Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization

A method for the development of Time-to-Collision (TTC) mathematical model for outdoor Unmanned Aerial Vehicle (UAV) using Particles Swarm Optimization (PSO), are presented. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking...

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Main Authors: Sabikan, Sulaiman, Nawawi, S. W., Aziz, N. A. A.
Format: Article
Published: Institute of Advanced Engineering and Science 2020
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Online Access:http://eprints.utm.my/id/eprint/91075/
http://dx.doi.org/10.11591/ijai.v9.i3.pp488-496
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spelling my.utm.910752021-05-31T13:29:21Z http://eprints.utm.my/id/eprint/91075/ Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization Sabikan, Sulaiman Nawawi, S. W. Aziz, N. A. A. TK Electrical engineering. Electronics Nuclear engineering A method for the development of Time-to-Collision (TTC) mathematical model for outdoor Unmanned Aerial Vehicle (UAV) using Particles Swarm Optimization (PSO), are presented. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their parameter, for instance, current speed and payload. However, this paper focus on the methodology of the implementation of PSO to develop the TTC model for 5 different set of payloads. This work utilizes a quadcopter as our testbed system that equipped with a Global Positioning System (GPS) receiver unit, a flight controller with data recording capability and ground control station for real-time monitoring. The recorded onboard flight mission data for 5 different set of payloads has been analyzed to develop a mathematical model of TTC through the PSO approach. The horizontal ground speed, throttle magnitudes and flight time stamp are extracted from the on-board quadcopter flight mission. PSO algorithm is used to find the optimal linear TTC model function, while the mean square error is used to evaluate the best fitness of the solution. The results of the TTC mathematical model for each payload are described. Institute of Advanced Engineering and Science 2020-09 Article PeerReviewed Sabikan, Sulaiman and Nawawi, S. W. and Aziz, N. A. A. (2020) Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization. IAES International Journal of Artificial IntelligenceOpen Access, 9 (3). pp. 488-496. ISSN 2089-4872 http://dx.doi.org/10.11591/ijai.v9.i3.pp488-496
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Sabikan, Sulaiman
Nawawi, S. W.
Aziz, N. A. A.
Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
description A method for the development of Time-to-Collision (TTC) mathematical model for outdoor Unmanned Aerial Vehicle (UAV) using Particles Swarm Optimization (PSO), are presented. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying any braking control system when the throttle is fully released. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their parameter, for instance, current speed and payload. However, this paper focus on the methodology of the implementation of PSO to develop the TTC model for 5 different set of payloads. This work utilizes a quadcopter as our testbed system that equipped with a Global Positioning System (GPS) receiver unit, a flight controller with data recording capability and ground control station for real-time monitoring. The recorded onboard flight mission data for 5 different set of payloads has been analyzed to develop a mathematical model of TTC through the PSO approach. The horizontal ground speed, throttle magnitudes and flight time stamp are extracted from the on-board quadcopter flight mission. PSO algorithm is used to find the optimal linear TTC model function, while the mean square error is used to evaluate the best fitness of the solution. The results of the TTC mathematical model for each payload are described.
format Article
author Sabikan, Sulaiman
Nawawi, S. W.
Aziz, N. A. A.
author_facet Sabikan, Sulaiman
Nawawi, S. W.
Aziz, N. A. A.
author_sort Sabikan, Sulaiman
title Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
title_short Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
title_full Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
title_fullStr Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
title_full_unstemmed Modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
title_sort modelling of time-to collision for unmanned aerial vehicle using particles swarm optimization
publisher Institute of Advanced Engineering and Science
publishDate 2020
url http://eprints.utm.my/id/eprint/91075/
http://dx.doi.org/10.11591/ijai.v9.i3.pp488-496
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score 13.211869