Construction of multi-modal perception model of communicative robot in non-structural cyber physical system environment based on optimized BT-SVM model

Abstract

With the rapid development of intelligent control technology, computer technology, bionics, artificial intelligence and other disciplines, more and more attention has been paid to the research of intelligent mobile robot technology, and autonomous positioning is the basis for mobile robots to conduct autonomous navigation and exploration research. Sensors assist each other to provide a wealth of perception information about the internal state of the robot and the external surrounding environment. This paper proposes a method for optimizing the Support Vector Machine (SVM) multi-classifier with a binary tree structure, which improves the accuracy of multi-modal tactile signal recognition. The improved particle swarm clustering algorithm is used to optimize the binary tree structure, reduce the error accumulation of the binary tree structure SVM multi-classifier, and further improve the accuracy of multi-modal tactile signal recognition. The effect of the method in this paper is verified by robot grasping experiments. The results show that the use of multi-modal information of two-dimensional images and three-dimensional point cloud images can effectively identify and locate target objects of different shapes. Compared with the processing method of two-dimensional or point cloud monomodal image information, the positioning error can be reduced by 54.8%, and the direction error can be reduced by 50.8%, which has better robustness and accuracy. The simulation results show that the improved PSOBT-SVM model has the best classification effect for artificial features, PCA features and spatio-temporal correlation features. The improved PSOBT-SVM optimizes the classification accuracy without changing the number of SVM classifiers, and proves its accuracy in classifying multimodal tactile signals.