Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization
Exploring a new environment is a huge challenge for the visually impaired. Hence there is a need for a system that is able to assist them safely during their journey. Here, in this paper, we propose a path planning with predetermined waypoints method using Particle Swarm Optimization (PSO) algorithm...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier Ltd.
2015
|
Subjects: | |
Online Access: | http://irep.iium.edu.my/49880/ http://irep.iium.edu.my/49880/ http://irep.iium.edu.my/49880/ http://irep.iium.edu.my/49880/1/49880_-_Path_Planning_for_Visually_Impaired_People_in_an_Unfamiliar.pdf |
id |
iium-49880 |
---|---|
recordtype |
eprints |
spelling |
iium-498802017-11-15T09:15:33Z http://irep.iium.edu.my/49880/ Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization Yusof, T. S. T. Toha, Siti Fauziah Md. Yusof, Hazlina T Technology (General) Exploring a new environment is a huge challenge for the visually impaired. Hence there is a need for a system that is able to assist them safely during their journey. Here, in this paper, we propose a path planning with predetermined waypoints method using Particle Swarm Optimization (PSO) algorithm. This method computes shortest possible path given the predetermined waypoints from initial position to final position. By using predetermined waypoints which is a collection of coordinates along the pedestrian walkaway, a resulting accessible pedestrian path could be offered to the visually impaired. The system consists of a destination selection process and path planning process. For the destination selection process, a list of available places is given and the user will select the places that they want to visit from the list. The path planning process calculates path length between all nodes which includes predetermined waypoints, start point and final destination using Euclidean distance formula. The PSO algorithm will optimise possible shortest route by minimizing the total cost for path length. The simulation analyses of the proposed method have shown promising results of optimal route for different destinations. Results from this paper will be used further to explore the potential development of path guidance system for the visually impaired people by allowing them to travel independently in an unfamiliar environment. Elsevier Ltd. 2015 Article PeerReviewed application/pdf en http://irep.iium.edu.my/49880/1/49880_-_Path_Planning_for_Visually_Impaired_People_in_an_Unfamiliar.pdf Yusof, T. S. T. and Toha, Siti Fauziah and Md. Yusof, Hazlina (2015) Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization. Procedia Computer Science, 76. pp. 80-86. ISSN 1877-0509 http://www.sciencedirect.com/science/article/pii/S1877050915037825 10.1016/j.procs.2015.12.281 |
repository_type |
Digital Repository |
institution_category |
Local University |
institution |
International Islamic University Malaysia |
building |
IIUM Repository |
collection |
Online Access |
language |
English |
topic |
T Technology (General) |
spellingShingle |
T Technology (General) Yusof, T. S. T. Toha, Siti Fauziah Md. Yusof, Hazlina Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
description |
Exploring a new environment is a huge challenge for the visually impaired. Hence there is a need for a system that is able to assist them safely during their journey. Here, in this paper, we propose a path planning with predetermined waypoints method using Particle Swarm Optimization (PSO) algorithm. This method computes shortest possible path given the predetermined waypoints from initial position to final position. By using predetermined waypoints which is a collection of coordinates along the pedestrian walkaway, a resulting accessible pedestrian path could be offered to the visually impaired. The system consists of a destination selection process and path planning process. For the destination selection process, a list of available places is given and the user will select the places that they want to visit from the list. The path planning process calculates path length between all nodes which includes predetermined waypoints, start point and final destination using Euclidean distance formula. The PSO algorithm will optimise possible shortest route by minimizing the total cost for path length. The simulation analyses of the proposed method have shown promising results of optimal route for different destinations. Results from this paper will be used further to explore the potential development of path guidance system for the visually impaired people by allowing them to travel independently in an unfamiliar environment. |
format |
Article |
author |
Yusof, T. S. T. Toha, Siti Fauziah Md. Yusof, Hazlina |
author_facet |
Yusof, T. S. T. Toha, Siti Fauziah Md. Yusof, Hazlina |
author_sort |
Yusof, T. S. T. |
title |
Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
title_short |
Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
title_full |
Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
title_fullStr |
Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
title_full_unstemmed |
Path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
title_sort |
path planning for visually impaired people in an unfamiliar environment using particle swarm optimization |
publisher |
Elsevier Ltd. |
publishDate |
2015 |
url |
http://irep.iium.edu.my/49880/ http://irep.iium.edu.my/49880/ http://irep.iium.edu.my/49880/ http://irep.iium.edu.my/49880/1/49880_-_Path_Planning_for_Visually_Impaired_People_in_an_Unfamiliar.pdf |
first_indexed |
2023-09-18T21:10:28Z |
last_indexed |
2023-09-18T21:10:28Z |
_version_ |
1777411218137415680 |