Human Inspired Robot Walking Pattern Planning
type: CreativeWork
description: Development of full size humanoid robots can be simplified along with the bipedal walking formulation methods. These robots consist of rigid bodies connected with actuated joints supposed to mimic human like walking. Although many researchers have tackled the problem of bipedal dynamic walking, it is far from matching human skills. This particular problem is of the special interest and the main subject of this research. Biped robot walking is a periodic path of an unstable phase, called Single Support Phase, following a stable phase, Double Support Phase. A periodic approach providing a scalable gait with characteristic parameters such as gait length, gait maximum height and gait time cycle is proposed. The methodology is divided in two parts, planning robot trajectory and dynamic stability examination. The lower body is responsible for the general bipedal walking trajectory where limited numbers of breakpoints in both stable and unstable phase are identified. Consequently, positions of ankle, hip, and knee joints are derived for a seven link biped robot. In order to generate a smooth walking trajectory, a search for fast and efficient computation algorithms resulted in exploring the field of Artificial Intelligence and Soft Computing with the purpose of finding a valid nonconventional approach. The represented approach for walking pattern planning based on Artificial Neural Networks using Radial Basis Function is intended to fit a curve on derived breakpoints. Biped robot stability during walking cycles is investigated using the Zero Moment Point criterion. In the dynamic stability study part, ZMP for a stable condition in a determined polygon of support is calculated in every single gait step. Then, for trunk motion adjustment in order to compensate for lower limb movement, Linear Inverted Pendulum model and ZMP criterion are employed to obtain upper body trajectory satisfying whole robot walking dynamic stability. Keywords: Artificial Neural Networks, Radial Basi…
abstract: Development of full size humanoid robots can be simplified along with the bipedal walking formulation methods. These robots consist of rigid bodies connected with actuated joints supposed to mimic human like walking. Although many researchers have tackled the problem of bipedal dynamic walking, it is far from matching human skills. This particular problem is of the special interest and the main subject of this research. Biped robot walking is a periodic path of an unstable phase, called Single Support Phase, following a stable phase, Double Support Phase. A periodic approach providing a scalable gait with characteristic parameters such as gait length, gait maximum height and gait time cycle is proposed. The methodology is divided in two parts, planning robot trajectory and dynamic stability examination. The lower body is responsible for the general bipedal walking trajectory where limited numbers of breakpoints in both stable and unstable phase are identified. Consequently, positions of ankle, hip, and knee joints are derived for a seven link biped robot. In order to generate a smooth walking trajectory, a search for fast and efficient computation algorithms resulted in exploring the field of Artificial Intelligence and Soft Computing with the purpose of finding a valid nonconventional approach. The represented approach for walking pattern planning based on Artificial Neural Networks using Radial Basis Function is intended to fit a curve on derived breakpoints. Biped robot stability during walking cycles is investigated using the Zero Moment Point criterion. In the dynamic stability study part, ZMP for a stable condition in a determined polygon of support is calculated in every single gait step. Then, for trunk motion adjustment in order to compensate for lower limb movement, Linear Inverted Pendulum model and ZMP criterion are employed to obtain upper body trajectory satisfying whole robot walking dynamic stability. Keywords: Artificial Neural Networks, Radial Basi…
author: Ranjbar, Mohammadreza
language: en
date published: 2016-03
date modified: 2026-07-24T04:03:38Z
provider: University of Regina
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Faculty of Graduate Studies and Research, University of Regina

PropertyValue
property ID: Handle
value: https://hdl.handle.net/10294/6830

PropertyValue
property ID: DOI
value: 10.82465/4397