NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT

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