MAV/UAV task coalition phased-formation method

doi:10.21629/JSEE.2019.02.18

Journal of Systems Engineering and Electronics ›› 2019, Vol. 30 ›› Issue (2): 402-414.doi: 10.21629/JSEE.2019.02.18

• Control Theory and Application • Previous Articles Next Articles

MAV/UAV task coalition phased-formation method

Zhiqiang JIAO*(), Peiyang YAO(), Jieyong ZHANG(), Yun ZHONG(), Xun WANG()

Information and Navigation College, Air Force Engineering University, Xi'an 710077, China

Received:2017-11-28 Online:2019-04-01 Published:2019-04-28
Contact: Zhiqiang JIAO E-mail:jzqpaper@163.com;ypy664@163.com;dumu3110728@126.com;pandawlj@126.com;wxkgdxy@163.com
About author:JIAO Zhiqiang was born in 1992. He received his B.S. degree in information and communication engineering from Air Force Engineering University in 2014, and his M.S. degree in information fusion from Air Force Engineering University in 2017. He is currently a Ph.D. candidate of Air Force Engineering University. His research interests include information fusion, command information system, and mission planning.E-mail:jzqpaper@163.com|YAO Peiyang was born in 1960. He received his B.S. degree in 1982, and his M.S. degree in 1991 from Xidian University. Currently he is a professor in Information and Navigation College, Air Force Engineering University. His research interests include command and control theory and command automation system.E-mail:ypy664@163.com|ZHANG Jieyong was born in 1983. He received his B.S. degree in 2006, his M.S. degree in 2008 and his Ph.D. degree in 2012 from Air Force Engineering University. Currently, he is a lecturer in Information and Navigation College, Air Force Engineering University. His research interests include mission planning technique and military organizational analysis.E-mail:dumu3110728@126.com|ZHONG Yun was born in 1990. He received his B.S. degree in network engineering from Air Force Engineering University in 2012, his M.S. degree in information and communication engineering from Air Force Engineering University in 2015. He is currently a Ph.D. candidate of Air Force Engineering University. His research interests include command information system, and mission planning.E-mail:pandawlj@126.com|WANG Xun was born in 1990. He received his B.S. degree in communication engineering from Shandong University in 2013, and his M.S. degree in command information system from Air Force Engineering University in 2016. He is currently a Ph.D. candidate of Air Force Engineering University. His research interests include command information system and mission planning.E-mail:wxkgdxy@163.com
Supported by:
the National Natural Science Foundation of China(61573017);the National Natural Science Foundation of China(61703425);the Aeronautical Science Fund(20175796014);This work was supported by the National Natural Science Foundation of China (61573017; 61703425), and the Aeronautical Science Fund (20175796014)

Abstract

Abstract:

The formation of the manned aerial vehicle/unmanned aerial vehicle (MAV/UAV) task coalition is considered. To reduce the scale of the problem, the formation progress is divided into three phases. For the task clustering phase, the geographical position of tasks is taken into consideration and a cluster method is proposed. For the UAV allocation phase, the UAV requirement for both constrained and unconstrained resources is introduced, and a multi-objective optimal algorithm is proposed to solve the allocation problem. For the MAV allocation phase, the optimal model is firstly constructed and it is decomposed according to the ideal of greed to reduce the time complexity of the algorithm. Based on the above phases, the MAV/UAV task coalition formation method is proposed and the effectiveness and practicability are demonstrated by simulation examples.

Key words: task coalition formation, task clustering, unmanned aerial vehicle (UAV) allocation, manned aerial vehicle (MAV) allocation

Zhiqiang JIAO, Peiyang YAO, Jieyong ZHANG, Yun ZHONG, Xun WANG. MAV/UAV task coalition phased-formation method[J]. Journal of Systems Engineering and Electronics, 2019, 30(2): 402-414.

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References 20

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Task	X	Y
1	52.926~3	37.850~1
2	59.252~9	63.944~3
3	35.608~6	62.479~4
4	95.674~1	57.349~1
5	43.188~7	10.441~9
6	89.885~8	37.302~7
7	76.749~1	20.377~6
8	74.007~4	18.791~3
9	59.356~1	27.958~9
10	92.572~7	62.732~6
11	85.820~5	42.462~4
12	54.876~6	6.157~6
13	84.207~9	44.416~7
14	30.728~1	90.725~8
15	96.304~2	26.537~1
16	24.876~0	31.608~4
17	53.830~3	43.126~0
18	19.580~0	32.201~2
19	63.690~0	23.836~4
20	98.756~9	98.963~7
21	97.200~6	26.733~3
22	69.410~6	61.507~3
23	68.555~9	99.821~9
24	11.883~1	30.592~1
25	65.531~9	70.449~3
26	92.941~1	19.266~5
27	53.087~4	9.597~6
28	51.007~1	76.005~1
29	68.071~5	37.551~1
30	86.977~7	99.223~1
31	15.148~6	46.001~5
32	32.690~2	59.942~1

Cluster	Tasks
1	T₄, T₁₀, T₂₀, T₂₃, T₃₀
2	T₆, T₁₁, T₁₃, T₁₅, T₂₁, T₂₆, T₂₉
3	T₅, T₇, T₈, T₉, T₁₂, T₁₉, T₂₇
4	T₁, T₁₆, T₁₇, T₁₈, T₂₄, T₃₁
5	T₂, T₃, T₁₄, T₂₂, T₂₅, T₂₈, T₃₂

Method	Mean	Variance	Time/s
GCBTD	452.43	2~147.23	0.013~6
k-means	471.77	6~838.63	0.001~6
GCWOICC	460.74	2~389.79	0.010~6

	C-index	S-index	MS-index
PFWCVD	0.551~7	0.722~2	149.561~6
PFWRS	0.393~6	0.723~5	144.610~4

(N_T, N_UAV)	Algorithm	C-index	S-index	MS-index
(50, 50)	PFWCVD	0.465~0	0.771~4	38.791~9
(50, 50)	PFWRS	0.221~6	0.559~0	22.380~0
(70, 70)	PFWCVD	0.631~4	0.722~7	47.885~3
(70, 70)	PFWRS	0.130~7	0.510~3	23.200~2
(90, 90)	PFWCVD	0.604~4	0.749~9	71.113~7
(90, 90)	PFWRS	0.113~4	0.568~9	25.781~1

MAV/UAV task coalition phased-formation method

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UAV	CR₁	CR₂	CR₃	CR₄	CR₅	UAV	CR₁	CR₂	CR₃	CR₄	CR₅
1	1	6	9	7	2	16	6	9	6	9	3
2	8	3	0	0	9	17	3	1	3	8	3
3	3	5	7	8	4	18	2	5	7	6	2
4	9	8	7	4	0	19	4	3	5	2	3
5	1	9	7	3	2	20	8	9	7	0	1
6	4	3	3	2	9	21	6	3	7	9	8
7	8	2	1	5	0	22	8	7	4	7	3
8	6	9	0	2	8	23	6	8	8	9	0
9	0	7	8	5	6	24	4	6	2	1	7
10	8	5	2	1	1	25	3	3	4	6	2
11	4	7	5	9	9	26	4	6	3	7	9
12	0	8	7	7	1	27	6	9	2	3	5
13	7	2	4	5	2	28	8	4	6	8	7
14	0	2	2	8	6	29	7	9	1	0	5
15	7	7	5	6	2	30	2	6	7	1	4

UAV	UR₁	UR₂	UR₃	UR₄	UR₅	UAV	UR₁	UR₂	UR₃	UR₄	UR₅
1	0	5	9	2	5	16	7	1	3	7	9
2	1	3	1	2	7	17	8	2	0	7	6
3	6	2	8	0	1	18	3	0	1	2	4
4	4	9	0	6	1	19	2	9	6	5	3
5	1	4	5	4	0	20	5	0	2	0	4
6	9	3	8	9	3	21	1	2	2	4	8
7	6	1	1	4	2	22	5	3	3	0	7
8	4	9	7	3	1	23	1	6	4	9	9
9	8	1	8	2	9	24	2	8	0	1	2
10	2	0	1	2	3	25	8	6	3	4	6
11	4	6	3	8	8	26	5	3	9	9	5
12	2	0	6	3	7	27	9	7	4	0	5
13	3	8	1	2	7	28	9	6	1	0	1
14	9	9	6	0	8	29	1	0	8	4	4
15	5	7	0	9	5	30	5	2	8	9	3

UAV	BR₁	BR₂	BR₃	BR₄	BR₅	UAV	BR₁	BR₂	BR₃	BR₄	BR₅
1	3	0	0	3	0	16	2	0	2	0	0
2	2	2	1	2	2	17	3	0	1	0	0
3	2	3	1	0	3	18	1	0	0	0	0
4	2	3	1	2	0	19	2	2	1	2	0
5	0	1	2	0	3	20	2	0	2	1	0
6	1	2	0	0	3	21	1	1	0	2	0
7	3	3	3	1	0	22	0	2	0	2	0
8	1	3	0	0	0	23	1	1	0	3	0
9	0	1	2	3	2	24	3	0	2	0	1
10	3	0	0	3	3	25	0	1	3	2	1
11	1	2	0	0	1	26	2	2	0	0	0
12	2	0	2	0	0	27	3	3	1	0	0
13	1	1	3	3	0	28	1	0	3	3	0
14	3	3	0	0	2	29	2	1	0	0	1
15	1	2	1	2	1	30	1	1	1	0	1

MAV	CB₁	CB₂	CB₃	CB₄	CB₅	MAV	CB₁	CB₂	CB₃	CB₄	CB₅
1	5	2	6	2	8	6	7	4	6	0	5
2	8	4	1	6	3	7	2	2	0	3	1
3	7	9	6	8	4	8	0	3	5	5	8
4	4	5	6	7	6	9	5	7	5	0	5
5	3	9	6	6	9	10	8	9	8	8	5

Cluster	UAV
1	U₂₀, U₂₁, U₂₅
2	U₃, U₁₀, U₁₁, U₁₇
3	U₁, U₈, U₁₉, U₂₆, U₂₇
4	U₅, U₆, U₁₈, U₂₄
5	U₂, U₇, U₉, U₁₃, U₃₀

Method	Fitness	Time/s	Time/s²
Method	(mean)	(mean)	(variance)
MAVAAWD	156.300~0	0.111~9	3.599 1e-04
GA	167.480~0	5.336~1	1.040~2
Branch and bound	150.160~0	75.473~3	4.563 0e+03

Method	15	20	25	30	35	40
MAVAAWD	0.14	0.18	0.23	0.45	0.66	0.82
GA	4.42	7.05	8.59	11.13	13.59	16.14

UAVs	MAV
1	M₅
2	M₄, M₆
3	M₂, M₉
4	M₁, M₇
5	M₃, M₈