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).

针对智能四旋翼无人机，本文提出了一种稳健可靠的自主规划系统，结合动态障碍物的跟踪和轨迹预测实现高效可靠的自主飞行，采用轻量级目标检测算法识别动态障碍物，利用卡尔曼滤波跟踪和估计其运动状态，在规划阶段不仅考虑静态障碍物而且考虑动态障碍物的潜在移动，使用基于 B 样条的轨迹搜索算法生成轨迹，并通过各种约束进一步优化以提高安全性和与无人机运动特征的一致性，在仿真和实际环境中进行实验，结果表明我们的方法能够实时检测和避开动态环境中的障碍物，相较现有方法在可靠性上有更大优势。此外，在自然语言处理（NLP）技术展示出卓越的零样本泛化能力的进展下，更友好的人机交互变得可行，本研究还探索了自主规划系统与大型语言模型（LLMs）的集成。