Search Results (21,035)

This research work presents an integrated method leveraging Convolutional Neural Networks and Recurrent Neural Networks (CNN-RNN) to enhance the accuracy of predictive maintenance and fault detection in DC motor drives of industrial robots. We propose a new hybrid deep learning framework that combines CNNs with RNNs to improve the accuracy of fault prediction that may occur on a DC motor drive during task processing. The CNN-RNN model determines the optimal maintenance strategy based on data collected from sensors, such as air temperature, process temperature, rotational speed, and so forth. The proposed AI model has the capacity to make highly accurate predictions and detect faults in DC motor drives, thus helping to ensure timely maintenance and reduce operational breakdowns. As a result, comparative analysis reveals that the proposed framework can achieve higher accuracy than the current existing method of combining CNN with Long Short-Term Memory networks (CNN-LSTM) as well as other CNNs, LSTMs, and traditional methods. The proposed CNN-RNN model can provide early fault detection for motor drives of industrial robots with a simpler architecture and lower complexity of the model compared to CNN-LSTM methods, which can enable the model to process faster than CNN-LSTM. It effectively extracts dynamic features and processes sequential data, achieving superior accuracy and precision in fault diagnosis, which can make it a practical and efficient solution for real-time fault detection in motor drive control systems of industrial robots. Full article

Accurate depth estimation is crucial for many fields, including robotics, navigation, and medical imaging. However, conventional depth sensors often produce low-resolution (LR) depth maps, making detailed scene perception challenging. To address this, enhancing LR depth maps to high-resolution (HR) ones has become essential, guided by HR-structured inputs like RGB or grayscale images. We propose a novel sensor fusion methodology for guided depth super-resolution (GDSR), a technique that combines LR depth maps with HR images to estimate detailed HR depth maps. Our key contribution is the Incremental guided attention fusion (IGAF) module, which effectively learns to fuse features from RGB images and LR depth maps, producing accurate HR depth maps. Using IGAF, we build a robust super-resolution model and evaluate it on multiple benchmark datasets. Our model achieves state-of-the-art results compared to all baseline models on the NYU v2 dataset for $\times 4$, $\times 8$, and $\times 16$ upsampling. It also outperforms all baselines in a zero-shot setting on the Middlebury, Lu, and RGB-D-D datasets. Code, environments, and models are available on GitHub. Full article

This paper presents the development of a virtual walker for wheelchair users designed for use in highly immersive environments, such as Cave Automatic Virtual Environments (CAVEs). The system allows users to navigate virtual environments using their natural wheelchair movements, providing haptic feedback based on the terrain they traverse. Both the control software and hardware have been developed from scratch and integrated into various CAVEs, including one with a six-degree-of-freedom (DOF) motion platform. To test the system, a comparative study was conducted with 21 users, measuring the time taken to complete the same course using different interaction methods and various feedback configurations with the virtual environment. The results show that the shortest times were achieved when users navigated using their natural interaction with their wheelchairs. Full article

The decline in the number of essential farmers has become a significant issue in Japanese agriculture. In response, there is increasing interest in the electrification and automation of agricultural machinery, particularly in relation to the United Nations Sustainable Development Goals (SDGs). This study focuses on the development of an electric vehicle (EV) crawler-type robot designed for weed cultivation operations, with the aim of reducing herbicide use in organic onion farming. Weed cultivation requires precise, delicate operations over extended periods, making it a physically and mentally demanding task. To alleviate the labor burden associated with weeding, we employed a color camera to capture crop images and used artificial intelligence (AI) to identify crop rows. An automated system was developed in which the EV crawler followed the identified crop rows. The recognition data were transmitted to a control PC, which directed the crawler’s movements via motor drivers equipped with Controller Area Network (CAN) communication. Based on the crop row recognition results, the system adjusted motor speed differentials, enabling the EV crawler to follow the crop rows with a high precision. Field experiments demonstrated the effectiveness of the system, with automated operations maintaining a lateral deviation of ±2.3 cm, compared to a maximum error of ±10 cm in manual operation. These results indicate that the automation system provides a greater accuracy and is suitable for weed cultivation tasks in organic farming. Full article

Background: Advancements in artificial intelligence (AI) and machine learning (ML) are set to revolutionize healthcare, particularly in fields like endoscopic surgery that heavily rely on digital imaging. However, to effectively integrate these technologies and drive future innovations, pediatric surgeons need specialized AI/ML skills. This survey evaluated the current level of readiness and educational needs regarding AI/ML among members of the European Society of Pediatric Endoscopic Surgeons (ESPES). Methods: A structured survey was distributed via LimeSurvey to ESPES members via email before and during the 2024 Annual Conference. Responses were collected over four weeks with voluntary, anonymous participation. Quantitative data were analyzed using descriptive statistics. Results: A total of 125 responses were received. Two-thirds (65%) of respondents rated their AI/ML understanding as basic, with only 6% reporting advanced knowledge. Most respondents (86%) had no formal AI/ML training. Some respondents (31%) used AI/ML tools in their practice, mainly for diagnostic imaging, surgical planning, and predictive analytics; 42% of the respondents used these tools weekly. The majority (95%) expressed interest in further AI/ML training, preferring online courses, workshops, and hands-on sessions. Concerns about AI/ML in pediatric surgery were high (85%), especially regarding data bias (98%). Half of respondents (51%) expect AI/ML to play a significant role in advancing robotic surgery, oncology, and minimally invasive techniques. A strong majority (84%) felt that the ESPES should lead AI education in pediatric surgery. Conclusions: This survey presents the ESPES with a unique opportunity to develop a competency map of its membership’s AI/ML skills and develop targeted educational programs, thus positioning the society to take the lead in AI education and the advancement of AI solutions in pediatric endosurgery. Full article

To address the issues of low trajectory planning efficiency, high motion impact, and poor operational stability in robotic arms during the automatic loading and unloading of aluminum blocks in coating machinery, a time-optimal trajectory optimization method based on a dynamically adaptive Particle Swarm Optimization (PSO) algorithm is proposed. First, the loading and unloading process of aluminum block components is described, followed by a kinematic analysis of the robotic arm in joint space. Then, the “3-5-3” hybrid polynomial interpolation method is used to fit the robotic arm’s motion trajectory and simulate the analysis. Finally, with the robotic arm’s operation time as the objective function, the dynamically adaptive PSO algorithm is applied to optimize the trajectory constructed by hybrid polynomial interpolation, achieving time-optimal trajectory planning for aluminum block handling. The results demonstrate that the proposed method successfully reduces the trajectory planning times for condition 1 and condition 2 from 6 s to 3.59 s and 3.14 s, respectively, improving overall efficiency by 40.2% and 47.7%. This confirms the feasibility of the method and significantly enhances the efficiency of automated loading and unloading tasks for aluminum blocks in coating machinery. The proposed method is highly adaptable and well-suited for real-time trajectory optimization of robotic arms. It can also be broadly applied to other robotic systems and manufacturing processes, enhancing operational efficiency and stability. Full article

Construction robots are pivotal in advancing the construction industry towards intelligent upgrades. To further explore the current research landscape in this domain, the CNKI Chinese database and the Web of Science core database were employed as data sources. CiteSpace software (version 6.2R4) was utilized to visualize and the analyze relevant literature on construction robots from 2007 to 2024, generating pertinent maps. The findings reveal an annual increase in the number of publications concerning construction robots. An analysis of institutions and authors indicates closer collaboration among English institutions, while Chinese authors exhibit stronger cooperation. However, overall institutional and author collaboration remains limited and fragmented, with no prominent core group of authors emerging. Research hotspots in both the Chinese and English literature are largely aligned, focusing on intelligent construction, human-robot collaboration, and path planning. Notably, the Chinese literature emphasizes technical aspects, whereas the English literature is more application-oriented. Future trends in the field are likely to include human-robot collaboration, intelligent construction, robot vision technology, and the cultivation of specialized talent. Full article

Trajectory tracking control is important in order to realize autonomous driving of mobile robots. From a control standpoint, trajectory tracking can be stated as the problem of stabilizing a tracking error system that describes both position and orientation errors of the mobile robot with respect to a time-parameterized path. In this paper, we address the problem for the trajectory tracking of nonholonomic wheeled mobile robots, and an exponential trajectory tracking controller is designed. The stability analysis is concerned with studying the local exponential stability property of a cascade system, provided that two isolated subsystems are exponentially stable and under certain bound conditions for the interconnection term. A theoretical stability analysis of the dynamic behaviors of the closed-loop system is provided based on the Lyapunov stability theory, and an exponential stability result is proven. An explicit estimate of the set of feasible initial conditions for the error variables is determined. Simulation results for verification of the proposed tracking controller under different operating conditions are given. The obtained results show that the problem of trajectory tracking control of nonholonomic wheeled mobile robots is solved over a large class of reference trajectories with fast convergence and good transient performance. Full article

Objective: Transoral robotic surgery (TORS) is becoming increasingly popular in head and neck surgery. Its applications have expanded beyond oncologic indications to obstructive sleep apnea syndrome (OSAS) and, more recently, to benign pathologies. Data Sources: A systematic search for articles published in the PubMed and Google Scholar databases between January 2003 and December 2023 was performed using the following combined search query (robot OR sleep OR apnea OR syndrome) AND (robot OR tongue OR base). Review methods: Given the limited literature, we conducted a systematic review focusing on the outcomes of TORS for benign pathologies of the base of the tongue. Our search methodology followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results: We found 16 articles that met our inclusion criteria. These were mainly case reports and a few case series. Conclusions: Compared to other transoral techniques, TORS offers better exposure, visualization, and access to the oropharynx, especially the base of the tongue, even in benign pathology. TORS should be considered a feasible, safe, and effective technique. Several more studies are needed to effectively evaluate the role of TORS in benign pathology that does not correlate with OSAS. Full article

The trajectory planning of manipulators plays a crucial role in industrial applications. This importance is particularly pronounced when manipulators operate in environments filled with obstacles, where devising paths to navigate around obstacles becomes a pressing concern. This study focuses on the environment of frame obstacles in industrial scenes. At present, many obstacle avoidance trajectory planning algorithms struggle to strike a balance among trajectory length, generation time, and algorithm complexity. This study aims to generate path points for manipulators in an environment with obstacles, and the trajectory for these manipulators is planned. The search direction adaptive RRT*Connect (SDA-RRT*Connect) method is proposed to address this problem, which adaptively adjusts the search direction during the search process of RRT*Connect. In addition, we design a path process method to reduce the length of the path and increase its smoothness. As shown in experiments, the proposed method shows improved performances with respect to path length, algorithm complexity, and generation time, compared to traditional path planning methods. On average, the configuration space’s path length and the time of generation are reduced by 38.7% and 57.4%, respectively. Furthermore, the polynomial curve trajectory of the manipulator was planned via a PSO algorithm, which optimized the running time of the manipulator. According to the experimental results, the proposed method costs less time during the manipulator’s traveling process with respect to other comparative methods. The average reduction in running time is 45.2%. Full article

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applications, lack the aspect of empathy, known to improve cooperation in human teams. Emphasizing both cognitive and emotional aspects of empathy, the introduced model navigates communication uncertainties and ambiguities, transforming these challenges into opportunities for learning and adaptation in dynamic environments. A significant feature of this model is its handling of imprecision through fuzzy logic, using fuzzy similarity measures in the decision process. The main objective of the presented research is to introduce a new model for improving cooperativeness in multi-agent systems with the use of cognitive empathy. Future research focus on implementing the model on physical platform and optimize the artificial empathy algorithms in the decision-making module. Full article

Human hands have over 20 degrees of freedom, enabled by a complex system of bones, muscles, and joints. Hand differences can significantly impair dexterity and independence in daily activities. Accurate assessment of hand function, particularly digit movement, is vital for effective intervention and rehabilitation. However, current clinical methods rely on subjective observations and limited tests. Smart gloves with inertial measurement unit (IMU) sensors have emerged as tools for capturing digit movements, yet their sensor accuracy remains underexplored. This study developed and validated an IMU-based smart glove system for measuring finger joint movements in individuals with hand differences. The glove measured 3D digit rotations and was evaluated against an industrial robotic arm. Tests included rotations around three axes at 1°, 10°, and 90°, simulating extension/flexion, supination/pronation, and abduction/adduction. The IMU sensors demonstrated high accuracy and reliability, with minimal systematic bias and strong positive correlations (p > 0.95 across all tests). Agreement matrices revealed high agreement (<1°) in 24 trials, moderate (1–10°) in 12 trials, and low (>10°) in only 4 trials. The Root Mean Square Error (RMSE) ranged from 1.357 to 5.262 for the 90° tests, 0.094 to 0.538 for the 10° tests, and 0.129 to 0.36 for the 1° tests. Likewise, mean absolute error (MAE) ranged from 0.967 to 4.679 for the 90° tests, 0.073 to 0.386 for the 10° tests, and 0.102 to 0.309 for the 1° tests. The sensor provided precise measurements of digit angles across 0–90° in multiple directions, enabling reliable clinical assessment, remote monitoring, and improved diagnosis, treatment, and rehabilitation for individuals with hand differences. Full article

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data. Full article

The successful separation of a single sheet from a stack of paper is considered a paper-handling goal when using a robot hand. Under the condition of uncertain friction coefficients, a stochastic algorithm introducing randomness is formulated, which converges a paper stack to a state of single-sheet separation through the repetition of simple robot operations. This formulation is based on the proposed motion strategy for a robotic hand, which introduces a state of a partially separated paper bundle to temporarily allow the simultaneous separation of multiple sheets and a return operation to return the paper to the original paper bundle. The experimental results indicate that a single sheet can be completely separated from a vertically standing stack of business-card-sized papers by shifting the paper in a high-speed translational movement using two fingers of the robot hand that grasp the paper from both sides. Full article

Addressing the issue of excessive manual intervention in discharging fermented grains from underground tanks in traditional brewing technology, this paper proposes an intelligent grains-out strategy based on a multi-degree-of-freedom hybrid robot. The robot’s structure and control system are introduced, along with analyses of kinematics solutions for its parallel components and end-effector speeds. According to its structural characteristics and working conditions, a visual-perception-based motion control method of discharging fermented grains is determined. The enhanced perception of underground tanks’ positions is achieved through improved Canny edge detection algorithms, and a YOLO-v7 neural network is employed to train an image segmentation model for fermented grains’ surface, integrating depth information to synthesize point clouds. We then carry out the downsampling and three-dimensional reconstruction of these point clouds, then match the underground tank model with the fermented grain surface model to replicate the tank’s interior space. Finally, a digging motion control method is proposed and experimentally validated for feasibility and operational efficiency. Full article