Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction

doi:10.23919/JSEE.2022.000098

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (6): 1550-1564.doi: 10.23919/JSEE.2022.000098

• SYSTEMS ENGINEERING • Previous Articles Next Articles

Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction

Jinming LIU(), Yingguo CHEN(), Rui WANG(), Yingwu CHEN()

¹ College of Systems Engineering, National University of Defense Technology, Changsha 410073, China

Received:2021-03-22 Online:2023-12-18 Published:2023-12-29
Contact: Yingguo CHEN E-mail:liujinming1998@163.com;argguo@163.com;ruiwangnudt@gmail.com;ywchen@nudt.edu.cn
About author:
LIU Jinming was born in 1998. He received his B.S. degree from Northeast Agricultural University in 2020. Now, he is pursuing his M.S. degree in National University of Defense Technology, Changsha, China. He is mainly engaged in intelligent optimization, mission planning, and scheduling methods. E-mail: liujinming1998@163.com

CHEN Yingguo was born in 1986. He received his B.S., M.S., and Ph.D. degrees from National University of Defense Technology, Changsha, China, in 2008, 2010 and 2014 respectively. He is currently an assistant professor with the College of Systems Engineering, National University of Defense Technology. His research interests include intelligent optimization, mission planning, and scheduling methods. E-mail: argguo@163.com

WANG Rui was born in 1986. He received his B.S. degree from National University of Defense Technology (NUDT), Changsha, China, in 2008 and Ph.D. degree from University of Sheffield, Sheffield, U.K., in 2013. He is a lecturer with the College of Systems Engineering, NUDT. His research interests include evolutionary computation, multi-objective optimization, machine learning, and various applications using evolutionary algorithms. E-mail: ruiwangnudt@gmail.com

CHEN Yingwu was born in 1963. He received his Ph.D. degree in engineering from National University of Defense Technology in 1994. He is a professor at the College of Information System and Management, National University of Defense Technology. His research interest covers system planning and decision making. E-mail: ywchen@nudt.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (72001212).

Abstract

Abstract:

With the new development trend of multi-resource coordinated Earth observation and the new goal of Earth observation application of “short response time, high observation accuracy, and wide coverage”, space-aeronautics cooperative complex task planning problem has become an urgent problem to be solved. The focus of this problem is to use multiple resources to perform collaborative observations on complex tasks. By analyzing the process from task assignment to receiving task observation results, we propose a multi-layer interactive task planning framework which is composed of a preprocessing method for complex tasks, a task allocation layer, a task planning layer, and a task coordination layer. According to the characteristics of the framework, a hybrid genetic parallel tabu (HGPT) algorithm is proposed on this basis. The algorithm uses genetic annealing algorithm (GAA), parallel tabu (PT) algorithm, and heuristic rules to achieve task allocation, task planning, and task coordination. At the same time, coding improvements, operator design, annealing operations, and parallel calculations are added to the algorithm. In order to verify the effectiveness of the algorithm, simulation experiments under complex task scenarios of different scales are carried out. Experimental results show that this method can effectively solve the problems of observing complex tasks. Meanwhile, the optimization effect and convergence speed of the HGPT is better than that of the related algorithms.

Key words: complex task, space-aeronautics cooperative, task planning framework, hybrid genetic parallel tabu (HGPT) algorithm

Jinming LIU, Yingguo CHEN, Rui WANG, Yingwu CHEN. Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction[J]. Journal of Systems Engineering and Electronics, 2023, 34(6): 1550-1564.

Figures/Tables 13

Table 1

Fig 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Fig 7

Table 2

Table 3

Fig 8

Fig 9

Table 4

References 32

1	SHI H R, LU F X, WANG H Y, et al Optimal observation configuration of UAVs based on angle and range measurements and cooperative target tracking in three-dimensional space. Journal of Systems Engineering and Electronics, 2020, 31 (5): 996- 1008. doi: 10.23919/JSEE.2020.000074
2	WANG Z N, LIN M, TANG X G, et al Multi-objective robust secure beamforming for cognitive satellite and UAV networks. Journal of Systems Engineering and Electronics, 2021, 32 (4): 789- 798. doi: 10.23919/JSEE.2021.000068
3	MORRIS R, DUNGAN J L, EDGINGTON W, et al. Coordinated science campaign scheduling for sensor webs. Proc. of the IEEE International Geoscience and Remote Sensing Symposium, 2005. DOI: 10.1109/IGARSS.2005.1526213.
4	MORRIS R, DUNGAN J L, BRESINA J L Challenges in coordinating remote sensing systems. Proc. of the AAAI Spring Symposium: Distributed Plan and Schedule Management, 2006, 65- 72.
5	HEROLD T, ABRAMSON M, BALAKRISHNAN H, et al. Asynchronous distributed optimization for the coordinated planning of air and space assets. Cambridge: Massachusetts Institute of Technology, 2010.
6	LIANG X X, CHEN C, LI J, et al Research on aerospace cooperative continuation observation strategy for maritime moving target. Proc. of the MATEC Web of Conferences, 2016, 81, 04001. doi: 10.1051/matecconf/20168104001
7	YU J Research on key technologies of cooperative task scheduling for airborne and spaceborne earth observing assets. Changsha: National University of Defense Technology, 2011.
8	LI J, ZHONG Z N, JING N, et al Space-air resources multi-phase cooperation task planning approach based on heterogeneous MAS model. Acta Aeronautica et Astronautica Sinica, 2013, 34 (7): 1682- 1697.
9	LI J, ZHONG Z N, HU W, et al A service model based on SWE for space-aeronautics cooperation earth observing operations. Journal of National University of Defense Technology, 2013, 35 (3): 108- 113.
10	LI X M, LIAO W K, WU G H, et al A two-stage iterative optimazation method for the coordinated task planning of space and air observation resources. Control and Decision, 2021, 36 (5): 1147- 1156.
11	WANG Y, SHENG M, ZHUANG W H, et al Multi-resource coordinate scheduling for earth observation in space information networks. IEEE Journal on Selected Areas in Communications, 2018, 36 (2): 268- 279. doi: 10.1109/JSAC.2018.2804045
12	HOU K W Research on air-space cooperative task planning for reconnaissance and surveillance. Changsha: National University of Defense Technology, 2009.
13	BAI G Q, XING L N, CHEN Y W Scheduling multi-platforms collaborative disasters monitoring based on coevolution algorithm. Research Journal of Chemistry and Environment, 2012, 16, 43- 50.
14	WU G H, PEDRYCZ W, LI H F, et al Coordinated planning of heterogeneous earth observation resources. IEEE Trans. on Systems Man and Cybernetics-Part A: Systems and Humans, 2015, 46 (1): 109- 125. doi: 10.1109/TSMC.2015.2431643
15	XIANG S, CHEN Y G, LI G L, et al Review on satellite autonomous and cooperative task scheduling planning. Acta Automatica Sinica, 2019, 45 (2): 252- 264.
16	JIA G W, WANG J F Research review of UAV swarm mission planning method. Systems Engineering and Electronics, 2021, 43 (1): 99- 111.
17	DU Y H, WANG T, XIN B, et al A data-driven parallel scheduling approach for multiple agile Earth observation satellites. IEEE Trans. on Evolutionary Computation, 2019, 24 (4): 679- 693.
18	WU G H, MA M H, ZHU J H, et al Multi-satellite observation integrated scheduling method oriented to emergency tasks and common tasks. Journal of Systems Engineering and Electronics, 2012, 23 (5): 723- 733. doi: 10.1109/JSEE.2012.00089
19	HE L, LIU X L, LAPORTE G, et al An improved adaptive large neighborhood search algorithm for multiple agile satellites scheduling. Computer & Operations Research, 2018, 100 (12): 12- 25.
20	WEN J, LIU X L, HE L Real-time online rescheduling for multiple agile satellites with emergent tasks. Journal of Systems Engineering and Electronics, 2021, 32 (6): 1407- 1420. doi: 10.23919/JSEE.2021.000120
21	CHEN H, YANG S, LI J, et al Exact and heuristic methods for observing task-oriented satellite cluster agent team formation. Mathematical Problems in Engineering, 2018, 2008, 2103625.
22	HE L, DE WEERDT M, YORKE-SMITH N Time/sequence-dependent scheduling: the design and evaluation of a general purpose tabu-based adaptive large neighbourhood search algorithm. Journal of Intelligent Manufacturing, 2019, 31, 1051- 1078.
23	JIN P, WANG C C, XIA W, et al Two-stage emergency mission planning with satellite instruction release. Systems Engineering and Electronics, 2019, 41 (4): 810- 818.
24	YAO J Y, JIN P, ZHU W M, et al Multi-satellite scheduling problem for regional targets with uneven income. Systems Engineering and Electronics, 2020, 42 (3): 638- 645.
25	ZHAO Z Y, LU G S Receding horizon control for cooperative search of multi-UAVs based on differential evolution. International Journal of Intelligent Computing and Cybernetics, 2012, 5 (1): 145- 158. doi: 10.1108/17563781211208260
26	EDISON E, SHIMA T Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms. Computers & Operations Research, 2011, 38 (1): 340- 356.
27	HU J Q, WU H S, ZHAN R J, et al Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior. Journal of Systems Engineering and Electronics, 2021, 32 (6): 1463- 1476. doi: 10.23919/JSEE.2021.000124
28	YUNUSOGLU P, TOPALOGLU YILDIZ S Constraint programming approach for multi-resource-constrained unrelated parallel machine scheduling problem with sequence-dependent setup times. International Journal of Production Research, 2021, 60 (7): 2212- 2229.
29	NATTAF M, DAUZERE-PERES S, YUGMA C, et al Parallel machine scheduling with time constraints on machine qualifications. Computers & Operations Research, 2019, 107 (7): 61- 76.
30	SHANHCARI O, LOGENDRAN R An enhanced tabu search algorithm to minimize a bi-criteria objective in batching and scheduling problems on unrelated-parallel machines with desired lower bounds on batch sizes. Computers & Operations Research, 2017, 77 (1): 154- 176.
31	SUN L Y, WANG Y K, HUANG W D, et al Inter-satellite communication and ranging link assignment for navigation satellite systems. GPS Solutions, 2018, 22 (2): 38. doi: 10.1007/s10291-018-0704-3
32	LUO C L, LI Y N, LIU Y, et al Task allocation in multi-agent system based on simulated annealing genetic algorithm. Application Research of Computers, 2012, 29 (6): 2114- 2116.

Task type	Definition
Stationary point target task	A point, a small observation area, or an observation of a building
Regional target task	Large continuous area
Cooperative target task	Coordinated observation of a target from multiple resources

Parameter	Satellite	UAV	Number of initial tasks	Number of meta-tasks
Observation rate/(km·h⁻¹）	3000	200	—	—
Observation radius/km	2	1	—	—
Platform dispatch cost	25	10	—	—
Observation angle/(°)	45	—	—	—
Scale $(\text{50}\times \text{50 k}{\text{m} }^{2})$	1	3	{20,10,20}	{20,63,40}
Scale $(\text{100}\times \text{100 k}{\text{m} }^{2})$	2	6	{40,20,40}	{40,130,80}
Scale $(150\times \text{150 k}{\text{m} }^{2})$	3	10	{60,30,60}	{60,191,120}

Scene scale/km²	Number of initial tasks	Number of Meta-tasks	Number of meta- tasks completed	Meta-task completion rate/%	Task completion rate/%	Algorithmic revenue value	Algorithmic revenue ratio/%
$\text{50}\times \text{50}$	50	123	116	94.3	96.0	457.00	94.5
$\text{100}\times \text{100}$	100	250	221	84.4	87.1	813.50	85.0
$150\times \text{150}$	150	371	297	80.5	83.3	1038.25	81.3

Algorithm	Number of completed meta-tasks			Time/min			Profit value			Standard deviation
Algorithm	50×50 km²	100×100 km²	150×150 km²	50×50 km²	100×100 km²	150×150 km²	50×50 km²	100×100 km²	150×150 km²	50×50 km²	100×100 km²	150×150 km²
HGPT	116	221	297	29.5	41.1	57.3	457.0	813.5	1038.3	0.9%	1.5%	1.9%
BHGPT	112	220	294	29.1	41.9	56.9	455.0	811.0	1034.5	1.1%	1.2%	1.5%
THGPT	109	205	278	29.2	40.7	56.7	446.0	794.0	1005.0	0.8%	1.4%	2.1%
HGT	115	219	298	125.4	167.6	227.9	455.0	811.0	1040.0	0.6%	0.9%	1.3%
GPT	91	157	223	30.5	40.3	58.6	409.5	672.0	801.5	2.3%	2.5%	1.7%
MNG	116	223	296	231.5	316.7	464.5	457.0	816.0	1044.0	0.4%	0.5%	0.7%

Complex task planning method of space-aeronautics cooperative observation based on multi-layer interaction

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

Share this article

Figures/Tables 13

References 32

Related Articles 0

Recommended Articles

Metrics

Comments