  For intelligent quadcopter UAVs, a robust and reliable autonomous planning
system is crucial. Most current trajectory planning methods for UAVs are
suitable 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 and
trajectory prediction of dynamic obstacles to achieve efficient and reliable
autonomous flight. We use a lightweight object detection algorithm to identify
dynamic obstacles and then use Kalman Filtering to track and estimate their
motion states. During the planning phase, we not only consider static obstacles
but also account for the potential movements of dynamic obstacles. For
trajectory generation, we use a B-spline-based trajectory search algorithm,
which is further optimized with various constraints to enhance safety and
alignment with the UAV's motion characteristics. We conduct experiments in both
simulation and real-world environments, and the results indicate that our
approach can successfully detect and avoid obstacles in dynamic environments in
real-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 this
study also explores the integration of autonomous planning systems with Large
Language Models (LLMs).
