scholarly journals The development of autonomous navigation and obstacle avoidance for a robotic mower using machine vision technique

2019 ◽  
Vol 52 (30) ◽  
pp. 173-177 ◽  
Author(s):  
Kosuke Inoue ◽  
Yutaka Kaizu ◽  
Sho Igarashi ◽  
Kenji Imou
Keyword(s):  
Machine Vision ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Vision Technique

Autonomous Navigation and Obstacle Avoidance

2009 ◽  
Author(s):  
Jesse Berger ◽  
Cory Carson ◽  
Massood Towhidnejad ◽  
Richard Stansbury
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation

Recent Reviews on Machine Vision-Based 3D Reconstruction

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Shaowei Ma ◽  
Meng Li ◽  
Hanghang Jiang ◽  
Jiaming Li
Keyword(s):  
Machine Vision ◽  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Robot Vision ◽  
Research Direction ◽  
Development Trend ◽  
Future Research ◽  
The Future ◽  
The Development Trend ◽  
Better Than

Background: In machine vision, the 3D reconstruction is widely used in medical system, autonomous navigation, aviation and remote sensing measurement, industrial automation and other fields, and the demand for reconstruction precision is significantly highlighted. Therefore, the 3D reconstruction is of great research value and will be an important research direction in the future. Objective: By reviewing the latest development and patent of 3D reconstruction, this paper provides references to researchers in related fields. Methods: Machine vision-based 3D reconstruction patents and literatures were analyzed from the aspects of the algorithm, innovation and application. Among them, there are more than 30 patents and nearly 30 literatures in the past ten years. Results: Researches on machine vision-based 3D reconstruction in recent 10 years are reviewed, and the typical characteristics were concluded. The main problems in its development were analyzed, the development trend was foreseen, and the current and future research on the productions and patents related to machine vision-based 3D reconstruction is discussed. Conclusion: The reconstruction result of binocular vision and multi-vision is better than monocular vision in most cases. Current researches of 3D reconstruction focus on robot vision navigation, intelligent vehicle environment sensing system and virtual reality. The aspects that need to be improved in the future include: improving robustness, reducing computational complexity, and reducing operating equipment requirements, and so on. Furthermore, more patents on machine vision-based 3D reconstruction also should be invented.

scholarly journals Autonomous system to control a mobile robot

2020 ◽  
Vol 9 (4) ◽  
pp. 1711-1717
Author(s):  
Ayman Abu Baker ◽  
Yazeed Yasin Ghadi
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Fuzzy Controller ◽  
Computational Time ◽  
Ongoing Effort ◽  
Unstructured Environment ◽  
Adaptive Inference ◽  
Real Time Applications ◽  
Linguistic Labels

This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.

scholarly journals Erratum to: Nondestructive Estimation of Lean Meat Yield of South Korean Pig Carcasses Using Machine Vision Technique

2019 ◽  
Vol 39 (3) ◽  
pp. 520-520
Author(s):  
Santosh Lohumi ◽  
Collins Wakholi ◽  
Jong Ho Baek ◽  
Byeoung Do Kim ◽  
Se Joo Kang ◽  
...  
Keyword(s):  
Machine Vision ◽  
Meat Yield ◽  
Lean Meat ◽  
South Korean ◽  
Pig Carcasses ◽  
Vision Technique ◽  
Nondestructive Estimation

INSPECTION AND QUALITY CHECKING OF CERAMIC CUP USING MACHINE VISION TECHNIQUE: DESIGN AND ANALYSIS

2017 ◽  
Vol 79 (5-2) ◽  
Author(s):  
Nursabillilah Mohd Ali ◽  
Mohd Safirin Karis ◽  
Siti Azura Ahmad Tarusan ◽  
Gao-Jie Wong ◽  
Mohd Shahrieel Mohd Aras ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Machine Vision ◽  
Image Sensor ◽  
Histogram Method ◽  
Inspection Method ◽  
Inspection Process ◽  
Set Up ◽  
Vision Application ◽  
Vision Technique

The development of inspection and quality checking using machine vision technique are discussed where the design of the algorithm mainly to detect the sign of defect when a sample product is used for inspection purposes. There are several constraints that a machine need to be improved based on technology used in vision application. CMOS image sensor as well as programming language and open source computer vision library were used in designing the inspection method. Experimental set-up was conducted to test the proposed technique for evaluate the effectiveness process. The experimental results were obtained and represented in graphical and image processing form. Besides, analysis and discussion were made according to obtained results. The proposed technique is able to perform the inspection process using good and defect ceramic cup based on detection technique. Moreover, based on the analysis gathered, the proposed technique able to differentiate between good and defect ceramic cup. The result shows that there is a difference frequency by 236 which is 2% of total value in pixels frequency. The frequency indicated as pixel frequency of image using histogram method based on scaled value of image.

scholarly journals Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4082 ◽  
Author(s):  
Zhengjun Qiu ◽  
Nan Zhao ◽  
Lei Zhou ◽  
Mengcen Wang ◽  
Liangliang Yang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Processing Speed ◽  
Paddy Field ◽  
Autonomous Navigation ◽  
Vision System ◽  
Obstacle Detection ◽  
Paddy Fields ◽  
Future Research ◽  
Moving Obstacles ◽  
Agricultural Machines

Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation.

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