System an Unmanned Aerial Vehicle Trajectory Generation in Real Time

This paper addresses the problem of real-time trajectory generation for unmanned aerial vehicles, emphasizing its importance for various applications such as search and rescue operations, environmental monitoring, and precision agriculture. The challenges associated with dynamic trajectory generation, including obstacle avoidance, adherence to mission constraints, and computational efficiency, are analyzed. A hybrid approach is proposed that integrates advanced path planning algorithms with real-time optimization techniques to ensure safe and efficient unmanned aerial vehicle navigation in complex environments. The system leverages onboard sensors and external data sources, such as GPS and LiDAR, for situational awareness and dynamic obstacle detection. A key feature of the proposed system is the ability to adapt the trajectory in response to real-time changes in the environment, ensuring robustness and reliability during autonomous flight. The implementation utilizes the PX4 autopilot platform, AirSim simulation environment, and QGroundControl software to validate the effectiveness of the proposed approach. The results demonstrate that the system achieves a balance between computational efficiency and trajectory accuracy, enabling its deployment in practical unmanned aerial vehicle applications.