Research On Path Planning Of A-star Algorithm Based On Improved R5DOS Model

UAV technology is widely used in agricultural monitoring,detection,plant protection and flight defense,etc.,and plays an increasingly important role in the healthy growth of crops.Path planning has important implications for autonomous flight of drones.As a development platform for UAVs,the embedded platform has the characteristics of strong specificity,low power consumption and small size.However,due to the relatively limited processing power,storage space and computing resources of the embedded platform,it is particularly important to improve the performance of path planning in the embedded development of UAVs.Due to the complex and changeable scenarios and environments of agricultural drones performing tasks,the rationality of the UAV’s path planning algorithm becomes very important.Taking this as the starting point,this paper mainly studies the UAV path planning algorithm,and focuses on the following research:1.Construction of UAV path planning model in complex three-dimensional space.According to the structural characteristics of the UAV and the real environment map,an analog digital map for the three-dimensional real environment of UAV path planning is established,and the model elements such as 3D map and nodes are used to abstract the actual map,and the evaluation indicators of surface obstacles and threats are designed.Based on its UAV path planning simulation experiment,it can allow the UAV to locate its own position,perceive the obstacles and threats around it,and independently avoid obstacles and fly safely;2.UAV three-dimensional path planning based on improved A-star algorithm based on R5 DOS model.A-star algorithm is an effective global direct search method for solving the shortest path in static road network,and an improved A-star algorithm based on R5 DOS model is proposed to adapt to the path planning problem in three-dimensional environment.The R5 DOS spatial inference model is used to determine the spatial position relationship of the A-star algorithm,and combined with the hopping point search method,the global optimal path is quickly found,and the number of search nodes of the A-star algorithm is reduced.Compared with the traditional A-star algorithm,the improved A-star algorithm can get rid of the local optimal path in the complex and changeable three-dimensional real environment,quickly find the global optimal path,and improve the convergence speed and computational complexity.