AI-Enhanced Collision Detection for Autonomous Drones Using LiDAR and Neural Network
Аннотация
This research introduces the enhanced collision detection system for the autonomous drones on which the LiDAR technology and neural networks are utilized in the increased safety and operational performance. Existing techniques of collision avoidance do not suffice when implemented in dynamic settings, hence the reason for advanced solutions. LiDAR has been employed as a 3D mapping tool while neural networks have been used to detect patterns with the proposed system using both to develop a real time collision detection system. It also improves the capability of detecting obstacles and avoid them through using deep learning models to process LiDAR data. The training and validation datasets include both synthetic and actual data with considerable enhancement over existing techniques shown in the work. The numerical performance shows high values of accuracy, precision, recall, and also low time complexity which establishes the effectiveness of the proposed system. Consequently, the results of the study to conclude that, LiDAR in conjunction with neural networks hold considerable promise for bringing improvements in the autonomy of drones. I will explain, how the idea of the proposed system is aimed not only at correcting the main drawbacks of traditional methods, but also offers more opportunities for delivering more complex operations. This study makes a small input to the existing literature of autonomous navigation and opens the door to progress concerning the collision detection and avoidance systems.