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 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：本文提出一种运动规划器，用于在布满障碍物的环境中通过多架无人机(UAVs)完成缆绳驱动载荷运输。该规划器具有动力学特性，即考虑了包含执行约束在内的运输系统完整动力学模型。由于规划问题维度较高，我们采用分层方法：首先使用基于采样的方法结合新型采样器进行几何运动规划，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的碰撞。通过软件在环仿真验证，相比仅单独规划载荷路径的标准方法，此类载荷运输系统的动力学运动规划在载荷跟踪误差与能耗方面具有显著优势。值得注意的是，在需要通过编队变形穿越狭窄空间的场景中，该方法展现出明显更高的成功概率。