A tethered marsupial robotics system comprises three components: an Unmanned Ground Vehicle (UGV), an Unmanned Aerial Vehicle (UAV), and a tether connecting both robots. Marsupial systems are highly beneficial in industry as they extend the UAV's battery life during flight. This paper introduces a novel strategy for a specific path planning problem in marsupial systems, where each of the three components must avoid collisions with ground and aerial obstacles modeled as 3D cuboids. Given an initial configuration in which the UAV is positioned atop the UGV, the goal is to reach an aerial target with the UAV. We assume that the UGV first moves to a position from which the UAV can take off and fly through a vertical plane to reach an aerial target. We propose an approach that discretizes the space to approximate an optimal solution, minimizing the sum of the lengths of the ground and air paths. First, we assume a taut tether and use a novel algorithm that leverages the convexity of the tether and the geometry of obstacles to efficiently determine the locus of feasible take-off points for the UAV. We then apply this result to scenarios that involve loose tethers. The simulation test results show that our approach can solve complex situations in seconds, outperforming a baseline planning algorithm based on RRT* (Rapidly exploring Random Trees).

翻译：系留式有袋机器人系统由三个部分组成：一辆无人地面车辆（UGV）、一架无人飞行器（UAV）以及连接两个机器人的系绳。有袋系统在工业中极具优势，因为它们能延长UAV在飞行过程中的电池续航时间。本文针对有袋系统中一个特定的路径规划问题提出了一种新颖策略，该问题要求三个组成部分均需避免与建模为三维长方体的地面及空中障碍物发生碰撞。给定UAV初始位于UGV顶部的配置，目标是通过UAV抵达一个空中目标点。我们假设UGV首先移动到一个位置，使得UAV能够从该位置起飞，并通过一个垂直平面飞行至空中目标点。本文提出了一种空间离散化方法以逼近最优解，从而最小化地面路径与空中路径的长度之和。首先，我们假设系绳处于绷紧状态，并利用一种新颖算法，该算法借助系绳的凸性及障碍物的几何形状，高效地确定了UAV可行的起飞点轨迹。随后，我们将此结果应用于涉及松弛系绳的场景。仿真测试结果表明，我们的方法能在数秒内解决复杂情况，其性能优于基于RRT*（快速探索随机树）的基线规划算法。