Search Results (41)

Sea cucumbers play a vital role in marine and coastal ecosystems, with some species holding significant economic value. Accurate growth assessment, particularly weight estimation, is crucial for their management and conservation. However, direct measurement poses challenges, as sea cucumbers expel internal fluids when handled, altering their body size and weight. This study evaluates the effectiveness of image analysis combined with ArUco markers to estimate the weight of three economically and ecologically important sea cucumber species found in Thailand: black sea cucumber (Holothuria leucospilota), pink warty sea cucumber (Cercodemas anceps), and sandfish (Holothuria scabra). The proposed method demonstrated high accuracy, with R² values of 0.9699, 0.9774, and 0.9882, respectively. Furthermore, no significant differences (p > 0.05) were observed between traditional hand measurements and image-based assessments, with relative errors of 7.71 ± 4.30% for black sea cucumber, 5.06 ± 3.37% for pink warty sea cucumber, and 4.50 ± 3.23% for sandfish. Unlike deep learning, which requires large datasets and high computation, this simple, cost-effective, and adaptable method highlights the potential of image analysis combined with ArUco markers as a non-invasive and accurate tool for estimating sea cucumber weight. This approach minimizes stress on the animals and can be extended to other aquatic species. However, challenges such as shadows, water turbidity, and the presence of similarly shaped objects near sea cucumbers should be considered when applying this technique in field conditions. Full article

Deformation monitoring of Gas-Insulated Transmission Lines (GILs) is critical for the early detection of structural issues and for ensuring safe power transmission. In this study, we introduce a rapid monocular measurement method that leverages deep learning for real-time monitoring. A YOLOv10 model is developed for automatically identifying regions of interest (ROIs) that may exhibit deformations. Within these ROIs, grayscale data is used to dynamically set thresholds for FAST corner detection, while the Shi–Tomasi algorithm filters redundant corners to extract unique feature points for precise tracking. Subsequent subpixel refinement further enhances measurement accuracy. To correct image tilt, ArUco markers are employed for geometric correction and to compute a scaling factor based on their known edge lengths, thereby reducing errors caused by non-perpendicular camera angles. Simulated experiments validate our approach, demonstrating that combining refined ArUco marker coordinates with manually annotated features significantly improves detection accuracy. Our method achieves a mean absolute error of no more than 1.337 mm and a processing speed of approximately 0.024 s per frame, meeting the precision and efficiency requirements for GIL deformation monitoring. This integrated approach offers a robust solution for long-term, real-time monitoring of GIL deformations, with promising potential for practical applications in power transmission systems. Full article

Among the several usages of unmanned aerial vehicles (UAVs), a wireless relay system is one of the most promising applications. Specifically, a small fixed-wing UAV is suitable to establish the system promptly. In the system, an antenna pointing control system directs an onboard antenna to a ground station in order to form and maintain a communication link between the UAV and the ground station. In this paper, we propose a sensor system to detect the direction of the ground station from the UAV by using color marker patterns for the antenna pointing control system. The sensor detects the difference between the antenna pointing direction and the ground station direction. The sensor is characterized by the usage of both the color information of multiple color markers and color marker pattern matching. These enable the detection of distant, low-resolution markers, a high accuracy of marker detection, and robust marker detection against motion blur. In this paper, we describe the detailed algorithm of the sensor, and its performance is evaluated by using the prototype sensor system. Experimental performance evaluation results showed that the proposed method had a minimum detectable drawing size of 10.2 pixels, a motion blur tolerance of 0.0175, and a detection accuracy error of less than 0.12 deg. This performance indicates that the method has a minimum detectable draw size that is half that of the ArUco marker (a common AR marker), is 15.9 times more tolerant of motion blur than the ArUco marker, and has a detection accuracy error twice that of the ArUco marker. The color markers in the proposed method can be placed farther away or be smaller in size than ArUco markers, and they can be detected by the onboard camera even if the aircraft’s attitude changes significantly. The proposed method using color marker patterns has the potential to improve the operational flexibility of radio relay systems utilizing UAVs and is expected to be further developed in the future. Full article

This paper introduces a team of Automated Guided Vehicles (AGVs) equipped with open-source, perception-enhancing rotating devices. Each device has a set of ArUco markers, employed to compute the relative pose of other AGVs. These markers also serve as landmarks, delineating a path for the robots to follow. The authors combined various control methodologies to track the ArUco markers on another rotating device mounted on the AGVs. Behavior trees are implemented to facilitate task-switching or to respond to sudden disturbances, such as environmental obstacles. The Robot Operating System (ROS) is installed on the AGVs to manage high-level controls. The efficacy of the proposed solution is confirmed through a real experiment. This research contributes to the advancement of AGV technology and its potential applications in various fields for example in a warehouse with a restricted and known environment where AGVs can transport goods while avoiding other AGVs in the same environment. Full article

Human–robot collaboration (HRC) remains an increasingly growing trend in the robotics research field. Despite the widespread usage of collaborative robots on the market, several safety issues still need to be addressed to develop industry-ready applications exploiting the full potential of the technology. This paper focuses on hand-guiding applications, proposing an approach based on a wearable device to reduce the risk related to operator fatigue or distraction. The methodology aims at ensuring operator’s attention during the hand guidance of a robot end effector in order to avoid injuries. This goal is achieved by detecting a region of interest (ROI) and checking that the gaze of the operator is kept within this area by means of a pair of eye-tracking glasses (Pupil Labs Neon, Berlin, Germany). The detection of the ROI is obtained primarily by the tracking camera of the glasses, acquiring the position of predefined ArUco markers, thus obtaining the corresponding contour area. In case of the misdetection of one or more markers, their position is estimated through the optical flow methodology. The performance of the proposed system is initially assessed with a motorized test bench simulating the rotation of operator’s head in a repeatable way and then in an HRC scenario used as case study. The tests show that the system can effectively identify a planar ROI in the context of a HRC application in real time. Full article

This paper presents a novel approach to enhance pose estimation accuracy and precision in mobile robotics using multiple graphical markers. While traditional single-marker methods using graphical markers such as ArUco offer simple implementation, their performance is susceptible to environmental variations and measurement errors. To address these limitations, we propose a robust pose estimation algorithm that leverages multiple markers simultaneously. Our approach incorporates two key mechanisms: spatial consistency verification to detect invalid markers within the marker array, and temporal stability analysis to identify and exclude outlier measurements. The algorithm enhances pose estimation accuracy by averaging measurements from valid markers while preventing any single marker from dominating the estimation process. The effectiveness of our approach was validated through experiments using both fixed and drone-mounted camera configurations. The results demonstrated that the pose estimation using multiple markers significantly improved both accuracy and precision compared with single-marker approaches. In fixed-camera experiments, the proposed method showed reduced mean errors and standard deviations in both position and orientation measurements across various camera poses. Similarly, in drone-mounted camera experiments, our approach exhibited superior stability with significantly lower measurement variations during hovering maneuvers. These improvements were particularly pronounced in challenging scenarios, such as when the camera was tilted at large angles relative to the marker plane. This research contributes to the advancement of reliable pose estimation methodologies in mobile robotics and autonomous systems, with potential applications across diverse environments where precise position and orientation measurements are crucial. Full article

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system. Full article

The present study proposes a fiducial marker for location systems that uses computer vision. The marker employs a set of tape-shaped markers that facilitate their positioning in the environment, allowing continuous reading to cover the entire perimeter of the environment and making it possible to minimize interruptions in the location service. Because the marker is present throughout the perimeter of the environment, it presents hierarchical coding patterns that allow it to be robust against multiple detection scales. We implemented an application to help the user generate the markers with a floor plan image. We conducted two types of tests, one in a 3D simulation environment and one in a real-life environment with a smartphone. The tests made it possible to measure the performance of the tape-shaped marker with readings at multiple distances compared to ArUco, QRCode, and STag with detections at distances of 10 to 0.5 m. The localization tests in the 3D environment analyzed the time of marker detection during the journey from one room to another in positioning conditions (A) with the markers positioned at the baseboard of the wall, (B) with the markers positioned at camera height, and (C) with the marker positioned on the floor. The localization tests in real conditions allowed us to measure the time of detections in favorable conditions of detections, demonstrating that the tape-shaped-marker-detection algorithm is not yet robust against blurring but is robust against lighting variations, difficult angle displays, and partial occlusions. In both test environments, the marker allowed for detection at multiple scales, confirming its functionality. Full article

In orthopedics, X-rays and computed tomography (CT) scans play pivotal roles in diagnosing and treating bone pathologies. Machine bulkiness and the emission of ionizing radiation remain the main problems associated with these techniques. The accessibility and low risks related to ultrasound handling make it a popular 2D imaging method. Indeed, 3D ultrasound assembles 2D slices into a 3D volume. This study aimed to implement a probe-tracking method for 6 DoF 3D ultrasound. The proposed method involves a dodecahedron with ArUco markers attached, enabling computer vision tracking of the ultrasound probe’s position and orientation. The algorithm focuses on the data acquisition phase but covers the basic reconstruction required for data generation and analysis. In the best case, the analysis revealed an average error norm of 2.858 mm with a standard deviation norm of 5.534 mm compared to an infrared optical tracking system used as a reference. This study demonstrates the feasibility of performing volumetric imaging without ionizing radiation or bulky systems. This marker-based approach shows promise for enhancing orthopedic imaging, providing a more accessible imaging modality for helping clinicians to diagnose pathologies regarding complex joints, such as the shoulder, replacing standard infrared tracking systems known to suffer from marker occlusion problems. Full article

Sensor technologies are increasingly used to monitor laboratory animal behaviour. The aim of this study was to investigate the added value of using accelerometers and video to monitor the activity and drinking behaviour of three rams from 5 days before to 22 days after inoculation with Toxoplasma gondii. We computed the activity from accelerometer data as the vectorial dynamic body acceleration (VDBA). In addition, we assessed individual drinking behaviour from video, using frame differencing above the drinker to identify drinking bouts, and Aruco markers for individual identification. Four days after inoculation, rams developed fever and activity decreased. The daytime VDBA from days 4 to 10 was 60–80% of that before inoculation. Animal caretakers scored rams as lethargic on days 5 and 6 and, for one ram, also on the morning of day 7. Video analysis showed that each ram decreased its number of visits to the drinker, as well as its time spent at the drinker, by up to 50%. The fever and corresponding sickness behaviours lasted until day 10. Overall, while we recognize the limited conclusiveness due to the small number of animals, the sensor technologies provided continuous, individual, detailed, and objective data and offered additional insights as compared to routine observations. We recommend the wider implementation of such technologies in animal disease trials to refine experiments and guarantee the quality of experimental results. Full article

Landing on unmanned surface vehicles (USV) autonomously is a critical task for unmanned aerial vehicles (UAV) due to complex environments. To solve this problem, an autonomous landing method is proposed based on a multi-level marker and linear active disturbance rejection control (LADRC) in this study. A specially designed landing board is placed on the USV, and ArUco codes with different scales are employed. Then, the landing marker is captured and processed by a camera mounted below the UAV body. Using the efficient perspective-n-point method, the position and attitude of the UAV are estimated and further fused by the Kalman filter, which improves the estimation accuracy and stability. On this basis, LADRC is used for UAV landing control, in which an extended state observer with adjustable bandwidth is employed to evaluate disturbance and proportional-derivative control is adopted to eliminate control error. The results of simulations and experiments demonstrate the feasibility and effectiveness of the proposed method, which provides an effective solution for the autonomous recovery of unmanned systems. Full article

Accurately docking mobile robots to various workstations on the factory floor is a common and essential task. The existing docking methods face three major challenges: intricate deployment procedures, susceptibility to ambient lighting, and incapacity to recognize product information during the docking process. This paper devises a novel approach that combines the features of ArUco and Data Matrix to form a composite marker termed “DataMatrix-ArUco-Hybrid” (DAH). The DAH pattern serves as a fiducial marker capable of concurrently providing both target pose information and product information. Detection of the DAH pattern is conducted by a cost-effective fiducial marker reader system, called “FiMa-Reader”, which comprises an embedded processing unit and an infrared camera equipped with a 940 nm fill-light to overcome lighting issues. The FiMa-Reader system effectively detects the DAH pattern under both well-lit and dimly lit conditions. Additionally, the implementation of the FiMa-Reader system leads to significant improvements in positioning accuracy, including an 86.42% improvement on the x-axis, a 44.7% improvement on the y-axis, and an 84.21% improvement in angular orientation when compared to traditional navigation methods. The utilization of FiMa-Reader presents an economically viable system capable of guiding mobile robots’ positioning with high precision in various indoor lighting conditions. Full article

Camera pose estimation is vital in fields like robotics, medical imaging, and augmented reality. Fiducial markers, specifically ArUco and Apriltag, are preferred for their efficiency. However, their accuracy and viewing angle are limited when used as single markers. Custom fiducial objects have been developed to address these limitations by attaching markers to 3D objects, enhancing visibility from multiple viewpoints and improving precision. Existing methods mainly use square markers on non-square object faces, leading to inefficient space use. This paper introduces a novel approach for creating fiducial objects with custom-shaped markers that optimize face coverage, enhancing space utilization and marker detectability at greater distances. Furthermore, we present a technique for the precise configuration estimation of these objects using multiviewpoint images. We provide the research community with our code, tutorials, and an application to facilitate the building and calibration of these objects. Our empirical analysis assesses the effectiveness of various fiducial objects for pose estimation across different conditions, such as noise levels, blur, and scale variations. The results suggest that our customized markers significantly outperform traditional square markers, marking a positive advancement in fiducial marker-based pose estimation methods. Full article

This paper presents a leader–follower mobile robot control approach using onboard sensors. The follower robot is equipped with an Intel RealSense camera mounted on a rotating platform. Camera observations and ArUco markers are used to localize the robots to each other and relative to the workspace. The rotating platform allows the expansion of the perception range. As a result, the robot can use observations that are not within the camera’s field of view at the same time in the localization process. The decision-making process associated with the control of camera rotation is implemented using behavior trees. In addition, measurements from encoders and IMUs are used to improve the quality of localization. Data fusion is performed using the EKF filter and allows the user to determine the robot’s poses. A 3D-printed cuboidal tower is added to the leader robot with four ArUco markers located on its sides. Fiducial landmarks are placed on vertical surfaces in the workspace to improve the localization process. The experiments were performed to verify the effectiveness of the presented control algorithm. The robot operating system (ROS) was installed on both robots. Full article

To enhance the crypticity and operational efficiency of unmanned underwater vehicle (UUV) charging, we propose an automatic alignment method for an underwater charging platform based on monocular vision recognition. This method accurately identifies the UUV number and guides the charging stake to smoothly insert into the charging port of the UUV through target recognition. To decode the UUV’s identity information, even in challenging imaging conditions, an encryption encoding method containing redundant information and an ArUco code reconstruction method are proposed. To address the challenge of underwater target location determination, a target location determination method was proposed based on deep learning and the law of refraction. The method can determine the two-dimensional coordinates of the target location underwater using the UUV target spray position. To meet the real-time control requirements and the harsh underwater imaging environment, we proposed a target recognition algorithm to guide the charging platform towards the target direction. The practical underwater alignment experiments demonstrate the method’s strong real-time performance and its adaptability to underwater environments. The final alignment error is approximately 0.5548 mm, meeting the required alignment accuracy and ensuring successful alignment. Full article