From a maneuverability perspective, the main advantage of tilting multirotor UAVs lies in the dynamic variability of the feasible executable wrench, which represents a key asset for physical interaction tasks. Accordingly, cant-angle selection should be optimized to ensure high performance while avoiding abrupt variations and preserving real-world feasibility. In this context, this work proposes a lightweight control framework for star-shaped interdependent cant-tilting hexarotor UAVs performing interaction tasks. The method uses an offline-computed look-up table of zero-moment force polytopes to identify feasible cant angles for a desired control force and select the optimal one by balancing efficiency and smoothness. The framework is integrated with a geometric full-pose controller and validated through Monte Carlo simulations in MATLAB/Simulink and compared against a baseline strategy. The results show a significant reduction in computation time, together with improved pose-tracking performance and competitive actuation efficiency. A final physics-based simulation of a complete wall inspection task in Simscape further confirms the feasibility of the proposed strategy in interacting scenarios.

翻译：从机动性角度来看，倾斜式多旋翼无人机的主要优势在于其可执行力旋量的动态可变性，这是实现物理交互任务的关键特性。因此，倾角的选择需经过优化，以确保高机动性能，同时避免突变并保持实际可行性。在此背景下，本文提出一种轻量级控制框架，适用于星形耦合倾转六旋翼无人机执行交互任务。该方法利用离线计算的零力矩力多面体查找表，为所需控制力识别可行的倾角，并通过平衡效率与平滑度来选取最优倾角。该框架与几何全姿态控制器集成，在MATLAB/Simulink中通过蒙特卡洛仿真进行验证，并与基线策略进行对比。结果表明，该方法显著降低了计算时间，同时提升了位姿跟踪性能并保持了具有竞争力的驱动效率。最后，在Simscape中完成完整墙面检测任务的物理仿真，进一步证实了所提策略在交互场景中的可行性。