Addressing the issues of long planning time, low iteration efficiency, and inability to be applied in dynamic scenarios of the Informed-RRT* algorithm, a dynamic path planning method is proposed by integrating the improved Informed- RRT* algorithm with the VFH+ algorithm. Firstly, the Informed-RRT* algorithm is optimized by adopting bidirectional expansion of target node biasing and adaptive step size strategy, enhancing its capability in global path planning. The VFH+ algorithm with dynamic threshold optimization is proposed for local path planning, effectively improving the algorithm's obstacle avoidance capability and path smoothness in local environments. By determining the optimal passage direction, dynamic threshold optimization, and setting local sub-target points. Finally, the integration of the improved Informed-RRT* algorithm and VFH+ algorithm achieves dynamic path planning based on local sub-target points. Simulation results demonstrate that the improved Informed-RRT* algorithm reduces the average path length by 9.4% and 5.8% compared to RRT* and standard Informed-RRT*, respectively, with an average increase in planning time of 35.2% and 23.5%. The integrated algorithm exhibits good dynamic path planning capabilities in complex environments, balancing global optimality with safe obstacle avoidance, and facilitating stable and efficient robot operation.
Aiming at the impact of multiple factors on the accuracy of monocular visual pose measurement system, grey correlation method is used to analyze the pose measurement errors of 11 independent variable parameters, including radial distortion error, tangential distortion error, image point error, normalized focal length error and center point error. The main factors affecting the pose accuracy are obtained, which proves the effectiveness of the grey correlation method for visual system error analysis. The calculation results show that the comprehensive correlation degree between absolute error sequence and zero sequence caused by five types of parameters of radial distortion, tangential distortion, image point coordinate error, normalized focal length error and center point error is less than 0.56, which is significantly lower than other parameters, indicating that its influence on measurement accuracy is significantly higher than other parameters.
To overcome the problem of collision with measured objects with multiple obstacles, a modified path planning method is proposed for a Measuring Robot (MR) based on rapid exploration of random trees. First, the kinematic model of the 6- DOF measuring robot is built by MDH modeling method to clear the measurement space. Second, as the measured object, a valve body is modeled to identify its characteristics and location. Third, the path is planned by optimizing the threshold, smoothness parameters of the traditional RRT model to improve the planning efficiency. Both the path length and planning time are evaluated to verify the advantages of the modified path planning method.
The inaccurate solution of the force measurement comes from the lateral attenuation of loads on mechanical structure. To correct this inaccurate solution, the impact force acting on hammer are reconstructed by a Hybrid Grey-Genetic Algorithm (HGGA). This algorithm integrates a modeling process and an optimization process. A fractional order differential model is built from the observed impact force by using the Fractional Order Accumulated Generating Operation (FAGO) method. After that, the growth coefficients of this model are refined through the Genetic Algorithm (GA). This method is applied to modify an impact force measurement system. The effectiveness of the HGGA is experimentally validated by comparing with the classical force reconstruction model, which shows its superiority.
Aiming at the high cost of tunnel surveying and mapping, the long time spent in the field, and the blind spots in the field of view when registering point clouds in the interior, this paper designed a measurement system based on lidar and camera data acquisition. First, the lidar is used to scan the cross section of the tunnel to obtain the geometric information of the inner wall of the tunnel. Secondly, the camera is used to observe the image information of the visual markers, and the EPNP algorithm is used to obtain the pose information of the camera. Then according to the external parameters calibrated by the camera and lidar and the pose information of the camera, the point cloud data measured by the lidar is normalized to complete the point cloud data splicing. Experimental results show that this method has the advantages of high degree of automation, comprehensive data, and high degree of visualization, and can meet the needs of three-dimensional reconstruction and quantitative analysis of long and narrow tunnels.
With the continuous development of manufacturing industries both domestically and internationally, there is an increasing demand for the 3D reconstruction of large-scale workpieces. In order to expand the measurement range while ensuring measurement accuracy, a 3D reconstruction method based on multi-camera wide-field tracking is proposed. First, calibrate the relative poses of the multi-camera system and establish the poses of all camera coordinate systems with respect to the global coordinate system. Secondly, attach circular markers around the line laser sensor and establish a marker coordinate system, calculating the relative pose between the marker coordinate system and the line laser sensor coordinate system. Thirdly, determine the camera field where the line laser sensor is located, and obtain the pose of circular markers using a pose matching algorithm based on geometric distance. Finally, combining the pose of circular markers and the relative poses of cameras, unify the locally acquired data from the line laser into the global coordinate system, achieving data stitching. The experimental results indicate that within a range of 1.1 meters, the average translation error of this method does not exceed 0.38mm, and the average angle error does not exceed 0.31°, demonstrating the capability for 3D reconstruction of large-scale workpieces.
With the continuous development of domestic and foreign manufacturing industries, there are limitations in the omnidirectional three-dimensional measurement of large parts. In order to improve the 3D measurement range, speed, and accuracy of single-line lasers, a combined measurement method based on binocular vision positioning technology is proposed. First, the circular markers are pasted around the line laser sensor, and a virtual intermediate coordinate system is constructed based on the centers of the marked points. Secondly, a feature point energy matching algorithm is used to complete the corresponding points matching of the circular markers, and the position of the circular markers is solved based on the SVD method. Third, the relative pose relationship between the virtual intermediate coordinate system and the line laser sensor coordinate system is completed based on the hand-eye calibration model. Fourth, combined with the circular markers pose and relative pose, the local point cloud data of the line laser is unified into the global coordinate system to realize point cloud splicing. The experimental results show that the average measurement error of the measurement system is less than 0.23 mm, which basically meets the requirements of general shape measurement.
KEYWORDS: Calibration, Data acquisition, Detection and tracking algorithms, Kinematics, Modeling, Robotics, Data modeling, Mathematical modeling, Connectors, Robotic systems
To reduce the absolute position error, this paper mainly uses Regularized Parameter Identification (RPI) method to in-situ calibrate the kinematic model of a manipulator. Firstly, the model is built according to the MDH modeling method to get the parameter error model. Secondly, as a parameter identification process, the regularization algorithm is used to compensate all the errors on the model of the manipulator. Thirdly, a calibration experiment is carried out to verify the RPI method by using a laser tracker. The experiment results show that the uncalibrated absolute position error of the manipulator is 0.553mm for RPI and 2.533mm for that without calibration. It shows that the regularization algorithm can effectively reduce the absolute position error of the robot after calibration.
Aiming at the problem of thread measurement with small pitch, a screw thread parameters measurement method based on machine vision is proposed, which solves the problem that the tip of the mechanical scanning probe is too large to measure the small size pitch diameter. This work developed a method of image processing method to detect the edge of screw thread parameters. The screw thread image is collected by calibrating the camera to correct the distortion error. Canny edge detection is used to detect the edge contours in the thread image, as well as mean filtering and median filtering. Gaussian smoothing filtering is also studied to obtain higher measurement accuracy: a sub-pixel subdivision technique is designed to improve the resolution of the imaging system. In order to rotate the screw thread image, a minimum rectangle fitting algorithm for the measurement area is developed. The affine transformation matrix is constructed by the center of the measurement area and the orientation of the long axis of the screw thread. This thread parameter edge detection technique has the advantages of simple and quick operation and high measurement accuracy.
To locate the impact source on a composite material structure, an energy eigenvector and correlation interpolation (EECI) method is proposed based on a multi-fiber Bragg grating (FBG) sensor array. The fundamental frequency interference is eliminated by the wavelet transformation. The signal energy feature vector is extracted by the wavelet packet transformation. A two-dimensional correlation matrix is obtained by analyzing the correlation between impact response signals and signals in the reference database. The linear interpolation is then applied to accurately predict the impact location. The EECI method is verified on a carbon fiber composite plate with an effective test size of 400 × 400 (mm). The experimental result shows that the mean absolute error is 16.18 mm and the mean relative error (MRE) is 4.04%. The influence of the FBG sensor arrays and the quantity on the localization accuracy is also discussed to optimize the layout of the localization system. It shows that the MRE is [25.32%, 44.50%] for a single FBG sensor array, [17.19%, 42.63%] for two FBG sensor arrays, [15.63%, 22.47%] for three FBG sensor arrays, which shows the advantages of multi-FBG sensor arrays.
To real time obtain the pose of a line laser sensor in a manipulator with a high accuracy, in this paper, a line laser three-dimensional(3D) measurement system is designed based on visual positioning method. First, the position and posture of the target in the camera coordinate system is obtained by the coplanar PNP model under isometric constraint and the absolute orientation problem. Second, the pose change matrices of the target relative to the initial position at different times are calculated by the pose of the target in the camera coordinate system to achieve the visual positioning. Third, the relative position and posture relationship matrix between the target and the line laser sensor is solved by the standard small ball method to achieve hand-eye calibration. Fourth, the measurement data of the line laser sensor are unified into the world coordinate system by the pose change matrices and the hand-eye calibration matrix to achieve the point cloud splicing. The experimental result shows that the displacement accuracy of the visual positioning is 0.039 mm, and the rotation accuracy is 4.2×10-3 rad. The contour measurement accuracy of the line laser 3D measurement system based on the vision positioning technology is 0.55 mm. It can be seen that the system meets the general industrial measurement requirements.
Aiming at the problems of unsmooth and low efficiency in path planning of the indoor mobile robot, a path planning obstacle avoidance system is proposed. First, the global path is generated in the environment grid map by A* algorithm. Second, as the pose constraint, the Reeds-Shepp path set can generate the executable control instructions by extracting the key points of the path. Third, the static/dynamic obstacle is recognized by a binocular camera and avoided by a decision-making method. The experimental result shows that the generated path can meet the actual motion constraints of the mobile robot, the relative error of the obstacle depth distance is 0.03%, which meets the requirements of the robot path planning.
Loop closure detection (LCD) is an important and challenging task in simultaneous localization and mapping (SLAM). To improve the efficiency and accuracy of an indoor LiDAR LCD, a geometric feature-based method is proposed. First, the rotation invariance of the keyframe pair to be detected is realized by the Fourier transform. Second, the main features of the keyframe are extracted and fitted into geometric features, which are stored in a ring-shaped semantic image. Third, the similarity score of the two keyframes is obtained by calculating the distance between two corresponding ring semantic images, which is verified by the ICP algorithm. The experimental result shows that the time cost of each detection is reduced to less than 30ms without sacrificing the detection precision by using our suggested method.
Since the traditional guidance technology of the AGV (Automatic Guidance Vehicle) has high environmental requirements and insufficient guidance flexibility, an positioning and guidance system of the AGV is proposed based on an UWB (Ultra WideBand) indoor positioning technology. Firstly, a time-based bidirectional ranging method is combined with a multi-base station positioning algorithm to locate the AGV. Secondly, the error generated by the positioning result is compensated to obtain the most accurate AGV actual coordinates. Thirdly the path of the AGV in the actual environment is programmed by using the weighted A* algorithm under different road conditions. The experimental result shows that the AGV with UWB indoor positioning technology has advantages of a high guiding flexibility, simple to use, good safety, stability, and high practical value.
To correct the uncertainty of the vision-based location system, a Hybrid Genetic-Newton Method (HGNM) is presented to calibrate its camera model. This method can minimize the uncertainty of the camera model by fusing the Genetic Algorithm (GA) and Newton method together. First, the camera model of the vision-based location system is built according to the image-forming rule and space geometry transformation principle of its visual measuring device. Second, the initial camera parameters generated by genetic process are iterated by Newton method until it meets the required accuracy. Otherwise, new populations will be generated again by GA and reiterated by Newton method. Third, a novel vision-based location system is designed to illustrate the application advantages of the modeling framework. The experimental result shows that the absolute error range of HGNM is [-1.1, 1.0] mm and the relative error range is [-9.49%, 0.11%]. It reveals that the accuracy of HGNM is about four times higher than LM method and up to six times higher than Newton method. In all, the HGNM is superior to traditional method when it comes to camera model calibration of the vision-based location system.
In this article, we provide a new testing method to evaluate the acceptable quality of the one-way clutch of automatic brake adjuster. To analysis the suitable adjusting brake moment which keeps the automatic brake adjuster out of failure, we build a mechanical model of one-way clutch according to the structure and the working principle of one-way clutch. The ranges of adjusting brake moment both clockwise and anti-clockwise can be calculated through the mechanical model of one-way clutch. Its critical moment, as well, are picked up as the ideal values of adjusting brake moment to evaluate the acceptable quality of one-way clutch of automatic brake adjuster. we calculate the ideal values of critical moment depending on the different structure of one-way clutch based on its mechanical model before the adjusting brake moment test begin. In addition, an experimental apparatus, which the uncertainty of measurement is ±0.1Nm, is specially designed to test the adjusting brake moment both clockwise and anti-clockwise. Than we can judge the acceptable quality of one-way clutch of automatic brake adjuster by comparing the test results and the ideal values instead of the EXP. In fact, the evaluation standard of adjusting brake moment applied on the project are still using the EXP provided by manufacturer currently in China, but it would be unavailable when the material of one-way clutch changed. Five kinds of automatic brake adjusters are used in the verification experiment to verify the accuracy of the test method. The experimental results show that the experimental values of adjusting brake moment both clockwise and anti-clockwise are within the ranges of theoretical results. The testing method provided by this article vividly meet the requirements of manufacturer’s standard.
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