多旋翼团队在杂乱环境中运输缆绳悬挂载荷的动力学运动规划
Kinodynamic Motion Planning for a Team of Multirotors Transporting a Cable-Suspended Payload in Cluttered Environments
AI总结：
本文提出一种面向多无人机缆绳悬挂载荷运输的动力学运动规划方法，通过分层几何采样与约束轨迹优化，在杂乱环境中显著降低跟踪误差和能耗，并提升狭窄空间队形变换场景的成功率。
摘要：
我们提出了一种在充满障碍物的环境中使用多架无人机（UAV）进行缆绳驱动载荷运输的运动规划器。我们的规划器是动力学的，即它考虑了运输系统的完整动力学模型，包括驱动约束。由于规划问题的高维度，我们采用分层方法：首先使用基于采样的方法结合一种新型采样器求解几何运动规划，然后进行考虑系统完整动力学的约束轨迹优化。两个规划阶段都考虑了机器人间碰撞以及机器人与障碍物之间的碰撞。我们在软件在环仿真和真实飞行实验中证明，与仅针对载荷进行规划的标准方法相比，对此类载荷运输系统进行动力学运动规划在载荷跟踪误差和能耗方面具有显著优势。值得注意的是，在需要通过队形变化来穿过狭窄空间的场景中，我们观察到显著更高的成功率。
英文摘要：
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.
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