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Coverage path planning for UAVs based on enhanced exact cellular decomposition method

机译:基于增强精确细胞分解方法的无人机覆盖路径规划

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In this paper, an enhanced exact cellular decomposition method to plan the coverage path of UAVs in a polygon area is proposed. To be more specific, the contributions of the paper are: firstly, the turning motion of UAVs is shown to be less efficient from the viewpoints of route length, duration and energy. Secondly, the problem of Coverage Path Planning (CPP) in a convex polygon area is transformed to width calculation of the convex polygon, and a novel algorithm to calculate the widths of convex polygons with time complexity of O(n) is developed. The path of the least number of turns for an UAV based on the widths of convex polygons is devised. Thirdly, a convex decomposition algorithm for minimum width sum based on the greedy recursive method which revolves around decomposing the concave area into convex subregions is developed. It is proved that the algorithm is a polynomial time algorithm. To avoid unnecessary back and forth motion, some entirely adjacent subregions are combined. Finally, comparing different weights of two joint-points, a subregion connection algorithm based on minimum traversal of weighted undirected graph is proposed to connect the coverage paths of the subregions. Simulation results show that the proposed method is feasible and effective.
机译:本文提出了一种改进的精确细胞分解方法,以规划无人机在多边形区域的覆盖路径。更具体地说,本文的贡献是:首先,从航路长度,持续时间和能量的角度来看,无人机的转向运动效率较低。其次,将凸多边形区域中的覆盖路径规划(CPP)问题转换为凸多边形的宽度计算,并提出了一种新的算法,该算法可计算时间复杂度为O(n)的凸多边形的宽度。设计了基于凸多边形宽度的无人机最小转弯路径。第三,提出了一种基于贪婪递归方法的最小宽度总和凸分解算法,该算法围绕将凹面区域分解为凸子区域展开。证明了该算法是多项式时间算法。为了避免不必要的来回运动,将一些完全相邻的子区域合并在一起。最后,通过比较两个连接点的不同权重,提出了一种基于加权无向图最小遍历的子区域连接算法,以连接子区域的覆盖路径。仿真结果表明,该方法是可行和有效的。

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