Aerial transportation robots using suspended cables have emerged as versatile platforms for disaster response and rescue operations. To maximize the capabilities of these systems, robots need to aggressively fly through tightly constrained environments, such as dense forests and structurally unsafe buildings, while minimizing flight time and avoiding obstacles. Existing methods geometrically over-approximate the vehicle and obstacles, leading to conservative maneuvers and increased flight times. We eliminate these restrictions by proposing PolyFly, an optimal global planner which considers a non-conservative representation for aerial transportation by modeling each physical component of the environment, and the robot (quadrotor, cable and payload), as independent polytopes. We further increase the model accuracy by incorporating the attitude of the physical components by constructing orientation-aware polytopes. The resulting optimal control problem is efficiently solved by converting the polytope constraints into smooth differentiable constraints via duality theory. We compare our method against the existing state-of-the-art approach in eight maze-like environments and show that PolyFly produces faster trajectories in each scenario. We also experimentally validate our proposed approach on a real quadrotor with a suspended payload, demonstrating the practical reliability and accuracy of our method.

翻译：使用悬挂缆索的空中运输机器人已成为灾害响应和救援行动的多功能平台。为最大化此类系统的性能，机器人需要在高度受限的环境中（如茂密森林和结构不安全的建筑物）进行激进飞行，同时最小化飞行时间并避开障碍物。现有方法对飞行器和障碍物进行几何上的过度近似，导致保守的机动策略和更长的飞行时间。我们通过提出PolyFly消除了这些限制，这是一种最优全局规划器，它通过将环境及机器人（四旋翼飞行器、缆索和载荷）的每个物理组件建模为独立多面体，实现了对空中运输的非保守表示。我们进一步通过构建方向感知多面体来纳入物理组件的姿态，从而提高了模型精度。通过利用对偶理论将多面体约束转换为光滑可微约束，可高效求解所得的最优控制问题。我们在八个迷宫式环境中将本方法与现有最先进方法进行比较，结果表明PolyFly在每种场景下都能生成更快的轨迹。我们还在搭载悬挂载荷的真实四旋翼飞行器上对所提方法进行了实验验证，证明了本方法在实际应用中的可靠性与准确性。