CN104932505A - Automatic navigation system based on camera detection, control method of system and vertical balance car - Google Patents

Abstract

The invention discloses an automatic navigation system based on camera detection, a control method of the system and a vertical balance car. The system comprises a vertical balance data collection module, a camera data collection module and a signal processing and control module, wherein the vertical balance data collection module and the camera data collection module are connected with the signal processing and control module, the vertical balance data collection data obtains present attitude angle data of the vertical balance car, the camera data collection module collects gray-level image data of a runway, and the signal processing and control module collects, stores and processes the gray-level image data of the runway, the present attitude angle data of the vertical balance car, and present speed and differential speed data of the vertical balance car, and controls the balance of the vertical balance car according to the present attitude angle data of the vertical balance car. Thus, the road condition in front can be effective predicted, prediction can be controlled more effectively, and the control precision is improved.

Description

Based on automated navigation system, control method and vertical balanced car that camera detects

Technical field

The present invention relates to a kind of automated navigation system, especially a kind of automated navigation system, control method and vertical balanced car detected based on camera, belongs to the technical fields such as infotech, automatic technology, automatic navigation technology, self-equilibrating technology.

Background technology

Automatic navigation technology is one of focus of studying of people always, the many employings 1 of automatic navigation technology system traditional at present) gps system navigation 2) photoelectric sensor 3) electromagnetic sensor; Adopt the system of GPS navigation, due to civilian GPS positioning precision and the restriction of data sample rates, often there is the phenomenon of " drift ", cause that control hysteresis, control accuracy are low, the consequence of poor real; Photoelectric sensor and electromagnetic sensor is adopted to carry out the system of navigating, although its sampling rate is higher, good measuring accuracy, but photoelectric sensor and electromagnetic sensor are " line " " point " formulas to be measured, namely " one-dimensional " is measured, can not measure the global feature of road pavement, cause system can only measure current road state and be difficult to obtain following pavement state.

Tradition Self-Balancing vehicle adopts 8 single-chip microcomputers of 8MHz, and operational precision is low, arithmetic speed is slow, sampling rate is slow.Meanwhile, current automated navigation system is applied on brougham more, and brougham, by the characteristic of its four-wheel, determines it and moves un-skill, to turn to inconvenience feature.

Along with the development of Self-Balancing vehicle technology, Self-Balancing vehicle plays the part of important role by many occasions, but the Self-Balancing vehicle technology using camera to carry out self-navigation still belongs to blank.In addition, the single-axis attitude algorithm that traditional Self-Balancing vehicle adopts is slightly inadequate in security compared with three-axis attitude algorithm.

Summary of the invention

First object of the present invention is the defect in order to solve above-mentioned prior art, and provide a kind of automated navigation system detected based on camera, this system can effectively predict front road conditions, carries out more effective PREDICTIVE CONTROL, improves control accuracy.

Second object of the present invention is the control method providing a kind of above-mentioned automated navigation system.

3rd object of the present invention is to provide a kind of vertical balanced car.

First object of the present invention can reach by taking following technical scheme:

Based on the automated navigation system that camera detects, described system comprises vertical balanced data acquisition module, camera data acquisition module and signal transacting and control module, and described vertical balanced data acquisition module, camera data acquisition module are connected with signal transacting and control module respectively; Wherein:

Described vertical balanced data acquisition module, for the current pose angular data of data acquisition vertical balanced car gathered by IMU sensor;

Described camera data acquisition module, for gathering the greyscale image data of runway by camera sensing device;

Described signal transacting and control module, for being collected by embedded computer, the greyscale image data of Storage and Processing runway, the current pose angular data of vertical balanced car, the current vehicle speed of vertical balanced car and differential data, and according to the current pose angular data of vertical balanced car, balance is carried out to vertical balanced car and controls.

As a kind of preferred version, described signal transacting and control module comprise:

Balance control module, for according to the current pose angular data obtained, carries out balance to vertical balanced car and controls;

Graphics processing unit, for carrying out computing to the runway greyscale image data gathered.

As a kind of preferred version, described IMU sensor is made up of L3G4200 gyroscope and MMA8451 accelerometer, and described gyroscope is connected with embedded computer by iic bus with accelerometer.

As a kind of preferred version, described camera sensing device adopts imageing sensor OV7620, and is connected with embedded computer by DMA passage.

As a kind of preferred version, described embedded computer adopts Freescale company Kinetis MK60DN512 Series MCU to form.

Second object of the present invention can reach by taking following technical scheme:

Based on the control method of above-mentioned automated navigation system, described method comprises:

In S1, IMU sensor, the data of gyroscope and accelerometer input embedded computer by iic bus, embedded computer is by the inverse trigonometric function computing to accelerometer data, in conjunction with the integration data of gyro data, carry out first-order low-pass ripple, obtain working as previous belief, calculate the current pose angular data of vertical balanced car; Meanwhile, the data input embedded computer of camera sensing device, obtains the greyscale image data of runway;

S2, in embedded computer, the attitude angle data that calculated by embedded computer of balance control module substitutes into PID controller, and the balance that the motor rotation controlling vertical balanced car realizes vertical balanced car controls; Simultaneously, graphics processing unit carries out Dynamic Binarization process to the runway greyscale image data that embedded computer obtains, identify that specifically to use with black line be the runway at edge, by carrying out edge feature calculating to the border of runway, obtain the road conditions of runway, and send differential signal to the motor of vertical balanced car by embedded computer, thus real-time follow-up track.

As a kind of preferred version, embedded computer resolving attitude angle data described in step S1, specific as follows:

Static attitude angle is resolved by following formulae discovery, and the attitude angle calculated is roll angle, the angle of pitch two attitudes:

${\mathrm{\ρ}}_o={\tan }^{-1}\left(\frac{a_x}{\sqrt{a_y^2+a_z^2}}\right)$

Wherein, a _x, a _y, a _zfor orthogonal 3-axis acceleration data, for the static angle of pitch, ρ _ofor static roll angle, a is set _yorthogonal with motor shaft, then effectively static attitude angular data is angle;

Add gyro data ω and do dynamic data combining, get ω _xparallel with motor shaft, then the dynamic angle of pitch after merging is:

Wherein, Confidence _gyrofor gyrostatic degree of confidence, Confidence _accefor the degree of confidence of accelerometer.

As a kind of preferred version, PID controller described in step S2 is by following formulae discovery:

$u=K_p*e+K_i\∫\mathrm{edt}+K_d\frac{\mathrm{de}}{\mathrm{dt}}$

Wherein, K _pfor controller proportional gain factor, K _ifor controller integral coefficient, K _dfor controller differential coefficient, e is error;

Use during Embedded computer system and make discretize:

$u_k=K_p*e_k+K_i*\underset{j=0}{\overset{k}{\mathrm{\Σ}}}{e}_j+K_d*(e_k-e_{k-1})$

Wherein, e is the numerical value that attitude angle departs from balance angle, and u is the dutycycle directly outputting to motor, e _kand e _k-1represent the deviation in k and k-1 moment respectively.

As a kind of preferred version, Dynamic Binarization described in step S2 adopts Bernsen algorithm, and this Bernsen arthmetic statement is as follows:

$T(i,j)=0.5\×(\underset{-\mathrm{\ω}\≤n\≤\mathrm{\ω}}{\underset{-\mathrm{\ω}\≤m\≤\mathrm{\ω}}{\max }}f(i+m,j+n)+\underset{-\mathrm{\ω}\≤n\≤\mathrm{\ω}}{\underset{-\mathrm{\ω}\≤m\≤\mathrm{\ω}}{\min }}f(i+m,j+n))$

Wherein, f (i, j) is for image is at the gray-scale value at pixel (i, j) place, and T (i, j) is the binary-state threshold of each pixel (i, j) in image;

Carry out binaryzation to pixel (i, j) each in image with b (i, the j) pointwise of binaryzation function, b (i, j) function is as follows:

$b(i,j)\left\{\begin{array}{cc}0& f(i,j)<T(i,j)\\ 1& f(i,j)\&GreaterEqual;T(i,j)\end{array}\right.$

Image after binaryzation, its runway edge, by having a strong rising edge, namely identifies runway edge by the saltus step of this rising edge of embedded computer identification.

3rd object of the present invention can reach by taking following technical scheme:

Vertical balanced car, comprises car body, left wheel, right wheel and motor, and described left wheel and right wheel are arranged on the both sides, bottom of car body, and described driven by motor left wheel and right wheel movement, also comprise above-mentioned automated navigation system; Wherein, described IMU sensor and embedded computer are arranged in car body, and described camera sensing device is arranged on car body top, and described embedded computer is connected with motor.

The present invention has following beneficial effect relative to prior art:

1, automated navigation system of the present invention adopts camera to carry out the navigation of plane formula, can effectively predict front road conditions, carries out more effective PREDICTIVE CONTROL, improves control accuracy; In addition, have benefited from the sampling rate of IMU sensor and the processing speed of embedded computer, control accuracy also significantly improves.

2, automated navigation system of the present invention can carry out Dynamic Binarization process to the runway greyscale image data obtained, identify that specifically to use with black line be the runway at edge, by the feature calculation to runway, the road conditions such as the steering characteristic that must go off the course, linear feature, cross characteristics, and send differential signal by embedded computer to the motor of vertical balanced car, real-time detecting and tracking can be carried out to given runway.

3, automated navigation system of the present invention can be applied on vertical balanced car, compared with traditional brougham, lighter, dexterous, and in energy consumption, and due to the own characteristic of vertical balanced car, its energy consumption is also low than brougham comparatively speaking.

4, light, the safety of vertical balanced car of the present invention, environmental protection, owing to applying automated navigation system, runway can be detected in real time, there is the advantage that recognition effect is good, tracking velocity is high and real-time, and adopt the upright algorithm of balance and image processing algorithm is efficiently convenient, practicability and effectiveness.

Accompanying drawing explanation

Fig. 1 is the automated navigation system structured flowchart of the embodiment of the present invention 1.

Fig. 2 is the inclination angle fusion results figure of the embodiment of the present invention 1.

Fig. 3 is the inverted pendulum model figure of the embodiment of the present invention 1.

Fig. 4 is the medium filtering process flow diagram of the embodiment of the present invention 1.

Fig. 5 is the Bernsen algorithm flow chart of the embodiment of the present invention 1.

The former figure of runway image that Fig. 6 obtains for the embodiment of the present invention 1.

Fig. 7 is the runway image of the embodiment of the present invention 1 after Bernsen algorithm process.

Fig. 8 is the vertical balanced bassinet structure schematic diagram of the embodiment of the present invention 2.

Embodiment

Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but embodiments of the present invention are not limited thereto.

Embodiment 1:

As shown in Figure 1, the automated navigation system of the present embodiment comprises vertical balanced data acquisition module, camera data acquisition module and signal transacting and control module, and described vertical balanced data acquisition module, camera data acquisition module are connected with signal transacting and control module respectively;

Described vertical balanced data acquisition module, for the current pose angular data of data acquisition vertical balanced car gathered by IMU sensor;

Described camera data acquisition module, for gathering the greyscale image data of runway by camera sensing device;

Described signal transacting and control module, for being collected by embedded computer, the greyscale image data of Storage and Processing runway, the current pose angular data of vertical balanced car, the current vehicle speed of vertical balanced car and differential data, and according to the current pose angular data of vertical balanced car, balance is carried out to vertical balanced car and controls; It can comprise:

Balance control module, for according to the current pose angular data obtained, carries out balance to vertical balanced car and controls;

Graphics processing unit, for carrying out computing to the runway greyscale image data gathered.

Described embedded computer adopts Freescale company Kinetis MK60DN512 Series MCU to form; Above-mentioned IMU sensor is made up of L3G4200 gyroscope and MMA8451 accelerometer, and described gyroscope is connected with embedded computer by iic bus with accelerometer; Described camera sensing device adopts imageing sensor OV7620.

The control procedure of the automated navigation system of the present embodiment is as follows:

In S1, IMU sensor, the data of gyroscope and accelerometer input embedded computer by iic bus, embedded computer is by the inverse trigonometric function computing to accelerometer data, in conjunction with the integration data of gyro data, carry out first-order low-pass ripple, obtain working as previous belief, calculate the current pose angular data of vertical balanced car; Meanwhile, the data input embedded computer of camera sensing device, obtains the greyscale image data of runway;

S11, embedded computer resolving attitude angle data, specific as follows:

1) static attitude angle is resolved by following formulae discovery, and the attitude angle calculated is roll angle, the angle of pitch two attitudes:

${\mathrm{\&rho;}}_o={\tan }^{-1}\left(\frac{a_x}{\sqrt{a_y^2+a_z^2}}\right)$

Wherein, a _x, a _y, a _zfor orthogonal 3-axis acceleration data, for the static angle of pitch, ρ _ofor static roll angle, a is set _yorthogonal with motor shaft, then effectively static attitude angular data is angle;

2) static attitude angular data is comparatively large at dynamic time-harmonic wave noise, should add gyro data ω thus and do dynamic data combining; Get ω _xparallel with motor shaft, then the dynamic angle of pitch after merging is:

Wherein, Confidence _gyrofor gyrostatic degree of confidence, Confidence _accefor the degree of confidence of accelerometer, the degree of confidence of usually getting accelerometer is 5%, and gyroscope degree of confidence is 95%, and degree of confidence refers to the credibility of the measured value of measured parameter;

As shown in Figure 2, input signal is the data waveform obtained as stated above+angular signal of-90 °, can see, can obtain reasonable effect according to the method described above from waveform.

S2, in embedded computer, the attitude angle data that calculated by embedded computer of balance control module substitutes into PID controller, and the balance that the motor rotation controlling vertical balanced car realizes vertical balanced car controls; Simultaneously, graphics processing unit carries out Dynamic Binarization process to the runway greyscale image data that embedded computer obtains, identify that specifically to use with black line be the runway at edge, by carrying out edge feature calculating to the border of runway, obtain the road conditions such as the steering characteristic of runway, linear feature, cross characteristics, and send differential signal to the motor of vertical balanced car by embedded computer, thus real-time follow-up track.

The attitude angle data that embedded computer calculates by S21, balance control module substitutes into PID controller, is specially:

1) reversible pendulum system is modeled as to vertical balanced car, as shown in Figure 3.

According to stress balance, the restoring force suffered by inverted pendulum is:

F＝mg*sinθ-ma*cosθ

Near working point, θ very little (≈ 0), therefore near working point, doing linearization process to above-mentioned equation, then above-mentioned equation turns to:

F＝mgθ-mk ₁θ

Consider the factors such as air resistance, then must add a damping force, this damping force direction should be consistent with inverted pendulum angular velocity direction, then after considering damping force factor, restoring force is:

F＝mgθ-mk ₁θ-mk ₂θ′

Suppose that power of motor is enough large, then motor input voltage and Driving Torque power are approximated to linear relationship:

F＝k ₃*u

Associating above formula obtains

u＝k ₁*θ+k ₂*θ′

Therefore show that the relation controlled between Motor torque and angle is approximated to PD control planning.

2) equation of motion is set up to system.

If system gravity and system bottom distance are L, a (t) is system motion acceleration, x (t) is disturbance acceleration, then the equation of motion of system is:

$L\frac{d^2\mathrm{\&theta;}\left(t\right)}{{\mathrm{dt}}^2}=g\sin \left[\mathrm{\&theta;}\left(t\right)\right]-a\left(t\right)\cos \left[\mathrm{\&theta;}\left(t\right)\right]+\mathrm{Lx}\left(t\right)$

Near working point, because θ is very little, therefore near working point, linearization process is done to the above-mentioned equation of motion:

$L\frac{d^2\mathrm{\&theta;}\left(t\right)}{{\mathrm{dt}}^2}=\mathrm{g\&theta;}\left(t\right)-a\left(t\right)+\mathrm{Lx}\left(t\right)$

During system balancing, a (t)==0;

Now:

$L\frac{d^2\mathrm{\&theta;}\left(t\right)}{{\mathrm{dt}}^2}=\mathrm{g\&theta;}\left(t\right)+\mathrm{Lx}\left(t\right)$

System transter is:

$H\left(s\right)=\frac{\mathrm{\&Theta;}\left(s\right)}{X\left(s\right)}=\frac{1}{s^2-\frac{g}{L}}$

Obviously now system has two limits, and one of them limit is at the right-half plane of s plane, and now system is unstable;

After additional proportion differential controls, system transter becomes:

$H\left(s\right)=\frac{\mathrm{\&Theta;}\left(s\right)}{X\left(s\right)}\frac{1}{s^2+\frac{k_2}{L}s+\frac{k_1-g}{L}}$

Two limits of system are:

$S_p=\frac{{-k}_2\&PlusMinus;\sqrt{k_2^2-4L(k_1-g)}}{2L}$

Make two limits all be positioned at the Left half-plane of s plane, then must k ₁> g, k ₂> 0;

The parameter using PD controller to make system stability can be drawn accordingly.

3) for improving stable state accuracy, adding integration control, adopting PID controller to control to this system.

Described PID controller is by following formulae discovery:

$u=K_p*e+K_i\&Integral;\mathrm{edt}+K_d\frac{\mathrm{de}}{\mathrm{dt}}$

Wherein, K _pfor controller proportional gain factor, K _ifor controller integral coefficient, K _dfor controller differential coefficient, e is error;

Use during Embedded computer system and make discretize:

$u_k=K_p*e_k+K_i*\underset{j=0}{\overset{k}{\mathrm{\&Sigma;}}}{e}_j+K_d*(e_k-e_{k-1})$

Wherein, e is the numerical value that attitude angle departs from balance angle, and u is the dutycycle directly outputting to motor, e _kand e _k-1represent the deviation in k and k-1 moment respectively.

S22, graphics processing unit carry out Dynamic Binarization process to the runway greyscale image data that embedded computer obtains, and identify that specifically to use with black line be the runway at edge, are specially:

1) view data owing to collecting has certain noise, uses median filtering algorithm to carry out filtering as shown in Figure 4 to signal; Median filtering method is a kind of nonlinear smoothing technology, the gray-scale value of each pixel is set to the intermediate value of all pixel gray-scale values in this some neighborhood window by it, its principle is that the Mesophyticum of each point value in a neighborhood of this point of value of any in digital picture or Serial No. is replaced, allow the actual value that the pixel value of surrounding is close, thus eliminate isolated noise spot.

2) for adapting to different illumination conditions, binary-state threshold should be made variable to the binary conversion treatment of image, threshold variable Dynamic Binarization Algorithm adopts Bernsen algorithm.

Bernsen arthmetic statement is as follows:

$T(i,j)=0.5\&times;(\underset{-\mathrm{\&omega;}\&le;n\&le;\mathrm{\&omega;}}{\underset{-\mathrm{\&omega;}\&le;m\&le;\mathrm{\&omega;}}{\max }}f(i+m,j+n)+\underset{-\mathrm{\&omega;}\&le;n\&le;\mathrm{\&omega;}}{\underset{-\mathrm{\&omega;}\&le;m\&le;\mathrm{\&omega;}}{\min }}f(i+m,j+n))---\left(1\right)$

Wherein, f (i, j) is for image is at the gray-scale value at pixel (i, j) place, and T (i, j) is the binary-state threshold of each pixel (i, j) in image;

Carry out binaryzation to pixel (i, j) each in image with b (i, the j) pointwise of binaryzation function, b (i, j) function is as follows:

$b(i,j)=\left\{\begin{array}{cc}0& f(i,j)<T(i,j)\\ 1& f(i,j)\&GreaterEqual;T(i,j)\end{array}\right.$

Bernsen binaryzation process flow diagram as shown in Figure 5; Bernsen Binarization methods is a kind of local binarization method of classics, and specific algorithm is as shown in above formula (1).

Runway image effect through binaryzation contrasts as shown in Figure 6, Figure 7; As can see from Figure 7, the image after binaryzation, its runway edge, by having a strong rising edge, namely identifies runway edge by the saltus step of this rising edge of embedded computer identification.

3) runway curvature radius calculation.

If the runway edge identified is f (i, j), then image radius-of-curvature is on centerline:

$k=\frac{f^{\&prime;\&prime;}(\mathrm{mid},\mathrm{mid})}{{(1+f^{\&prime;}{(\mathrm{mid},\mathrm{mid})}^2)}^{3/2}}$

If curvature and the linear change of differential, then curvature is as follows to left and right motor PWM differential computing formula:

PWM _Difference＝K _Diff*k

Wherein, K _difffor differential gain.

In sum, automated navigation system of the present invention adopts camera to carry out the navigation of plane formula, can effectively predict front road conditions, carries out more effective PREDICTIVE CONTROL, improves control accuracy; In addition, have benefited from the sampling rate of IMU sensor and the processing speed of embedded computer, control accuracy also significantly improves.

Embodiment 2:

The present embodiment is an application example, the automated navigation system of above-described embodiment 1 is applied in vertical balanced car.

As shown in Figure 8, described vertical balanced car comprises car body 1, left wheel 2, right wheel 3, motor (not shown) and automated navigation system, described left wheel 2 and right wheel 3 are arranged on the both sides, bottom of car body 1, and described driven by motor left wheel 2 and right wheel 3 move; Wherein, described IMU sensor 4 and embedded computer 5 are arranged in car body 1, and described camera sensing device 6 is arranged on car body 1 top, and described embedded computer 5 is connected with motor.

The above; be only patent preferred embodiment of the present invention; but the protection domain of patent of the present invention is not limited thereto; anyly be familiar with those skilled in the art in the scope disclosed in patent of the present invention; be equal to according to the technical scheme of patent of the present invention and patent of invention design thereof and replaced or change, all belonged to the protection domain of patent of the present invention.

Claims (10)

1. based on the automated navigation system that camera detects, it is characterized in that: described system comprises vertical balanced data acquisition module, camera data acquisition module and signal transacting and control module, described vertical balanced data acquisition module, camera data acquisition module are connected with signal transacting and control module respectively; Wherein:

Described vertical balanced data acquisition module, for the current pose angular data of data acquisition vertical balanced car gathered by IMU sensor;

Described camera data acquisition module, for gathering the greyscale image data of runway by camera sensing device;

Described signal transacting and control module, for being collected by embedded computer, the greyscale image data of Storage and Processing runway, the current pose angular data of vertical balanced car, the current vehicle speed of vertical balanced car and differential data, and according to the current pose angular data of vertical balanced car, balance is carried out to vertical balanced car and controls.

2. the automated navigation system detected based on camera according to claim 1, is characterized in that: described signal transacting and control module comprise:

Balance control module, for according to the current pose angular data obtained, carries out balance to vertical balanced car and controls;

Graphics processing unit, for carrying out computing to the runway greyscale image data gathered.

3. the automated navigation system detected based on camera according to claim 1 and 2, it is characterized in that: described IMU sensor is made up of L3G4200 gyroscope and MMA8451 accelerometer, and described gyroscope is connected with embedded computer by iic bus with accelerometer.

4. the automated navigation system detected based on camera according to claim 1 and 2, be is characterized in that: described camera sensing device adopts imageing sensor OV7620, and is connected with embedded computer by DMA passage.

5. the automated navigation system detected based on camera according to claim 1 and 2, is characterized in that: described embedded computer adopts Freescale company Kinetis MK60DN512 Series MCU to form.

6., based on the control method of automated navigation system described in claim 3, it is characterized in that described method comprises:

In S1, IMU sensor, the data of gyroscope and accelerometer input embedded computer by iic bus, embedded computer is by the inverse trigonometric function computing to accelerometer data, in conjunction with the integration data of gyro data, carry out first-order low-pass ripple, calculate the current pose angular data of vertical balanced car; Meanwhile, the data input embedded computer of camera sensing device, obtains the greyscale image data of runway;

S2, in embedded computer, the attitude angle data that calculated by embedded computer of balance control module substitutes into PID controller, and the balance that the motor rotation controlling vertical balanced car realizes vertical balanced car controls; Simultaneously, graphics processing unit carries out Dynamic Binarization process to the runway greyscale image data that embedded computer obtains, identify that specifically to use with black line be the runway at edge, by carrying out edge feature calculating to the border of runway, obtain the road conditions of runway, and send differential signal to the motor of vertical balanced car by embedded computer, thus real-time follow-up track.

7. the control method of automated navigation system according to claim 6, is characterized in that: embedded computer resolving attitude angle data described in step S1, specific as follows:

Static attitude angle is resolved by following formulae discovery, and the attitude angle calculated is roll angle, the angle of pitch two attitudes:

${\mathrm{\&rho;}}_o={\tan }^{-1}\left(\frac{a_x}{\sqrt{a_y^2+a_z^2}}\right)$

Wherein, a _x, a _y, a _zfor orthogonal 3-axis acceleration data, for the static angle of pitch, ρ _ofor static roll angle, a is set _yorthogonal with motor shaft, then effectively static attitude angular data is angle;

Add gyro data ω and do dynamic data combining, get ω _xparallel with motor shaft, then the dynamic angle of pitch after merging is:

Wherein, Confidence _gyrofor gyrostatic degree of confidence, Confidence _accefor the degree of confidence of accelerometer.

8. the control method of automated navigation system according to claim 6, is characterized in that: PID controller described in step S2 is by following formulae discovery:

$u=K_p*e+K_i\&Integral;\mathrm{edt}+K_d\frac{\mathrm{de}}{\mathrm{dt}}$

Wherein, K _pfor controller proportional gain factor, K _ifor controller integral coefficient, K _dfor controller differential coefficient, e is error;

Use during Embedded computer system and make discretize:

$u_k=K_p*e_k+K_i*\underset{j=0}{\overset{k}{\mathrm{\&Sigma;}}}{e}_j+K_d*(e_k-e_{k-1})$

Wherein, e is the numerical value that attitude angle departs from balance angle, and u is the dutycycle directly outputting to motor, e _kand e _k-1represent the deviation in k and k-1 moment respectively.

9. the control method of automated navigation system according to claim 6, is characterized in that: Dynamic Binarization described in step S2 adopts Bernsen algorithm, and this Bemsen arthmetic statement is as follows:

$T(i,j)=0.5\&times;(\underset{\underset{-\mathrm{\&omega;}\&le;n\&le;\mathrm{\&omega;}}{-\mathrm{\&omega;}\&le;m\&le;\mathrm{\&omega;}}}{\max }f(i+m,j+n)+\underset{\underset{-\mathrm{\&omega;}\&le;n\&le;\mathrm{\&omega;}}{-\mathrm{\&omega;}\&le;m\&le;\mathrm{\&omega;}}}{\min }f(i+m,j+n))$

Wherein, f (i, j) is for image is at the gray-scale value at pixel (i, j) place, and T (i, j) is the binary-state threshold of each pixel (i, j) in image;

Carry out binaryzation to pixel (i, j) each in image with b (i, the j) pointwise of binaryzation function, b (i, j) function is as follows:

$b(i,j)=\left\{\begin{array}{cc}0& f(i,j)<T(i,j)\\ 1& f(i,j)\&GreaterEqual;T(i,j)\end{array}\right.$

Image after binaryzation, its runway edge, by having a strong rising edge, namely identifies runway edge by the saltus step of this rising edge of embedded computer identification.

10. vertical balanced car, comprise car body, left wheel, right wheel and motor, described left wheel and right wheel are arranged on the both sides, bottom of car body, and described driven by motor left wheel and right wheel movement, is characterized in that: also comprise automated navigation system according to claim 1; Wherein, described IMU sensor and embedded computer are arranged in car body, and described camera sensing device is arranged on car body top, and described embedded computer is connected with motor.