scholarly journals A Path Planning Method Based on Robot Automatic Grinding of Drills

2021 ◽  
Vol 2133 (1) ◽  
pp. 012024
Author(s):  
Weidong Hao ◽  
Chaoquan Tan ◽  
Yulu Sun ◽  
Chuan Zheng ◽  
Jingsen Jin
Keyword(s):  
Path Planning ◽  
Point Cloud ◽  
Industrial Robots ◽  
Planning Method ◽  
Motion Path ◽  
Point Cloud Data ◽  
End Effector ◽  
Cloud Data ◽  
Pdc Bit ◽  
Actual Operation

Abstract This paper proposes a path planning method for grinding excess material after PDC bit repair using industrial robots. Firstly, a 3D scanning instrument is used to obtain point cloud data of the bit to be ground, secondly, this data are imported into Geomagic Studio for processing to obtain the triangular sheet file of the area to be ground. Finally, the software based on MATLAB is used to process the file and calculate the motion path of the robot end-effector. The generated path is imported into ROS for simulation. By comparing the generated path with the grinding area, it was verified that the generated path could be used in actual operation.

scholarly journals Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

2020 ◽  
Vol 10 (23) ◽  
pp. 8641
Author(s):  
Xuan Ye ◽  
Lan Luo ◽  
Li Hou ◽  
Yang Duan ◽  
Yang Wu
Keyword(s):  
Laser Ablation ◽  
Path Planning ◽  
Point Cloud ◽  
Ant Colony Algorithm ◽  
Planning Method ◽  
Ant Colony ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Coverage Path Planning ◽  
Improved Ant Colony Algorithm

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.

3D Laser Remanufacturing of Damaged Parts

2013 ◽  
Vol 690-693 ◽  
pp. 3350-3353
Author(s):  
Yan Fang ◽  
Xi Chen Yang ◽  
Jian Bo Lei
Keyword(s):  
Path Planning ◽  
Image Matching ◽  
Point Cloud ◽  
Vision System ◽  
Processing Parameters ◽  
Point Cloud Data ◽  
Robot System ◽  
Cloud Data ◽  
Binocular Stereo Vision ◽  
Binocular Stereo

A repair method for damaged parts based on laser robot system is proposed. First, to obtain 3D point cloud in the robot coordinate system, damaged part surfaces are scanned with binocular stereo vision system, including camera calibration and image matching. Secondly, with the point cloud data and laser processing parameters, system accomplishes path planning, generates robot programs. Experimental results show that system can effectively achieves 3D reconstruction and path planning, which further enable high precision remanufacturing.

scholarly journals Traversability-Based RRT*for Planetary Rover Path Planning in Rough Terrain with LIDAR Point Cloud Data

2017 ◽  
Vol 29 (5) ◽  
pp. 838-846 ◽  
Author(s):  
Reiya Takemura ◽  
◽  
Genya Ishigami
Keyword(s):  
Path Planning ◽  
Point Cloud ◽  
Random Trees ◽  
Rough Terrain ◽  
Point Cloud Data ◽  
Continuous Space ◽  
Cloud Data ◽  
Tree Expansion ◽  
Planning Algorithm ◽  
Path Planner

Sampling-based search algorithms such as Rapidly-Exploring Random Trees (RRT) have been utilized for mobile robot path planning and motion planning in high dimensional continuous spaces. This paper presents a path planning method for a planetary exploration rover in rough terrain. The proposed method exploits the framework of a sampling-based search, the optimal RRT (RRT*) algorithm. The terrain geometry used for planning is composed of point cloud data close to continuous space captured by a light detection and ranging (LIDAR) sensor. During the path planning phase, the proposed RRT*algorithm directly samples a point (node) from the LIDAR point cloud data. The path planner then considers the rough terrain traversability of the rover during the tree expansion process of RRT*. This process improves conventional RRT*in that the generated path is safe and feasible for the rover in rough terrain. In this paper, simulation study on the proposed path planning algorithm in various real terrain data confirms its usefulness.

Path planning method based on discontinuous grid partition algorithm of point cloud for in situ printing

2019 ◽  
Vol 25 (3) ◽  
pp. 602-613 ◽  
Author(s):  
Qin Lian ◽  
Xiao Li ◽  
Dichen Li ◽  
Heng Gu ◽  
Weiguo Bian ◽  
...  
Keyword(s):  
Path Planning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Planning Method ◽  
Content Type ◽  
Cloud Data ◽  
Grid Partition ◽  
Point Density ◽  
Path Point

Purpose Path planning is an important part of three-dimensional (3D) printing data processing technology. This study aims to propose a new path planning method based on a discontinuous grid partition algorithm of point cloud for in situ printing. Design/methodology/approach Three types of parameters (i.e. structural, process and path interruption parameters) were designed to establish the algorithm model with the path error and the computation amount as the dependent variables. The path error (i.e. boundary error and internal error) was further studied and the influence of each parameter on the path point density was analyzed quantitatively. The feasibility of this method was verified by skin in situ printing experiments. Findings Path point density was positively correlated with Grid_size and negatively related to other parameters. Point_space, Sparsity and Line_space had greater influence on path point density than Indentation and Grid_size. In skin in situ printing experiment, two layers of orthogonal printing path were generated, and the material was printed accurately in the defect, which proved the feasibility of this method. Originality/value This study proposed a new path planning method that converted 3D point cloud data to a printing path directly, providing a new path planning solution for in situ printing. The discontinuous grid partition algorithm achieved controllability of the path planning accuracy and computation amount that could be applied to different processes.

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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