Name
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/rs13081525 | REMOTE SENSING |
Keywords | DocType | Volume |
coverage path planning, region optimal decomposition, depth-first-search algorithm, remote sensing | Journal | 13 |
Issue | Citations | PageRank |
8 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Tang | 1 | 0 | 2.03 |
| Congqiang Tang | 2 | 0 | 0.34 |
| Hao Zhou | 3 | 0 | 0.34 |
| Christophe Claramunt | 4 | 0 | 1.01 |
| Shaoyang Men | 5 | 0 | 1.35 |