A spanning tree algorithm with adaptive pruning with low redundancy coverage rate

Abstract

In reality, when UAV is used in region reconnaissance, the shape of region is usually irregular. Selecting an algorithm to find a complete coverage path with smaller coverage redundancy rate, lower path motion complexity and theoretical energy consumption has certain research significance for the complete coverage path planning (CCPP). In existing algorithms, there is always a high redundant coverage rate under irregularly shaped regions. The motion complexity and the theoretical energy consumption of the path also increase. In this paper, a kind of spanning tree with adaptive pruning (STAP) algorithm based on the grid map is proposed to solve the above problems: on the one hand, it uses a greedy growth strategy to determine the branching growth direction, and proposes an adaptive pruning method for self-optimization of the initial path; on the other hand, a kind of nearest neighbor sub-region connection rule of adjacent paths is proposed to reduce the complexity and the coverage redundancy rate. The STAP algorithm searches for all grid centers, grows, and optimizes the spanning tree by creating paths between the centers. At the end of the optimization, it creates a complete coverage path based on the distribution of the spanning tree. Experiments show that when the STAP algorithm is applied to unobstructed regions, it plans the path with the lowest redundant coverage rate, which reaches 2.6296% and 4.9402%; when it is applied to the regions include obstacles, the advantages of the algorithm are more significant: On the one hand, the path planned by STAP has the least total length, the lowest number of turning and turning back. On the other hand, the redundant coverage rate of the path is still the lowest, reaching 4.1362% and 2.7765%, respectively. The STAP is primarily suitable for situations about CCPP under grid maps formed by irregularly shaped regions, and it holds certain significance for ground reconnaissance of UAVs.