Research On Mobile Robot Navigation Based On Monocular Camera

Nowadays,more and more robots are used in daily life.With the rapid development of artificial intelligence and image processing technology,more and more people begin to devote themselves to the research of visual robots.This paper mainly studies the navigation of visual robot in static indoor environment.Firstly,a monocular camera is used to obtain the image of the robot's environment,then image processing is performed to extract feature points and obstacles.Through the extracted feature points,the scale invariant feature transformation algorithm is applied to target recognition,and the landmarks are identified.The distance between the robot and the obstacle and landmark is obtained by calculating the 3D distance of the camera.Then,the robot is positioned by the obtained landmark to obtain the specific position of the robot.In this paper,a novel multi-innovation Calman filtering algorithm is proposed to reduce the positioning accuracy of the mobile robot in the process of localization,which is caused by the sensor measurement error and the robot pose error.On the basis of standard Calman filtering,robust weighting coefficients and adaptive factors are introduced to resist measurement errors and pose errors.At the same time,we introduce multi-innovation,that is,the new time vector,to further improve the accuracy of the system.Finally,the path planning of the robot is realized by using fuzzy neural network,which can make the robot avoid the obstacle from the starting point and find an optimal path to reach the target point.In this paper,path planning is combined with global path planning and local path planning.The robot to determine its position in the global path planning to find an optimal target,and then use the local path planning of the robot,the fuzzy neural network controller order cycle,until the robot reaches the ultimate goal.The simulation environment is established by grid method,and the feasibility of the algorithm is verified in three environments: barrier free environment,small amount of obstacles,simple environment and complex environment with large number of obstacles.The simulation results show that the algorithm can quickly find an optimal path from the point of departure to the target point.