Abstract
Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.
Highlights
A multi-rotor Unmanned Aerial Vehicle (UAV) has many advantages, such as small size, high reliability, and hovering [1], and has been widely used in many fields of production, safety, and maintenance operations in many domains, such as agricultural plant protection [2], film and television shooting [3], structure detection [4], and river inspection [5]
The second one is coverage path planning (CPP) [7], in which the main task is to search all areas except obstacles in the working area, such as sweeping robots, industrial detection robots, and agricultural robots [8,9,10]
One of the innovations of this paper is to introduce a method of region optimization One of the innovations of this paper paths, is to introduce method of the region optimization decomposition to reduce non-working thereby aimproving coverage search decomposition to reduce non-working paths, thereby improving the coverage search effiefficiency of the UAVs
Summary
A multi-rotor Unmanned Aerial Vehicle (UAV) has many advantages, such as small size, high reliability, and hovering [1], and has been widely used in many fields of production, safety, and maintenance operations in many domains, such as agricultural plant protection [2], film and television shooting [3], structure detection [4], and river inspection [5]. This paper introduces a Region Optimal Decomposition (ROD) method to decompose the concave polygonal areas. A recent work suggested a method that divides the search area by the working radius of an unmanned equipment [27] It appears that most of the coverage algorithms are only applicable to convex polygons, while a concave polygon should be decomposed into multiple convex polygons. Most of the above studies did not conduct in-depth research on the decomposition of concave polygonal regions and the merging of sub-regions, nor did they discuss the optimal CPP algorithm to reduce the number of UAV turns. An improved DFS algorithm optimizes the concave polygonal area’s segmentation to tation to minimize the number of sub-regions and reduce the sub-regions to be Secondly, the minimum width method determines the search direction of each su to reduce the number of turns of the UAV.
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