BUILDING A MAP OF REFERENCE SURFACES TO SOLVE THE PROBLEM OF PLANNING THE MOVEMENT OF A GROUP OF GROUND ROBOTS

Abstract

The purpose of the study is to form a geometric model of the environment containing information about the parameters of the underlying surface for use in a system for planning the movements of a group of robots in formation at high speed. The article examines the problem of con structing a map of support surfaces. An analysis of existing research on the topic of determining the characteristics of supporting surfaces by mobile robots is presented. A classification of methods for assessing the characteristics of a supporting surface into remote and contact ones is given. Based on an analysis of the advantages and disadvantages of known remote and contact methods, the work proposes a combined approach that makes it possible to use the advantages of both methods. The approach is based on remote division of space into clusters according to the external parameters of the underlying surface with potentially identical internal properties, simultaneous determination of the internal parameters of the underlying surface by the contact method and their further combination. In this case, the surface parameters are constantly updated during movement. The approach uses a limited list of standard on-board means of a mobile robot and does not require large computational costs compared to machine learning methods. A description is given of the remote determination of the external parameters of the underlying surface, which are based on point cloud segmentation algorithms that do not require preliminary training. The arguments for segmentation are: the coordinates of the cloud points, the color of each point, and the height difference in the vicinity of each point. An algorithm for determining the internal characteristics of a surface using the contact method is described. The friction coefficients between each wheel and the current surface are considered as internal parameters. These coefficients make it possible to determine the maximum accelerations for each robot in the group, which are necessary to implement the motion planning system. The paper presents the results of experimental studies of remote determination of the parameters of the underlying surface within the framework of the proposed approach using data from the public KITTI dataset. The results of the study confirm the possibility of forming a geometric model of the environment, segmented into areas with different characteristics of the supporting surface without training using standard hardware capabilities of the robot