Drone Situation Awareness and Path Planning Based on Deep Learning

Abstract

With the rapid development of artificial intelligence and drone technology, drones are increasingly being used in military reconnaissance, disaster monitoring and rescue, agriculture, logistics and distribution, and other fields. Situational awareness and path planning, as key capabilities of drones, are critical to their ability to perform efficient and autonomous operations. This paper reviews the current application of deep learning in drone situational awareness and path planning, analyses its advantages and challenges, and discusses future development directions. Deep learning technology, through its powerful data processing and feature extraction capabilities, significantly improves the accuracy of situational awareness and the efficiency of path planning for drones in complex environments. However, current deep learning applications in drones still face challenges such as high data requirements, insufficient model generalization ability, limited computing resources, and security and robustness issues. Future research directions should focus on developing more efficient deep learning models, improving the interpretability and generalization ability of models, and optimizing drone hardware systems to meet real-time requirements. The introduction of interdisciplinary research will further promote the development of drone technology, enabling it to achieve efficient and safe applications in more fields.