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).

翻译：对于智能四旋翼无人机而言，一个稳健可靠的自主规划系统至关重要。当前大多数无人机轨迹规划方法适用于静态环境，但在处理动态障碍物时存在困难，这给飞行带来了挑战甚至危险。为解决此问题，本文提出一种结合动态障碍物跟踪与轨迹预测的视觉规划系统，以实现高效可靠的自主飞行。我们采用轻量级目标检测算法识别动态障碍物，并利用卡尔曼滤波对其运动状态进行跟踪与估计。在规划阶段，我们不仅考虑静态障碍物，还同时考虑动态障碍物的潜在运动。对于轨迹生成，我们采用基于B样条的轨迹搜索算法，并通过多种约束条件进行优化，以提升安全性及与无人机运动特性的匹配度。我们在仿真与实际环境中开展实验，结果表明该方法能够实时成功检测并避开动态环境中的障碍物，较现有方法具有更高可靠性。此外，随着自然语言处理技术展现出卓越的零样本泛化能力，更友好的人机交互成为可能，本研究还探讨了自主规划系统与大型语言模型的集成。