Design of Dancing Robot Based on Machine Vision

Abstract

Aiming at the application requirements of artificial intelligence and robotics, this paper adopts a lightweight and low-cost scheme to construct a dancing-robot system integrating machine vision, mechanical structure and motion control so as to improve its accurate human-motion imitation capability. It provides an important reference for the engineering application of motion imitation of humanoid robots. A human-pose visual-recognition model is built based on Python and deep-learning techniques. A monocular camera captures two-dimensional images, from which skeletal key-point coordinates are extracted via 3D reconstruction. Joint angles are calculated by inverse kinematics to supply core input data for motion imitation. Mechanically, a humanoid joint structure composed of 16 servos is adopted to realize one-to-one mapping and execution of joint angles. In hardware, a collaborative control circuit is constructed with the main-control module and servo-drive module as the core. Software implements data-parsing, instruction generation and closed-loop control. After system integration and debugging, the robot can stably and accurately reproduce simple human dance movements, which verifies the feasibility and effectiveness of the proposed scheme.