Search Results (263)

This paper introduces an efficient localization algorithm for robotic systems, utilizing deep learning to identify and exploit natural fiduciary patterns within the environment. Diverging from conventional localization techniques that depend on artificial markers, this method capitalizes on the inherent environmental features to enhance both accuracy and computational efficiency. By integrating advanced deep learning frameworks with natural scene analysis, the proposed algorithm facilitates robust, real-time localization in dynamic and unstructured settings. The resulting approach offers significant improvements in adaptability, precision, and operational efficiency, representing a substantial contribution to the field of autonomous robotics. We are aiming at analyzing an automotive manufacturing scenario to achieve robotic localization related to a moving target. To work with a simpler and more accessible scenario we have chosen a demonstrative context consisting of a laboratory wall containing some elements. This paper will focus on the first part of the case study, with a continuation planned for future work. It will demonstrate a scenario in which a camera is mounted on a robot, capturing images of the underside of a car (which we assume to be represented by a gray painted surface with specific elements to be described in Materials and Methods). These images are processed by a convolutional neural network (CNN), designed to detect the most distinctive features of the environment. The extracted information is crucial, as the identified characteristic areas will serve as reference points for the real-time localization of the industrial robot. In this work, we have demonstrated the potential of leveraging natural fiduciary patterns for efficient and accurate robot localization. By utilizing deep learning, specifically convolutional neural networks. The experimental results suggest that this approach is not only feasible but also scalable across a wide range of applications, including industrial automation autonomous vehicles, and aerospace navigation. As robots increasingly operate in environments where computational efficiency and adaptability are paramount, our methodology offers a viable solution to enhance localization without compromising accuracy or speed. The proposal of an algorithm that enables the application of the proposed method for natural fiduciary patterns based on neural networks to more complex scenarios is highlighted, along with the efficiency of the method for robot localization compared to others. Full article

This paper presents a comprehensive approach to evaluating the ability of multi-legged robots to traverse confined and geometrically complex unstructured environments. The proposed approach utilizes advanced point cloud processing techniques integrating voxel-filtered cloud, boundary and mesh generation, and dynamic traversability analysis to enhance the robot’s terrain perception and navigation. The proposed framework was validated through rigorous simulation and experimental testing with humanoid robots, showcasing the potential of the proposed approach for use in applications/environments characterized by complex environmental features (navigation inside collapsed buildings). The results demonstrate that the proposed framework provides the robot with an enhanced capability to perceive and interpret its environment and adapt to dynamic environment changes. This paper contributes to the advancement of robotic navigation and path-planning systems by providing a scalable and efficient framework for environment analysis. The integration of various point cloud processing techniques into a single architecture not only improves computational efficiency but also enhances the robot’s interaction with its environment, making it more capable of operating in complex, hazardous, unstructured settings. Full article

Background/Objectives: EnBloc resections of bone tumors of the spine are very demanding as the target to achieve a tumor-free margin specimen (sometimes impossible due to the extracompartimental tumor extension) is sometimes conflicting with the integrity of neurological functions and spine stability. Methods: The surgical treatment of a huge multi-level chordoma of the thoracic spine with unusual extension is reported. Anteriorly, the tumor widely invaded the mediastinum and displaced the aorta; on the left side, it expanded in the subpleuric region; posteriorly, it was uncommonly distant 13 mm from the posterior wall. Results: EnBloc resection is largely performed for primary bone tumors of the spine and many reports have been published concerning brilliant solutions to difficult issues of surgical anatomy. One of the major challenges is still the compatibility between oncological and functional requirements. Conclusions: Oncological staging, careful imaging analysis, a multidisciplinary surgical team, and utilization of the most recent technologies like navigation and robotics have made an oncologically appropriate EnBloc resection of a multi-level chordoma of the thoracic spine possible without affecting the continuity of the spinal canal and without any involvement of its content by an original “L”-shaped osteotomy. Full article

Carrier-phase measurements are essential in precise Global Navigation Satellite System (GNSS) positioning applications. The quality of those observations, as well as the final positioning result, is influenced by an extensive list of GNSS error sources, one of which is the receiver antenna phase center (PC) model. It has been well established that the antenna PC exhibits variability depending on the frequency, direction, and intensity of the incoming GNSS signal. To mitigate the corresponding range errors, phase center corrections (PCCs) are determined through a specialized procedure known as receiver antenna calibration and subsequently applied in data processing. In 2023, the Laboratory for Measurements and Measuring Technique (LMMT) of the Faculty of Geodesy, University of Zagreb, Croatia, initiated the development of a new robotic GNSS receiver antenna calibration system. The system implements absolute field calibration and PCC modeling through triple-difference (TD) carrier-phase observations and spherical harmonics (SH) expansion. This study presents and documents dual-frequency (L1 and L2) Global Positioning System (GPS) calibration results for several distinct receiver antennas. Furthermore, the main goals of this contribution are to evaluate the accuracy of dual-frequency GPS calibration results on the pattern level with respect to independent calibrations obtained from Geo++ GmbH and to extensively experimentally validate LMMT calibration results in the spatial (coordinate) domain, i.e., to investigate how the application of LMMT PPC models reflects on geodetic-grade GNSS positioning. Our experimental research results showed a submillimeter calibration accuracy, i.e., 0.36 mm for GPS L1 and 0.54 mm for the GPS L2 frequency. Furthermore, our field results confirmed that the application of LMMT PCC models significantly increases baseline accuracy and GNSS network solution accuracy when compared to type-mean PCC models of the International GNSS Service (IGS). 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 ×4, ×8, and ×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

Background and Objectives: Spine surgery has undergone significant advancements, particularly with regard to robotic systems that enhance surgical techniques and improve patient outcomes. As these technologies become increasingly integrated into surgical practice, it is essential to evaluate their added value and cost savings. Hence, this study compared robot-assisted and navigation-based spine surgery, focusing on surgical efficiency. Materials and Methods: We conducted a single-center, retrospective cohort study of patients undergoing single- and double-level transforaminal lumbar interbody fusion (TLIF) and oblique lumbar interbody fusion (OLIF) surgeries. Patients were divided into two groups: those who had robot-assisted and navigation-based surgeries, stratified by surgery type (TLIF or OLIF) and fusion levels (one or two). A comparative analysis of factors related to surgical efficiency, including operative duration, blood loss, and length of hospital stay, was conducted. Results: Our results showed a statistically significant reduction in operative duration for robot-assisted one- and two-level OLIF cases, with average time savings of 50 and 62 min, respectively, compared to navigation-based surgery. These time savings translated to an estimated cost reduction of SGD 1500 for the hospital for each patient for a two-level OLIF procedure and could be higher as the number of operated levels increase. Conclusions: These results indicated that robot-assisted spine surgery offers superior surgical efficiency and cost savings, particularly with increased numbers of surgical levels. As robotic technologies evolve, their integration into spine surgery is justified, promising improved patient outcomes and cost-effectiveness. Full article

Developing autonomous navigation techniques for surface vehicles remains an important research area, and accurate global path planning is essential. For mobile robots—particularly for Unmanned Surface Vehicles (USVs)—a key challenge is ensuring that sharp turns and sharp breaks are avoided. Therefore, global path planning must not only calculate the shortest path but also provide smoothness. Bézier Curves are one of the main methods used for smoothing paths in the literature. Some studies have focused on turns alone; however, continuous path smoothness across the entire trajectory enhances navigational quality. Contrary to similar studies, we applied Bézier Curves whose control polygon is defined by an RRT path and thus avoided a multi-objective formulation. In the final stage of our approach, we proposed a control point reduction method in order to decrease the time complexity without affecting the feasibility of the path. Our experimental results suggest significant improvements for multiple map sizes, in terms of path smoothness. Full article

Inspection robots, which improve hazard identification and enhance safety management, play a vital role in the examination of high-risk environments in many fields, such as power distribution, petrochemical, and new energy battery factories. Currently, the position precision of the robots is a major barrier to their broad application. Exact kinematic model and control system of the robots is required to improve their location accuracy during movement on the unstructured surfaces. By a virtual engine and digital twins, this study put forward a visualization monitoring and control system framework which can address the difficulties in the intelligent factories while managing a variety of data sources, such as virtual–real integration, real-time feedback, and other issues. To develop a more realistic dynamic model for the robots, we presented a neural-network-based compensation technique for the nonlinear dynamic model parameters of outdoor mobile robots. A physical prototype was applied in the experiments, and the results showed that the system is capable of controlling and monitoring outdoor mobile robots online with good visualization effects and high real-time performance. By boosting the positional accuracy of robots by 18% when navigating obstacles, the proposed precise kinematic model can increase the inspection efficiency of robots. The visualization monitoring and control system enables visual, digital, multi-method, and complete real-time inspections in high-risk factories, such as new energy battery factories, to ensure the safe and stable operations. Full article

This paper presents a comprehensive review of path planning in dynamic environments. This review covers the entire process, starting from obstacle detection techniques, through path-planning strategies, and also extending to formation control and communication styles. The review discusses the key trends, challenges, and gaps in current methods to emphasize the need for more efficient and robust algorithms that can handle complex and unpredictable dynamic environments. Moreover, it discusses the importance of collaborative decision making and communication between robots to optimize path planning in dynamic scenarios. This work serves as a valuable resource for advancing research and practical applications in dynamic obstacle navigation. Full article

Autonomous technologies have revolutionized transportation, military operations, and space exploration, necessitating precise localization in environments where traditional GPS-based systems are unreliable or unavailable. While widespread for outdoor localization, GPS systems face limitations in obstructed environments such as dense urban areas, forests, and indoor spaces. Moreover, GPS reliance introduces vulnerabilities to signal disruptions, which can lead to significant operational failures. Hence, developing alternative localization techniques that do not depend on external signals is essential, showing a critical need for robust, GPS-independent localization solutions adaptable to different applications, ranging from Earth-based autonomous vehicles to robotic missions on Mars. This paper addresses these challenges using Visual odometry (VO) to estimate a camera’s pose by analyzing captured image sequences in GPS-denied areas tailored for autonomous vehicles (AVs), where safety and real-time decision-making are paramount. Extensive research has been dedicated to pose estimation using LiDAR or stereo cameras, which, despite their accuracy, are constrained by weight, cost, and complexity. In contrast, monocular vision is practical and cost-effective, making it a popular choice for drones, cars, and autonomous vehicles. However, robust and reliable monocular pose estimation models remain underexplored. This research aims to fill this gap by developing a novel adaptive framework for outdoor pose estimation and safe navigation using enhanced visual odometry systems with monocular cameras, especially for applications where deploying additional sensors is not feasible due to cost or physical constraints. This framework is designed to be adaptable across different vehicles and platforms, ensuring accurate and reliable pose estimation. We integrate advanced control theory to provide safety guarantees for motion control, ensuring that the AV can react safely to the imminent hazards and unknown trajectories of nearby traffic agents. The focus is on creating an AI-driven model(s) that meets the performance standards of multi-sensor systems while leveraging the inherent advantages of monocular vision. This research uses state-of-the-art machine learning techniques to advance visual odometry’s technical capabilities and ensure its adaptability across different platforms, cameras, and environments. By merging cutting-edge visual odometry techniques with robust control theory, our approach enhances both the safety and performance of AVs in complex traffic situations, directly addressing the challenge of safe and adaptive navigation. Experimental results on the KITTI odometry dataset demonstrate a significant improvement in pose estimation accuracy, offering a cost-effective and robust solution for real-world applications. Full article

Obstacle contact detection is not commonly employed in autonomous robots, which mainly depend on avoidance algorithms, limiting their effectiveness in cluttered environments. Current contact-detection techniques suffer from blind spots or discretized detection points, and rigid platforms further limit performance by merely detecting the presence of a collision without providing detailed feedback. To address these challenges, we propose an innovative contact sensor design that improves autonomous navigation through physical contact detection. The system features an elastic collision platform integrated with flex sensors to measure displacements during collisions. A neural network-based contact-detection algorithm converts the flex sensor data into actionable contact information. The collision system was validated with collisions through manual flights and autonomous contact-based missions, using sensor feedback for real-time collision recovery. The experimental results demonstrated the system’s capability to accurately detect contact events and estimate collision parameters, even under dynamic conditions. The proposed solution offers a robust approach to improving autonomous navigation in complex environments and provides a solid foundation for future research on contact-based navigation systems. Full article

This paper examines the critical role of indoor positioning for robots, with a particular focus on small and confined spaces such as homes, warehouses, and similar environments. We develop an algorithm by integrating deep neural networks (DNNs) with the extended Kalman filter (EKF) method, which is known as DNN-EKF, to obtain an accurate indoor localization for ensuring precise and reliable robot movements within the use of Ultra-Wideband (UWB) technology. The study introduces a novel methodology that combines advanced technology, including DNN, filtering techniques, specifically the EKF and UWB technology, with the objective of enhancing the accuracy of indoor localization systems. The objective of integrating these technologies is to develop a more robust and dependable solution for robot navigation in challenging indoor environments. The proposed approach combines a DNN with the EKF to significantly improve indoor localization accuracy for mobile robots. The results clearly show that the proposed model outperforms existing methods, including NN-EKF, LPF-EKF, and other traditional approaches. In particular, the DNN-EKF method achieves optimal performance with the least distance loss compared to NN-EKF and LPF-EKF. These results highlight the superior effectiveness of the DNN-EKF method in providing precise localization in indoor environments, especially when utilizing UWB technology. This makes the model highly suitable for real-time robotic applications, particularly in dynamic and noisy environments. Full article

Lung cancer is a major global health issue, with 2.21 million cases and 1.80 million deaths reported in 2020. It is the leading cause of cancer death worldwide. Most lung cancers have been linked to tobacco use, with changes in cigarette composition over the years contributing to shifts in cancer types and tumor locations within the lungs. Additionally, there is a growing incidence of lung cancer among never-smokers, particularly in East Asia, which is expected to increase the global burden of the disease. The classification of non-small cell lung cancer (NSCLC) into distinct subtypes is crucial for treatment efficacy and patient safety, especially as different subtypes respond differently to chemotherapy. For instance, certain chemotherapeutic agents are more effective for adenocarcinoma than for squamous carcinoma, which has led to the exclusion of squamous carcinoma from treatments like Bevacizumab due to safety concerns. This necessitates accurate histological diagnosis, which requires sufficient tissue samples from biopsies. However, acquiring adequate tissue is challenging due to the complex nature of lung tumors, patient comorbidities, and potential complications from biopsy procedures, such as bleeding, pneumothorax, and the purported risk of local recurrence. The need for improved diagnostic techniques has led to the development of advanced technologies like electromagnetic navigation bronchoscopy (ENB), radial endobronchial ultrasound (rEBUS), and robotic bronchoscopy. ENB and rEBUS have enhanced the accuracy and safety of lung biopsies, particularly for peripheral lesions, but both have limitations, such as the dependency on the presence of a bronchus sign. Robotic bronchoscopy, which builds on ENB, offers greater maneuverability and stability, improving diagnostic yields. Additionally, new imaging adjuncts, such as Cone Beam Computed Tomography (CBCT) and augmented fluoroscopy, further enhance the precision of these procedures by providing real-time, high-resolution imaging. These advancements are crucial as lung cancer is increasingly being detected at earlier stages due to screening programs, which require minimally invasive, accurate diagnostic methods to improve patient outcomes. This review aims to provide a comprehensive overview of the current challenges in lung cancer diagnostics and the innovative technological advancements in this rapidly evolving field, which represents an increasingly exciting career path for aspiring pulmonologists. Full article

Knee arthroplasty, commonly performed to treat osteoarthritis, necessitates precise surgical techniques for optimal outcomes. The introduction of systems such as the Persona Knee System (Zimmer Biomet, Warsaw, IN, USA) has revolutionized knee arthroplasty, promising enhanced precision and better patient outcomes. This study investigates the application of robotic planning specifically in knee prosthetic surgeries, with a focus on Persona Knee System prostheses. We conducted a retrospective analysis of 300 patients who underwent knee arthroplasty using the Persona Knee System between January 2020 and November 2023, including demographic data, surgical parameters, and preoperative imaging. Robotic planning was employed to simulate surgical procedures. The planning process integrated preoperative imaging data from a specific program adopted for conducting digital preoperative planning, and statistical analyses were conducted to assess correlations between patient characteristics and surgical outcomes. Out of 300 patients, 85% presented with minor deformities, validating the feasibility of robotic planning. Robotic planning demonstrated precise prediction of optimal arthroplasty sizes and alignment, closely aligning with preoperative imaging data. This study highlights the potential benefits of robotic planning in knee arthroplasty surgeries, particularly in cases with minor deformities. By leveraging preoperative imaging data and integrating advanced robotic technologies, surgeons can improve precision and efficacy in knee arthroplasty. Moreover, robotic technology allows for a reduced level of constraint in the intraoperative choice between Posterior-Stabilized and Constrained Posterior-Stabilized liners compared with an imageless navigated procedure. Full article

As part of the Horizon 2020 InfraROB project aimed at enhancing road safety through innovative robotic solutions, a compact autonomous vehicle has been developed to repair asphalt potholes. Central to this system is a 3D printer capable of extruding a novel cold-asphalt mixture, specifically designed for patching road surfaces. The printer is mounted on a small robot that autonomously navigates to potholes, while the human operator controls the operation from a secure location outside the traffic area. The system’s development involved several key steps: designing the repair mixture, constructing the 3D printer for mixture extrusion, implementing a photogrammetric technique to accurately measure pothole geometry for printing, and integrating the extrusion system with the robotic platform. Two preliminary tests were conducted in controlled environments at Sapienza University of Rome to check the reliability of calculation of the amount of material needed to fill in the potholes. Finally, the entire procedure was tested on an Italian motorway, demonstrating the system’s functionality without encountering operational issues. Full article