MASPA：一种用于有袋机器人系统路径规划的高效策略
MASPA: An efficient strategy for path planning with a tethered marsupial robotics system
AI总结：
本文针对有袋机器人系统提出了一种基于空间离散化的路径规划策略，通过利用系绳凸性和障碍物几何特征高效确定UAV可行起飞点，显著缩短路径长度并在数秒内求解，优于RRT*基线算法。
摘要：
有袋机器人系统由三个组件组成：无人地面车辆（UGV）、无人飞行器（UAV）以及连接两者的系绳。有袋系统在工业中极具优势，因为它能在飞行过程中延长UAV的电池寿命。本文针对有袋系统中的特定路径规划问题提出了一种新策略，其中三个组件各自必须避免与被建模为3D长方体的地面和空中障碍物发生碰撞。给定UAV位于UGV顶部的初始配置，目标是让UAV到达空中目标。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穿过垂直平面到达空中目标。我们提出了一种通过离散化空间来近似最优解的方法，旨在最小化地面和空中路径长度之和。首先，我们假设系绳处于紧绷状态，并使用一种新算法，该算法利用系绳的凸性和障碍物的几何形状，高效地确定UAV可行起飞点的轨迹。然后，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，我们的方法能够在几秒钟内解决复杂情况，优于基于RRT*（快速探索随机树）的基线规划算法。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
