Path Planning Of UAV In Unknown Environment Based On Ant Colony Algorithm

UAV is widely used in military and civil fields because of its low cost,strong maneuverability and low risk.With the development of UAV intelligence,path planning in unknown environment has become a research hotspot.In order to make the UAV have the ability of three-dimensional environment perception and autonomous path planning after detecting obstacles,this paper studies the problem of environment perception with binocular camera as the main sensor of the rotorcraft,analyzes the problem of path planning after the rotorcraft obtains the environment information,and puts forward the adaptive dynamic window ant colony algorithm in the two-dimensional unknown environment The improved method is verified by simulation.The main contents of this paper are as follows:Firstly,the environment perception system of UAV is built by using binocular vision camera.The principle of binocular stereo vision and the realization method of threedimensional measurement are described in detail.In order to better realize the subsequent environment sensing function,the camera is calibrated and image corrected by experiment,and the internal and external parameters of the camera are obtained,and the calibration error is controlled in the actual application range.Secondly,using the depth image of binocular camera output and the single frame point cloud image calculated by combining the gray image data,the feature extraction and matching of multi frame gray image are based on the algorithm,and the point cloud splicing of adjacent multi frames is realized by ICP point cloud registration algorithm,which achieves better 3D scene reconstruction results.Based on the depth information of obstacles,the UAV obstacle detection standards are divided,and the obstacles are segmented from the image by clustering algorithm to achieve the purpose of obstacle detection.In the outdoor environment,the obstacle detection experiment is carried out.The results show that under the condition of general light source,it can detect multiple types of obstacles.According to the error analysis of single obstacle distance,it can obtain a more accurate detection range by keeping the UAV safety distance within 2-3m.Finally,the path planning problem of ant colony algorithm in unknown environment is studied.Aiming at the two-dimensional environment,an improved ant colony algorithm based on adaptive dynamic window is proposed: the improved ant colony algorithm is used to re plan the path in the dynamic window which can be adjusted automatically to avoid obstacles.The simulation results show that the path re planning ability of the algorithm UAV under the condition of sudden obstacles is strong and the time is short.Aiming at the three-dimensional environment,the ant colony algorithm and artificial potential field method are optimized at the same time.By using the cubic B-spline interpolation method to smooth the path,the simulation test is carried out.The results show that the improved three-dimensional ant colony algorithm has strong ability of optimization and fast convergence speed,and the fusion algorithm can effectively plan the obstacle avoidance path suitable for UAV flight characteristics in the unknown environment.