CN106767796B - Fusion algorithm of unmanned ship distance measuring unit and inertia measuring unit in aqueduct-like environment - Google Patents
Fusion algorithm of unmanned ship distance measuring unit and inertia measuring unit in aqueduct-like environment Download PDFInfo
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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
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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
- G01C21/20—Instruments for performing navigational calculations
- G01C21/203—Specially adapted for sailing ships
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Abstract
The invention relates to a fusion algorithm of a unmanned ship distance measuring unit and an inertia measuring unit in a similar aqueduct environment, which comprises the following steps: calculating to obtain a measured deflection angle of the ship body by the distance measuring unit; constructing a system state model by an inertial measurement unit by adopting a Kalman filtering technology; constructing a system measurement model by an inertial measurement unit by adopting a Kalman filtering technology; predicting a state model at the k moment according to the k-1 moment to obtain a predicted deflection angle of the ship body at the k moment; combining the measured deflection angle of the ship body at the moment k with the predicted deflection angle of the ship body, and obtaining the optimal predicted estimation value at the moment k according to the optimal estimation model; repeating the steps to obtain the optimal prediction estimated value at the moment k +1, k +2 … k + n by performing autoregressive operation; and according to the obtained optimal prediction estimation value, the control system of the unmanned ship controls the propeller and the rudder to adjust the movement speed and direction of the ship body to finish the aqueduct detection. The invention can control the unmanned ship to carry out stable automatic navigation under the completely closed condition or the GPS-free environment.
Description
Technical Field
The invention relates to a fusion algorithm of a unmanned ship distance measuring unit and an inertia measuring unit in a similar aqueduct environment, which is used for assisting the distance observation and detection of an unmanned ship on the water surface in the similar aqueduct environment and belongs to the field of hydraulic engineering.
Background
At present, the safety detection of the existing water delivery aqueduct can only depend on manual inspection after closing a gate and stopping water. The method has long detection period and has great influence on downstream normal water supply. Many established water delivery projects cannot provide long-time water cut-off conditions for manual inspection, so that detection is not carried out so far, and potential safety hazards are large.
More advanced is to adopt a detection robot (unmanned surface vessel) to carry out detection, and the unmanned surface vessel disclosed by the prior art is in a monohull vessel shape and mainly comprises a vessel body, a power system, a sensor system, a control system and an image system. The power system comprises a single propeller or a plurality of propellers which are matched with the rudders to control the movement speed and the movement direction of the ship body; an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) form a sensor system and provide bottom data support for a control system to automatically control the movement of a ship body; the image system is provided with a plurality of cameras, and videos and images of a part needing to be detected are recorded and transmitted in the moving process of the ship body.
The existing unmanned ship on water mainly adopts two modes to control: 1. manual remote control and 2, automatic control.
The most important part of the aqueduct detection is the quality of video and images, in order to ensure that the video and the images shot by an image system are clear, the relative speed of the ship body relative to the movement of the aqueduct is less than 0.5m/s, and the distance between the ship body and the observed side wall and the water bottom needs to be stable. In water conservancy projects of aqueduct (including open troughs and hidden troughs) and similar environments, the water flow speed can often reach 3m/s or even more, and the environment is relatively closed due to the complex internal environment. With manual remote control, the operator is required to maintain stable operation of the hull at high flow rates and at a constant distance from the wall being measured. This is extremely demanding for the operator and does not guarantee that the relative speed of operation is stable within 0.5m/s, nor does it keep the same distance from the observation wall. By adopting automatic control, the unmanned water surface ship disclosed by the prior art adopts a Global Positioning System (GPS) and an inertial unit (IMU) to control the automatic operation of a ship body in a combined manner, but the GPS can hardly receive signals under a closed environment, so that data is inaccurate, the automatic control system can not operate normally, and the effect of stable observation can not be achieved. And the control system controls the propeller and the rudder to adjust the movement speed and direction of the ship body according to the parameters fed back by the inertia measuring unit and the distance measuring unit. The distance measurement unit is used for replacing a GPS, the problem that the similar aqueduct environment is closed and has no signal is solved, the parameters collected by the distance measurement unit and the inertia measurement unit provide bottom data support for the control system to automatically control the movement of the ship body, and the distance measurement unit is particularly suitable for distance observation and detection in the similar aqueduct environment.
Disclosure of Invention
In order to solve the above problems, an object of the present invention is to provide a fusion algorithm of a unmanned ship ranging unit and an inertial measurement unit in a aqueduct-like environment, which utilizes an external ranging unit in combination with an internal calculation to fuse into an accurate value, thereby enabling a stable automatic navigation operation under a completely closed condition or a GPS-free environment.
The purpose of the invention is realized by the following technical scheme:
a fusion algorithm of an unmanned ship distance measuring unit and an inertia measuring unit in a similar aqueduct environment comprises the following steps:
step 1, calculating by a distance measuring unit to obtain a ship body measurement deflection angle theta;
step 2, constructing a system state model by an inertial measurement unit by adopting a Kalman filtering technology:
where X (k) is the system state at time k,
ωimu(k-1) is the angular velocity value at the time k-1, Wimu(k) T is Gaussian white noise, and T is a sampling period;
and 3, constructing a system measurement model by an inertial measurement unit by adopting a Kalman filtering technology:
Z(k)＝HX(k)+Wlaser(k)
z (k) is the measured value at time k, H is the measurement matrix [ 10 ]]，Wlaser(k) Gaussian white noise of the distance measurement unit at the moment k;
step 4, the covariance matrix of the system process is
qlaserAnd q isimuThe weight constants of the inertia measurement unit and the distance measurement unit are respectively, the covariance matrix of the measurement process is R, and the reliability of the inertia measurement unit is greater than that of the distance measurement unit; and predicting a state model at the k moment according to the k-1 moment to obtain a ship body predicted deviation angle at the k moment, wherein the state model for predicting the k moment according to the k-1 moment is as follows:
X(k|k-1)＝A1X(k-1|k-1)+B1ωimu(k)，
wherein X (k-1| k-1) is the optimal result obtained at the moment k-1, and X (k | k-1) is the prediction result by using the state at the moment k-1;
and the covariance of X (k | k-1) is: p (k | k-1) ═ A1P(k-1|k-1)A1 T+Q；
Step 5, combining the measured deflection angle of the ship body at the moment k with the predicted deflection angle of the ship body, and obtaining the optimal predicted estimation value at the moment k according to an optimal estimation model, wherein the optimal estimation model is as follows:
X(k|k)＝X(k|k-1)+K(k)(Z(k)-HX(k|k-1))
wherein k (k) ═ P (k | k-1) HT[HP(k|k-1)HT+R]-1Is a Kalman gain matrix;
6, repeating the steps 1-5 to perform autoregressive operation on the optimal prediction estimated value at the moment k +1, k +2 … k + n to obtain the optimal prediction estimated value;
and 7, controlling a propeller and a rudder by a control system of the unmanned ship to adjust the movement speed and the movement direction of the ship body to finish the aqueduct detection according to the optimal prediction estimation value obtained in the step 6.
Further, the distance measuring unit is a laser distance measuring device, an ultrasonic distance measuring device or a sonar distance measuring device.
Further, when the distance measuring unit is a laser distance measuring device, 3 laser distance measuring sensors are respectively assembled on two sides of the unmanned ship at equal intervals, and the laser distance measuring sensors on each side are arranged as follows:
the laser ranging sensors at the middle are perpendicular to the edge of the ship body and are positioned at the center, and the laser ranging sensors at the two ends respectively form α angles with the perpendicular line of the edge of the ship body.
Furthermore, the distance between the laser ranging sensors is 300mm, and the α angle is 30-45 degrees.
Further, in the step 1, the distance measuring unit calculates the first deviation angle theta of the ship body according to the linear distance L of the laser distance measuring sensor from the wall surface along the arrangement angle read by the laser distance measuring sensor and the distance d of the laser distance measuring sensor perpendicular to the wall surface.
Further, the inertial measurement unit comprises an accelerometer, a gyroscope and a magnetic compass.
The ship body of the invention adopts a multi-hull ship structure, and the parameters collected by the distance measuring unit and the inertia measuring unit provide bottom data support for the control system to automatically control the motion of the ship body, so the invention has the advantages of stable motion and accurate control, and is particularly suitable for distance observation and detection in a similar aqueduct environment.
Drawings
FIG. 1 is a schematic diagram of arrangement positions of 3 laser ranging units on one side of an unmanned ship;
FIG. 2 is a schematic diagram of a unmanned ship ranging unit calculating a ship body measuring deflection angle.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The method is based on a fusion algorithm of an unmanned ship ranging unit and an inertial measurement unit, wherein the ranging unit can be a laser ranging device, an ultrasonic ranging device or a sonar ranging device, when the ranging unit is the laser ranging device, 3 laser ranging sensors are respectively arranged on two sides of the unmanned ship at equal intervals, the laser ranging sensors on each side are arranged as shown in figure 1, the laser ranging sensor in the middle is perpendicular to the edge of the ship body and is positioned at the center, the laser ranging sensors at two ends are respectively at α degrees with the perpendicular line of the edge of the ship body, the intervals of the laser ranging sensors are determined according to the length of the used unmanned ship, the interval D of the laser ranging sensors is 300mm, the angle α is 30-45 degrees, the reverse extension line of the α angle can cover the bow and the stern, the angle can be calculated theoretically and checked, the angle is in the range so as to ensure that the laser ranging sensors, or other similar sensors can be replaced, the mutual interference can be avoided, and the inertial measurement unit in the embodiment is α preferably 30 degrees.
A fusion algorithm of an unmanned ship distance measuring unit and an inertia measuring unit in a similar aqueduct environment comprises the following steps:
step 1, calculating by a distance measuring unit to obtain a ship body measurement deflection angle theta;
specifically, the distance measuring unit calculates a first deviation angle theta of the ship body according to a linear distance L of the laser distance measuring sensor from the wall surface along the arrangement angle of the laser distance measuring sensor read by the laser distance measuring sensor and a distance d of the laser distance measuring sensor perpendicular to the wall surface.
As shown in FIG. 2, L1，L2，L3The linear distances from the wall surface to the laser ranging sensor along the arrangement angle are respectively read by the laser ranging sensor. L is1Is the bow of a ship, L2Is a middle position, L3Is the tail. Thus, d1，d2，d3Is L1，L2，L3Distance data perpendicular to the wall surface.
This is one of the operating situations in which the unmanned ship is moving in the direction of the arrow, L1-L3Is ≧ 0, in which case L1Too far away, the error is large, and the discussion is excluded. So according to the trigonometric function correlation theorem, d2＝L2cosθ，d3＝L3cos(θ-30°)，sinθ＝(d2-d3) D, combining the three formulas to obtain D1＝L1cos (theta +30 degrees), so that the distance measuring unit calculates the measured deflection angle theta of the ship body.
Step 2, calculating a predicted deviation angle phi by an Inertial Measurement Unit (IMU) by using a Kalman filtering algorithm:
where c is the offset error constant, ωimuAs angular velocity value, WimuIs Gaussian white noise of an inertial measurement unit, A is a state matrix
B is a control input matrix
WlaserWhite gaussian noise as a ranging unit;
and constructing a system state model by an inertial measurement unit by adopting a Kalman filtering technology:
where X (k) is the system state at time k,
ωimu(k-1) is the angular velocity value at the time k-1, Wimu(k) T is Gaussian white noise, and T is a sampling period;
and 3, constructing a system measurement model by an inertial measurement unit by adopting a Kalman filtering technology:
Z(k)＝HX(k)+Wlaser(k)
z (k) is the measured value at time k, H is the measurement matrix [ 10 ]]，Wlaser(k) Gaussian white noise of the distance measurement unit at the moment k;
step 4, the covariance matrix of the system process is
qlaserAnd q isimuThe weight constants of the inertia measurement unit and the distance measurement unit are respectively, the covariance matrix of the measurement process is R, and the reliability of the inertia measurement unit is greater than that of the distance measurement unit; and predicting a state model at the k moment according to the k-1 moment to obtain a ship body predicted deviation angle at the k moment, wherein the state model for predicting the k moment according to the k-1 moment is as follows:
X(k|k-1)＝A1X(k-1|k-1)+B1ωimu(k)，
and the covariance of X (k | k-1) is: p (k | k-1) ═ A1P(k-1|k-1)A1 T+Q；
Step 5, combining the measured deflection angle of the ship body at the moment k with the predicted deflection angle of the ship body, and obtaining the optimal predicted estimation value at the moment k according to an optimal estimation model, wherein the optimal estimation model is as follows:
X(k|k)＝X(k|k-1)+K(k)(Z(k)-HX(k|k-1))
wherein k (k) ═ P (k | k-1) HT[HP(k|k-1)HT+R]-1Is a Kalman gain matrix;
6, repeating the steps 1-5 to perform autoregressive operation on the optimal prediction estimated value at the moment k +1, k +2 … k + n to obtain the optimal prediction estimated value;
and 7, controlling a propeller and a rudder by a control system of the unmanned ship to adjust the movement speed and the movement direction of the ship body to finish the aqueduct detection according to the optimal prediction estimation value obtained in the step 6.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A fusion algorithm of an unmanned ship distance measuring unit and an inertia measuring unit in a similar aqueduct environment is characterized by comprising the following steps:
step 1, calculating by a distance measuring unit to obtain a ship body measurement deflection angle theta;
step 2, constructing a system state model by an inertial measurement unit by adopting a Kalman filtering technology:
where X (k) is the system state at time k,
ωimu(k-1) is the angular velocity value at the time k-1, Wimu(k) T is Gaussian white noise, and T is a sampling period;
and 3, constructing a system measurement model by an inertial measurement unit by adopting a Kalman filtering technology:
Z(k)＝HX(k)+Wlaser(k)
z (k) is the measured value at time k, H is the measurement matrix [ 10 ]]，Wlaser(k) Gaussian white noise of the distance measurement unit at the moment k;
step 4, the covariance matrix of the system process is
qlaserAnd q isimuThe weight constants of the inertia measurement unit and the distance measurement unit are respectively, the covariance matrix of the measurement process is R, and the reliability of the inertia measurement unit is greater than that of the distance measurement unit; and predicting a state model at the k moment according to the k-1 moment to obtain a ship body predicted deviation angle at the k moment, wherein the state model for predicting the k moment according to the k-1 moment is as follows:
X(k|k-1)＝A1X(k-1|k-1)+B1ωimu(k)，
and the covariance of X (k | k-1) is: p (k | k-1) ═ A1P(k-1|k-1)A1 T+Q；
Step 5, combining the measured deflection angle of the ship body at the moment k with the predicted deflection angle of the ship body, and obtaining the optimal predicted estimation value at the moment k according to an optimal estimation model, wherein the optimal estimation model is as follows:
X(k|k)＝X(k|k-1)+K(k)(Z(k)-HX(k|k-1))
wherein k (k) ═ P (k | k-1) HT[HP(k|k-1)HT+R]-1Is a Kalman gain matrix;
6, repeating the steps 1-5 to perform autoregressive operation on the optimal prediction estimated value at the moment k +1, k +2 … k + n to obtain the optimal prediction estimated value;
and 7, controlling a propeller and a rudder by a control system of the unmanned ship to adjust the movement speed and the movement direction of the ship body to finish the aqueduct detection according to the optimal prediction estimation value obtained in the step 6.
2. The fusion algorithm of the unmanned ship ranging unit and the inertial measurement unit in the aqueduct-like environment according to claim 1, wherein the ranging unit is a laser ranging device, an ultrasonic ranging device or a sonar ranging device.
3. The fusion algorithm of the unmanned ship ranging unit and the inertial measurement unit in the aqueduct-like environment as claimed in claim 2, wherein when the ranging unit is a laser ranging device, 3 laser ranging sensors are respectively arranged on two sides of the unmanned ship at equal intervals, and the laser ranging sensors on each side are arranged as follows:
the laser ranging sensors at the middle are perpendicular to the edge of the ship body and are positioned at the center, and the laser ranging sensors at the two ends respectively form α angles with the perpendicular line of the edge of the ship body.
4. The fusion algorithm of the unmanned ship ranging unit and the inertial measurement unit in the aqueduct-like environment of claim 3, wherein the distance between the laser ranging sensors is 300mm, and the α angle is 30-45 degrees.
5. The fusion algorithm of the unmanned ship distance measuring unit and the inertia measuring unit in the aqueduct-like environment as claimed in claim 3, wherein the distance measuring unit calculates the first deviation angle θ of the ship body according to the linear distance L of the laser distance measuring sensor from the wall surface along the arrangement angle thereof and the distance d of the laser distance measuring sensor perpendicular to the wall surface, which are read by the laser distance measuring sensor in the step 1.
6. The fusion algorithm of the unmanned ship ranging unit and the inertial measurement unit in the aqueduct-like environment of claim 1, wherein the inertial measurement unit comprises an accelerometer, a gyroscope and a magnetic compass.
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