The Development of Supervised Motion Learning and Vision System for Humanoid Robot

Abstract

With the development of the concept of Industry 4.0, research relating to robots is being paid more and more attention, among which the humanoid robot is a very important research topic. The humanoid robot is a robot with a bipedal mechanism. Due to the physical mechanism, humanoid robots can maneuver more easily in complex terrains, such as going up and down the stairs. However, humanoid robots often fall from imbalance. Whether or not the robot can stand up on its own after a fall is a key research issue. However, the often used method of hand tuning to allow robots to stand on its own is very inefficient. In order to solve the above problems, this paper proposes an automatic learning system based on Particle Swarm Optimization (PSO). This system allows the robot to learn how to achieve the motion of rebalancing after a fall. To allow the robot to have the capability of object recognition, this paper also applies the Convolutional Neural Network (CNN) to let the robot perform image recognition and successfully distinguish between 10 types of objects. The effectiveness and feasibility of the motion learning algorithm and the CNN based image classification for vision system proposed in this paper has been confirmed in the experimental results.

A comprehensive survey on humanoid robot development

“…Humanoid robots use some ranges of sensors to support path planning, mapping, and localization tasks in uncertain and dynamic environments. In this way, ultrasonic (Dutta & Fernie, 2005), infrared (Abrate et al, 2007), laser scanner (Cheng et al, 2001;Duran et al, 2003), or camera-based vision (Sabe et al, 2004;Lin et al, 2019) are employed. There are many unresolved issues in the case of robot vision, including walking visual guidance (Kim et al, 2005), remote meeting (Morita et al, 2007), stereo vision (Gutmann et al, 2004), etc.…”