Optimized autonomous UAV design with obstacle avoidance capability

Jun Jet Tai; Swee King Phang; Yen Myan Felicia Wong

doi:10.1063/5.0001372

Optimized autonomous UAV design with obstacle avoidance capability

Jun Jet Tai, Swee King Phang, Yen Myan Felicia Wong

Taylor's University

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

5 Citations (Scopus)

Abstract

Obstacle Avoidance and Navigation (OAN) algorithms are an active research field dominated by either offline or online methods. The former method is fast but requires a prior known map while the latter method can function without a prior known map at the expense of high computational requirements. To bring OAN algorithm to mass produced mobile robots, more precisely multirotor Unmanned Aerial Vehicles (UAVs), the computational requirement of robust algorithms must be brought low enough such that the computation can be done on an onboard companion computer, while being able to operate without prior knowledge of the map. This article introduces a novel OAN algorithm-dubbed the Closest Obstacle Avoidance and A∗ Algorithm (COAA*)-that bridges the capabilities of current offline and online OAN algorithms. The proposed algorithm takes into account the UAV performance limits, and is very easy to calibrate and incorporate for many other classes of mobile robots. The main contributions of this research work are that COAA∗ has guaranteed convergence to a global minimum for the navigational trajectory, while being very computationally lightweight due to its first principles formulation.

Original language	English
Title of host publication	13th International Engineering Research Conference, EURECA 2019
Editors	Swee King Phang, Ghafour Amouzad Mahdiraji, Chockalingam Aravind Vaithilingam
Publisher	American Institute of Physics Inc.
Number of pages	12
ISBN (Electronic)	9780735419926
DOIs	https://doi.org/10.1063/5.0001372
Publication status	Published - 5 May 2020
Externally published	Yes
Event	13th International Engineering Research Conference, EURECA 2019 - Selangor Darul Ehsan, Malaysia Duration: 27 Nov 2019 → …

Publication series

Name	AIP Conference Proceedings
Volume	2233
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Conference

Conference	13th International Engineering Research Conference, EURECA 2019
Abbreviated title	EURECA 2019
Country/Territory	Malaysia
City	Selangor Darul Ehsan
Period	27/11/19 → …

Bibliographical note

Funding Information:
This research is done with the support of Taylor's Internal Research Grant Scheme-Emerging Research Funding Scheme (TiGRS/ERFS/2/2018/SOE/004).

Publisher Copyright:
© 2020 Author(s).

Funding

This research is done with the support of Taylor's Internal Research Grant Scheme-Emerging Research Funding Scheme (TiGRS/ERFS/2/2018/SOE/004).

ASJC Scopus subject areas

General Physics and Astronomy

Access to Document

10.1063/5.0001372

Cite this

Tai, J. J., Phang, S. K., & Wong, Y. M. F. (2020). Optimized autonomous UAV design with obstacle avoidance capability. In S. K. Phang, G. A. Mahdiraji, & C. A. Vaithilingam (Eds.), 13th International Engineering Research Conference, EURECA 2019 Article 020026 (AIP Conference Proceedings; Vol. 2233). American Institute of Physics Inc.. https://doi.org/10.1063/5.0001372

Optimized autonomous UAV design with obstacle avoidance capability. / Tai, Jun Jet; Phang, Swee King; Wong, Yen Myan Felicia.
13th International Engineering Research Conference, EURECA 2019. ed. / Swee King Phang; Ghafour Amouzad Mahdiraji; Chockalingam Aravind Vaithilingam. American Institute of Physics Inc., 2020. 020026 (AIP Conference Proceedings; Vol. 2233).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding › peer-review

Tai, JJ, Phang, SK & Wong, YMF 2020, Optimized autonomous UAV design with obstacle avoidance capability. in SK Phang, GA Mahdiraji & CA Vaithilingam (eds), 13th International Engineering Research Conference, EURECA 2019., 020026, AIP Conference Proceedings, vol. 2233, American Institute of Physics Inc., 13th International Engineering Research Conference, EURECA 2019, Selangor Darul Ehsan, Malaysia, 27/11/19. https://doi.org/10.1063/5.0001372

@inproceedings{d4ede82197304272800d49ddf0906e95,

title = "Optimized autonomous UAV design with obstacle avoidance capability",

abstract = "Obstacle Avoidance and Navigation (OAN) algorithms are an active research field dominated by either offline or online methods. The former method is fast but requires a prior known map while the latter method can function without a prior known map at the expense of high computational requirements. To bring OAN algorithm to mass produced mobile robots, more precisely multirotor Unmanned Aerial Vehicles (UAVs), the computational requirement of robust algorithms must be brought low enough such that the computation can be done on an onboard companion computer, while being able to operate without prior knowledge of the map. This article introduces a novel OAN algorithm-dubbed the Closest Obstacle Avoidance and A∗ Algorithm (COAA*)-that bridges the capabilities of current offline and online OAN algorithms. The proposed algorithm takes into account the UAV performance limits, and is very easy to calibrate and incorporate for many other classes of mobile robots. The main contributions of this research work are that COAA∗ has guaranteed convergence to a global minimum for the navigational trajectory, while being very computationally lightweight due to its first principles formulation.",

author = "Tai, \{Jun Jet\} and Phang, \{Swee King\} and Wong, \{Yen Myan Felicia\}",

note = "Funding Information: This research is done with the support of Taylor's Internal Research Grant Scheme-Emerging Research Funding Scheme (TiGRS/ERFS/2/2018/SOE/004). Publisher Copyright: {\textcopyright} 2020 Author(s).; 13th International Engineering Research Conference, EURECA 2019, EURECA 2019 ; Conference date: 27-11-2019",

year = "2020",

month = may,

day = "5",

doi = "10.1063/5.0001372",

language = "English",

series = "AIP Conference Proceedings",

publisher = "American Institute of Physics Inc.",

editor = "Phang, \{Swee King\} and Mahdiraji, \{Ghafour Amouzad\} and Vaithilingam, \{Chockalingam Aravind\}",

booktitle = "13th International Engineering Research Conference, EURECA 2019",

address = "United States",

}

TY - GEN

T1 - Optimized autonomous UAV design with obstacle avoidance capability

AU - Tai, Jun Jet

AU - Phang, Swee King

AU - Wong, Yen Myan Felicia

N1 - Funding Information: This research is done with the support of Taylor's Internal Research Grant Scheme-Emerging Research Funding Scheme (TiGRS/ERFS/2/2018/SOE/004). Publisher Copyright: © 2020 Author(s).

PY - 2020/5/5

Y1 - 2020/5/5

N2 - Obstacle Avoidance and Navigation (OAN) algorithms are an active research field dominated by either offline or online methods. The former method is fast but requires a prior known map while the latter method can function without a prior known map at the expense of high computational requirements. To bring OAN algorithm to mass produced mobile robots, more precisely multirotor Unmanned Aerial Vehicles (UAVs), the computational requirement of robust algorithms must be brought low enough such that the computation can be done on an onboard companion computer, while being able to operate without prior knowledge of the map. This article introduces a novel OAN algorithm-dubbed the Closest Obstacle Avoidance and A∗ Algorithm (COAA*)-that bridges the capabilities of current offline and online OAN algorithms. The proposed algorithm takes into account the UAV performance limits, and is very easy to calibrate and incorporate for many other classes of mobile robots. The main contributions of this research work are that COAA∗ has guaranteed convergence to a global minimum for the navigational trajectory, while being very computationally lightweight due to its first principles formulation.

AB - Obstacle Avoidance and Navigation (OAN) algorithms are an active research field dominated by either offline or online methods. The former method is fast but requires a prior known map while the latter method can function without a prior known map at the expense of high computational requirements. To bring OAN algorithm to mass produced mobile robots, more precisely multirotor Unmanned Aerial Vehicles (UAVs), the computational requirement of robust algorithms must be brought low enough such that the computation can be done on an onboard companion computer, while being able to operate without prior knowledge of the map. This article introduces a novel OAN algorithm-dubbed the Closest Obstacle Avoidance and A∗ Algorithm (COAA*)-that bridges the capabilities of current offline and online OAN algorithms. The proposed algorithm takes into account the UAV performance limits, and is very easy to calibrate and incorporate for many other classes of mobile robots. The main contributions of this research work are that COAA∗ has guaranteed convergence to a global minimum for the navigational trajectory, while being very computationally lightweight due to its first principles formulation.

UR - https://www.scopus.com/pages/publications/85096401918

U2 - 10.1063/5.0001372

DO - 10.1063/5.0001372

M3 - Conference proceeding

AN - SCOPUS:85096401918

T3 - AIP Conference Proceedings

BT - 13th International Engineering Research Conference, EURECA 2019

A2 - Phang, Swee King

A2 - Mahdiraji, Ghafour Amouzad

A2 - Vaithilingam, Chockalingam Aravind

PB - American Institute of Physics Inc.

T2 - 13th International Engineering Research Conference, EURECA 2019

Y2 - 27 November 2019

ER -

Optimized autonomous UAV design with obstacle avoidance capability

Abstract

Publication series

Conference

Bibliographical note

Funding

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this