Problem
Research questions and friction points this paper is trying to address.
minimum-pressure path
vine robots
polytopic obstacles
growth pressure
An Efficient Algorithm for Minimum-Pressure Growth Planning of Vine Robots
🤖 AI Summary
本文提出一种高效算法，通过转化为带时间依赖权重的最短路径问题并用改进的Dijkstra算法求解，寻找藤状机器人在多边形障碍物环境中生长的最小压力路径。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Minimum-Pressure Path
Growth Pressure Equation
Piecewise-Linear Path
Modified Dijkstra's Algorithm
VinePlanner
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