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UAV control system with Time to Collision (TTC) prediction capability

This paper presents the development of an Unmanned Aerial Vehicle (UAV) control system simulation with collision avoidance prediction capability using the Time-to-Collision (TTC) model. TTC is the time required for a UAV either to collide with any static obstacle or completely stop without applying...

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Main Authors: Sabikan, Sulaiman, Nawawi, Sophan Wahyudi, Ab. Aziz, Nor Azlina
Format: Book Section
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/100620/
http://dx.doi.org/10.1007/978-981-16-8129-5_78
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spelling my.utm.1006202023-04-17T07:19:54Z http://eprints.utm.my/id/eprint/100620/ UAV control system with Time to Collision (TTC) prediction capability Sabikan, Sulaiman Nawawi, Sophan Wahyudi Ab. Aziz, Nor Azlina TK Electrical engineering. Electronics Nuclear engineering This paper presents the development of an Unmanned Aerial Vehicle (UAV) control system simulation with collision avoidance prediction capability using the Time-to-Collision (TTC) model. 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. Flight mission data collected from the quadcopter testbed platform experiments in the real environment in order to develop TTC model. The horizontal ground speed, throttle magnitudes, and flight time stamp are downloaded from the onboard quadcopter, filtered, analyzed, and optimize using Particles Swarm Optimization (PSO) algorithm to find the optimal TTC model. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their current parameters, for instance, current speed and payload. This development of UAV’s control system implemented in Matlab/Simulik. The PID-based controller is utilized to stabilize the quadcopter and collision avoidance control systems with the TTC model to assist the system in order to avoid a collision from happening. Simulation tests performed proved the capability of UAV to stop at a safe distance and avoid collisions with the obstacles that existed based on TTC model prediction during flight successfully. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Sabikan, Sulaiman and Nawawi, Sophan Wahyudi and Ab. Aziz, Nor Azlina (2022) UAV control system with Time to Collision (TTC) prediction capability. In: Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications Enhancing Research and Innovation through the Fourth Industrial Revolution. Lecture Notes in Electrical Engineering, 829 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 507-512. ISBN 978-981168128-8 http://dx.doi.org/10.1007/978-981-16-8129-5_78 DOI:10.1007/978-981-16-8129-5_78
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, Sophan Wahyudi
Ab. Aziz, Nor Azlina
UAV control system with Time to Collision (TTC) prediction capability
description This paper presents the development of an Unmanned Aerial Vehicle (UAV) control system simulation with collision avoidance prediction capability using the Time-to-Collision (TTC) model. 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. Flight mission data collected from the quadcopter testbed platform experiments in the real environment in order to develop TTC model. The horizontal ground speed, throttle magnitudes, and flight time stamp are downloaded from the onboard quadcopter, filtered, analyzed, and optimize using Particles Swarm Optimization (PSO) algorithm to find the optimal TTC model. This model provides predictions of time before UAV will collide with the obstacle in the same path based on their current parameters, for instance, current speed and payload. This development of UAV’s control system implemented in Matlab/Simulik. The PID-based controller is utilized to stabilize the quadcopter and collision avoidance control systems with the TTC model to assist the system in order to avoid a collision from happening. Simulation tests performed proved the capability of UAV to stop at a safe distance and avoid collisions with the obstacles that existed based on TTC model prediction during flight successfully.
format Book Section
author Sabikan, Sulaiman
Nawawi, Sophan Wahyudi
Ab. Aziz, Nor Azlina
author_facet Sabikan, Sulaiman
Nawawi, Sophan Wahyudi
Ab. Aziz, Nor Azlina
author_sort Sabikan, Sulaiman
title UAV control system with Time to Collision (TTC) prediction capability
title_short UAV control system with Time to Collision (TTC) prediction capability
title_full UAV control system with Time to Collision (TTC) prediction capability
title_fullStr UAV control system with Time to Collision (TTC) prediction capability
title_full_unstemmed UAV control system with Time to Collision (TTC) prediction capability
title_sort uav control system with time to collision (ttc) prediction capability
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2022
url http://eprints.utm.my/id/eprint/100620/
http://dx.doi.org/10.1007/978-981-16-8129-5_78
_version_ 1765296678582091776
score 13.211869

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