The path planning algorithm in the FlyNeRF system can be enhanced to optimize the drone's trajectory for more efficient data collection and reconstruction quality improvement. One approach is to implement a reinforcement learning-based path planning algorithm that learns optimal trajectories based on the quality assessment feedback from the Image Evaluation Module. By training the algorithm to prioritize areas with lower render quality for additional image capture, the drone can focus on improving the reconstruction in challenging regions. Furthermore, incorporating real-time obstacle detection sensors such as LiDAR or ultrasonic sensors can enable the algorithm to dynamically adjust the drone's path to avoid obstacles and ensure complete coverage of the environment. Additionally, integrating a predictive modeling component that anticipates the optimal positions for future image capture based on the current reconstruction progress can further enhance the efficiency of the path planning algorithm.