International Journal of Science, Engineering and TechnologyAuthors: Professor Dr. R.N.Patil, Mr.Siyang Prafulla Kamble
Abstract: The advancement of autonomous aerial systems is transforming military operations, offering enhanced situational awareness, precision, and safety. This project focuses on developing a comprehensive 3D simulation framework within the Godot engine to model UAVs operating in complex military environments. The primary objective is to enable these autonomous drones to navigate dynamically around obstacles, assess threats, and execute missions effectively in real-time scenarios. To achieve this, the simulation incorporates sophisticated algorithms powered by machine learning techniques for obstacle detection and decision-making, physics-based models for realistic flight behavior, and advanced path planning methods to chart optimal routes amidst challenging terrains. A crucial aspect of the framework involves simulating sensor inputs such as radar, LIDAR, and visual sensors, which allow UAVs to perceive and interpret their environment accurately. These sensor simulations are integrated with real-time data processing systems that facilitate immediate response and adaptive navigation. The simulation environment is designed to replicate realistic military scenarios, including urban landscapes, rugged terrains, and obstacle-rich zones, providing a robust platform for testing autonomous behaviors under various operational conditions. By leveraging Godot's capabilities for 3D visualization and physics, this project aims to deliver a scalable and flexible tool for developing and evaluating autonomous UAV technologies. The ultimate goal is to enhance the operational proficiency of military drones, ensuring safer and more efficient deployment in complex battlefield environments. Furthermore, this simulation framework can serve as a foundation for future research in defense technology, contributing to the development of autonomous systems that are capable of performing in high-stakes, real-world scenarios with minimal human intervention. Through this work, we seek to bridge the gap between simulation and real-world application, supporting the ongoing evolution of autonomous aerospace systems in defense.
