Research On Navigation Algorithm Of Mobile Water Quality Monitoring Platform And System Design And Implementation

The existing water environment monitoring methods mainly use manual sampling,fixed-point buoys.These ways not only consumes a lot of manpower and material resources,but also have problems such as small monitoring range,untimely feedback of monitoring results and low monitoring frequency.With the development of sensing,communication,unmanned systems and other technologies,integrating various types of water quality monitoring sensors into Unmanned Surface Vehicle(USV)can form a mobile water quality monitoring platform.This method has become a hot research direction in the field of water environment monitoring for intelligent monitoring of water environment.USV mainly includes navigation,guidance and control systems.As an important part of it,the autonomous navigation system can provide USV with information such as position,speed,angle,which is the basis of guidance and control.This paper focuses on the research and development of the autonomous navigation system in the following two aspects: First,this paper conduct theoretical research on the data fusion algorithm in the USV autonomous navigation system,and proposes a robust adaptive unscented Kalman filter algorithm.Then,based on the USV,this paper design and implement a complete mobile water quality monitoring platform(including an autonomous navigation system).The water quality monitoring platform completed the debugging and testing of the platform in the actual water environment.On the one hand,autonomous navigation system is an important part of USV.Data fusion algorithm is the key of USV autonomous navigation system.In the actual complex and uncertain water environment,the traditional Kalman filter algorithm has the problem that the prior noise distribution does not match the actual noise distribution.The problem reduces the accuracy of the autonomous navigation system.Therefore,this paper proposes a robust adaptive unscented Kalman filter algorithm.Firstly,considering the traditional unscented Kalman filter(UKF)algorithm,the online fault diagnosis mechanism is used to judge whether the current noise covariance matrix needs to be updated.The residual error-based method is used to correct it.Then,the prior noise covariance matrix and the corrected noise covariance matrix are weighted to obtain an updated noise covariance matrix.Finally,the updated noise covariance matrix is used to correct the previous state estimation values.Compared with traditional UKF algorithm and adaptive UKF algorithm based on window,simulation results show that the proposed method can provide more accurate position and heading angle estimation.On the other hand,according to the specific requirements of water quality monitoring,this paper designs and implements a complete mobile water quality monitoring platform(including autonomous navigation system)based on USV.Firstly,the overall scheme of mobile water quality monitoring platform is designed.In the hardware system of the platform,we designed and implemented the ship control and drive module,motion sensor module,wireless communication module,water quality acquisition and analysis module and power supply module.In the platform software system,we designed and implemented the system driver subroutine,system solution operator subroutine,navigation subroutine,position and speed control subroutine and communication subroutine.Then,we built a mobile water quality monitoring platform.online water quality detection can be carried out along the preset straight line or curve track in the actual water environment.During autonomous navigation,we implemented position and speed control to ensure movement stability and position accuracy requirements.It can be used for debugging and experiment of collecting water samples in current waters for subsequent high-precision off-line water quality analysis.The results show that the USV navigation system can provide accurate position,speed and angle information.The USV can meet the follow-up guidance and control requirements,which is helpful for the mobile water quality monitoring platform to detect water quality autonomously and intelligently.