Push Recovery for Humanoid Robot in Dynamic Environment and Classifying the Data Using K-Mean

Anubha Parashar,Somya Goyal,Apoorva Parashar

doi:10.9781/ijimai.2016.425

Abstract

Push recovery is prime ability that is essential to be incorporated in the process of developing a robust humanoid robot to support bipedalism. In real environment it is very essential for humanoid robot to maintain balance. In this paper we are generating a control system and push recovery controller for humanoid robot walking. We apply different kind of pushes to humanoid robot and the algorithm that can bring a change in the walking stage to sustain walking. The simulation is done in 3D environment using Webots. This paper describes techniques for feature selection to foreshow push recovery for hip, ankle and knee joint. We train the system by K-Mean algorithm and testing is done on crouch data and tested results are reported. Random push data of humanoid robot is collected and classified to see whether push lie in safer region and then tested on given proposed system.

Highlights

IntroductionBipedal robots are comparable to human walking
A day’s human robots are developed to perform many human activities
In fifth stage we find out the push recovery strategy of humanoid robot on given training system to get better results or push recovery

Summary

Introduction

Bipedal robots are comparable to human walking. Though we can say human locomotion seems effortless but it is exceptionally complex [1]. When we talk about the push recovery features in humanoid robot we face difficulties as compared to animals and humans. Humans and animals are completely versed where as humanoid robots have certain other features [2]. It’s contemplated to make a humanoid robot that can work smoothly and practically. The goal of this paper is to search easy, lucid way for push recovery walking in humanoid robots. We collect the push data by pushing humanoid robot from behind and we train the system by applying K-Mean algorithm and using crouch as data. We get a trained system which can analyse and work on any push recovery strategy. We get a trained system which can analyse and work on any push recovery strategy. [18]- [20]

Objectives

Methods

Results

Conclusion