TRUST-Planner：面向具有不确定障碍物时空避让的AAV拓扑引导鲁棒轨迹规划器
TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance
AI总结：
提出TRUST-Planner拓扑引导分层规划框架，通过动态增强可见概率图、无终端最小控制多项式和动态距离场实现复杂动态环境下的鲁棒时空避障，达到96%成功率和毫秒级计算效率。
摘要：
尽管自主飞行器（AAV）的运动规划已取得广泛进展，但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。为了解决这些问题，我们提出了TRUST-Planner，一种拓扑引导的分层规划框架，用于鲁棒的时空避障。在前端，提出了一种动态增强可见概率图（DEV-PRM），以快速探索拓扑路径进行全局引导。后端利用统一的无终端最小控制多项式（UTF-MINCO）和动态距离场（DDF），实现高效的预测性避障和快速并行计算。此外，引入了一种增量式多分支轨迹管理框架，以实现时空拓扑决策，同时有效利用历史信息减少重规划时间。仿真结果表明，TRUST-Planner优于基线竞争对手，在测试的复杂环境中实现了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和实用性。
英文摘要：
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.
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