The Analysis of Drone Track Planning Based on Machine-visual.

Abstract

With the proliferation of drones, there has been a surge in demand for highly efficient navigation systems. This article delves deep into the evolution of machine vision-based drone trajectory planning to meet this demand. By harnessing machine vision technology, including visual SLAM, three-dimensional vision, depth cameras, convolutional neural networks, and other methods, drones have significantly enhanced their operational capabilities in complex environments. Through case studies, this article illustrates how machine vision technology optimizes trajectory planning by facilitating obstacle detection, target recognition, and tracking. Furthermore, it underscores the challenges posed by computing resource constraints, deep learning model training, and optimization. Addressing these challenges is crucial for the continued advancement of machine vision-based navigation systems for drones. Additionally, the article proposes future research directions such as improving environmental perception, integrating multiple sensors, and optimizing algorithms and hardware to further enhance drone navigation systems. By exploring these avenues, researchers can pave the way for more efficient, reliable, and adaptable drone navigation systems capable of operating seamlessly in diverse environments and scenarios.