Graph generation using point cloud data for path planning of autonomous mobile robots

2021 ◽  
Author(s):  
Kan Ichikawa ◽  
Tsuyoshi Amano ◽  
Isao Miyagawa ◽  
Kazuhito Murakami
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Point Cloud ◽  
Autonomous Mobile Robots ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Graph Generation

scholarly journals Performance Evaluation of Robot Localization Using 2D and 3D Point Clouds

2017 ◽  
Vol 29 (5) ◽  
pp. 928-934
Author(s):  
Kiyoaki Takahashi ◽  
◽  
Takafumi Ono ◽  
Tomokazu Takahashi ◽  
Masato Suzuki ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Autonomous Mobile Robots ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Point Clouds ◽  
Laser Range Finders ◽  
2D And 3D

Autonomous mobile robots need to acquire surrounding environmental information based on which they perform their self-localizations. Current autonomous mobile robots often use point cloud data acquired by laser range finders (LRFs) instead of image data. In the virtual robot autonomous traveling tests we have conducted in this study, we have evaluated the robot’s self-localization performance on Normal Distributions Transform (NDT) scan matching. This was achieved using 2D and 3D point cloud data to assess whether they perform better self-localizations in case of using 3D or 2D point cloud data.

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.

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.

Robust stairway-detection and localization method for mobile robots using a graph-based model and competing initializations

2018 ◽  
Vol 37 (12) ◽  
pp. 1463-1483 ◽  
Author(s):  
Thomas Westfechtel ◽  
Kazunori Ohno ◽  
Bärbel Mertsching ◽  
Ryunosuke Hamada ◽  
Daniel Nickchen ◽  
...  
Keyword(s):  
Mobile Robots ◽  
Point Cloud ◽  
Large Scale ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Average Error ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Depth Sensors ◽  
Main Challenge

One of the major challenges for mobile robots in human-shaped environments is navigating stairways. This study presents a method for accurately detecting, localizing, and estimating the characteristics of stairways using point cloud data. The main challenge is the wide variety of different structures and shapes of stairways. This challenge is often aggravated by an unfavorable position of the sensor, which leaves large parts of the stairway occluded. This can be further aggravated by sparse point data. We overcome these difficulties by introducing a three-dimensional graph-based stairway-detection method combined with competing initializations. The stairway graph characterizes the general structural design of stairways in a generic way that can be used to describe a large variety of different stairways. By using multiple ways to initialize the graph, we can robustly detect stairways even if parts of the stairway are occluded. Furthermore, by letting the initializations compete against each other, we find the best initialization that accurately describes the measured stairway. The detection algorithm utilizes a plane-based approach. We also investigate different planar segmentation algorithms and experimentally compare them in an application-orientated manner. Our system accurately detects and estimates the stairway parameters with an average error of only [Formula: see text] for a variety of stairways including ascending, descending, and spiral stairways. Our method works robustly with different depth sensors for either small- or large-scale environments and for dense and sparse point cloud data. Despite this generality, our system’s accuracy is higher than most state-of-the-art stairway-detection methods.

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.

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.

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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