Enhancing social robot's direct gaze expression through vestibulo-ocular movements

Non-verbal communication, especially eye contact, plays a crucial role in human interaction. Integrating eye contact capabilities into robotic behavior presents challenges that require comprehensive interdisciplinary collaboration from the fields of vision science and robotics. This study introduces an innovative methodology to improve the user's perception of the tabletop social robot Haru's direct gaze by explaining how eye and head movements can be managed through simulating visual perception processing. By considering the mechanisms of eye and head movements during visual perception processing, the robot's eye and head gaze behaviors are intended to replicate human gaze movements with Vestibulo-Ocular Movement (VOM) during face-to-face interaction, ultimately enhancing the user's perception of the robot's direct gaze.

Students’ Perceptions of Workplace Robots in the Republic of Serbia

Research Question: The key research question is what the key positive and negative perceptions of students from the Republic of Serbia regarding workplace robots are. Motivation: Academics, practitioners, and decision-makers are more and more interested in the impact of new technological solutions in the field of robotics on organizations and their functioning. Ongoing technological advancements have accelerated the adoption and use of robots in the workplace. As a result, organizations that plan to introduce robots should consider all effects on employees, both positive and negative. Idea: The main idea is to examine and analyse the perceptions and attitudes of students from the Republic of Serbia regarding robots at the workplace. The results of this study regarding students' perceptions and attitudes towards robots in the workplace are crucial for the future of work and job design. Data: Data were collected via online questionnaire consisting of five profile questions and 13 statements organized in two five-point Likert scales. From January to March 2023, the questionnaire was completed by 164 students from the Republic of Serbia. Tools: The collected answers were analysed using the Statistical Package for Social Sciences – SPSS, version 21.0. (Armonk, NY: IBM Corporation). The Cronbach's Alpha coefficient, Kolmogorov-Smirnov test, descriptive statistics, student t-test and one-way ANOVA test was used for data analysis. Findings: Research results showed that most respondents believe that robots can do dangerous jobs that humans cannot, that robots can free humans from routine and monotonous jobs, and that humans will have more time for creative tasks. Robots in the workplace, on the other hand, will have some negative consequences. Many respondents agree that working with robots without people would make them feel lonely; robots would not know how to react in some unexpected circumstances; and they are not as flexible and mobile as humans. Contribution: The findings of this study could make a significant contribution to a better understanding of students’ attitudes towards robots in the workplace and they may help decision makers, employers, leaders, and managers on how to increase workplace acceptance of robots.

Real-time Sorting of Broiler Chicken Meat with Robotic arm: XAI-enhanced Deep Learning and LIME Framework for Freshness Detection

Ensuring food safety in poultry processing is crucial due to the risk of contamination, which can lead to health hazards. Traditional methods for sorting chicken meat based on freshness are often manual and prone to error, necessitating the development of an automated, accurate sorting system. This research focuses on integrating Convolutional Neural Networks (CNNs) with Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy and efficiency of sorting broiler chicken meat into fresh and rotten categories. This study employed the InceptionV3 CNN model combined with LIME for transparent decision-making. The model was trained and tested on a dataset of chicken meat images to guide a robotic arm for real-time sorting. The system achieved an impressive sorting accuracy of 96.3 %, outperforming traditional manual methods. With the guidance of the CNN-LIME integration, the robotic arm successfully processed 1000 samples of fresh and 300 samples of rotten chicken meat, achieving a precision of 94.19% for fresh meat and 97.24% for rotten meat. This integration significantly improved sorting accuracy and efficiency in poultry processing, providing a reliable method for distinguishing between fresh and rotten chicken meat. This system improves food safety and operational efficiency in automated food processing. It shows the effectiveness of combining explainable AI with robotics, reducing human exposure to harmful contaminants ethically. It also contributes to public health by ensuring safer food products. This research presents a new approach to automated food sorting by integrating CNNs with LIME for transparency and accuracy. This can be beneficial for food processing industries and researchers in the field of AI and robotics.

ORB Visual and WiFi Online RSSI fusion SLAM

Simultaneous Localization and Mapping (SLAM) technologies are indispensable for indoor service robots, enabling them to navigate through and interact with environments. Visual SLAM systems often encounter significant challenges such as dynamic obstacles, variable lighting, feature scarcity, and perceptual aliasing in real-world scenarios. By merging the precise environmental mapping capabilities of visual SLAM with the ubiquity and stability of WiFi signals, our method effectively addresses the limitations typically associated with visual SLAM. Notably, our fusion technique leverages existing WiFi infrastructure, thus providing a cost-effective improvement in spatial awareness without the extensive offline database requirements of WiFi RSSI-based localization. Comparative performance evaluations highlight that our graph optimization-based approach not only surpasses the original ORBSLAM3 method but also significantly outperforms the Extended Kalman Filter (EKF) in terms of accuracy, particularly in environments characterized by poor lighting, feature-less scenes, and significant occlusions. This is evidenced by a reduced Root Mean Square Error (RMSE) in localization: 3.09m for our method versus 4.02m for EKF. This enhancement in precision underscores the potential of our integrated system to advance indoor navigation technologies, making it a crucial development in the field of robotics and automated systems.

Mechanism design and mechanical analysis of pipeline inspection robot

Purpose This study aims to address the issues of limited pipe diameter adaptability and low inspection efficiency of current pipeline inspection robots, a new type of pipeline inspection robot capable of adapting to various pipe diameters was designed. Design/methodology/approach The diameter-changing mechanism uses a multilink elastic telescopic structure consisting of telescopic rods, connecting rods and wheel frames, driven by a single motor with a helical drive scheme. A geometric model of the position relationships of the hinge points was established based on the two extreme positions of the diameter-changing mechanism. Findings A pipeline inspection robot was designed using a simple linkage agency, which significantly reduced the weight of the robot and enhanced its adaptive pipe diameter ability. The analysis determined that the robot could accommodate pipe diameters ranging from 332 mm to 438 mm. A static equilibrium equation was established for the robot in the hovering state, and the minimum pressing force of the wheels against the pipe wall was determined to be 36.68 N. After experimental testing, the robots could successfully pass a height of 15 mm, demonstrating the good obstacle capacity of the robot. Practical implications This paper explores and proposes a new type of multilink elastic telescopic variable diameter pipeline inspection robot, which has the characteristics of strong adaptability and flexible operation, which makes it more competitive in the field of pipeline inspection robots and has great potential market value. Originality/value The robot is characterized by the innovative design of a multilink elastic telescopic structure and the use of a single motor to drive the wheel for spiral motion. On the basis of reducing the weight of the robot, it has good pipeline adaptability, climbing ability and obstacle-crossing ability.