Abstract
To cover an area of interest by an autonomous vehicle, such as an Unmanned Aerial Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process. However, coverage path planning is often problematic, especially when the boundary of the area is complicated and the area contains several obstacles. A common solution for this situation is to decompose the area into disjoint convex sub-polygons and to obtain coverage paths for each sub-polygon using a simple back-and-forth pattern. Aligned with the solution approach, we propose a new convex decomposition method which is simple and applicable to any shape of target area. The proposed method is designed based on the idea that, given an area of interest represented as a polygon, a convex decomposition of the polygon mainly occurs at the points where an interior angle between two edges of the polygon is greater than 180 degrees. The performance of the proposed method is demonstrated by comparison with existing convex decomposition methods using illustrative examples.
Highlights
Vehicle (UAV), planning a coverage path which guides the unit to cover the area is an essential process
As a high-resolution grip map for a high-quality coverage path exponentially increases the complexity of a path finding algorithm and memory usage, grid-based coverage methods are suitable only for relatively small-sized areas [1], which highlights the applicability of the Coverage path planning (CPP) with the convex decomposition
This paper proposes a new convex decomposition method, which is essential for deriving a coverage path for an autonomous unit
Summary
Coverage path planning (CPP) is the problem of determining a path that guides a vehicle or a machine to cover an area of interest, while avoiding obstacles inside of the area [1]. The boustrophedon method is applied to the CPP such that the number of turns in a path generated is minimized, because turning a vehicle often requires more energy and time than a straight movement As a high-resolution grip map for a high-quality coverage path exponentially increases the complexity of a path finding algorithm and memory usage, grid-based coverage methods are suitable only for relatively small-sized areas (e.g., from indoor mobile operation) [1], which highlights the applicability of the CPP with the convex decomposition. In this paper, considering their importance, we review existing convex decomposition methods and propose a new convex decomposition method which is simple but well-performing
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