A method of robot human-computer interaction

Abstract

The invention belongs to the technical fields of robot active visual perception, language interaction, radar obstacle avoidance and deep learning, and in particular relates to a robot human-computer interaction method. This method captures the RGB image and depth map of the environment, detects obstacle information to obtain a laser radar array, normalizes the acquired data, constructs a problem encoding network in human-computer interaction to encode the problem; constructs an image feature extraction network, Extract RGB image and depth image information into a feature matrix, splice lidar data, question code and feature matrix to get feature fusion matrix; use convolutional network to obtain data fusion matrix as the data fusion matrix of surrounding environment; train a cyclic neural network As a navigator, the network takes the data fusion matrix as input, outputs the navigation result, and controls the robot's movement direction. The method realizes the robot's self-navigation, self-exploration, human-computer interaction and other functions, and improves the intelligence of the robot.

Description

technical field [0001] The invention belongs to the technical fields of robot active visual perception, language interaction, radar obstacle avoidance and deep learning, and in particular relates to a robot human-computer interaction method. Background technique [0002] Today, autonomous robots can operate independently to complete specific tasks without human intervention. Autonomous locomotion, a major attribute of autonomous robots, depends primarily on accurate motion estimation and high-level environmental perception. However, in some cases, artificial landmarks are unknowable, or the robot is in a GPS-missing environment, so that ego motion estimation or obtaining scene information encounters great difficulties. Technically, the mobile robot gradually constructs a map consistent with the overall environment by sensing the environment, and at the same time uses this map to realize self-positioning. For a long time, robot navigation problems have been basically solved...

AI Technical Summary

Problems solved by technology

Although they have made great achievements in computer vision and navigation, there is not much progress in the fusion of vision and LiDAR information for map-free navigation, and there are few studies on using the above information for navigation and knowledge question answering.

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