Failure analysis of unmanned autonomous swarm considering cascading effects

doi:10.23919/JSEE.2022.000069

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 759-770.doi: 10.23919/JSEE.2022.000069

• • 上一篇

收稿日期:2020-12-04 出版日期:2022-06-18 发布日期:2022-06-24

Failure analysis of unmanned autonomous swarm considering cascading effects

Bei XU^1,²(), Guanghan BAI¹(), Yun’an ZHANG¹(), Yining FANG¹(), Junyong TAO^1,*()

¹ Laboratory of Science and Technology on Integrated Logistics Support, College of Intelligent Sciences and Technology, National University of Defense Technology, Changsha 410073, China
² School of General Aviation, Nanchang Hangkong University, Nanchang 330063, China

Received:2020-12-04 Online:2022-06-18 Published:2022-06-24
Contact: Junyong TAO E-mail:70611@nchu.edu.cn;baiguanghan@nudt.edu.cn;yazhang@nudt.edu.cn;fangyining@nchu.edu.cn;taojunyong@nudt.edu.cn
About author:|XU Bei was born in 1987. She is currently working toward her Ph.D. degree with National University of Defense Technology, Changsha, China. She is also currently a lecturer with the School of General Aviation, Nanchang Hangkong University, Jiangxi, China. Her research interests include network reliability and system resilience. E-mail: 70611@nchu.edu.cn||BAI Guanghan was born in 1986. He received his Ph.D. degree in mechanical engineering from University of Alberta, Edmonton, AB, Canada, in 2016. He is currently a lecturer with the Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology. His research interests include network reliability and system resilience. E-mail: baiguanghan@nudt.edu.cn||ZHANG Yun’an was born in 1983. He received his Ph.D. degree in mechanical engineering from National University of Defense Technology, Changsha, China, in 2014. He is currently an associate professor with the Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology. His research interest focuses on system reliability. E-mail: yazhang@nudt.edu.cn||FANG Yining was born in 1991. She received her Ph.D. degree in mechanical engineering from University of Alberta, Edmonton, AB, Canada, in 2019. She is currently a lecturer with the Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology. Her research interests include system resilience. E-mail: fangyining@nchu.edu.cn||TAO Junyong was born in 1969. He received his Ph.D. degree in mechanical engineering from National University of Defense Technology, Changsha, China, in 2000. He is currently a professor with the Laboratory of Science and Technology on Integrated Logistics Support, National University of Defense Technology. His research interests include reliability test and evaluation. E-mail: taojunyong@nudt.edu.cn
Supported by:
This work was supported by the Science and Technology on Reliability & Environmental Engineering Laboratory (6142004004-2), and the Science Technology Commission of the CMC (2019-JCJQ-JJ-180, ZZKY-YX-10-3).

摘要/Abstract

Abstract:

In this paper, we focus on the failure analysis of unmanned autonomous swarm (UAS) considering cascading effects. A framework of failure analysis for UAS is proposed. Guided by the framework, the failure analysis of UAS with crash fault agents is performed. Resilience is used to analyze the processes of cascading failure and self-repair of UAS. Through simu-lation studies, we reveal the pivotal relationship between resilience, the swarm size, and the percentage of failed agents. The simulation results show that the swarm size does not affect the cascading failure process but has much influence on the process of self-repair and the final performance of the swarm. The results also reveal a tipping point exists in the swarm. Meanwhile, we get a counter-intuitive result that larger-scale UAS loses more resilience in the case of a small percentage of failed individuals, suggesting that the increasing swarm size does not necessarily lead to high resilience. It is also found that the temporal degree failure strategy performs much more harmfully to the resilience of swarm systems than the random failure. Our work can provide new insights into the mechanisms of swarm collapse, help build more robust UAS, and develop more efficient failure or protection strategies.

Key words: unmanned autonomous swarm (UAS), failures analy-sis, cascading failure, resilience

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 759-770.

Bei XU, Guanghan BAI, Yun’an ZHANG, Yining FANG, Junyong TAO. Failure analysis of unmanned autonomous swarm considering cascading effects[J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 759-770.

图/表 15

参考文献 33

1	REYNOLDS C W Flocks, herds, and schools: a distributed behavioral model. Computer Graphics, 1987, 21 (4): 25- 34. doi: 10.1145/37402.37406
2	VICSEK T, CZIROK A, BEN-JACOB E, et al Novel type of phase transition in a system of self-driven particles. Physical Review Letters, 1995, 75 (6): 1226- 1229. doi: 10.1103/PhysRevLett.75.1226
3	COUZIN I D, KRAUSE J, JAMES R, et al Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology, 2002, 218 (1): 1- 11. doi: 10.1006/jtbi.2002.3065
4	COUZIN I D, KRAUSE J, FRANKS N R, et al Effective leadership and decision-making in animal groups on the move. Nature, 2005, 433 (7025): 513- 516. doi: 10.1038/nature03236
5	OLFATI-SABER R Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. on Automatic Control, 2006, 51 (3): 401- 420. doi: 10.1109/TAC.2005.864190
6	CUCKER F, SMALE S Emergent behavior in flocks. IEEE Trans. on Automatic Control, 2007, 52 (5): 852- 862. doi: 10.1109/TAC.2007.895842
7	CHAMANBAZ M, MATEO D, ZOSS B M, et al Swarmenabling technology for multi-robot systems. Frontiers in Robot and AI, 2017, 4 (12): 1- 12.
8	PARKER L. Reliability and fault tolerance in collective robot systems. Handbook of Collective Robotics. KERNBACH S, ed.. Stanford: Pan Stanford, 2013.
9	BAI G H, LI Y J, FANG Y N, et al Network approach for resilience evaluation of a UAV swarm by considering communication limits. Reliability Engineering & System Safety, 2020, 193, 106602.
10	CHENG C C, BAI G H, ZHANG Y A, et al Resilience evaluation for UAV swarm performing joint reconnaissance mission. Chaos, 2019, 29, 053132. doi: 10.1063/1.5086222
11	HOLLING C S Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 1973, 4 (1): 1- 23. doi: 10.1146/annurev.es.04.110173.000245
12	PLUMMER R, ARMITAGE D A resilience-based framework for evaluating adaptive co-management: linking ecology, economics and society in a complex world. Ecological Economics, 2007, 61 (1): 62- 74. doi: 10.1016/j.ecolecon.2006.09.025
13	MADNI A, JACKSON S Towards a conceptual framework for resilience engineering. IEEE Systems Journal, 2009, 3 (2): 181- 191. doi: 10.1109/JSYST.2009.2017397
14	HOSSEINI S, BARKER K, RAMIREZ-MARQUEZ J E A review of definitions and measures of system resilience. Reliability Engineering & System Safety, 2016, 145, 47- 61.
15	BRUNEAU M, CHANG S E, EGUCHI R T, et al A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 2003, 19 (4): 733- 752. doi: 10.1193/1.1623497
16	HOLLNAGEL E, WOODS D D, LEVESON N. Resilience engineering: concepts and precepts. London: CRC Press, 2006.
17	SAULNIER K, SALDANA D, PROROK A, et al Resilient flocking for mobile robot teams. IEEE Robotics & Automation Letters, 2017, 2 (2): 1039- 1046.
18	ADAMS T M, BEKKEM K R, TOLEDO-DURAN E J Freight resilience measures. Journal of Transportation Engineering, 2012, 138 (11): 1403- 1409. doi: 10.1061/(ASCE)TE.1943-5436.0000415
19	SAHEBJAMNIA N, TORABI S A, MANSOURI S A Integrated business continuity and disaster recovery planning: towards organizational resilience. European Journal of Operational Research, 2015, 242 (1): 261- 273. doi: 10.1016/j.ejor.2014.09.055
20	ZHANG L M, ZENG G W, LI D Q, et al Scale-free resilience of real traffic jams. Proc. of the National Academy of Sciences of the United States of America, 2019, 116 (18): 8673- 8678. doi: 10.1073/pnas.1814982116
21	EROL S, WILLIAM S. Swarm robotics. Santa Monica: Springer, 2004.
22	BRAMBILLA M, FERRANTE E, BIRATTARI M, et al Swarm intelligence swarm robotics: a review from the swarm engineering perspective. Swarm Intelligence, 2013, 7 (1): 1- 41. doi: 10.1007/s11721-012-0075-2
23	STAMATIS D H. Failure mode and effect analysis: FMEA from theory to execution. Milwaukee: Quality Press, 2003.
24	MOHAMMAD K Resilience and controllability of dynamic collective behaviors. PLOS, 2013, 8 (12): e82578.
25	KOMAREJI M, SHANG Y, BOUFFANAIS R Consensus in topologically interacting swarms under communication constraints and time-delays. Nonlinear Dynamics, 2018, 93 (3): 1287- 1300. doi: 10.1007/s11071-018-4259-1
26	ALBERT R, JEONG H, BARABASI A Error and attack tolerance of complex networks. Nature, 2000, 406 (6794): 378- 382. doi: 10.1038/35019019
27	FENG H F, LI C H, XU Y J Invulnerability analysis of vehicular ad hoc networks based on temporal networks. Proc. of the 2nd IEEE Invulnerability Analysis of Vehicular Ad Hoc Networks based on Temporal Networks, 2016, 2198- 2202.
28	SUR S, GANGULY N, MUKHERJEE A Attack tolerance of correlated time-varying social networks with well-defined communities. Physica A: Statistical Mechanics and Its Applications, 2015, 420, 98- 107. doi: 10.1016/j.physa.2014.08.074
29	ZHONG J L, ZHANG F M, LI Z X Identification of vital nodes in complex network via belief propagation and node reinsertion. IEEE Access, 2018, 6, 29200- 29210. doi: 10.1109/ACCESS.2018.2843532
30	WINFIELD A, NEMBRINI J Safety in numbers: fault-tolerance in robot swarms. International Journal of Modelling, Identification and Control, 2006, 1 (1): 30- 37. doi: 10.1504/IJMIC.2006.008645
31	LEBLANC H J, ZHANG H T, SUNDARAM S, et al Resilient continuous-time consensus in fractional robust networks. Proc. of the IEEE American Control Conference, 2013, 1237- 1242.
32	LIU K K, ZHONG J L, BAI G H, et al Complex networks approach for reliability evaluation of swarm systems under malicious attacks. IEEE Access, 2020, 8, 81209- 81219. doi: 10.1109/ACCESS.2020.2991211
33	SIMONS A Many wrongs: the advantage of group navigation. Trends in Ecology & Evolution, 2004, 19 (9): 453- 455.

Parameter	Symbol	Value
Speed/(m/s)	$ {v}_{0} $	1
Zone of interaction/m	r	6
Zone of repulsion/m	$ {r}_{0} $	1
The max turning angle/(rad/s)	$ {\vartheta }_{\max} $	2
Time step/s	$ \Delta t $	0.2
Number of individuals	$ N $	10, 50, 100, 200, 500
Failure proportion	$ P $	0—1

Failure analysis of unmanned autonomous swarm considering cascading effects

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 33

相关文章 0

编辑推荐

Metrics

本文评价