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

收稿日期:2021-01-11 出版日期:2022-10-27 发布日期:2022-10-27

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).

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1151-1160.

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.

图/表 14

参考文献 26

1	QUAN Q, LI G, BAI Y Q, et al An overview and suggestions on traffic management of low-altitude UAV. Acta Aeronautica et Astronautica Sinica, 2020, 41 (1): 6- 34.
2	ZHANG Q R, WEI R X, HE R K, et al Flight path planning of urban dense irregular obstacle space UAV. Control Theory & Applications, 2015, 32 (10): 1407- 1413.
3	LEVASSEUR B, BERTRAND S, RABALLAND N, et al. Accurate ground impact footprints and probabilistic maps for risk analysis of UAV missions. Proc. of the IEEE Aerospace Conference, 2019. DOI: 10.1109/AERO.2019.8741718.
4	LUO Q, WANG H B, ZHENG Y, et al Research on path planning of mobile robot based on improved ant colony algorithm. Neural Computing and Applications, 2020, 32 (6): 1555- 1566.
5	BAO S T, LU Y G, et al Research on path planning of UAV based on ant colony algorithm with angle factor. Journal of Physics: Conference Series, 2020, 1627 (1): 1- 8.
6	XU Z, ZHANG E Z, CHEN Q W Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization. Journal of Systems Engineering and Electronics, 2020, 31 (1): 130- 141.
7	ZHANG Q Q, WANG Z Y, ZHANG H H, et al A complex low-altitude multi-aircraft conflict resolution method based on SMILO-VTAC model. Journal of Traffic and Transportation Engineering, 2019, 19 (6): 125- 136.
8	SARIM M, RADMANESH M, DECHERING M, et al Distributed detect-and-avoid for multiple unmanned aerial vehicles in national airspace. Journal of Dynamic Systems, Measurement and Control, 2019, 141 (7): 071014.
9	FAN X J, GUO Y J, LIU H, et al. Improved Artificial Potential Field Method Applied for AUV Path Planning. Mathematical Problems in Engineering, 2020: 6523158. DOI: 10.1155/2020/6523158.
10	ANGEL M, ABDULLA A, DAVID M, et al 3D Trajectory planning method for UAVs swarm in building emergencies. Sensors, 2020, 20 (3): 642. doi: 10.1109/JSEN.2020.2964488
11	ZHANG Y Y, LI S, GUO H L A type of biased consensus-based distributed neural network for path planning. Nonlinear Dynamics, 2017, 89, 1803- 1815.
12	CHANG Y, WANG Y Q, ALSAADI F E, et al Adaptive fuzzy output-feedback tracking control for switched stochastic pure-feedback nonlinear systems. International Journal of Adaptive Control and Signal Processing, 2019, 33 (10): 1567- 1582.
13	GAO X, FANG Y W, WU Y L Fuzzy Q learning algorithm for dual-aircraft path planning to cooperatively detect targets by passive radars. Journal of Systems Engineering and Electronics, 2013, 24 (5): 800- 810. doi: 10.1109/JSEE.2013.00093
14	ALEXANDRA G, ESTEN I G, DAC-TU H, et al UAVs trajectory planning by distributed MPC under radio communication path loss constraints. Journal of Intelligent and Robotic Systems, 2015, 79 (1): 115- 134. doi: 10.1007/s10846-014-0090-1
15	WU J F, WANG H L, LI N, et al Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm. Aerospace Science and Technology, 2017, 70, 497- 510. doi: 10.1016/j.ast.2017.08.037
16	GUAN X M, LU R L A conflict resolution method for complex low-altitude flight based on satisfactory game theory. Acta Aeronautica et Astronautica Sinica, 2017, 38 (S1): 120- 128.
17	SISLAK D Agent-based cooperative decentralized airplane-collision avoidance. IEEE Trans. on Intelligent Transportations Systems, 2011, 12 (1): 36- 46.
18	PRIMATESTA S, GUGLIERI G, RIZZO A A risk aware path planning strategy for UAVs in urban environments. Journal of Intelligent & Robotic Systems, 2019, 95 (2): 629- 643.
19	ZHANG H H, GAN X S, WU Y R, et al. Evaluation of safety target level of UAV system based on Monte-Carlo. http://kns.cnki.net/kcms/detail/41.1227.TN.20201016.0848.002.html. (in Chinese)
20	HU X T, WU YU. Risk-based discrete multi-path planning method for UAVs in urban environments. http://kns.cnki.net/kcms/detail/11.1929.V.20200821.1328.006.html.
21	KOH C H, LOW K H, LI L, et al Weight threshold estimation of falling UAVs (unmanned aerial vehicles) based on impact energy. Transportation Research Part C: Emerging Technologies, 2018, 93, 228- 255. doi: 10.1016/j.trc.2018.04.021
22	ZHANG Z J, ZHANG S G, LIU X, et al Estimated method of target level of safety for unmanned aircraft system. Journal of Aerospace Power, 2018, 33 (4): 1017- 1024.
23	ZHANG X J, LIU Y, ZHANG Y. Safety Assessment and Risk Estimation for Unmanned Aerial Vehicles Operating in National Airspace System. Journal of Advanced Transportation, 2018: 4731585. DOI: 10.1155/2018/4731585.
24	LI Q H, WEI A X, ZHANG Z H Application of economic load distribution of power system based on BAS-PSO. IOP Conference Series: Materials Science and Engineering, 2019, 490 (7): 2727.
25	FAN Y Q, SHAO J P, SUN G T Optimized PID controller based on beetle antennae search algorithm for electro-hydraulic position servo control system. Sensors, 2020, 902, 54- 64.
26	NGOC H T, ANH D N, THANH N N A genetic algorithm application in planning path using B-Spline model for autonomous underwater vehicle (AUV). Applied Mechanics and Materials, 2020, 4796, 54- 64.

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

UAV safe route planning based on PSO-BAS algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 14

参考文献 26

相关文章 0

编辑推荐

Metrics

本文评价