航空人工智能概念与应用发展综述

doi:10.7527/S1000-6893.2021.25150

综述

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

航空人工智能概念与应用发展综述

卢新来, 杜子亮, 许赟

航空工业发展研究中心, 北京 100029

收稿日期:2020-12-23 修回日期:2021-01-05 发布日期:2021-02-02
通讯作者: 杜子亮 E-mail:duziliang2012@126.com

Review on basic concept and applications for artificial intelligence in aviation

LU Xinlai, DU Ziliang, XU Yun

Aviation Industry Development Research Center, Beijing 100029, China

Received:2020-12-23 Revised:2021-01-05 Published:2021-02-02

摘要/Abstract

摘要： 针对航空人工智能发展的迫切需要，对人工智能定义、智能等级划分两个基本问题进行讨论，指出人工智能近期难以存在公认定义，需辩证看待人工智能航空应用的不确定性和确定性，对执行特定任务的智能系统进行智能等级划分因不符合当前主流认知规律而没有必要。从总体历史沿革、机载导弹、机载系统及可信等角度阐述航空人工智能应用的发展特点和态势，突出了可信航空人工智能研究作为行业应用前提性条件的重要性。

关键词: 人工智能, 智能等级, 自主等级, 机载导弹, 机载系统, 可信

Abstract: Recent years have witnessed rapid development of artificial intelligence application in aviation industry. In order to illustrate some basic but fundamental concepts, the definition of artificial intelligence and intelligence levels are discussed. Experts generally agree that it will not be a commonly accepted definition of AI in decades, and the industrial community should deal with the certainty and uncertainty of state of art artificial intelligence with dialectical perspectives. For intelligence systems that execute specific tasks in certain military context, classifying their intelligence level is unnecessary. From the perspective of general history of development, airborne missiles, airborne system and trustworthiness, the characteristics and trends of artificial intelligence adoption in aviation industry are illustrated. The importance of trustworthiness of artificial intelligence in aviation as a precondition for better adoption is emphasized.

Key words: artificial intelligence, intelligence level, autonomous level, airborne missiles, airborne system, trustworthiness

中图分类号:

V271

卢新来, 杜子亮, 许赟. 航空人工智能概念与应用发展综述[J]. 航空学报, 2021, 42(4): 525150-525150.

LU Xinlai, DU Ziliang, XU Yun. Review on basic concept and applications for artificial intelligence in aviation[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(4): 525150-525150.

参考文献

[1] TRUMP D J. Executive order on maintaining american leadership in artificial intelligence[EB/OL]. (2019-02-11)[2020-11-01].http://www.whitehouse.gov/presidential-actions/excutieve-order-maintaining-american-lead-ership-artificial-intelligence.
[2] US Department of Defense. Summary of the 2018 department of defense artificial intelligence strategy[R]. Washington, D.C.:Department of Defense, 2019.
[3] US Department of Defense. 2019 The United States Air Force artificial intelligence annex to the Department of Defense artificial intelligence strategy[R]. Washington,D.C.:Department of The Air Force, 2019.
[4] Artificial intelligence and national security (updated November 10, 2020)[R]. Washington, D.C.:Congressional Research Service, 2020.
[5] SCHARRE P, RⅡKONEN A. Defense technology strategy[R]. Washington D. C.:Center for a New American Security, 2020.
[6] MCCARTHY J, MINSKY M L, ROCHESTER N, et al. A proposal for the dartmouth summer research project on artificial intelligence[J]. AI Magazine, 2006, 27(4):1-2.
[7] RUSSELL S J, NORVIG P. 人工智能:一种现代方法(第3版)[M]. 北京:清华大学出版社, 2013:3-18. RUSSELL S J, NORVIG P. Artificial intelligence a modern approach, third edition[M].Beijing:Tsinghua University Press, 2013:3-18(in Chinese).
[8] 李德毅, 于剑. 人工智能导论[M]. 北京:中国科学技术出版社, 2018:2-8. LI D Y, YU J. Introduction to artificial intelligence[M].Beijing:China Science & Technology Press, 2018:2-8(in Chinese).
[9] 陈小平. 人工智能的历史进步、目标定位和思维演化[J]. 开放时代, 2018,6:31-48. CHEN X P. Artificial intelligence:Its historical progress,reconsideration of its final target,and evolution of its thinking mode[J]. Open Times, 2018,6:31-48(in Chinese).
[10] National defense authorization act for fiscal year 2019:116-333[R]. Washington, D.C.:House of Representatives, 2018.
[11] 雷宏杰,姚呈康. 面向军事应用的航空人工智能技术架构研究[J]. 导航定位与授时, 2020,7(1):1-11. LEI H J, YAO C K. Technical architecture of aviation artificial intelligence for military application[J]. Navigation Positioning & Timing, 2020,7(1):1-11(in Chinese).
[12] 黄铁军,余肇飞,刘怡俊. 类脑机的思想与体系结构综述[J]. 计算机研究与发展, 2019, 56(6):1135-1148. HUANG T J, YU Z F, LIU Y J. Brain-like machine:Thought and architecture[J]. Journal of Computer Research and Development, 2019, 56(6):1135-1148(in Chinese).
[13] NILSON N J. Artificial intelligence[R]. Information Processing, 1974:778-801.
[14] 陈小平. 封闭性场景:人工智能的产业化路径[J]. 文化纵横, 2020(2):34-42. CHEN X P. Closed scenario:Industrialization path of AI[J]. Beijing Cultural Review, 2020(2):34-42(in Chinese).
[15] HINTZ E A. Understanding the four types of AI, from reactive robots to self-aware beings[EB/OL]. (2016-11-18)[2020-11-01]. https://observer.com/2016/11/understanding-the-four-types-of-ai-from-reactive-robots-to-self-aware-beings/.
[16] STEPHAN D S, MATTHIJS M, TIM S. Artificial intelligence and the future of defense[M]. Hague:The Hague Centre for Strategic Studies, 2017.
[17] 中国电子技术标准化研究院. 人工智能标准化白皮书(2018版)[EB/OL]. (2018-01-24)[2020-11-01].http://www.cesi.cn/images/editor/20180124/20180124135528742.pdf. China Electronics Standardization Institute. Artificial intelligence white paper (2018)[EB/OL]. (2018-01-24)[2020-11-01]. http://www.cesi.cn/images/editor/20180124/20180124135528742.pdf (in Chinese).
[18] National Highway Traffic Safety Administration. Preliminary statement of policy concerning automated vehicles[EB/OL]. (2013-05-30)[2020-11-01]. https://www.nhtsa.gov/staticfiles/rulemaking/pdf/Automated_Vehicles_Policy.pdf.
[19] SAE International. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles[EB/OL]. (2016-10-02)[2020-11-01].https://pdfs.semanticscholar.org/5962/a3287865a8453ddc7832340df322ea0f0bd0.pdf?_ga=2.33077626.551604543.1608343293-699443072.1608191097.
[20] ILACHINSKI A. AI, robots, and swarms (issues, questions, and recommended studies)[R]. Center for Naval Analyses, 2017.
[21] SHERIDAN T B. Automation, authority and angst revisitedin human factors society[M]. Washington,D.C.:Human Factors &Ergonomics Society Press, 1991.
[22] 刘树光,茹乐,王柯. 无人机自主性评价方法新进展[J].飞航导弹, 2019(2):43-49. LIU S G, RU L, WANG K. New development of evaluation methods for UAV autonomous level[J]. Aerodynamic Missile Journal, 2019(2):43-49(in Chinese).
[23] PARASURAMAN R, SHERIDAN T B, WICKENS C D. A model for types and levels of human interaction with automation[J]. IEEE Transactions on Systems Man and Cybernetics Part A, 2000, 30(3):286-297.
[24] Office of the Secretary of Defense.Unmanned aerial vehicles roadmap 2000-2025[R]. Washington, D. C.:Office of the Secretary of Defense, 2001.
[25] CLOUGH B T. Metrics, schmetrics! How do you track a UAV's autonomy?[C]//AIAA's 1 st Technical Conference and Workshop on Unmanned Aerospace Vehicles.Reston:AIAA, 2002.
[26] PROUD R W, HART J J, MROZINSKI R B. Methods for determining the level of autonomy to design into a human spaceflight vehicle:a function specific approach[EB/OL]. (2015-03-16)[2020-11-01]. https://www.researchgate.net/publication/235167645_Methods_for_Determining_the_Level_of_Autonomy_to_Design_into_a_Human_Spaceflight_Vehicle_A_Function_Specific_Approach.
[27] Office of the Secretary of Defense.Unmanned aerial vehicles roadmap:2002-2027[R]. Washington, D.C.:Office of the Secretary of Defense, 2002.
[28] Office of the Secretary of Defense.Unmanned aircraft system roadmap:2005-2030[R]. Washington, D.C.:Office of the Secretary of Defense, 2005.
[29] HUANG H M. Autonomy levels for unmanned systems (ALFUS) framework volume I:Terminology (Version 1.1)[R]. Gaithersburg:NIST Special Publication, 2004.
[30] HUANG H M, ALBUS J S, MESSINA E R, et al. Specifying autonomy levels for unmanned systems:Interim report[C]//SPIE Defense and Security Symposium, 2004.
[31] Unmanned systems integrated roadmap FY2011-2036[R]. Washington, D.C.:Department of Defense, 2011.
[32] The role of autonomy in DoD systems[R]. Washington, D.C.:Department of Defense, Defense Science Board, 2012.
[33] United States Air Force Office of the Chief Scientist Authors.Autonomous horizons system autonomy in the air force-a path to the future (volume I:Human-autonomy teaming)[R]. Washington, D.C.:United States Air Force, Office of the Chief Scientist, 2015.
[34] ZACHARIAS G L. Autonomous horizons:The way forward[R]. Washington, D.C.:United States Air Force, Office of the Chief Scientist, 2019.
[35] RUTH D, PAUL N. Summer study on autonomy[R]. Washington, D. C.:Defense Science Board, 2016.
[36] Congressional Research Service.Artificial intelligence and national security (updated April 26, 2018)[R]. Washington, D.C.:Congressional Research Service, 2018.
[37] Congressional Research Service.Artificial intelligence and national security (updated January 30, 2019)[R]. Washington, D.C.:Congressional Research Service, 2019.
[38] MCDERMOTT J. R1:A rule-based configurer of computer systems[J]. Artificial Intelligence, 1982, 19(1):39-88.
[39] SHELA B, CARL S. Lizza pilot's associate "a cooperative, knowledge-based system application"[R]. Washington, D.C.:Wright-Patterson Air Force Base, 1991.
[40] MILLER C A, HANNEN M D. The rotorcraft pilot's associate:Design and evaluation of an intelligent user interface for cockpit information management[J]. Knowledge-Based Systems, 1999(12):443-456.
[41] JONES R M, LAIRD J E, NIELSEN P E, et al. Automated intelligent pilots for combat flight simulation[C]//AAAI'98/IAAI'98:Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, 1999.
[42] ERNEST N, CARROLL D, SCHUMACHER C, et al. Genetic fuzzy based artificial intelligence for unmanned combat aerial vehicle control in simulated air combat missions[J]. Journal of Defense Management, 2016, 6(1):1-7.
[43] SCHRITTWIESER J, ANTONOGLOU I, HUBERT T, et al. Mastering atari, go, chess and shogi by planning with a learned model[EB/OL]. (2019-11-19)[2020-12-15]. https://arxiv.org/pdf/1911.08265v2.pdf.
[44] 樊会涛, 闫俊. 自主化——机载导弹重要的发展方向[J]. 航空兵器, 2019, 26(1):1-10. FAN H T, YAN J. The important development direction of airborne missile:Autonomization[J]. Aero Weaponry, 2019, 26(1):1-10(in Chinese).
[45] 刘代军, 王超磊. 空空导弹智能化技术的发展与展望[J]. 航空兵器, 2019, 26(1):25-29. LIU D J, WANG C L. Development and prospect of air-to-air missile intelligentization[J]. Aero Weaponry, 2019, 26(1):25-29(in Chinese).
[46] 程进, 齐航, 袁健全,等. 关于导弹武器智能化发展的思考[J]. 航空兵器, 2019, 26(1):20-24. CHENG J, QI H, YUAN J Q, et al. Discussion on the development of intelligent missile technology[J]. Aero Weaponry, 2019, 26(1):20-24(in Chinese).
[47] 孙毓凯, 孙斐, 任宏光,等. 直升机载空空导弹关键技术研究[J]. 航空兵器, 2020, 27(1):17-25. SUN Y K, SUN F, REN H G, et al. Study on key technologies of helicopter-borne air-to-air missiles[J]. Aero Weaponry, 2020, 27(1):17-25(in Chinese).
[48] 陈伟, 孙洪忠, 齐恩勇,等. 智能化时代雷达导引头信号处理关键技术展望[J]. 航空兵器, 2019, 26(1):76-82. CHEN W, SUN H Z, QI E Y, et al. Key technology prospects of radar seeker signal processing in intelligent age[J]. Aero Weaponry, 2019, 26(1):76-82(in Chinese).
[49] 郭玉霞, 刘功斌, 崔炳喆,等. 空空导弹雷达导引头信息处理智能化思考[J]. 航空兵器, 2020, 27(5):23-27. GUO Y X, LIU G B, CUI B Z, et al. Intelligentization of the radar guiding technology of air-to-air missile[J]. Aero Weaponry, 2020, 27(5):23-27(in Chinese).
[50] GAUDET B, FURFARO R. Missile homing-phase guidance law design using reinforcement learning[C]//Proceedings of the AIAA Guidance, Navigation, and Control Conference.Reston:AIAA, 2012.
[51] GAUDET B, LINARES R. Adaptive guidance with reinforcement meta-learning[EB/OL].(2019-01-12)[2020-11-01]. https://arxiv.org/pdf/1901.04473.pdf.
[52] GAUDET B, FURFARO R. Reinforcement meta-learning for angle-only intercept guidance of maneuvering targets[C]//AIAA SciTech Forum.Reston:AIAA,2020.
[53] HONG D, KIM M, PARK S. Study on reinforcement learning-based missile guidance law[J]. Applied Science, 2020, 10:6567.
[54] LEI S, LEI Y L, ZHU Z. Research on missile intelligent penetration based on deep reinforcement learning[C]//3rd International Symposium on Big Data and Applied Statistics, 2020.
[55] 雷永林, 姚剑, 朱宁,等. 武器装备作战效能仿真系统WESS[J]. 系统仿真学报, 2017, 29(6):1244-1252. LEI Y L, YAO J, ZHU N, et al. Weapon effectiveness simulation system (WESS)[J]. Journal of System Simulation. 2017, 29(6):1244-1252(in Chinese).
[56] YAN X H, ZHU J H, KUANG M C, et al. Missile aerodynamic design using reinforcement learning and transfer learning[J]. Science China Information Sciences, 2018, 61(11):119204:1-119204:3.
[57] 王晓海. 认知雷达系统技术发展综述[J]. 数字通信世界, 2018(S1):40-43. WANG X H. Overview of cognitive radar system[J]. Digital Communication World, 2018(S1):40-43(in Chinese).
[58] HAYKIN S. Cognitive radar——a way of the future[J]. IEEE Signal Processing Magazine, 2006, 23(1):30-40.
[59] 金林. 智能化认知雷达综述[J]. 现代雷达, 2013, 35(11):6-11. JIN L. Overview of cognitive radar with intelligence[J]. Modern Radar, 2013, 35(11):6-11(in Chinese).
[60] HINTON G E, OSINDERO S, THE Y. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7):1527-1554.
[61] KRIZHEVSKY A, SUTSKEVER H, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Advances in Neural Information Processing Systems, 2012, 25(2):1097-1105.
[62] GURBUZ S Z, GRIFFITHS H D, CHARLISH A, et al. An overview of cognitive radar:past, present, and future[J]. IEEE Aerospace and Electronic Systems Magazine, 2019, 34(12):6-18.
[63] 何积丰. 安全可信人工智能[J]. 信息安全与通信保密, 2019(10):5-8. HE J F. Secure and trusted artificial intelligence[J]. Information Security and Communications Privacy, 2019(10):5-8(in Chinese).
[64] 崔鹏, 邓柯, 王国豫,等. 安全可信智能的可能技术路径[J]. 中国计算机学会通讯, 2020,16(11):23-28. CUI P, DENG K, WANG G Y, et al. The possible roadmaps to safe and trustworthy AI[J]. Communications of the CCF, 2020,16(11):23-28(in Chinese).
[65] EASA.Artificial intelligence roadmap——a human-centric approach to AI in aviation[R]. Cologne:European Union Aviation Safety Agency,2020.
[66] NIKOS V.Ethics guidelines for trustworthy AI[R]. European Commission High-level Expert Group on Artificial Intelligence,2019.
[67] EASA.Concepts of design assurance for neural networks(CoDANN)[R]. Cologne:European Union Aviation Safety Agency,2020.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

航空人工智能概念与应用发展综述

Review on basic concept and applications for artificial intelligence in aviation

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	陈勇, 钟科林, 罗悦, 王淼. 支线客机关键技术与发展方向[J]. 航空学报, 2023, 44(2): 26697-026697.
[2]	惠俊鹏, 汪韧, 俞启东. 基于强化学习的再入飞行器“新质”走廊在线生成技术[J]. 航空学报, 2022, 43(9): 325960-325960.
[3]	王皓, 陈根良. 机器人型装备在航空装配中的应用现状与研究展望[J]. 航空学报, 2022, 43(5): 626128-626128.
[4]	锁斌, 闫英. 基于面积度量的加速退化试验可信性评价方法[J]. 航空学报, 2022, 43(3): 325207-325207.
[5]	胡庆雷, 邵小东, 杨昊旸, 段超. 航天器多约束姿态规划与控制:进展与展望[J]. 航空学报, 2022, 43(10): 527351-527351.
[6]	蔡国飙, 张百一, 贺碧蛟, 翁惠焱, 刘立辉. 真空羽流智能化计算[J]. 航空学报, 2022, 43(10): 527352-527352.
[7]	孙智孝, 杨晟琦, 朴海音, 白成超, 葛俊. 未来智能空战发展综述[J]. 航空学报, 2021, 42(8): 525799-525799.
[8]	石健, 王少萍, 罗雪松. 基于不确定传感器状态的机载系统多层故障诊断方法[J]. 航空学报, 2021, 42(6): 624376-624376.
[9]	张伟伟, 寇家庆, 刘溢浪. 智能赋能流体力学展望[J]. 航空学报, 2021, 42(4): 524689-524689.
[10]	丁国如, 孙佳琛, 王海超, 焦雨涛. 复杂电磁环境下频谱智能管控技术探讨[J]. 航空学报, 2021, 42(4): 524750-524750.
[11]	李霓, 布树辉, 尚柏林, 李永波, 汤志荔, 张伟伟. 飞行器智能设计愿景与关键问题[J]. 航空学报, 2021, 42(4): 524752-524752.
[12]	陈志杰, 汤锦辉, 王冲, 程季锃, 曹珊, 邵欣. 人工智能赋能空域系统,提升空域分层治理能力[J]. 航空学报, 2021, 42(4): 525018-525018.
[13]	祝学军, 赵长见, 梁卓, 谭清科. OODA智能赋能技术发展思考[J]. 航空学报, 2021, 42(4): 524332-524332.
[14]	袁利, 王淑一. 航天器控制系统智能健康管理技术发展综述[J]. 航空学报, 2021, 42(4): 525044-525044.
[15]	杨志刚, 张炯, 李博, 曾锐, 毛研勋. 民用飞机智能飞行技术综述[J]. 航空学报, 2021, 42(4): 525198-525198.