无人水面艇自主导航技术
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摘要
作为无人水面艇的关键技术之一,自主导航技术决定着无人水面艇自主航行能力的高低。结合无人水面艇在实际航行和作业过程中自主导航所面临的问题,对无人水面艇组合导航及完好性监测技术、协同导航技术、路径规划技术和环境感知技术目前的现状、不足和发展趋势进行了总结和分析。为了更加精确、可靠地进行安全导航定位,即插即用全源导航是无人水面艇可采用的一种导航方式,并在此基础上开展了组合导航完好性监测工作。多目标规划综合最优及基于分层策略的自适应路径规划方法是解决动态时变环境下路径规划问题的有效途径。深度学习、数据挖掘、信息融合等人工智能技术将在提高环境感知信息的本质特征表述、充分挖掘多元异构数据间的有效信息,以及构建信息全面、立体、可靠的三维环境模型方面发挥主导性作用。
As one of the key technologies of unmanned surface vehicle(USV), autonomous navigation technology detennines the autonomous sailing capability of USV. Considering the problems faced by autonomous navigation in the actual sailing environment and operation process of USV, this paper summarizes and analyzes the current status, shortcomings and development trend of integrated navigation technology, integrity monitoring technology, collaborative navigation technology,path planning technology and environment perception technology of USV. Plug-and-play full-source navigation is a kind of navigation method that can be used by USV to get a safe navigation and positioning function which is more accurate and reliable. On this basis, the integrated navigation integrity monitoring work is carried out. The comprehensive optimal multi-objective programming and adaptive path planning method based on hierarchical strategy is an effective way to solve the path planning problem in dynamic time-varying environment. Deep learning, data mining, information fusion and other artificial intelligence technologies will play leading roles in improving the expression of the essential characteristics of environmentally-perceived information, fully exploiting effective information between diverse and heterogeneous data and constructing a comprehensive, stereoscopic, and reliable three-dimensional environment model.
As one of the key technologies of unmanned surface vehicle(USV), autonomous navigation technology detennines the autonomous sailing capability of USV. Considering the problems faced by autonomous navigation in the actual sailing environment and operation process of USV, this paper summarizes and analyzes the current status, shortcomings and development trend of integrated navigation technology, integrity monitoring technology, collaborative navigation technology,path planning technology and environment perception technology of USV. Plug-and-play full-source navigation is a kind of navigation method that can be used by USV to get a safe navigation and positioning function which is more accurate and reliable. On this basis, the integrated navigation integrity monitoring work is carried out. The comprehensive optimal multi-objective programming and adaptive path planning method based on hierarchical strategy is an effective way to solve the path planning problem in dynamic time-varying environment. Deep learning, data mining, information fusion and other artificial intelligence technologies will play leading roles in improving the expression of the essential characteristics of environmentally-perceived information, fully exploiting effective information between diverse and heterogeneous data and constructing a comprehensive, stereoscopic, and reliable three-dimensional environment model.
引文
[1] Wang Q Y, Cui X F, Li Y B, et al. Performance enhancement of a USV INS/CNS/DVL integration navigation system based on an adaptive information sharing factor federated filter[J]. Sensors, 2017, 17(2):239.
[2] Xia G Q, Wang G Q. INS/GNSS tightly-coupled integration using quaternion-based AUPF for USV[J]. Sensors, 2016, 16(8):1215.
[3]白淼淼.协同导航网络多传感器信息融合技术研究[D].哈尔滨工程大学,2014.BAI Miao-miao. Research on multi sensor information fusion technology in collaborative navigation network[D].Harbin Engineering University, 2014.
[4]汤世松.水面无人艇组合导航系统的研究与设计[D].东南大学,2015.TANG Shi-song. Research and design on integrated navigation system for the surface unmanned vehicles[D]. Southeast University, 2015.
[5]蒋德杰.基于惯性辅助卫星导航完好性监测研究[D].南京航空航天大学,2012.JIANG De-jie. Research on integrity monitoring of GNSS based on INS[D]. Nanjing University of Aeronautics and Astronautics, 2012.
[6]孔雪博.基于惯性多信息辅助的北斗接收机完好性关键技术研究[D].南京航空航天大学,2017.KONG Xue-bo. Research on the key technology of Beidou receiver's integrity based on inertial multi-information[D].Nanjing University of Aeronautics and Astronau-tics, 2017.
[7] Martin T. Integrated GPS/inertial navigation for aviation[C]. Symposium Gyro Technology:Inertial Sensors and Systems, 2011.
[8]刘超.无人艇主从协同编队定位技术研究[Dl.哈尔滨工程大学,2016.LIU Chao. Research on master-slave cooperative positiontechnology of USVs formation[D]. Harbin Engineering University, 2016.
[9]张玉奎.水面无人艇路径规划技术研究[Dl.哈尔滨工程大学,2008.ZHANG Yu-kui. Research on path planning technology of unmanned surface vehicle[D]. Harbin Engineering University, 2008.
[10]吴博,文元桥,吴贝,等.水面无人艇避碰方法回顾与展望[J].武汉理工大学学报(交通科学与工程版),2016, 40(3):456-461.WU Bo, WEN Yuan-qiao, WU Bei, et al. Review and expectation on collision avoidance method of unmanned surface vessel[J]. Journal of Wuhan University of Technology(Transportation Science&Engineering), 2016, 40(3):456-461.
[11] Hu C Q, Fu L, Yang Y Y. Cooperative navigation and control for surface-underwater autonomous marine vehicles[C]. Proceedings of IEEE 2~(nd)Information Technology,Networking, Electronic and Automatic Control Conference, 2017.
[12] Campbell S, Naeem W, Irwin G W. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres[J]. Annual Reviews in Control, 2012, 36(2):267-283.
[13] Casalino G, Turetta A, Simetti E. A three-layered architecture for real time path planning and obstacle avoidance for surveillance USVs operating in harbour fields[C].Oceans, 2009:1-8.
[14]杨怀.USV路径规划算法的研究[Dl.大连海事大学,2016.YANG Huai. Research on USV path planning algorithm[D]. Dalian Maritime University, 2016.
[15]唐平鹏.复杂海况下水面无人艇分层危险规避方法研究[D].哈尔滨工程大学,2014.TANG Ping-peng. Research on hierarchical obstacle avoidance for unmanned surface vehicles in complicated marine environments[D]. Harbin Engineering University, 2014.
[16] Bai Y M, Zhao Y S, Fan Y S. Fuzzy identification and control compensation of unmanned surface vehicle steering system[C]. Proceedings of the Joint International Mechanical, Electronic and Information Technology Conference, 2015.
[17]马忠丽,文杰,梁秀梅,等.无人艇视觉系统多类水面目标特征提取与识别[J].西安交通大学学报,2014, 48(8):60-66.MA Zhong-li, WEN Jie, LIANG Xiu-mei, et al.Extraction and recognition of features from multi-types of surface targets for visual systems in unmanned surface vehicle[J]. Journal of Xi'an Jiaotong University, 2014, 48(8):60-66.
[18]陆峰,徐友春,李永乐,等,基于信息融合的智能车障碍物检测方法[J].计算机应用,2017, 37(S2):.115-119.LU Feng, XU You-chun, LI Yong-le, et al. Obstacle detection method based on fusion information[J]. Journal of Computer Applications, 2017, 37(S2):115-119.
[2] Xia G Q, Wang G Q. INS/GNSS tightly-coupled integration using quaternion-based AUPF for USV[J]. Sensors, 2016, 16(8):1215.
[3]白淼淼.协同导航网络多传感器信息融合技术研究[D].哈尔滨工程大学,2014.BAI Miao-miao. Research on multi sensor information fusion technology in collaborative navigation network[D].Harbin Engineering University, 2014.
[4]汤世松.水面无人艇组合导航系统的研究与设计[D].东南大学,2015.TANG Shi-song. Research and design on integrated navigation system for the surface unmanned vehicles[D]. Southeast University, 2015.
[5]蒋德杰.基于惯性辅助卫星导航完好性监测研究[D].南京航空航天大学,2012.JIANG De-jie. Research on integrity monitoring of GNSS based on INS[D]. Nanjing University of Aeronautics and Astronautics, 2012.
[6]孔雪博.基于惯性多信息辅助的北斗接收机完好性关键技术研究[D].南京航空航天大学,2017.KONG Xue-bo. Research on the key technology of Beidou receiver's integrity based on inertial multi-information[D].Nanjing University of Aeronautics and Astronau-tics, 2017.
[7] Martin T. Integrated GPS/inertial navigation for aviation[C]. Symposium Gyro Technology:Inertial Sensors and Systems, 2011.
[8]刘超.无人艇主从协同编队定位技术研究[Dl.哈尔滨工程大学,2016.LIU Chao. Research on master-slave cooperative positiontechnology of USVs formation[D]. Harbin Engineering University, 2016.
[9]张玉奎.水面无人艇路径规划技术研究[Dl.哈尔滨工程大学,2008.ZHANG Yu-kui. Research on path planning technology of unmanned surface vehicle[D]. Harbin Engineering University, 2008.
[10]吴博,文元桥,吴贝,等.水面无人艇避碰方法回顾与展望[J].武汉理工大学学报(交通科学与工程版),2016, 40(3):456-461.WU Bo, WEN Yuan-qiao, WU Bei, et al. Review and expectation on collision avoidance method of unmanned surface vessel[J]. Journal of Wuhan University of Technology(Transportation Science&Engineering), 2016, 40(3):456-461.
[11] Hu C Q, Fu L, Yang Y Y. Cooperative navigation and control for surface-underwater autonomous marine vehicles[C]. Proceedings of IEEE 2~(nd)Information Technology,Networking, Electronic and Automatic Control Conference, 2017.
[12] Campbell S, Naeem W, Irwin G W. A review on improving the autonomy of unmanned surface vehicles through intelligent collision avoidance manoeuvres[J]. Annual Reviews in Control, 2012, 36(2):267-283.
[13] Casalino G, Turetta A, Simetti E. A three-layered architecture for real time path planning and obstacle avoidance for surveillance USVs operating in harbour fields[C].Oceans, 2009:1-8.
[14]杨怀.USV路径规划算法的研究[Dl.大连海事大学,2016.YANG Huai. Research on USV path planning algorithm[D]. Dalian Maritime University, 2016.
[15]唐平鹏.复杂海况下水面无人艇分层危险规避方法研究[D].哈尔滨工程大学,2014.TANG Ping-peng. Research on hierarchical obstacle avoidance for unmanned surface vehicles in complicated marine environments[D]. Harbin Engineering University, 2014.
[16] Bai Y M, Zhao Y S, Fan Y S. Fuzzy identification and control compensation of unmanned surface vehicle steering system[C]. Proceedings of the Joint International Mechanical, Electronic and Information Technology Conference, 2015.
[17]马忠丽,文杰,梁秀梅,等.无人艇视觉系统多类水面目标特征提取与识别[J].西安交通大学学报,2014, 48(8):60-66.MA Zhong-li, WEN Jie, LIANG Xiu-mei, et al.Extraction and recognition of features from multi-types of surface targets for visual systems in unmanned surface vehicle[J]. Journal of Xi'an Jiaotong University, 2014, 48(8):60-66.
[18]陆峰,徐友春,李永乐,等,基于信息融合的智能车障碍物检测方法[J].计算机应用,2017, 37(S2):.115-119.LU Feng, XU You-chun, LI Yong-le, et al. Obstacle detection method based on fusion information[J]. Journal of Computer Applications, 2017, 37(S2):115-119.
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