Research Of Environment Modeling Method For Supermarket Robot

Based on supermarket environment of robot operating, this paper conducts a depth research on knowledge representation, merchandise modeling, shelf modeling and map modeling. Created sample libraries so that the supermarket robot can establish the environmental model quickly and accurately. Using the LVQ neural network system to learn environmental samples to distinguish the type of obstacles accurately. These methods provides a data support for path planning and obstacle avoidance in an unknown area for supermarket robot.This paper gives an overview introduction to the background of the subject, the theory of robotics and artificial intelligence, the development of robot and environment modeling. By analyzing the advantages and disadvantages of several modeling methods in different applications, this paper gives models for the supermarket environment both in semantic and structure. This paper makes a classification and encoding for merchandise and conducts a research on definition, geometry modeling, location and pose of the merchandise. Gives a modeling method based on sample characteristics libraries using multi-knowledge representation, LVQ neural network, 2.5D description.Makes a definition and management for the knowledge and semantic of environment using C + + Builder development platform. Realizes map modeling, shelf modeling, merchandise modeling and obstacle recognition. Based on knowledge representation this paper constructs a relationship model between environment, shelves, and merchandise using a combination of multi-knowledge representation methods. Gives solutions to the key problems, described the system designing, experiment and the test results, points out the scientific value of this subject, its shortcomings and further research objectives.