阅读说明：本技术 一种gps信息缺失情况下的无人车导航方法及系统 (Unmanned vehicle navigation method and system under condition of GPS information loss ) 是由 任章 梁源 李清东 于 2019-11-11 设计创作，主要内容包括：本发明公开了一种GPS信息缺失情况下的无人车导航方法及系统。该方法及系统均应用于一种无人车导航定位装置。该导航定位装置包括无人车和至少一架具有自主导航能力的无人机；无人车与无人机无线通讯连接；该无人车导航方法包括：获取无人车自身定位系统的定位信息、无人机的广播信息以及接收到的可见星信息；根据广播信息和可见星信息计算几何精度因子；构建卡尔曼滤波模型,并根据几何精度因子的大小确定卡尔曼滤波模型量测噪声的协方差矩阵；将定位信息、广播信息、可见星信息输入构建好的卡尔曼滤波模型,得到更新后的无人车定位信息。本发明的方法及系统能够在GPS信息缺失的情况下实现对无人车的导航定位。(The invention discloses an unmanned vehicle navigation method and system under the condition of GPS information loss. The method and the system are both applied to an unmanned vehicle navigation positioning device. The navigation positioning device comprises an unmanned vehicle and at least one unmanned vehicle with autonomous navigation capability; the unmanned vehicle is in wireless communication connection with the unmanned aerial vehicle; the unmanned vehicle navigation method comprises the following steps: acquiring positioning information of a self positioning system of the unmanned vehicle, broadcasting information of the unmanned vehicle and received visible star information; calculating a geometric precision factor according to the broadcast information and the visible star information; constructing a Kalman filtering model, and determining a covariance matrix of measurement noise of the Kalman filtering model according to the size of the geometric accuracy factor; and inputting the positioning information, the broadcast information and the visible star information into the constructed Kalman filtering model to obtain updated positioning information of the unmanned vehicle. The method and the system can realize navigation positioning of the unmanned vehicle under the condition of GPS information loss.)

1. An unmanned vehicle navigation method under the condition of GPS information loss is characterized in that the method is applied to an unmanned vehicle navigation positioning device; the navigation positioning device comprises an unmanned vehicle and at least one unmanned vehicle with autonomous navigation capability; the unmanned vehicle is in wireless communication connection with the unmanned aerial vehicle;

the unmanned vehicle navigation method comprises the following steps:

acquiring positioning information of a self positioning system of the unmanned vehicle, broadcasting information of the unmanned vehicle and received visible star information;

calculating a geometric precision factor according to the broadcast information and the visible star information;

constructing a Kalman filtering model, and determining a covariance matrix of measurement noise of the Kalman filtering model according to the size of the geometric precision factor;

and inputting the positioning information, the broadcast information and the visible star information into a constructed Kalman filtering model to obtain updated positioning information of the unmanned vehicle.

2. The unmanned vehicle navigation method under the condition of GPS information absence according to claim 1, wherein the calculating a geometric precision factor according to the broadcast information and the visible star information specifically comprises:

calculating position vectors from the unmanned vehicles to all unmanned vehicles according to the positioning information of the unmanned vehicles and the broadcast information;

calculating the position vector from the unmanned vehicle to each visible star according to the positioning information of the unmanned vehicle and the visible star information;

combining the position vectors from the unmanned vehicles to all unmanned aerial vehicles and the position vectors from the unmanned vehicles to all visible stars to construct a vector matrix M;

according to the formula DOP ═ M^TM)^-1Calculating the intermediate matrix DOP;

according to the formula

3. The unmanned vehicle navigation method under the condition of GPS information deficiency according to claim 1, wherein the constructing of the Kalman filtering model and the determining of the covariance matrix of the measurement noise of the Kalman filtering model according to the size of the geometric accuracy factor specifically comprise:

establishing a state transition model of a Kalman filtering model by taking positioning information of the unmanned vehicle as a state variable;

constructing a measurement model of a Kalman filtering model by taking the relative distance from the unmanned vehicle to each unmanned vehicle and the pseudo distance from the unmanned vehicle to each visible star as observed quantities;

constructing a middle covariance matrix R by taking the variance of the measurement noise of each observation quantity as a diagonal element;

according to the size of the geometric precision factor, the intermediate covariance matrix R is utilized to construct a covariance matrix for measuring noise

4. The unmanned aerial vehicle navigation method under the condition of GPS information loss according to claim 1, wherein the step of inputting the positioning information, the broadcast information and the visible star information into a constructed Kalman filtering model to obtain updated positioning information of the unmanned aerial vehicle specifically comprises the steps of:

inputting the positioning information, the broadcast information and the visible star information into a constructed Kalman filtering model to obtain an estimation result matrix of the current state;

and taking each element in the estimation result matrix of the current state as coordinate data of the unmanned vehicle to obtain the positioning information of the unmanned vehicle.

5. An unmanned vehicle navigation system under the condition of GPS information loss is characterized in that the unmanned vehicle navigation system is applied to an unmanned vehicle navigation positioning device; the navigation positioning device comprises an unmanned vehicle and at least one unmanned vehicle with autonomous navigation capability; the unmanned vehicle is in wireless communication connection with the unmanned aerial vehicle;

this unmanned vehicle navigation includes:

the information acquisition module is used for acquiring positioning information of a self positioning system of the unmanned vehicle, broadcasting information of the unmanned vehicle and received visible star information;

the geometric precision factor calculation module is used for calculating a geometric precision factor according to the broadcast information and the visible star information;

the Kalman filtering model building module is used for building a Kalman filtering model and determining a covariance matrix of measurement noise of the Kalman filtering model according to the size of the geometric accuracy factor;

and the positioning information updating module is used for inputting the positioning information, the broadcast information and the visible star information into the constructed Kalman filtering model to obtain updated positioning information of the unmanned vehicle.

6. The unmanned aerial vehicle navigation system in the absence of GPS information as recited in claim 5, wherein the geometric form factor calculation module comprises:

the vehicle-to-machine vector calculation unit is used for calculating position vectors from the unmanned vehicle to all unmanned vehicles according to the positioning information of the unmanned vehicle and the broadcast information;

the vehicle-to-satellite vector calculation unit is used for calculating position vectors from the unmanned vehicle to each visible satellite according to the positioning information of the unmanned vehicle and the visible satellite information;

the vector matrix construction unit is used for combining the position vectors from the unmanned vehicles to all the unmanned vehicles and the position vectors from the unmanned vehicles to all the visible stars to construct a vector matrix M;

an intermediate matrix calculation unit for calculating (M) according to the formula DOP^TM)^-1Calculating the intermediate matrix DOP;

a geometric figure of merit calculation unit for calculating the geometric figure of merit according to the formula

7. The unmanned aerial vehicle navigation system under GPS information loss condition of claim 5, wherein the Kalman filtering model construction module comprises:

the state transition model building unit is used for building a state transition model of the Kalman filtering model by taking the positioning information of the unmanned vehicle as a state variable;

the measurement model building unit is used for building a measurement model of the Kalman filtering model by taking the relative distance from the unmanned vehicle to each unmanned vehicle and the pseudo distance from the unmanned vehicle to each visible star as observed quantities;

the intermediate covariance matrix construction unit is used for constructing an intermediate covariance matrix R by taking the variance of the measurement noise of each observation quantity as a diagonal element;

a covariance matrix construction unit for measuring noise, which is used for constructing the covariance matrix of the measured noise by using the middle covariance matrix R according to the size of the geometric accuracy factor

8. The unmanned aerial vehicle navigation system in the absence of GPS information as recited in claim 5, wherein the positioning information update module comprises:

the filtering unit is used for inputting the positioning information, the broadcasting information and the visible star information into a constructed Kalman filtering model to obtain an estimation result matrix of the current state;

and the positioning information determining unit is used for taking each element in the estimation result matrix of the current state as the coordinate data of the unmanned vehicle to obtain the positioning information of the unmanned vehicle.

Technical Field

The invention relates to the field of navigation positioning, in particular to an unmanned vehicle navigation method and system under the condition of GPS information loss.

Background

With the development of robot technology, people can be liberated from complicated labor to a certain extent, the existing robot has good application prospects in many fields, and since the birth of the sixty years, the robot plays a great role in reducing the labor intensity of people, improving the social production efficiency, changing the mode of production and life and the like. Through rapid and cumulative development for many years, robots have been applied in various fields of industrial automation, and become key mechatronic equipment. Meanwhile, with the rapid development of society and the continuous and rapid improvement of living standards of people, robots in non-manufacturing fields are also developed in succession, for example, various robots for various purposes such as agricultural application robots, service robots, underwater exploration robots, medical robots, entertainment robots, military robots, and even some special robots are on the market, and are being stepped in toward practical life and production at a rapid speed. It can be said that the mobile robot becomes an important branch in the development of the economic society. However, mobile robots widely used at present are basically Ground mobile robots (UGVs), which have strong superiority in engineering exploration, military detection, emergency rescue and the like, and can help human beings to complete tasks under various severe conditions, but under some complex working conditions, many problems still need to be solved for effectively carrying out related work of the UGVs.

For UGV, realizing accurate navigation positioning per se is a necessary premise for executing all tasks. At present, it is preferred to utilize a satellite navigation system (e.g., gps (global Positioning system)) to realize UGV navigation, taking hardware cost, system reliability, navigation accuracy, and other factors into comprehensive consideration. However, the GPS satellite signals are greatly affected by the environment, and accurate navigation and positioning cannot be performed in a complex building group (e.g. in an urban environment), a canyon, a forest and the like due to signal attenuation or loss.

Disclosure of Invention

The invention aims to provide a method and a system for navigating an unmanned vehicle under the condition of GPS information loss, which can realize navigation and positioning of the unmanned vehicle under the condition of GPS information loss.

In order to achieve the purpose, the invention provides the following scheme:

an unmanned vehicle navigation method under the condition of GPS information loss is applied to an unmanned vehicle navigation positioning device; the navigation positioning device comprises an unmanned vehicle and at least one unmanned vehicle with autonomous navigation capability; the unmanned vehicle is in wireless communication connection with the unmanned aerial vehicle;

the unmanned vehicle navigation method comprises the following steps:

acquiring positioning information of a self positioning system of the unmanned vehicle, broadcasting information of the unmanned vehicle and received visible star information;

calculating a geometric precision factor according to the broadcast information and the visible star information;

constructing a Kalman filtering model, and determining a covariance matrix of measurement noise of the Kalman filtering model according to the size of the geometric precision factor;

and inputting the positioning information, the broadcast information and the visible star information into a constructed Kalman filtering model to obtain updated positioning information of the unmanned vehicle.

Optionally, the calculating a geometric accuracy factor according to the broadcast information and the visible star information specifically includes:

calculating position vectors from the unmanned vehicles to all unmanned vehicles according to the positioning information of the unmanned vehicles and the broadcast information;

calculating the position vector from the unmanned vehicle to each visible star according to the positioning information of the unmanned vehicle and the visible star information;

combining the position vectors from the unmanned vehicles to all unmanned aerial vehicles and the position vectors from the unmanned vehicles to all visible stars to construct a vector matrix M;

according to the formula DOP ═ M^TM)^-1Calculating the intermediate matrix DOP;

according to the formula

A geometric precision factor GDOP is calculated, where trace () represents the trace of the matrix.

Optionally, the constructing a kalman filter model, and determining a covariance matrix of measurement noise of the kalman filter model according to the size of the geometric precision factor specifically includes:

establishing a state transition model of a Kalman filtering model by taking positioning information of the unmanned vehicle as a state variable;

constructing a measurement model of a Kalman filtering model by taking the relative distance from the unmanned vehicle to each unmanned vehicle and the pseudo distance from the unmanned vehicle to each visible star as observed quantities;

constructing a middle covariance matrix R by taking the variance of the measurement noise of each observation quantity as a diagonal element;

according to the size of the geometric precision factor, the intermediate covariance matrix R is utilized to construct a covariance matrix for measuring noise

Optionally, the positioning information, the broadcast information, and the visible star information are input into a constructed kalman filtering model to obtain updated positioning information of the unmanned vehicle, which specifically includes:

inputting the positioning information, the broadcast information and the visible star information into a constructed Kalman filtering model to obtain an estimation result matrix of the current state;

and taking each element in the estimation result matrix of the current state as coordinate data of the unmanned vehicle to obtain the positioning information of the unmanned vehicle.

An unmanned vehicle navigation system under the condition of GPS information loss is applied to an unmanned vehicle navigation positioning device; the navigation positioning device comprises an unmanned vehicle and at least one unmanned vehicle with autonomous navigation capability; the unmanned vehicle is in wireless communication connection with the unmanned aerial vehicle;

this unmanned vehicle navigation includes:

the information acquisition module is used for acquiring positioning information of a self positioning system of the unmanned vehicle, broadcasting information of the unmanned vehicle and received visible star information;

the geometric precision factor calculation module is used for calculating a geometric precision factor according to the broadcast information and the visible star information;

the Kalman filtering model building module is used for building a Kalman filtering model and determining a covariance matrix of measurement noise of the Kalman filtering model according to the size of the geometric accuracy factor;

and the positioning information updating module is used for inputting the positioning information, the broadcast information and the visible star information into the constructed Kalman filtering model to obtain updated positioning information of the unmanned vehicle.

Optionally, the geometric precision factor calculating module includes:

the vehicle-to-machine vector calculation unit is used for calculating position vectors from the unmanned vehicle to all unmanned vehicles according to the positioning information of the unmanned vehicle and the broadcast information;

the vehicle-to-satellite vector calculation unit is used for calculating position vectors from the unmanned vehicle to each visible satellite according to the positioning information of the unmanned vehicle and the visible satellite information;

the vector matrix construction unit is used for combining the position vectors from the unmanned vehicles to all the unmanned vehicles and the position vectors from the unmanned vehicles to all the visible stars to construct a vector matrix M;

an intermediate matrix calculation unit for calculating (M) according to the formula DOP^TM)^-1Calculating the intermediate matrix DOP;

a geometric figure of merit calculation unit for calculating the geometric figure of merit according to the formula

A geometric precision factor GDOP is calculated, where trace () represents the trace of the matrix.

Optionally, the kalman filtering model building module includes:

the state transition model building unit is used for building a state transition model of the Kalman filtering model by taking the positioning information of the unmanned vehicle as a state variable;

the measurement model building unit is used for building a measurement model of the Kalman filtering model by taking the relative distance from the unmanned vehicle to each unmanned vehicle and the pseudo distance from the unmanned vehicle to each visible star as observed quantities;

the intermediate covariance matrix construction unit is used for constructing an intermediate covariance matrix R by taking the variance of the measurement noise of each observation quantity as a diagonal element;

a covariance matrix construction unit for measuring noise, which is used for constructing the covariance matrix of the measured noise by using the middle covariance matrix R according to the size of the geometric accuracy factor

Optionally, the positioning information updating module includes:

the filtering unit is used for inputting the positioning information, the broadcasting information and the visible star information into a constructed Kalman filtering model to obtain an estimation result matrix of the current state;

and the positioning information determining unit is used for taking each element in the estimation result matrix of the current state as the coordinate data of the unmanned vehicle to obtain the positioning information of the unmanned vehicle.

According to the specific embodiment provided by the invention, the invention discloses the following technical effects: according to the unmanned vehicle navigation method and system under the condition of GPS information loss, the unmanned vehicle is positioned by combining the broadcast information of the unmanned vehicle with the information of the GPS visible satellites, and the unmanned vehicle can still be positioned through the broadcast information of the unmanned vehicle under the condition of the visible satellite information loss. In addition, by calculating the geometric accuracy factor and determining the covariance matrix of the measurement noise of the Kalman filtering model on the basis of the geometric accuracy factor, the positioning information of the unmanned vehicle is updated by Kalman filtering, the calculation of the confidence coefficient of the visible star information and the broadcast information of the unmanned vehicle by the geometric accuracy factor is realized, the proportion of the visible star information and the broadcast information in Kalman filtering positioning is adjusted according to the confidence coefficient, the interference to navigation positioning under the condition of GPS visible star information loss is reduced, and the navigation positioning accuracy is improved.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.

Fig. 1 is a flowchart of a method of an unmanned vehicle navigation method in the absence of GPS information according to embodiment 1 of the present invention;

fig. 2 is a system configuration diagram of the unmanned vehicle navigation system in the case of GPS information loss in embodiment 2 of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.

An unmanned vehicle navigation method and system under the condition of GPS information loss are both applied to an unmanned vehicle navigation positioning device. The navigation positioning device meets the following conditions: 1. the UAV navigation system is provided with at least one UAV (Unmanned Aerial Vehicle), the number of the UAVs depends on the actual use condition, and the UAV has an autonomous navigation capability with sufficient navigation accuracy (i.e. the UAV has an autonomous navigation positioning capability, for example, a GPS navigation device is carried on the UAV, and the GPS device can normally and stably operate, the navigation device includes but is not limited to the GPS navigation device, the navigation accuracy of the UAV can meet specific use requirements, and the use requirements depend on specific situations and tasks and are generally determined by related technical personnel). 2. The UAV has the capability of sending information to the UGV via wireless communication, and with the wireless communication, the UGV can measure and calculate the relative distance between the UAV and the UGV. 3. The UGV is provided with a GPS navigation device, and has certain autonomous navigation positioning capability, and can maintain certain navigation precision in a short time (for example, the UGV is provided with a MEMS gyroscope, a MEMS accelerometer, a magnetic compass, a code disc and the like).

The selection of the number of UAVs needs to consider the following factors, including the running range of the UGV, the coverage of the communication system signal, the navigation accuracy requirement of the UGV, the shielding condition of the GPS signal, the hardware cost of the system, and the like. The autonomous navigation capability of the UGV refers to the navigation capability of navigating in other ways without depending on GPS information.

In a communication period of each UAV and UGV, the UAVs broadcast self navigation positioning results outwards, and in the communication period of each UGV, all receivable broadcast information of the UAVs are received, and meanwhile, the relative distance is estimated by utilizing a related algorithm; on the basis of obtaining the information, the UGV searches and receives satellite signals transmitted by all the received GPS satellites (GPS visible satellites) at the same time.

The basic principle of the technical scheme of the invention is as follows:

after acquiring the GPS satellite signal and the broadcast signal of the UAV, performing autonomous evaluation on confidence coefficients of the GPS satellite signal and the broadcast signal of the UAV, and realizing adaptive fusion of navigation information by the UGV by using a self-fusion algorithm on the basis of the information, wherein the confidence coefficient autonomous evaluation and the adaptive fusion algorithm refer to: the method comprises the steps of measuring and calculating a GDOP (Geometric Precision factor) value formed by a GPS satellite signal and a broadcast signal of the UAV according to the existing navigation information, dynamically adjusting the reliability of the GPS information and the UAV broadcast information according to the measured and calculated GDOP value, and dynamically adjusting the proportion of the GPS visible star information and the UAV broadcast information in a navigation information fusion algorithm according to the value of the GDOP because the value of the GDOP is inversely proportional to the Precision of the GPS visible star information and the UAV broadcast information, thereby realizing the following steps: under the condition that the confidence degrees of the GPS visible star information and the UAV broadcast information are high, the GPS visible star information and the UAV broadcast information are introduced into a navigation information fusion algorithm in a 'confidence' state, so that the purpose of improving the navigation positioning precision is achieved; and under the condition that the confidence degrees of the GPS visible star information and the UAV broadcast information are insufficient, the ratio of the GPS visible star information to the UAV broadcast information in the navigation fusion algorithm is reduced, the GPS visible star information and the UAV broadcast information are introduced into the navigation information fusion algorithm in a low confidence state, and the influence of the GPS visible star information and the UAV broadcast information with low confidence degrees on the UGV navigation positioning precision is prevented.