Navigation Strategy for Mobile Robot Based on Computer Vision and Yolov5 Network in the Unknown Environment

Abstract

Intelligent mobile robots must possess the ability to navigate in complex environments. The field of mobile robot navigation is continuously evolving, with various technologies being developed. Deep learning has gained attention from researchers, and numerous navigation models utilizing deep learning have been proposed. In this study, the YOLOv5 model is utilized to identify objects to aid the mobile robot in determining movement conditions. However, the limitation of deep learning models being trained on insufficient data, leading to inaccurate recognition in unforeseen scenarios, is addressed by introducing an innovative computer vision technology that detects lanes in real-time. Combining the deep learning model with computer vision technology, the robot can identify different types of objects, allowing it to estimate distance and adjust speed accordingly. Additionally, the paper investigates the recognition reliability in varying light intensities. The findings of this study offer promising directions for future breakthroughs in mobile robot navigation

An Effective Method for the Detection of Wall Brick Defects using Machine Vision

“…In today's era of the 4.0 industrial revolution, robots are becoming increasingly popular [1][2][3] and manipulator robots, in particular, have found wide applicability across various industries [4][5][6], including the wall brick production lines. Traditionally, taking bricks has been a labor-intensive task requiring manual effort and precision.…”