USV Path Planning Based on Sparse Visibility Graph

The path planning of unmanned surface vehicle is the key to realize the intelligent driving of unmanned vehicle. Aiming at the problem of low search efficiency caused by the increase of vertex connections in the existing Visibility Graph, this paper presents a path planning algorithm based on Sparse Visibility Graph, which improves the planning efficiency of Visibility Graph by reducing the complexity of visibility graph and improving the search algorithm. Firstly, Sparse Visibility Graph construction is introduced, which reduces the complexity of the Visibility Graph by clipping unnecessary edges to reduce the degree of vertices. Secondly, the improved Lazy Theta* is introduced, the weighted valuation function is introduced to analyze the influence of the actual cost and the estimated cost on the planning effect. Aiming at the problem that the basic A * search path is constrained by the grid and the Theta* planning path is not optimal and the search efficiency is low. Through delayed the line-of-sight check and improvement in checking generations limits, the improved Lazy Theta*algorithm improves the efficiency of planning and the authenticity of the path. Finally, simulation experiments are carried out in a two-dimensional grid environment. The results show that, compared with the search algorithm based on Visibility Graph, the path planning based on Sparse Visibility Graph has a shorter search time, can achieve more efficient local path planning, and the path is more reasonable.