CN111025269B - Underwater robot sensor installation deviation estimation method - Google Patents
Underwater robot sensor installation deviation estimation method Download PDFInfo
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- CN111025269B CN111025269B CN201911314228.7A CN201911314228A CN111025269B CN 111025269 B CN111025269 B CN 111025269B CN 201911314228 A CN201911314228 A CN 201911314228A CN 111025269 B CN111025269 B CN 111025269B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/52—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S15/00
- G01S7/52004—Means for monitoring or calibrating
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S15/00—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems
- G01S15/02—Systems using the reflection or reradiation of acoustic waves, e.g. sonar systems using reflection of acoustic waves
- G01S15/50—Systems of measurement, based on relative movement of the target
- G01S15/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
Abstract
The invention discloses an underwater robot sensor installation deviation estimation method, which is characterized in that a factor graph model of a water surface navigation state robot navigation pose is established, based on truth value feedback of a satellite positioning module, a nonlinear optimization method is utilized, estimation of a constant linear additive error alpha of the underwater robot heading measurement and nonlinear multiplicative errors cos (beta) and sin (beta) of the underwater robot advancing speed and traversing speed is realized, and the position deviation between a dead reckoning module and the satellite positioning module is compensated. The robust estimation of the installation deviation of the sensor is realized, the calculation precision of the dead reckoning module is improved, and the positioning precision of the underwater robot is further improved. The calibration frequency of the robot during water outlet is reduced, the operation efficiency is improved, and the requirement of long-time underwater navigation is met.
Description
Technical Field
The invention belongs to the field of underwater robot positioning, relates to an underwater robot sensor installation deviation estimation method, and particularly relates to an underwater robot sensor installation deviation estimation method based on graph optimization.
Background
The underwater positioning technology is an important research direction in the field of underwater robots and is a key for the underwater robots to perform effective task operation, however, sensor installation errors easily bring unnecessary attitude and speed measurement errors to the underwater robots, the accuracy of a dead reckoning method is reduced, the underwater robots need to continuously discharge water in the operation process to complete position correction, and the operation efficiency is reduced.
The method for estimating the installation deviation of the sensor of the underwater robot realizes the nonlinear estimation of the installation deviation by utilizing the position information of the underwater robot provided by a satellite positioning system, combining an estimation module based on graph optimization, establishing a factor graph model based on dead reckoning position estimation and satellite positioning measurement and utilizing a nonlinear optimization algorithm under the condition that the robot has the installation deviation of the sensor.
Disclosure of Invention
In view of the above prior art, the technical problem to be solved by the present invention is to provide an underwater robot sensor installation deviation estimation method capable of correcting installation deviation and reducing the number of times of water discharge calibration. The estimation of the installation deviation of the sensor of the underwater robot is completed by a graph optimization method, the correction of the measurement data of the attitude sensor and the Doppler velocimeter is realized, the positioning accuracy of the multi-underwater robot is improved, and the long-time navigation operation requirement of the underwater robot is met.
In order to solve the technical problem, the invention provides an underwater robot sensor installation deviation estimation method, which comprises the following steps:
step 1: the underwater robot starts to sail on the water surface, the underwater robot acquires speed information by using a Doppler velocimeter module, completes correction on Doppler installation deviation through a speed measurement correction link, obtains a speed value of the underwater robot in a geodetic coordinate system, and inputs the speed value into an underwater robot dead reckoning module;
and 2, step: the underwater robot acquires attitude information by using an attitude sensor module, finishes the correction of installation deviation of the attitude sensor through an attitude measurement correction link, acquires an attitude value of the underwater robot under a geodetic coordinate system, and inputs the attitude information to a robot position estimation module;
and 3, step 3: the dead reckoning module adopts a dead reckoning algorithm according to the input speed information and the input attitude information to complete the estimation of the position of the robot per se, and iteratively updates variable nodes of the underwater robot position factor graph according to the output position information;
and 4, step 4: the underwater robot acquires real position information of the underwater robot through a satellite positioning module, the information is used as an updatable variable node of a factor graph, and meanwhile, installation deviation is used as a variable node;
and 5: the graph optimization estimation module establishes factor nodes of the factor graph by using estimation and observation values among variable nodes, and solves the maximum posterior estimation of the factor graph through nonlinear optimization to obtain estimation of the self position and installation deviation of the underwater robot;
step 6: after the installation deviation information is obtained, the installation deviation information is respectively fed back to the Doppler velocimeter module and the attitude sensor module;
and 7: judging whether the installation deviation estimation is finished according to the change condition of the installation deviation estimation: and when the variation of the installation deviation estimation is smaller than the expected threshold value, judging that the estimation process is finished, ending the task, and otherwise returning to the step 1.
The invention also includes:
1. the map optimization estimation module establishes a nonlinear optimization target by utilizing the position information of the dead reckoning module and the position information of the satellite positioning module and adopting a factor map optimization method, and the optimal estimation of the sensor installation deviation is completed.
2. The Doppler velocimeter module utilizes the acoustic Doppler effect and 4 array elements to realize the measurement of speed information in a typical seabed environment.
3. The attitude sensor module utilizes an inertia measurement unit comprising a fiber optic gyroscope and an accelerometer to complete the measurement of the attitude information of the underwater robot.
4. And the satellite positioning module utilizes a satellite positioning system to complete the position measurement of the underwater robot in the water surface navigation state.
The invention has the beneficial effects that: the invention relates to a sensor installation deviation problem of an underwater robot in a dead reckoning process. In the underwater dead reckoning process of the underwater robot, a certain error is brought to a dead reckoning algorithm by the installation deviation of the Doppler velocimeter and the attitude sensor, so that dead reckoning precision is rapidly dispersed. By establishing a factor graph model of the navigation pose of the robot in the water surface navigation state and utilizing a nonlinear optimization method, the estimation of a constant linear additive error alpha of the heading measurement of the underwater robot and nonlinear multiplicative errors cos (beta) and sin (beta) of the advancing speed and the traversing speed of the underwater robot is realized. The underwater dead reckoning method has the advantages that the underwater robot can correct the installation deviation in the process of a navigation task, the accuracy of an underwater dead reckoning algorithm is improved, the times of calibration of the underwater robot during water outlet are reduced, the operation efficiency is improved, and the requirement of long-time underwater navigation is met. The method has clear logic and simple practice.
The invention combines a diagram optimization estimation method, realizes the nonlinear estimation of the installation deviation of the Doppler velocimeter and the attitude sensor based on the true value feedback of the satellite positioning module, and compensates the position deviation between the dead reckoning module and the satellite positioning module. The robust estimation of the installation deviation of the sensor is realized, the calculation precision of the dead reckoning module is improved, and the positioning precision of the underwater robot is further improved.
Drawings
FIG. 1 is a block diagram of an installation deviation estimation system for a sensor of an underwater robot;
FIG. 2 is a view showing a model configuration of an installation deviation estimation map;
fig. 3 is a flow chart of installation deviation estimation.
Detailed Description
The following description of the embodiments of the present invention will be made with reference to the accompanying drawings.
The nonlinear estimation module based on graph optimization realizes mathematical abstraction of the problem of estimation of the installation deviation of the sensor of the underwater robot by utilizing a factor graph, establishes an optimization equation aiming at the installation deviation of the attitude sensor and the Doppler velocimeter and finishes the optimal estimation of the installation deviation. The influence of sensor installation deviation on the horizontal plane position estimation is considered, and in order to acquire satellite positioning information, the deviation estimation method needs to be completed in the water surface navigation process. The installation deviation of the attitude sensor is the fixed installation angle deviation between the X axis of the attitude sensor and the heading of the underwater robot, and brings a constant linear additive error alpha of the heading measurement of the underwater robot. The installation deviation of the Doppler velocimeter is the fixed angle deviation between the X axis of the Doppler velocimeter and the heading of the underwater robot, and brings nonlinear multiplicative errors cos (beta) and sin (beta) of the advancing speed and the traversing speed of the underwater robot.
Referring to fig. 1, the system structure of the underwater robot sensor installation deviation estimation method is as follows:
as shown in fig. 1, the underwater robot target estimation system structure includes a diagram optimization estimation module, a doppler velocimeter module, an attitude sensor module, a dead reckoning module, and a satellite positioning module.
The graph optimization estimation module: and establishing a nonlinear optimization target by using the position information of the dead reckoning module and the position information of the satellite positioning module and adopting a factor graph optimization method, thereby realizing the optimal estimation of the installation deviation of the sensor.
Doppler velocimeter module: the measurement of the velocity information in a typical submarine environment is achieved by using 4 array elements, using the acoustic Doppler effect.
An attitude sensor module: with an inertial measurement unit: and the fiber optic gyroscope and the accelerometer are used for measuring the attitude information of the underwater robot.
Dead reckoning module: and calculating the position and the posture of the underwater robot by using a Doppler velocimeter and a posture sensor and combining a Dead Reckoning positioning algorithm.
A satellite positioning module: and the position measurement of the underwater robot under the water surface navigation state is realized by utilizing satellite positioning systems such as GPS/Beidou/GLONASS and the like.
A graph optimization model is established by using dead reckoning position information with sensor installation deviation and position information of a satellite positioning system, and optimal estimation of Doppler velocimeter installation deviation and attitude sensor installation deviation is achieved by using a graph optimization estimation method in a certain time window. Wherein, the deviation of measuring the speed is multiplicative error, and the attitude deviation is additive error.
Referring to FIG. 2, a model structure diagram of a mounting deviation estimation diagram is described
Variable X t Representing the self-position estimation information of the robot at the moment t, alpha and beta are installation deviation variables to be estimated, G t And for the satellite positioning information of the underwater robot at the time t, namely variable nodes of a factor graph, black solid points represent factor relationships among variables, namely factor nodes of the factor graph, and represent deviation between position observation and position estimation.
With reference to fig. 3, a flow of the underwater robot sensor installation deviation estimation method:
1. starting sensor installation deviation estimation, and starting water surface navigation of the underwater robot;
2. the underwater robot acquires speed information by using a Doppler velocimeter, corrects Doppler installation deviation through a speed measurement correction link, obtains a speed value of the underwater robot in a geodetic coordinate system, and inputs the speed value into a robot position estimation module;
3. the underwater robot acquires attitude information by using an attitude sensor, corrects the installation deviation of the attitude sensor through an attitude measurement correction link, acquires an attitude value of the underwater robot under a geodetic coordinate system, and inputs the attitude information to a robot position estimation module;
4. the robot position estimation module adopts a dead reckoning algorithm according to the input speed information and the input attitude information to realize the estimation of the self position of the robot, and establishes/updates variable nodes of the underwater robot position factor graph according to the output position information;
5. the underwater robot acquires the real position information thereof by using a positioning system such as a GPS/Beidou and the like, and the information is used as a variable node which is not updatable on a factor graph. Meanwhile, the installation deviation is used as a variable node;
6. the estimation and observation values among the variable nodes are utilized to establish factor nodes of the factor graph, and the maximum posterior estimation of the factor graph is solved through nonlinear optimization, so that the estimation of the self position and the installation deviation of the underwater robot is realized;
7. after obtaining the installation deviation information, feeding back the installation deviation information to the measurement correction module to realize the update of the correction module;
8. and judging whether the installation deviation estimation is finished or not according to the change condition of the installation deviation estimation. And when the variation of the installation deviation estimation is smaller than an expected threshold value, judging that the estimation process is finished, ending the task, and otherwise returning to the step 2.
Claims (4)
1. An underwater robot sensor installation deviation estimation method is characterized by comprising the following steps:
step 1: the underwater robot starts to sail on the water surface, the underwater robot acquires speed information by using a Doppler velocimeter module, completes correction on Doppler installation deviation through a speed measurement correction link, obtains a speed value of the underwater robot in a geodetic coordinate system, and inputs the speed value into an underwater robot dead reckoning module;
step 2: the underwater robot acquires attitude information by using an attitude sensor module, finishes correction of installation deviation of the attitude sensor through an attitude measurement correction link, acquires an attitude value of the underwater robot under a geodetic coordinate system, and inputs the attitude information to a robot position estimation module;
and 3, step 3: the dead reckoning module adopts a dead reckoning algorithm according to the input speed information and the input attitude information to complete the estimation of the position of the robot per se, and iteratively updates variable nodes of the underwater robot position factor graph according to the output position information;
and 4, step 4: the underwater robot acquires real position information of the underwater robot through a satellite positioning module, the information is used as an updatable variable node of a factor graph, and meanwhile, installation deviation is used as a variable node;
and 5: the graph optimization estimation module establishes factor nodes of the factor graph by using estimation and observation values among variable nodes, and solves the maximum posterior estimation of the factor graph through nonlinear optimization to obtain estimation of the self position and installation deviation of the underwater robot;
and 6: after the installation deviation information is obtained, the installation deviation information is respectively fed back to the Doppler velocimeter module and the attitude sensor module;
and 7: judging whether the installation deviation estimation is finished according to the change condition of the installation deviation estimation: and when the variation of the installation deviation estimation is smaller than the expected threshold value, judging that the estimation process is finished, ending the task, and otherwise returning to the step 1.
2. The underwater robot sensor installation deviation estimation method according to claim 1, characterized in that: the Doppler velocimeter module utilizes the acoustic Doppler effect and 4 array elements to realize the measurement of speed information in a typical seabed environment.
3. The underwater robot sensor installation deviation estimation method according to claim 1, characterized in that: the attitude sensor module utilizes an inertia measurement unit comprising a fiber optic gyroscope and an accelerometer to complete the measurement of the attitude information of the underwater robot.
4. The underwater robot sensor installation deviation estimation method according to claim 1, characterized in that: and the satellite positioning module utilizes a satellite positioning system to complete the position measurement of the underwater robot in the water surface navigation state.
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