This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning challenges in densely cluttered environments. In Stage I, an Enhanced Tube-RRT* algorithm is developed by integrating active hybrid sampling and an adaptive expansion strategy, enabling rapid generation of a safe and feasible virtual tube in environments with dense obstacles. Moreover, a trajectory smoothness cost is explicitly incorporated into the edge cost to reduce excessive turns and thereby mitigate cable-induced oscillations. Simulation results demonstrate that the proposed Enhanced Tube-RRT* achieves a higher success rate and effective sampling rate than mixed-sampling Tube-RRT* (STube-RRT*) and adaptive-extension Tube-RRT* (AETube-RRT*), while producing a shorter optimal path with a smaller cumulative turning angle. In Stage II, a convex quadratic program is formulated by considering payload translational and rotational dynamics, cable tension constraints, and collision-safety constraints, yielding a smooth, collision-free desired payload trajectory. Finally, a centralized geometric control scheme is applied to the cascaded system to validate the effectiveness and feasibility of the proposed planning framework, offering a practical solution for payload attitude maneuvering in densely cluttered environments.

翻译：本文针对多无人机刚体载荷级联运输系统，提出了一种两阶段轨迹规划框架，旨在解决密集杂乱环境中的规划挑战。在第一阶段，通过融合主动混合采样与自适应扩展策略，开发了增强型Tube-RRT*算法，使其能够在密集障碍物环境中快速生成安全可行的虚拟管道。同时，将轨迹平滑度代价显式纳入边代价函数，以减少过度转向并抑制缆绳引起的振荡。仿真结果表明，与混合采样Tube-RRT*（STube-RRT*）和自适应扩展Tube-RRT*（AETube-RRT*）相比，所提出的增强型Tube-RRT*具有更高的成功率和有效采样率，同时能够生成更短的最优路径和更小的累积转向角。在第二阶段，通过考虑载荷平移与旋转动力学、缆绳张力约束以及碰撞安全约束，构建了一个凸二次规划问题，从而生成光滑无碰撞的期望载荷轨迹。最后，将集中式几何控制方案应用于级联系统，验证了所提出规划框架的有效性和可行性，为密集杂乱环境中的载荷姿态机动提供了实用解决方案。