船舶远程驾驶人机主从博弈控制方法研究

doi:10.16097/j.cnki.1009-6744.2024.03.003

交通运输系统工程与信息 ›› 2024, Vol. 24 ›› Issue (3): 21-31.DOI: 10.16097/j.cnki.1009-6744.2024.03.003

船舶远程驾驶人机主从博弈控制方法研究

李晨^{a, b, c}，严新平^{a, c, d}，刘佳仑^{*a, c, d}，黄亚敏^{a, c, d}，李诗杰^{a, b}

武汉理工大学，a. 水路交通控制全国重点实验室；b. 交通与物流工程学院； c. 国家水运安全工程技术研究中心；d. 智能交通系统研究中心，武汉 430063

收稿日期:2024-02-06 修回日期:2024-03-05 接受日期:2024-03-12 出版日期:2024-06-25 发布日期:2024-06-23
作者简介:李晨(1998- )，男，山西忻州人，博士生
基金资助:
国家重点研发计划项目(2022YFB4301402)；国家自然科学基金重点国际(地区)合作项目(51920105014)；国家自然科学基金(52272425)

Stackelberg Game-based Control Method for Driver-automation Collaboration in Ship Remote-control

LI Chen^{a, b, c}, YAN Xinping^{a, c, d}, LIU Jialun^{*a, c, d}, HUANG Yamin^{a, c, d}, LI Shijie^{a, b}

a. State Key Laboratory of Maritime Technology and Safety; b. School of Transportation and Logistics Engineering; c. National Engineering Research Center for Water Transport Safety; d. Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan 430063, China

Received:2024-02-06 Revised:2024-03-05 Accepted:2024-03-12 Online:2024-06-25 Published:2024-06-23
Supported by:
National Key Research and Development Program of China (2022YFB4301402)；Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (51920105014)；National Natural Science Foundation of China (52272425)

摘要/Abstract

摘要： 针对船舶远程驾驶场景下的人机目标非一致性问题，本文设计了系统人主机辅的运行模式，提出一种主从博弈框架下的共享控制方法，将船舶协同避碰转向任务中的人机交互作用描述为完全信息条件下的非合作博弈关系，通过构造驾驶员和共驾控制器的状态空间推导主从博弈的 Stackelberg 微分对策，使用 Fan-GFlicksberg 不动点定理证明纳什均衡解的存在性与唯一性。根据驾驶风格和操纵技能预分配驾驶权重，基于模型预测控制方法设计轨迹跟踪器，采取反馈校正方式在有限时域内滚动优化，综合考虑船舶安全航行边界、碰撞风险和人机冲突程度在线进行调节。选取船舶横向偏移误差和驾驶员操作负荷等评价指标，在内河操纵环境下验证了方法的有效性。仿真结果表明：本文所提出的主从博弈控制方法能够为具备不同驾驶风格和操纵技能的作业人员提供个性化辅助，当驾驶员和共驾控制器发生意图冲突时，根据航行风险对预分配权重进行调节，在确保航行安全的前提下使船舶尽可能满足驾驶员的操纵意图。

关键词: 水路运输, 船舶远程驾驶, 人机协同, 共享转向控制, 模型预测, 控制权重调节

Abstract: To solve the problem of human-machine control objective non-consistency in the ship remote-control process, this paper proposes a shared steering control method within the Stackelberg framework and considers the human-dominated and machine-auxiliary operating mode of the system. The human-machine interaction in ship collision avoidance collaborative steering task is described as a non-cooperative game relationship under complete information conditions. By constructing the state space of the driver and the co-pilot controller, the differential strategy is derived for Stackelberg game, and the uniqueness and existence of Nash equilibrium solution is proved with FanGlicksberg fixed points theorem. Based on model predictive control method, the trajectory tracking controller is designed with pre-allocating driving weight for different driving style and maneuvering skills, rolling and optimization in a finite time domain through feedback correction. And the control authority will be adjusted online in combination of the safety navigation boundary, collision risk and degree of human-machine conflicts. Taking the lateral displacement and driver's operational load as evaluation indexes, the effectiveness of method is verified in inland maneuvering scenarios. Simulation results show that the proposed method could provide personalized assistance for remote operators with different driving styles and maneuvering skills, while there exists intention conflict between the driver and the co-pilot controller, it can adjust the pre-allocated weight in accordance with the navigating risk dynamically, so as to make the ship motion more compliant with driver's maneuvering intentions under the premise of ensuring navigating safety.

Key words: waterway transportation, ship remote-control, driver-automation collaboration, shared steering control; model predictive, control authority adjustment

中图分类号:

U675.9

李晨, 严新平, 刘佳仑, 黄亚敏, 李诗杰. 船舶远程驾驶人机主从博弈控制方法研究[J]. 交通运输系统工程与信息, 2024, 24(3): 21-31.

LI Chen, YAN Xinping, LIU Jialun, HUANG Yamin, LI Shijie. Stackelberg Game-based Control Method for Driver-automation Collaboration in Ship Remote-control[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 21-31.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/10.16097/j.cnki.1009-6744.2024.03.003

http://www.tseit.org.cn/CN/Y2024/V24/I3/21

参考文献

[1] 严新平, 李晨, 刘佳仑, 等. 新一代航运系统体系架构与关键技术研究[J]. 交通运输系统工程与信息, 2021, 21(5): 22-29, 76. [YAN X P, LI C, LIU J L. Architecture and key technologies for new generation of waterborne transportation system[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(5): 22-29, 76.]

[2] FENG L, WILTSCHE C, HUMPHREY L, et al. Synthesis of human-in-the-loop control protocols for autonomous systems[J]. IEEE Transactions on Automation Science and Engineering, 2016, 13(2): 450-462.

[3] ZHENG N, LIU Z, REN P, et al. Hybrid-augmented intelligence: Collaboration and cognition[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 153-179.

[4] DEMIR M, MCNEESE N J, GORMAN J C, et al. Exploration of teammate trust and interaction dynam ICS in human-autonomy teaming[J]. IEEE Transactions on Human-machine Systems, 2021, 51(6): 69 6-705.

[5] MONARCH R M. Human-in-the-loop machine learning: Active learning and annotation for human-centered AI [M]. New York: Simon and Schuster, 2021.

[6] YE P, WANG X, ZHENG W, et al. Parallel cognition: Hybrid intelligence for human-machine interaction and management[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(12): 1765-1779.

[7] 王玉虎, 刘伟. 一种基于人机融合的态势认知模型[J]. 指挥与控制学报, 2023, 9(1): 76-84. [WANG Y H, LIU W. A situation cognition model based on human-machine hybrid fusion[J]. Journal of Command and Control, 2023, 9(1): 76-84.]

[8] 张旋武, 谢磊, 初秀民, 等. 无人船路径跟随控制方法综述 [J]. 交通信息与安全, 2020, 38(1): 20-26. [ZHANG X W, XIE L, CHU X M, et al. An overview of path following control methods for unmanned surface vehicles[J]. Journal of Transport Information and Safety, 2020, 38(1): 20-26.]

[9] LAZCANO A M R, NIU T, AKUTAIN X C, et al. MPC-based haptic shared steering system: A driver modeling approach for symbiotic driving[J]. IEEE/ASME Transactions on Mechatronics, 2021, 26(3): 1201-1211.

[10] LI C, CHEN Y H, ZHAO H, et al. Stackelberg game theory-based optimization of high-order robust control for fuzzy dynamical systems[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2020, 52(2): 1254-1265.

[11] LIU J, GUO H, SHI W, et al. Driver-automation shared steering control considering driver neuromuscular delay characteristics based on stackelberg game[J]. Green Energy and Intelligent Transportation, 2022, 1(2): 100027.

[12] JIANG A X, LEYTON-BROWN K. A Tutorial on the proof of the existence of nash equilibria[R/OL]. University of British Columbia Technical Report TR- 2007-25, (2007-11-09) [2024-02-03]. https://www.cs. ubc.ca/tr/2007/tr-2007-25.

[13] LIU J, YU G. Fuzzy kakutani-fan-glicksberg fixed point theorem and existence of nash equilibria for fuzzy games [J]. Fuzzy Sets and Systems, 2022, 447: 100-112.

[14] NA X, COLE D. Theoretical and experimental investigation of driver noncooperative-game steering control behavior[J]. IEEE/CAA Journal of Automatica Sinica, 2020, 8(1): 189-205.

[15] 王建强, 吴剑, 李洋. 基于人-车-路协同的行车风险场概念、原理及建模[J]. 中国公路学报, 2016, 29(1): 105- 114. [WANG J Q, WU J, LI Y. Concept, principle and modeling of driving risk field based on driver-vehicle-road system[J]. China Journal of Highway and Transport, 2016, 29(1): 105-114.]

[16] JI J, KHAJEPOUR A, MELEK W W, et al. Path planning and tracking for vehicle collision avoidance based on model predictive control with multiconstraints[J]. IEEE Transactions on Vehicular Technology, 2016, 66(2): 952- 964.

[17] ZHEN R, SHI Z Q, LIU J L, et al. A novel arena-based regional collision risk assessment method of multi-ship encounter situation in complex waters[J]. Ocean Engineering, 2022, 246: 110531.

[18] BOINK R, VAN PAASSEN M M, MULDER M, et al. Understanding and reducing conflicts between driver and haptic shared control[C]//2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE, 2014: 1510-1515.

船舶远程驾驶人机主从博弈控制方法研究

Stackelberg Game-based Control Method for Driver-automation Collaboration in Ship Remote-control

PDF

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

[1]	刘美岐, 金楷然, 李雅澜, 郭戈. 城市道路智能网联车辆轨迹鲁棒控制方法[J]. 交通运输系统工程与信息, 2024, 24(4): 31-40.
[2]	郑建风, 肖懿灵, 赵志昊, 褚艳玲. 集装箱舱位分配与舱位租赁的感知价格研究[J]. 交通运输系统工程与信息, 2024, 24(3): 83-93.
[3]	刘涛, 尤海林. 基于人机协同决策的运输调度优化研究[J]. 交通运输系统工程与信息, 2024, 24(2): 136-148.
[4]	赵瑞嘉, 孙宇轩, 牛东翔, 谢新连. 混合蒸发气动力下液化天然气海运航线配船优化[J]. 交通运输系统工程与信息, 2024, 24(2): 304-312.
[5]	郑建风, 王鑫珏, 刘惠斌. 混合泊位分配与专用泊位租赁的联合优化研究[J]. 交通运输系统工程与信息, 2024, 24(1): 262-271.
[6]	计明军, 胡寒霖, 高振迪, 方婉薇. 考虑风浪影响下的船舶节能航线优化[J]. 交通运输系统工程与信息, 2023, 23(6): 274-283.
[7]	王直欢, 胡炜琴, 王逸文. 基于复杂网络的长三角和粤港澳航运网络韧性评估[J]. 交通运输系统工程与信息, 2023, 23(5): 184-193.
[8]	郑琰, 巴文婷, 肖玉杰. 考虑碳排放的长三角港口群动态效率测度[J]. 交通运输系统工程与信息, 2023, 23(4): 34-46.
[9]	林俊亭, 倪铭君. 基于扩张状态观测器的虚拟编组触发模型预测控制[J]. 交通运输系统工程与信息, 2023, 23(4): 134-146.
[10]	侯涛, 唐丽, 牛宏侠. 基于数据驱动的高速列车速度复合控制研究[J]. 交通运输系统工程与信息, 2023, 23(3): 145-152.
[11]	杨华龙, 王豪, 邢玉伟. 考虑退订费的远洋集装箱班轮舱位分配优化[J]. 交通运输系统工程与信息, 2023, 23(3): 235-242.
[12]	赵旭, 陈家钦, 黄瑞. 不确定条件下考虑横纵联盟策略的船期协调研究[J]. 交通运输系统工程与信息, 2023, 23(3): 243-252.
[13]	封学军, 王苏阳, 许博, 张艳, 黄建梁, 顾卫华. 原油接卸港区单线航道避让区选址与规模仿真研究[J]. 交通运输系统工程与信息, 2023, 23(3): 253-264.
[14]	高天航, 徐杏, 吴宏宇, 毕珊珊, 田佳. 考虑托运人行为决策的集装箱港口腹地划分模型[J]. 交通运输系统工程与信息, 2023, 23(1): 10-16.
[15]	李晓东, 匡海波, 何鸿宇. 政府环境规制下港航系统减排演化博弈研究[J]. 交通运输系统工程与信息, 2023, 23(1): 17-29.