Feature selection and planning are integral parts of visual servoing systems. Because many irrelevant and nonreliable image features usually exist, higher accuracy and robustness can be expected by selecting and planning good features. Assumption of perfect radiometric conditions is common in visual servoing. The following paper discusses the issue of radiometric constraints for feature selection in the context of visual servoing. Here, radiometric constraints are presented and measures are formulated to select the optimal features (in a radiometric sense) from a set of candidate features. Simulation and experimental results verify the effectiveness of the proposed measures.
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http://dx.doi.org/10.1109/tsmcb.2003.818534 | DOI Listing |
PeerJ Comput Sci
November 2024
University Center for Exact Sciences and Engineering, Universidad de Guadalajara, Guadalajara, Jalisco, Mexico.
This article presents an implementation of visual servoing (VS) for a redundant mobile manipulator in an eye-in-hand configuration. We used the image based visual servoing (IBVS) scheme, which means the pose control of the robot is based on the error features in the image of a camera. Conventional eye-in-hand VS requires the inversion of a Jacobian matrix, which can become rank deficient, provoking kinematic singularities.
View Article and Find Full Text PDFSensors (Basel)
October 2024
Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China.
To address the issue of accurately recognizing and locating picking points for tea-picking robots in unstructured environments, a visual positioning method based on RGB-D information fusion is proposed. First, an improved T-YOLOv8n model is proposed, which improves detection and segmentation performance across multi-scale scenes through network architecture and loss function optimizations. In the far-view test set, the detection accuracy of tea buds reached 80.
View Article and Find Full Text PDFISA Trans
October 2023
Department of Mechanical, Industrial and Aerospace Engineering, Concordia University, Montreal, Quebec H3G 1M8, Canada. Electronic address:
The trajectory tracking control of parallel robots is challenging due to their complicated dynamics and kinematics. This paper proposes a position-based visual servoing (PBVS) approach for a 6-Revolute-Spherical-Spherical (6-RSS) parallel robot using adaptive sliding mode control in Cartesian space. A photogrammetry sensor C-Track 780 in the eye-to-hand configuration is adopted to measure the real-time pose of the robot end-effector, which can avoid the calculation of robot forward kinematics and provide more flexibility for controller design.
View Article and Find Full Text PDFMicromachines (Basel)
October 2024
State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Micro-stereolithography (μSL) is an advanced additive manufacturing technique that enables the fabrication of highly precise microstructures with fine feature resolution. One of the primary challenges in μSL is achieving and maintaining precise focus throughout the fabrication process. For the successful application of μSL, it is essential to maintain the sample surface within a focal depth of several microns.
View Article and Find Full Text PDFFront Robot AI
October 2024
Department of Mechanical Engineering, Polytechnique Montréal, Montréal, QC, Canada.
In response to the costly and error-prone manual satellite tracking on the International Space Station (ISS), this paper presents a deep neural network (DNN)-based robotic visual servoing solution to the automated tracking operation. This innovative approach directly addresses the critical issue of motion decoupling, which poses a significant challenge in current image moment-based visual servoing. The proposed method uses DNNs to estimate the manipulator's pose, resulting in a significant reduction of coupling effects, which enhances control performance and increases tracking precision.
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