Research on Applications of Image Recognition in the Design of Autonomous Navigation Robots

Abstract

This study evaluates traditional and deep learning-based image recognition technologies in autonomous navigation robots, detailing their strengths and limitations. Traditional image recognition techniques, which rely on predefined algorithms and pattern recognition, have proven efficient and stable for navigation in controlled environments. Conversely, deep learning approaches, notably through convolutional neural networks (CNNs), excel in dynamic and unpredictable settings by adapting more effectively to complex environmental interactions. The core challenges in this field include the need for real-time data processing, achieving consistent accuracy across diverse environments, and managing the computational demands of sophisticated algorithms. This research highlights the significant improvements deep learning techniques bring to autonomous navigation, particularly in terms of adaptability and robustness. The study also addresses the necessity for advancements in both technology domains to meet the evolving demands of autonomous robotics, emphasizing the ongoing need to enhance computational efficiency and environmental adaptability. The findings suggest a growing potential for future research to explore hybrid models that leverage the predictability of traditional methods with the flexibility of deep learning to optimize autonomous navigational tasks.