车辆姿态多传感器检测系统与信息融合算法研究
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摘要
田间农业机器人是国际上提高农业自动化水平的有效手段之一,它可以满足未来农业生产中的高产、安全、高效率、环保、农艺高精度的要求。田间车辆的自动导航技术是一个首先需要解决的基础问题,本文研究的主要内容是基于GPS/DR导航原理和3G通讯的导航系统开发,同时对导航过程中的多传感器数据融合算法中的关键问题,如作业车辆的状态估计、作业车辆的路线跟踪、作业车辆的导航信息事后评估等问题进行了讨论,具体内容如下：
①利用GPS导航与航位推算法相结合,实现了低成本田间作业车辆导航定位系统的开发,所使用到的传感器包括GPS模块,电子罗盘、光纤陀螺仪等惯性传感器,利用Labview这一图形化编程工具,进行了上位机程序编写,程序主要完成系统的串口配置,车辆姿态信息提取等功能,并利用Labview与Matlab(?)昆合编程的功能,实现了对多传感器采集到的数据在线分析的功能。导航系统所使用的GPS信号为差分GPS信号,差分信号数据选择3G网络作为通信链路,传递给移动站,用来实现对移动站采集到的GPS数据进行修正。
②在MATLAB中的Simulink环境下,实现了田间作业机械的动力学模型开发,其中包括：前轮侧向力计算单元,后轮侧向力计算单元、稳态转向半径计算单元、横摆角稳态增益响应单元、车辆不足转向参数计算单元等六个单元,对相应单元输入功能参数以及作业车辆的机械参数,可以方便的计算车辆的横摆角以及车辆的质心运行轨迹。
③多传感器数据融合方面,采用基于迭代的方法来得到在最小均方误差意义下系统状态的最优估计的Kalman滤波方法,实现作业车辆行驶姿态的最优估计,同时在GPS/DR导航系统中,由于引入了惯性传感器,系统的状态方程为非线性方程,因此利用扩展Kalman滤波,先对状态方程进行线性化处理之后,再进行滤波操作,试验结果证明可行。
④GPS/DR导航属于多传感器共同完成测量任务的情况,如果采用集中式滤波的方法,就会造成子系统的故障会影响全局的情况.本课题采用了分布式滤波的方式,设计联邦Kalman滤波器,提高了系统的容错性,同时又解决了集中式Kalman滤波的计算量大的缺陷。
⑤导航过程中,一旦GPS子系统失效,系统将迅速切换成DR导航系统继续完成测量任务,而DR系统存在着误差累计的缺点。针对这一问题,结合测量结果,利用最有平滑理论,对采集到的数据进行最优平滑,取得了很好的效果,并进一步验证了,平滑算法在车辆导航中有很好的表现,是一种行之有效的事后处理方法。
⑥车辆自主驾驶过程中,为了避免车辆行走过程中挂伤作物,同时也为了避免作业过程中产生衔接行的遗漏,减少作业能耗、降低成本,经常会按照预定路线行走。在按照预定路线行走时,针对融合信息的不确定性,构建了自动导航系统的模糊控制器,可以对作业车辆进行直线跟踪和曲线跟踪。同时考虑到在直线行走跟踪和曲线行走跟踪时,横向位置偏差和航向偏差对系统输出的影响不同,设计了参数自调整的模糊控制器,试验结果证明,较一般的模糊控制器而言,具有超调量小、反应灵敏的特点。
Agricultural robot is one of the most effective methods to increase agricultural automation level in the world. It can meet the future agriculture of good harvest, safety, high efficiency, environmental protection, agronomic's high precision requirements. Automatic vehicle navigation technology is the basic problem. A navigation system is built based on GPS/DR and 3G technology. Key problems of sensor data fusion in navigation process, such as state estimation of agricultural vehicles, route tracking of agricultural vehicles, later evaluation of navigation information, was discussed as below:
①Using global positioning system technology, combined with DR technology, a navigation system of low cost equipped on agricultural vehicles was developed. The sensors used in the system are GPS, Inertial navigation sensors such as electronic compass, fiber optic gyro. The software was developed with Labview. The software is used to configure serial port, extract vehicle attitude information and so on. Differential GPS Signal was used in the system to observe vehicle attitude, which was transmitted through the 3G network. The DGPS signal is used to correct the GPS data the mobile station collected.
②Using the Simulink in the MATLAB, the dynamic model of field work machinery was developed. It includes:the front wheel side force calculation unit, the back wheel side force calculation unit, steady turning radius calculation unit, yaw angles gain steady-state response unit, Insufficient steering parameters calculation unit and so on. When function parameters and structure parameters is given to the calculation model, vehicle's yaw angles and centroid running trajectory can be calculated quickly.
③Refers to multi sensor data fusion, using the Kalman filtering method based on iteration which has the minimum mean square error(MMSE), The attitude's optimal estimation of the agricultural vehicles can be obtained. Considering the state equation of the system is nonlinear equation, extended kalman filtering (EKF) method is used to solve this defect. The test result shows that the method is feasible.
④The navigation system's work depends on different sensors, when using centralized filter method to deal with the signals sensors get, the damage of the subsystem can cause paralysis of the whole system. Distributed filter method is used here. A federal kalman filtering system is designed here, using this method, the system's fault tolerance is improved. At the same time, the amount of calculation of the system is reduced.
⑤In the process of navigation of the vehicles, when GPS fails to get the signal, inertial navigation can be used instead of GPS navigation system. Inertial navigation system's error will accumulate over time. We use the optimal smooth theory to smooth the data get from sensors, the test results shows that this method is useful.
⑥In the case of autonomous vehicles, special route is given to the vehicle to avoid scratching crops, reduce energy consumption, reduce cost of production A fuzzy controller is designed for linear tracking and curve tracking. To deduce system overshoot, the fuzzy controller's parameters can be adjusted. The test results shows that using this method, the system overshoot is deduced.
①利用GPS导航与航位推算法相结合,实现了低成本田间作业车辆导航定位系统的开发,所使用到的传感器包括GPS模块,电子罗盘、光纤陀螺仪等惯性传感器,利用Labview这一图形化编程工具,进行了上位机程序编写,程序主要完成系统的串口配置,车辆姿态信息提取等功能,并利用Labview与Matlab(?)昆合编程的功能,实现了对多传感器采集到的数据在线分析的功能。导航系统所使用的GPS信号为差分GPS信号,差分信号数据选择3G网络作为通信链路,传递给移动站,用来实现对移动站采集到的GPS数据进行修正。
②在MATLAB中的Simulink环境下,实现了田间作业机械的动力学模型开发,其中包括：前轮侧向力计算单元,后轮侧向力计算单元、稳态转向半径计算单元、横摆角稳态增益响应单元、车辆不足转向参数计算单元等六个单元,对相应单元输入功能参数以及作业车辆的机械参数,可以方便的计算车辆的横摆角以及车辆的质心运行轨迹。
③多传感器数据融合方面,采用基于迭代的方法来得到在最小均方误差意义下系统状态的最优估计的Kalman滤波方法,实现作业车辆行驶姿态的最优估计,同时在GPS/DR导航系统中,由于引入了惯性传感器,系统的状态方程为非线性方程,因此利用扩展Kalman滤波,先对状态方程进行线性化处理之后,再进行滤波操作,试验结果证明可行。
④GPS/DR导航属于多传感器共同完成测量任务的情况,如果采用集中式滤波的方法,就会造成子系统的故障会影响全局的情况.本课题采用了分布式滤波的方式,设计联邦Kalman滤波器,提高了系统的容错性,同时又解决了集中式Kalman滤波的计算量大的缺陷。
⑤导航过程中,一旦GPS子系统失效,系统将迅速切换成DR导航系统继续完成测量任务,而DR系统存在着误差累计的缺点。针对这一问题,结合测量结果,利用最有平滑理论,对采集到的数据进行最优平滑,取得了很好的效果,并进一步验证了,平滑算法在车辆导航中有很好的表现,是一种行之有效的事后处理方法。
⑥车辆自主驾驶过程中,为了避免车辆行走过程中挂伤作物,同时也为了避免作业过程中产生衔接行的遗漏,减少作业能耗、降低成本,经常会按照预定路线行走。在按照预定路线行走时,针对融合信息的不确定性,构建了自动导航系统的模糊控制器,可以对作业车辆进行直线跟踪和曲线跟踪。同时考虑到在直线行走跟踪和曲线行走跟踪时,横向位置偏差和航向偏差对系统输出的影响不同,设计了参数自调整的模糊控制器,试验结果证明,较一般的模糊控制器而言,具有超调量小、反应灵敏的特点。
Agricultural robot is one of the most effective methods to increase agricultural automation level in the world. It can meet the future agriculture of good harvest, safety, high efficiency, environmental protection, agronomic's high precision requirements. Automatic vehicle navigation technology is the basic problem. A navigation system is built based on GPS/DR and 3G technology. Key problems of sensor data fusion in navigation process, such as state estimation of agricultural vehicles, route tracking of agricultural vehicles, later evaluation of navigation information, was discussed as below:
①Using global positioning system technology, combined with DR technology, a navigation system of low cost equipped on agricultural vehicles was developed. The sensors used in the system are GPS, Inertial navigation sensors such as electronic compass, fiber optic gyro. The software was developed with Labview. The software is used to configure serial port, extract vehicle attitude information and so on. Differential GPS Signal was used in the system to observe vehicle attitude, which was transmitted through the 3G network. The DGPS signal is used to correct the GPS data the mobile station collected.
②Using the Simulink in the MATLAB, the dynamic model of field work machinery was developed. It includes:the front wheel side force calculation unit, the back wheel side force calculation unit, steady turning radius calculation unit, yaw angles gain steady-state response unit, Insufficient steering parameters calculation unit and so on. When function parameters and structure parameters is given to the calculation model, vehicle's yaw angles and centroid running trajectory can be calculated quickly.
③Refers to multi sensor data fusion, using the Kalman filtering method based on iteration which has the minimum mean square error(MMSE), The attitude's optimal estimation of the agricultural vehicles can be obtained. Considering the state equation of the system is nonlinear equation, extended kalman filtering (EKF) method is used to solve this defect. The test result shows that the method is feasible.
④The navigation system's work depends on different sensors, when using centralized filter method to deal with the signals sensors get, the damage of the subsystem can cause paralysis of the whole system. Distributed filter method is used here. A federal kalman filtering system is designed here, using this method, the system's fault tolerance is improved. At the same time, the amount of calculation of the system is reduced.
⑤In the process of navigation of the vehicles, when GPS fails to get the signal, inertial navigation can be used instead of GPS navigation system. Inertial navigation system's error will accumulate over time. We use the optimal smooth theory to smooth the data get from sensors, the test results shows that this method is useful.
⑥In the case of autonomous vehicles, special route is given to the vehicle to avoid scratching crops, reduce energy consumption, reduce cost of production A fuzzy controller is designed for linear tracking and curve tracking. To deduce system overshoot, the fuzzy controller's parameters can be adjusted. The test results shows that using this method, the system overshoot is deduced.
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