UAV safe route planning based on PSO-BAS algorithm

doi:10.23919/JSEE.2022.000111

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (5): 1151-1160.doi: 10.23919/JSEE.2022.000111

• SYSTEMS ENGINEERING • Previous Articles Next Articles

UAV safe route planning based on PSO-BAS algorithm

Honghong ZHANG^1,²(), Xusheng GAN^1,^2,*(), Shuangfeng LI^1,²(), Zhiyuan CHEN¹()

¹ Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China
² National Key Laboratory of Air Traffic Collision Prevention, Xi’an 710051, China

Received:2021-01-11 Online:2022-10-27 Published:2022-10-27
Contact: Xusheng GAN E-mail:anhuifuyangzhh@sina.com;gxsh15934896556@qq.com;77625747@qq.com;838115488@qq.com
About author:|ZHANG Honghong was born in 1995. He received his B.S. degree in 2018 from Air Force Engineering University, now he is a master’s student of Air Force Engineering University. His main research interests include UAV mission planning and safety assessment. E-mail: anhuifuyangzhh@sina.com||GAN Xusheng was born in 1971. He received his B.S. degree in 1997 from Air Force Engineering College, M.S. degree in 2004 from Air Force Engineering University, and Ph.D. degree in 2008 from Northwestern Polytechnical University. Now he is an associate professor of the Air Force Engineering University. His main research interests include UAV air traffic management and air traffic control.E-mail: gxsh15934896556@qq.com||LI Shuangfeng was born in 1980. He received his doctorate in combat command from the Air Force Command Academy in 2014. His research direction is combat command and air traffic control.E-mail: 77625747@qq.com||CHEN Zhiyuan was born in 1993. He received his B.S. degree in 2016 from Air Force Engineering University, now he is a master’s student of Air Force Engineering University. His main research interests include air traffic management and safety. E-mail: 838115488@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61601497), the Natural Science Basic Research Plan in Shaanxi Province of China (2022JM-412), and the Air Force Engineering University Principal Fund (XZJ2020005).

Abstract

Abstract:

In order to solve the current situation that unmanned aerial vehicles (UAVs) ignore safety indicators and cannot guarantee safe operation when operating in low-altitude airspace, a UAV route planning method that considers regional risk assessment is proposed. Firstly, the low-altitude airspace is discretized based on rasterization, and then the UAV operating characteristics and environmental characteristics are combined to quantify the risk value in the low-altitude airspace to obtain a 3D risk map. The path risk value is taken as the cost, the particle swarm optimization-beetle antennae search (PSO-BAS) algorithm is used to plan the spatial 3D route, and it effectively reduces the generated path redundancy. Finally, cubic B-spline curve is used to smooth the planned discrete path. A flyable path with continuous curvature and pitch angle is generated. The simulation results show that the generated path can exchange for a path with a lower risk value at a lower path cost. At the same time, the path redundancy is low, and the curvature and pitch angle continuously change. It is a flyable path that meets the UAV performance constraints.

Key words: unmanned aerial vehicle (UAV), low-attitude airspace, mission planning, risk assessment, particle swarm optimization, beetle antennae search (BAS), cubic B-spline

Honghong ZHANG, Xusheng GAN, Shuangfeng LI, Zhiyuan CHEN. UAV safe route planning based on PSO-BAS algorithm[J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1151-1160.

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References 26

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Coefficient	Shelter parameter
0	No shelter
0.25	Sparse trees
0.50	Trees or low buildings
0.75	High-rise building
1	Industrial zone

Parameter	Value	Parameter	Value
${f_G}/{\rm{h} }^{-1}$	$6.4 \times {10^{ - 5} }$	${\rho _A}/({\rm{kg/{m^3} } })$	$1.225$
m/kg	1.38	$\beta/{\rm{J}}$	34
$\rho/{\rm{k}}{{\rm{m}}^{-2} }$	$30\;000$	$ {R_I} $	0.3
${r_p}/{\rm{m}}$	0.25	${ {\bar S_{ {\rm{car} } } } }/{\rm{ {m} }^2}$	8.4
${h_p}/{\rm{m}}$	1.65	$ K $	0.1
${R_{ {\rm{uav} } } }/{\rm{m}}$	0.2	${D_{ {\rm{road} } } }/{\rm{m}}$	3
${v_{\max } }/({\rm{m/s}})$	16	T	3
g /(m/s²)	$9.8$	${H_{ {\rm{safe} } } }/{\rm{m}}$	3
$A/{{\rm{m}}^2} $	0.018 8	—	—

Parameter	Value	Parameter	Value
$ N $	20	$ {c_1} $	1.5
$ {\omega _1} $	0.5	$ {c_2} $	2
$ {\omega _2} $	0.2	$ m $	3
$ k $	200	${\rm{eta}}\_d$	0.95
$ \omega $	1	${\rm{eta}}\_\delta$	0.95

Type	Path risk value	Path length/m
Planning methods considering risk assessment	$ 8.75 \times {10^{ - 7}} $	156.74
Planning methods without considering risk assessment	$ 6.45 \times {10^{ - 6}} $	136.48

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UAV safe route planning based on PSO-BAS algorithm

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