We propose a motion planner for cable-driven payload transportation using multiple unmanned aerial vehicles (UAVs) in an environment cluttered with obstacles. Our planner is kinodynamic, i.e., it considers the full dynamics model of the transporting system including actuation constraints. Due to the high dimensionality of the planning problem, we use a hierarchical approach where we first solve the geometric motion planning using a sampling-based method with a novel sampler, followed by constrained trajectory optimization that considers the full dynamics of the system. Both planning stages consider inter-robot and robot/obstacle collisions. We demonstrate in a software-in-the-loop simulation and real flight experiments that there is a significant benefit in kinodynamic motion planning for such payload transport systems with respect to payload tracking error and energy consumption compared to the standard methods of planning for the payload alone. Notably, we observe a significantly higher success rate in scenarios where the team formation changes are needed to move through tight spaces.

翻译：本文提出了一种用于多架无人机在障碍物密集环境中进行缆索驱动负载运输的运动规划方法。我们的规划器是动力学规划器，即它考虑了包括驱动约束在内的整个运输系统的完整动力学模型。由于规划问题的高维特性，我们采用分层方法：首先使用基于采样的方法配合新型采样器解决几何运动规划问题，随后进行考虑系统完整动力学的约束轨迹优化。两个规划阶段均考虑了机器人间及机器人与障碍物间的碰撞。通过软件在环仿真和真实飞行实验，我们证明与此类负载运输系统中仅针对负载进行规划的标准方法相比，动力学运动规划在负载跟踪误差和能耗方面具有显著优势。值得注意的是，在需要通过狭窄空间而需改变编队队形的场景中，我们观察到成功率显著提高。