任务决策是实现无人艇智能自主的关键环节，其存在着场景多样性、状态不确定性、约束动态性等问题。为此，本文提出动态多实体贝叶斯网络模型。首先设计了面向无人艇的语义推理框架，对无人艇本体信息进行知识表示。进一步，采用概率本体语言方法描述不确定性知识，同时扩展动态贝叶斯网络的结构，从而提高了无人艇任务决策在不确定性因素和时序因素影响下的推理能力。最后针对岛礁区域防守的任务场景，与多实体贝叶斯网络模型进行对比，结果表明DMEBN模型在动态条件下具有执行策略的连续性，验证了模型的可行性和有效性。
Mission decision-making is the key component to realize the intelligent autonomy of unmanned surface vehicles, which has the problems of scenario diversity, state uncertainty, and constraint dynamics. To solve the above limitations, the dynamic multi-entity Bayesian network (DMEBN) model is proposed. In order to perform knowledge representation of unmanned surface vehicle ontology information, a semantic reasoning framework for unmanned surface vehicles is designed. The probabilistic ontology language is used to describe uncertainty knowledge while extending the structure of dynamic Bayesian networks, thus enhancing the reasoning ability of unmanned surface vehicle decision-making under the influence of uncertainty factors and temporal sequence. The model is applied to the mission scenario of island area defense and compared with the model of multi-entity Bayesian network to show the continuity of the DMEBN model in executing strategies under dynamic conditions and to verify the feasibility and validity of the model.
2023,45(8): 78-83 收稿日期：2022-06-27
DOI：10.3404/j.issn.1672-7649.2023.08.016
分类号：TP181
基金项目：国家自然科学基金资助项目(61991412)
作者简介：刘力源(1998-),男,硕士研究生,研究方向为任务决策、知识推理
参考文献：
[1] WOO J, KIM N. Collision avoidance for an unmanned surface vehicle using deep reinforcement learning[J]. Ocean Engineering, 2020, 199: 107001
[2] GAO M, SHI G. Ship-collision avoidance decision-making learning of unmanned surface vehicles with automatic identification system data based on encoder—decoder automatic-response neural networks[J]. Journal of Marine Science and Engineering, 2020, 8(10): 754–771
[3] ZHANG H, HUANG C. Maneuver decision-making of deep learning for UCAV thorough azimuth angles[J]. IEEE Access, 2020, 8: 12976–12987
[4] CHEN C, SEFF A, KORNHAUSER A, et al. Deepdriving: learning affordance for direct perception in autonomous driving[C]// International Conference on Computer Vision (ICCV). Santiago, Chile, 2015: 2722-2730.
[5] LI G, YANG Y, ZHANG T, et al. Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios[J]. Transportation Research Part C:Emerging Technologies, 2021, 122: 102820
[6] CAPITAN J, MERINO L, OLLERO A. Cooperative decision-making under uncertainties for multi-target surveillance with multiples UAVs[J]. Journal of Intelligent & Robotic Systems, 2016, 84(1–4): 371–386
[7] LI X, BILBAO S, MARTÍN-WANTON T, et al. Swarms ontology: a common information model for the cooperation of underwater robots[J]. Sensors-Basel, 2017, 17(3): 569–589
[8] HUANG C, DONG K, HUANG H, et al. Autonomous air combat maneuver decision using Bayesian inference and moving horizon optimization[J]. Journal of systems engineering and electronics, 2018, 29(1): 86–97
[9] 任佳, 高晓光, 郑景嵩, 等. 复杂环境下的无人机任务决策模型[J]. 系统工程与电子技术, 2010, 32(1): 100–103
REN Jia, GUANG Xiao-guang, ZHENG Jing-song, et al. Mission decision-making for UAV under dynamic environment[J]. Systems Engineering and Electronics, 2010, 32(1): 100–103
[10] LASKEY K B. MEBN: a language for first-order Bayesian knowledge bases[J]. Artificial Intelligence, 2008, 172(2–3): 140–178
[11] 肖秦琨, 高嵩, 高晓光. 动态贝叶斯网络推理学习理论及应用[M]. 北京: 国防工业出版社, 2007: 28-45.
[12] LIU Z, ZHANG Y, YU X, et al. Unmanned surface vehicles: an overview of developments and challenges[J]. Annual Reviews in Control, 2016, 41: 71–93
[13] 朱英富. 水面舰船设计新技术[M]. 哈尔滨: 哈尔滨工程大学出版社, 2019: 9-14.
[14] DING Z, PENG Y. A probabilistic extension to ontology language owl[C]// 37th Annual Hawaii International Conference on System Sciences. Big Island, HI, USA, 2004: 1-10.
[15] YANG Y, CALMET J. Ontobayes: an ontology-driven uncertainty model[C]// International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06). Vienna, Austria, 2005: 457-463.
[16] GAO C, GUO Y, ZHONG M, et al. Reliability analysis based on dynamic Bayesian networks: a case study of an unmanned surface vessel[J]. Ocean Engineering, 2021, 240: 109970
