基于拓扑引导的无人机路径规划，有效避免动态障碍物碰撞
TRUST-Planner: Topology-guided Robust Trajectory Planner for AAVs with Uncertain Obstacle Spatial-temporal Avoidance
- 用拓扑结构快速生成全局路径，提升规划鲁棒性
- 96%成功率，毫秒级计算速度，实测表现优越
- 适合复杂动态环境下的无人飞行器实时避障
尽管自主空中飞行器（AAVs）运动规划技术已取得显著进展，现有框架在复杂动态环境中仍面临局部极小值和死锁问题，导致碰撞风险增加。为此，本文提出TRUST-Planner，一种基于拓扑引导的分层规划框架，实现稳健的时空障碍物避让。前端采用动态增强可见概率图（DEV-PRM）快速探索拓扑路径，提供全局引导；后端结合无终端最小控制多项式（UTF-MINCO）与动态距离场（DDF），实现高效预测避障与快速并行计算。此外，引入增量多分支轨迹管理框架，支持时空拓扑决策，并利用历史信息降低重规划时间。仿真结果表明，该方法在复杂环境中成功率高达96%，计算效率达毫秒级；真实场景实验进一步验证了其可行性与实用性。
原文摘要 · Abstract (English)
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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