发表机构
University of California Santa Barbara（加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种高效算法，为藤蔓机器人规划绕过凸多面体障碍物的最小生长压力路径，在二维保证全局最优、三维近似最优，并通过模拟和硬件实验验证。
AI 中文摘要
藤蔓机器人通过从尖端延伸来在杂乱环境中导航。尽管其在此类环境中操作的能力已被广泛证明，但很少有工作涉及生长规划，即寻找最优生长路径。此外，现有规划器未考虑沿给定路径所需的生长压力，当压力过高时可能导致机器人爆裂。本文研究了在凸多面体障碍物周围生长的藤蔓机器人的最小压力路径问题。我们提出了一种高效算法，该算法保证在二维中找到全局最优解，在三维中找到近似解，且误差随离散化参数趋近于零而消失。首先，我们推导了任意形状藤蔓机器人的生长压力方程，并利用该方程证明始终存在一条最小压力路径，该路径是分段线性的，且只能在障碍物上的特定点弯曲。然后，我们利用这一观察将生长规划问题简化为具有时间相关权重的短路径问题，并通过改进的Dijkstra算法高效求解。我们通过数值模拟展示了方法的速度和可扩展性。我们还通过硬件实验验证了算法，并在Python包VinePlanner中提供了开源高性能实现：此https URL。
英文摘要
Vine robots navigate cluttered environments by extending from their tip. Although their ability to operate in such environments has been extensively demonstrated, little work has addressed growth planning, i.e., finding optimal growth paths. Moreover, existing planners do not account for the growth pressure necessary to follow a given path, which can cause the robot to burst when it is too high. In this paper, we address the problem of finding minimum-pressure paths for vine robots growing around polytopic obstacles. We propose an efficient algorithm that is guaranteed to find globally optimal solutions in 2D and approximate solutions in 3D, with an error that vanishes as a discretization parameter approaches zero. First, we derive a growth pressure equation for vine robots of arbitrary shape, which we use to show that there always exists a minimum-pressure path that is piecewise-linear and can bend only at specific points on the obstacles. We then leverage this observation to reduce the growth-planning problem to a shortest-path problem with time-dependent weights, which we efficiently solve using a modified Dijkstra's algorithm. We demonstrate the speed and scalability of our approach through numerical simulations. We also validate our algorithm with hardware experiments and provide an open-source and high-performance implementation in the Python package, VinePlanner: https://github.com/Ahsoka/VinePlanner.
