随着无人船系统复杂度的增加,对其进行故障预测和健康管理(prognostic and health management,PHM)的需求也随之提升。采用贝叶斯网络建立无人船的可靠性模型,并基于此开展无人船的PHM技术研究。主要探究贝叶斯网络模型在PHM方面的应用...随着无人船系统复杂度的增加,对其进行故障预测和健康管理(prognostic and health management,PHM)的需求也随之提升。采用贝叶斯网络建立无人船的可靠性模型,并基于此开展无人船的PHM技术研究。主要探究贝叶斯网络模型在PHM方面的应用,包括利用动态贝叶斯网络进行预测故障,通过参数学习自动生成贝叶斯网络模型。以实验室开发的智能无人船为具体研究对象,针对其动力系统常见的故障点监测模式进行研究,并设计相应的在线监测方案。通过实时监测完整地表达无人船的健康程度,并开发了无人船的健康管理模型。对开发设计的健康管理模型进行实时性和准确性双方面评价,所研发的健康管理技术可以准确地还原无人船的故障分布情况,并快速响应做出故障预测,可以充分评估无人船在不同状态下的实际工作能力。展开更多
In recent decades,path planning for unmanned surface vehicles(USVs)in complex environments,such as harbours and coastlines,has become an important concern.The existing algorithms for real-time path planning for USVs a...In recent decades,path planning for unmanned surface vehicles(USVs)in complex environments,such as harbours and coastlines,has become an important concern.The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles.In this study,we developed a novel path planning method based on the D^(*) lite algorithm for real-time path planning of USVs in complex environments.The proposed method has the following advantages:(1)the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles;(2)a constrained artificial potential field method is employed to enhance the safety of the planned paths;and(3)the method is practical in terms of vehicle performance.The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms.The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.展开更多
文摘随着无人船系统复杂度的增加,对其进行故障预测和健康管理(prognostic and health management,PHM)的需求也随之提升。采用贝叶斯网络建立无人船的可靠性模型,并基于此开展无人船的PHM技术研究。主要探究贝叶斯网络模型在PHM方面的应用,包括利用动态贝叶斯网络进行预测故障,通过参数学习自动生成贝叶斯网络模型。以实验室开发的智能无人船为具体研究对象,针对其动力系统常见的故障点监测模式进行研究,并设计相应的在线监测方案。通过实时监测完整地表达无人船的健康程度,并开发了无人船的健康管理模型。对开发设计的健康管理模型进行实时性和准确性双方面评价,所研发的健康管理技术可以准确地还原无人船的故障分布情况,并快速响应做出故障预测,可以充分评估无人船在不同状态下的实际工作能力。
基金financially supported by the Cultivation of Scientific Research Ability of Young Talents of Shanghai Jiao Tong University(Grant No.19X100040072)the Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education(Grant No.MIES-2020-07)。
文摘In recent decades,path planning for unmanned surface vehicles(USVs)in complex environments,such as harbours and coastlines,has become an important concern.The existing algorithms for real-time path planning for USVs are either too slow at replanning or unreliable in changing environments with multiple dynamic obstacles.In this study,we developed a novel path planning method based on the D^(*) lite algorithm for real-time path planning of USVs in complex environments.The proposed method has the following advantages:(1)the computational time for replanning is reduced significantly owing to the use of an incremental algorithm and a new method for modelling dynamic obstacles;(2)a constrained artificial potential field method is employed to enhance the safety of the planned paths;and(3)the method is practical in terms of vehicle performance.The performance of the proposed method was evaluated through simulations and compared with those of existing algorithms.The simulation results confirmed the efficiency of the method for real-time path planning of USVs in complex environments.
