Research On Path Planning Of Unmanned Aerial Vehicle In Urban Environment

In urban environments,building distribution is irregular and airspace structures are complex and diverse.Dynamic path planning technology is the key to ensuring the successful execution of tasks such as logistics delivery,facility inspection,environmental monitoring and disaster relief by unmanned aerial vehicle(UAV)in urban environments,while providing feasibility support for UAVs’ autonomous obstacle avoidance and flight.Therefore,this paper conducts in-depth research and improvement on the path planning problem of UAVs in dynamic environments.The main work is as follows:For UAV global path planning,the key to solving the problem for low-altitude logistics UAVs in urban environments is to simultaneously adapt to simple and complex environments,and how to establish planning models and design algorithms.After analyzing the Theta* algorithm and its limitations in the grid space,we propose an improved UAV path planning algorithm based on an improved spatial layering approach.Firstly,a layered node model that adopts spatial stratification is designed,and the hierarchical node structure effectively saves data storage space.In combination with UAV constraints,it further filters the number of search nodes in complex environments.Secondly,an adaptive initial planning level and cost factor are designed for the hierarchical structure,which can automatically adjust the size of the hierarchical structure and search speed according to the complexity and resolution of the environment,while meeting the needs of lowprecision space fuzzy search and high-precision space accurate search.The simulation results on different complexity maps show that the improved algorithm compared with D*Lite algorithm can save about 3% of the time to generate a path that fits the optimal solution in simple environments,reduce 4% of the path length and improve 20% of the path smoothness in complex maps,verifying the adaptability and feasibility of the algorithm.For the local replanning problem of unmanned aerial vehicles,the local planning of unmanned aerial vehicles in environments containing dynamic obstacles should focus on timeliness and obstacle avoidance.How to design effective local replanning strategies is the key to solving the problem.On the basis of the principle and defect analysis of the dynamic random expansion tree algorithm,a reprogramming method based on breakpoint tree repair is proposed.Firstly,the initial global path is quickly generated through a full planning algorithm in the sampling space.For known static obstacles,prior information is generated through random expansion tree sampling.Feasibility detection domain and collision domain are introduced on the drone’s travel path to detect dangerous nodes that may collide during the drone’s operation process.By combining the collision detection conditions of unknown and known obstacles,accurate positioning of dangerous nodes is achieved? Secondly,a hierarchical map model is used to locate the optimal repair unit for the breakpoint tree generated by pruning after collision detection,transforming the reprogramming problem into a repair problem for the breakpoint tree,improving the quality of the repair path and reducing the time required for reprogramming.Monte Carlo experiments targeting multiple performance have shown that the BRD-RRT algorithm can control the replanning time between 0.2 seconds and 0.5 seconds in maps of low-speed and high-speed obstacles,and ensure an obstacle avoidance rate of 100% in simple maps and over 90% in complex maps.This verifies the timeliness and obstacle avoidance success rate of the algorithm in various environments.