The Establishment Of Environmental Electronic Map Of Excavating Robot

It has always been a hot issue in the field of robot research that mining robot conducts autonomous navigation in the wild environment.It requires the robot to realize autonomous motion in the wild environment.Therefore,localization and mapping technology is particularly important.Accurate positioning is helpful for building the robot environment modeling.In combination with the mining robot environment perception project at a research institute,this paper studies self-positioning and mapping of the excavating robot in an unknown environment.In this paper,a grid calibration method is used to jointly calibrate sensors to achieve multi-sensor information complementarity.In order to improve the detection efficiency of obstacles,we first get the approximate area of obstacles by lidar and map the information to the image.After image preprocessing,the Slic algorithm and Grabcut algorithm are used to cluster and segment pixels to obtain accurate obstacle information.In addition,since the CCD camera can't be photographed in all directions,the improved progressive algorithm is applied to image stitching to get the best fusion image.For the uncertainty of single sensor collecting information,fuzzy inference algorithm is applied to fuse the environment information acquired by sensors.Experimental simulations show that the accuracy of obstacle recognition is greatly improved after fusion.Then we use C4.5 algorithm for obstacle classification and optimizationFinally,we deeply study the simultaneous localization and mapping algorithm based on multi-sensor fusion.The obstacle is set as a static landmark in the environment,which assists robot in self localization and mapping in the field.The fuzzy inference optimization algorithm is used to improve the EKF-SLAM,and the simulation is carried out on the simulation platform.The results are analyzed and compared.The results show that the accuracy of creating the environment map is improved,and the effectiveness and stability of the improved algorithm are proved.In addition,an excavating robot is used to establish an environmental electronic map of laboratory corridors in a laboratory environment.