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For intelligent quadcopter UAVs, a robust and reliable autonomous planningsystem is crucial. Most current trajectory planning methods for UAVs aresuitable for static environments but struggle to handle dynamic obstacles,which can pose challenges and even dangers to flight. To address this issue,this paper proposes a vision-based planning system that combines tracking andtrajectory prediction of dynamic obstacles to achieve efficient and reliableautonomous flight. We use a lightweight object detection algorithm to identifydynamic obstacles and then use Kalman Filtering to track and estimate theirmotion states. During the planning phase, we not only consider static obstaclesbut also account for the potential movements of dynamic obstacles. Fortrajectory generation, we use a B-spline-based trajectory search algorithm,which is further optimized with various constraints to enhance safety andalignment with the UAV's motion characteristics. We conduct experiments in bothsimulation and real-world environments, and the results indicate that ourapproach can successfully detect and avoid obstacles in dynamic environments inreal-time, offering greater reliability compared to existing approaches.Furthermore, with the advancements in Natural Language Processing (NLP)technology demonstrating exceptional zero-shot generalization capabilities,more user-friendly human-machine interactions have become feasible, and thisstudy also explores the integration of autonomous planning systems with LargeLanguage Models (LLMs).

A Safer Vision-based Autonomous Planning System for Quadrotor UAVs with Dynamic Obstacle Trajectory Prediction and Its Application with LLMs