Despite extensive developments in motion planning of autonomous aerial vehicles (AAVs), existing frameworks faces the challenges of local minima and deadlock in complex dynamic environments, leading to increased collision risks. To address these challenges, we present TRUST-Planner, a topology-guided hierarchical planning framework for robust spatial-temporal obstacle avoidance. In the frontend, a dynamic enhanced visible probabilistic roadmap (DEV-PRM) is proposed to rapidly explore topological paths for global guidance. The backend utilizes a uniform terminal-free minimum control polynomial (UTF-MINCO) and dynamic distance field (DDF) to enable efficient predictive obstacle avoidance and fast parallel computation. Furthermore, an incremental multi-branch trajectory management framework is introduced to enable spatio-temporal topological decision-making, while efficiently leveraging historical information to reduce replanning time. Simulation results show that TRUST-Planner outperforms baseline competitors, achieving a 96\% success rate and millisecond-level computation efficiency in tested complex environments. Real-world experiments further validate the feasibility and practicality of the proposed method.

翻译：尽管自主飞行器（AAV）的运动规划已取得广泛进展，现有框架在复杂动态环境中仍面临局部极小值和死锁的挑战，导致碰撞风险增加。为解决这些问题，我们提出TRUST-Planner——一种用于鲁棒时空障碍物避让的拓扑引导分层规划框架。在前端，提出动态增强可见概率路线图（DEV-PRM）以快速探索全局引导的拓扑路径。后端采用均匀无终端最小控制多项式（UTF-MINCO）和动态距离场（DDF），实现高效预测性避障与快速并行计算。此外，引入增量式多分支轨迹管理框架，支持时空拓扑决策制定，同时高效利用历史信息以减少重规划时间。仿真结果表明，TRUST-Planner优于基线方法，在所测试的复杂环境中达到96%的成功率和毫秒级计算效率。真实世界实验进一步验证了所提方法的可行性与实用性。