Research On Path Planning Of UAVs Based On Beetle Antennae Search Algorithm In 2D And 3D Space

UAVs are increasingly used in military and civilian environments to perform mission-critical tasks because they can perform missions in hazardous conditions or extreme weather.Sporting aircraft and vehicles all have high-speed dynamics.To meet the low complexity requirements of planning,the most important task required to perform tasks is to achieve path planning with enforceability and low computational complexity.In recent years,many well-established and probabilistic path planning methods have been proposed in the path planning field.Some of these methods transform the path planning environment into a specific form of node graph,and then use heuristic-based algorithms for heuristic path planning.However,since the path planning problem is an NP complete problem,these methods are often inefficient and cannot be used for online path planning tasks.In order to effectively solve the problem of inefficiency of path planning method based on heuristic algorithm,this thesis proposes OABAS,an efficient dynamic path planning and obstacle avoidance method.Firstly,this thesis designs a solution and optimization scheme for the model in two-dimensional space.This method models the obstacles in the path point and the environment in the two-dimensional environment,and then uses the navigation point as the optimization object.For a nonlinear time-varying system such as a drone,the input of the system also includes constraints such as maximum turning angle and minimum safety interval.Secondly,in the three-dimensional environment,using the efficiency of the OABAS algorithm,this thesis designs a pre-planning scheme based on the minimum threat surface and the observation window method,which makes the drone have the ability of dynamic path planning and obstacle avoidance in three-dimensional space.In this thesis,under the same model design scheme,the particle swarm optimization algorithm,the bee colony algorithm and the BAS algorithm are used for verification test.The simulation results show that the proposed scheme can quickly plan the available paths and carry out further optimization process.The proposed method not only has nearly 99%probability completeness,but also has progressive optimal planning results.At the same time,noise is added to the control input of the UAV path planning system,and the robustness test is carried out.The simulation results show that the path planning system has strong robustness.Based on the simulation results benchmark in the 3D environment,the method can complete the obstacle avoidance task in the dynamic environment and significantly improve the planning efficiency.