Object Tracking And Path Planning Of Indoor Mobile Robot Based On Visual Information

Autonomous Navigation of the mobile robot needs to perceive environmental information,locate obstacles and track targets.With the information of the targets,obstacles and environment,it can build the real-time map,so the mobile robot can make path planning.Aiming at indoor environmental,this paper studies the problems of environment detection,dynamic target tracking,path planning and optimization in autonomous navigation of mobile robots based on multi-vision information.The main contents are as follows:The autonomous navigation vision system of the mobile robot is constructed.A framework of monocular and binocular multi-vision system is proposed and analyzed.The binocular vision system of mobile robot is designed,the binocular calibration and correction are also researched.Aiming at the problem of shadow interference in indoor environment,a single-scale Retinex shadow elimination algorithm based on color saturation information is proposed.According to the environmental brightness information,the algorithm can adjust and enhance the gray difference between the ground and obstacles in the fusion image adaptively,and extract obstacles effectively.After the shadow is eliminated,the image is processed by seed filling and morphological operations.The environmental scene is segmented into obstacles,backgrounds,and ground areas.In order to meet the real-time requirements of obstacle measurement of mobile robots,a binocular vision measurement method was proposed based on templatematching.And a simple and fast binocular vision obstacle measurement was implemented on the mobile robot platform.In the obstacle detection and measurement experiment,the average measurement relative error is 1.77%,the average time is 45.5ms,and it has good accuracy and real-time performance.Aiming at tracking problems in complex environments,such as background interference,target rotation,occlusion and fast motion,a spatio-temporal context tracking algorithm is proposed based on key points.The optical flow tracking method and key points matching are used to track key points of the target,and the target position is estimated by clustering and other operations;a relationship model between the change rate of target key points and the update rate of spatio-temporal context model is established,which can control update rate at real-time and prevent the error information from being introduced;A context appearance model is constructed at the prediction position of the target,and a confidence map is obtained by calculating the correlation with the spatio-temporal context model to accurately locate the final position of the target.Compared with the four types of current major target tracking algorithms,the algorithm has the best comprehensive performance in tracking success rate and tracking error.In the dynamic target tracking experiment,the tracking success rate of mobile robot for random moving target tracking is 97.4%.A real-time map of obstacles,the target and the mobile robot is established based on multiinformation fusion method.An improved velocity vector field path planning algorithm is proposed.The attenuation field of the attraction velocity is used to avoid the robot oscillating near the target;The logarithmic form of repulsive velocity and its attenuation strategy is proposed to solve the problem of robot track jitter and target's unreachable problem when the target is in the repulsive field of obstacles;The direction selection of the tangential velocity of static and dynamic obstacles is optimized to ensure the shortest safety path and effectively get rid of the local minimum.Based on the monocular and binocular vision system proposed in this paper,the indoor autonomous navigation experiment of mobile robot is designed with the obstacle detection algorithm,the obstacle measurement algorithm,the dynamic target tracking algorithm and the path planning algorithms in this paper.It achieves the goal of autonomous,adaptive and realtime path planning and he experimental results verify the feasibility and effectiveness of the proposed method.