CN210895129U - Intelligent vehicle control device based on gray level camera detection - Google Patents
Intelligent vehicle control device based on gray level camera detection Download PDFInfo
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- CN210895129U CN210895129U CN202020115202.1U CN202020115202U CN210895129U CN 210895129 U CN210895129 U CN 210895129U CN 202020115202 U CN202020115202 U CN 202020115202U CN 210895129 U CN210895129 U CN 210895129U
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Abstract
The utility model belongs to the technical field of artificial intelligence and intelligent control, and discloses an intelligent vehicle control device based on gray level camera detection, which is provided with a sensor acquisition device for information acquisition; the signal processing device is connected with the sensor acquisition device and is used for analyzing and processing the acquired information; the singlechip is connected with the signal processing device and is used for carrying out control algorithm on the operation data; and the actuating mechanism is connected with the singlechip and used for executing the data generated by the singlechip. The single chip microcomputer is used as a core processor, and sufficient guarantee is provided for motion control of the intelligent trolley. The sensor detection part acquires road information by adopting an image acquisition module, an ultrasonic ranging module and an electromagnetic sensor so as to meet the requirement of acquiring complex road information. When the camera is abnormal in acquisition or does not scan the track, the track is searched by the electromagnetic sensor according to the electromagnetic guide line on the ground, so that the trolley can continue to stably run on the track, and further guarantee is provided.
Description
Technical Field
The utility model belongs to the technical field of artificial intelligence, intelligent control, especially, relate to an intelligent car controlling means based on grey level camera detects.
Background
Currently, the current state of the art commonly used in the industry is such that: in many countries in the world, the attention is directed to the front end of an information chain, namely information acquisition and processing, high-end sensors and intelligent sensing are developed into leading-edge technologies of modern technologies, and the third information wave with basic characteristics of wireless, ubiquitous, intelligent and networked functions and the like is lifted, so that the intelligent information acquisition and intelligent information processing are enhanced.
The robot is a product of high and new technology research, realizes the movement of the whole robot through mechanical structure design, control circuit design and software design, and completes specified tasks. The tracing intelligent car (tracing robot) is one kind of robot, and has been in history for decades. At present, the tracing intelligent car has higher and higher application value in the fields of entertainment, adolescent science popularization, innovation competition and the like, and the research level of a control system of the intelligent car is promoted. In the future, in the fields of automatic driving, intelligent control, logistics transportation and the like, the research results of the intelligent vehicle have wide application prospects, the intelligent vehicle for tracing is more intelligent by means of the technologies of image recognition, electromagnetic detection, intelligent control and the like, human beings can be replaced in more fields, and the development of the automation field is promoted.
However, many current intelligent tracking trolleys are generally provided with a plurality of infrared photoelectric sensors for detecting and tracking, and have the defects of low detection efficiency, application to few occasions, no obstacle avoidance capability, intelligent parking, incomplete control system and the like, and the success rate of completing tasks of the tracking intelligent trolleys is seriously influenced. Therefore, a control system scheme capable of ensuring that the trolley strictly follows a preset track to run, effectively judging a complex path and improving the tracking efficiency of the trolley is needed.
In summary, the problems of the prior art are as follows: the conventional intelligent tracing trolley is not high in detection efficiency, is only suitable for extremely few occasions, does not have the defects of obstacle avoidance capability, intelligent parking, incomplete control system and the like, and the success rate of completing tasks of the tracing intelligent trolley is seriously influenced. Therefore, the utility model discloses an improve inspection efficiency, multisensor auxiliary detection with the inspection of grey scale camera and acquire more road information, plan decision-making, intelligent information processing's control system for solve keep away the problem that barrier, parking and can go in complicated road.
SUMMERY OF THE UTILITY MODEL
Problem to prior art existence, the utility model provides an intelligence car controlling means based on grey level camera detects.
The utility model is realized in such a way that an intelligent vehicle control device based on gray level camera detection is provided with a sensor acquisition device for information acquisition;
furthermore, the intelligent vehicle control device takes an MK60FX512VLQ15 (32-bit) single chip microcomputer of Enzhipu semiconductor company as a core processor, adopts a CMOS gray camera MT9V032 image acquisition module as a main part, adopts an ultrasonic ranging module and adopts an electromagnetic induction device to assist in acquiring road information, directly processes a gray number group through an image processing algorithm to identify track elements and obtains an expected speed; meanwhile, an incremental encoder is adopted to obtain the real-time speed of the trolley, and then the closed-loop control of the speed of the trolley and the steering control of the steering engine are realized by utilizing a PID algorithm.
The signal processing device is connected with the sensor acquisition device and is used for analyzing and processing the acquired information; as shown in fig. 1, the signal processing apparatus is configured such that a signal for image processing is transmitted from the CMOS camera, a signal for obstacle detection is transmitted from the ultrasonic transmission distance sensor, and a signal for orthogonal decoding is transmitted from the incremental encoder.
The singlechip is connected with the signal processing device and is used for carrying out control algorithm on the operation data; as shown in fig. 1, signals acquired by each sensor are transmitted into a single chip microcomputer, and a control algorithm is performed on operating data to complete steering control and speed regulation closed-loop control of a steering engine.
And the actuating mechanism is connected with the singlechip and used for executing the data generated by the singlechip. As shown in fig. 1, the S3010 steering engine and the RN380 motor respectively execute control data of steering and speed regulation in the single chip microcomputer.
Further, the sensor acquisition device includes:
the image acquisition module is used for directly processing the gray number group by adopting a gray camera through an image processing algorithm to identify the track elements and obtain the expected speed;
the ultrasonic ranging module acquires the real-time speed of the trolley by adopting an incremental encoder;
and the electromagnetic induction device assists in acquiring road information.
Furthermore, the grayscale camera is arranged behind the steering engine, the electromagnetic induction device is arranged in front of the steering engine, and the ultrasonic ranging module is arranged in front of the steering engine.
Furthermore, the intelligent vehicle control device based on the gray-scale camera detection is also provided with a power supply module for providing required power supply for each module of the system, and a voltage regulating device and a voltage stabilizing device are installed in the power supply module.
Furthermore, a driving device is installed in the executing mechanism, the driving device is connected with a driving circuit through a driving module, and the driving circuit is connected with a driving motor.
In summary, the advantages and positive effects of the invention are: the intelligent vehicle control device based on the gray-scale camera detection takes an MK60FX512VLQ15 (32-bit) single chip microcomputer of Enzhipu semiconductor company as a core processor, the processor is provided with a 256KB flash memory, and can expand abundant analog, digital, communication, timing and control peripherals and access MCU I/O pins. The MCU product adopts an ARM Cortex-M4 inner core, the operating frequency is 150MHz, the MCU product not only has stronger performance, but also has higher storage capacity, and sufficient guarantee is provided for the motion control of the intelligent trolley. The sensor detection part adopts a CMOS gray camera MT9V032 image acquisition module, an ultrasonic ranging module and an electromagnetic sensor to acquire road information so as to meet the requirement of acquiring complex road information. The MT9V032 camera acquires a gray image, and the information quantity acquired by the camera is more sufficient than that acquired by a traditional black-and-white camera; the ultrasonic sensor and the camera sensor are combined to identify the obstacle, so that the judgment is more accurate than that of the single judgment of the ultrasonic sensor; when the camera is abnormal in acquisition or does not scan the track, the track is searched by the electromagnetic sensor according to the electromagnetic guide line on the ground, so that the trolley can continue to stably run on the track, and further guarantee is provided. In the aspect of hardware system design, the circuit is simple and compact, and particularly a power supply management part reduces the system load and improves the flexibility of the trolley; an RN380 motor, a Futaba S3010 steering engine and the like are used as actuating mechanisms, so that the structure is simple and the performance is good; and the incremental encoder is adopted to obtain the real-time speed of the trolley to realize the closed-loop speed regulation of the trolley. Compared with the traditional intelligent vehicle control device, the intelligent vehicle control device based on the gray level camera detection provided by the invention has the advantages that the adopted scheme improves the line searching precision and efficiency, and not only can judge straight roads and curved roads, but also can effectively judge complex road elements such as crossroads, transverse roadblocks, circular rings, starting lines and the like; when the image recognition of the trolley is abnormal, electromagnetic line seeking is used, so that the trolley can be prevented from being incapable of image recognition operation due to overlarge light; when the front obstacle is in the condition of setting for the distance, turn to and keep away the obstacle, ultrasonic sensor and camera sensor combine in order to detect the place ahead obstacle distance this moment, can judge more accurately than single ultrasonic sensor.
Drawings
Fig. 1 is the embodiment of the utility model provides an intelligent car controlling means design based on grey level camera detects.
Fig. 2 is a flow chart of design and manufacture of a control system according to an embodiment of the present invention.
Fig. 3 is a circuit structure of a power module according to an embodiment of the present invention.
Fig. 4 is a flow chart of a fitting center line provided by an embodiment of the present invention.
Fig. 5 is a general flow chart of software system design provided by the embodiment of the present invention.
Fig. 6 is a control method and a flowchart of an actuator according to an embodiment of the present invention.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings.
To solve the problems in the prior art, the present invention provides an intelligent vehicle control device based on gray level camera detection, and the present invention is described in detail below with reference to fig. 1 to 6.
The intelligent vehicle control device based on the gray level camera detection is a comprehensive device integrating functions of environment perception, planning decision, intelligent information processing, multifunctional assistance and the like, and is a typical high and new technology comprehensive body by intensively applying computer, modern sensing, information fusion, communication, artificial intelligence and automatic control technologies. And the control system adopts multi-sensor auxiliary detection to acquire more road information, makes planning decision and processes intelligent information.
The intelligent vehicle control device based on the gray level camera detection takes an MK60FX512VLQ15 (32-bit) single chip microcomputer of Enzhipu semiconductor company as a core processor, adopts a CMOS gray level camera MT9V032 image acquisition module as a main part, adopts an ultrasonic ranging module and an electromagnetic induction device to assist in acquiring road information, directly processes a gray level number group through an image processing algorithm to identify track elements and obtains an expected speed; meanwhile, an incremental encoder is adopted to obtain the real-time speed of the trolley, and the closed-loop control of the speed of the trolley and the steering control of a steering engine are realized by utilizing a PID algorithm. In the test, the whole control system design can effectively ensure that the trolley stably and quickly runs on the track.
The design of a control system of an intelligent vehicle is a core link for manufacturing the intelligent vehicle. The system consists of four parts, namely sensor acquisition, signal processing, a control algorithm and an actuating mechanism, wherein a singlechip is used as a core and is provided with a sensor, and the actuating mechanism and a driving circuit thereof form hardware of a control system; the signal processing and control algorithm is controlled by software in the single chip microcomputer. Therefore, the design of the control system of the intelligent vehicle can be divided into two parts of hardware system design and software system design.
The hardware system is a power module circuit, a sensor module circuit, a driving circuit, and a circuit (a clock circuit, a reset circuit, an indicator light circuit, and the like) which is relatively simple.
More specifically, in the power module circuit of the camera detection-based intelligent vehicle control system and the hardware circuit, the power module provides required power for other modules in the system, which is a basis and precondition for the motion of the intelligent vehicle, and therefore, it is important to design a stable and reliable power circuit. The power supply of all hardware circuits is provided by 18650 lithium batteries (7.2V, 2000mA), and the required power supply voltage and current are different because the circuit system consists of different module circuits. Therefore, a plurality of voltage stabilizing circuits are required to be designed.
Mainly comprises the following steps:
voltage of 3.3V: mainly for MK60FX512VLQ15(K60) singlechip, CMOS camera MT9V032 provides voltage.
Voltage of 5V: ultrasonic sensor, incremental encoder and interface circuit such as OLED display, pilot lamp.
Voltage of 6V: the working voltage is mainly provided for the S3010 steering engine.
Voltage of 7.2V: the lithium battery is mainly used for motor driving, and the voltage at two ends of the lithium battery can be directly used.
The 3.3V part is a core part and is easy to interfere, and a voltage stabilizing chip can be added independently, so that the mutual interference among modules is reduced, and the noise is reduced.
More specifically, in the camera detection-based intelligent vehicle control system and the drive circuit of the hardware circuit, the model of the rear wheel drive motor of the intelligent vehicle is an RN380 motor, the intelligent vehicle operates under the voltage of 7.2V, the no-load rotation speed is 15000+500rpm, and the maximum output power is 20W. The motor driving module controls the voltage at two ends of the motor drive, so that the trolley can be accelerated or braked. The motor driving circuit of the design adopts an MOS chip BTN7971, and has the characteristics of large current and high driving; the isolation chip 74HC244, the supply voltage is 5V, protect the one-chip computer to the maximum extent; the filter capacitor is arranged, so that the power supply can be effectively filtered.
More specifically, in the intelligent vehicle control system based on camera detection, in the circuit board design of the hardware circuit, the PCB is designed by adopting an Altium Designer software platform, and the designed PCB has good circuit performance and heat dissipation performance. On the premise of satisfying reliability and high efficiency, the use quantity of components is reduced as much as possible, the area of a PCB is reduced, the weight of the whole vehicle is effectively reduced, and the gravity center position of the model vehicle is reduced.
More specifically, in the intelligent vehicle control system based on camera detection, a camera is arranged behind a steering engine, an electromagnetic induction device is arranged in front of the steering engine, and an ultrasonic sensor is arranged in front of the steering engine, so that gravity center distribution, electromagnetic signal acquisition and blind area and look-ahead matching are facilitated;
the software system of the intelligent vehicle is a key part of design, and the software system of the scheme can be mainly divided into cascade PID control of system initialization, image acquisition and processing, and vehicle steering and speed regulation.
More specifically, in the system initialization of the intelligent vehicle control system based on camera detection and the software system, the operation of the system needs to be initialized first, and then some module parameters are adjusted. The initialization sequence is as follows: the method comprises the steps of clock initialization, encoder quadrature decoding initialization, steering engine and motor PID parameter initialization, camera initialization, steering engine and motor initialization, key initialization, interrupt initialization and LCD initialization.
More specifically, in the intelligent vehicle control system based on camera detection, in the image acquisition and processing of a software system, a problem inevitably occurs when a camera is adopted to extract road information, namely, the influence of camera distortion on track information, the track is aligned by the camera and transmitted into an upper computer, then partial images are intercepted, the images are guided into Matlab to be subjected to trapezoidal correction, inverse perspective transformation and parameter adjustment, and an image correction table and an inverse correction table are generated. The generated correction table is imported into the MK60FX512VLQ15 core processor, and the acquired image is corrected. The default acquisition of the CMOS grayscale camera MT9V032 is a 188 × 120 grayscale image, wherein the width is 188 and the height is 120. The information content of one gray image is far larger than that of a black-and-white image, and if proper image processing is used, the judgment of the track elements is more accurate; meanwhile, through testing, under the condition of uneven sunlight, the left and right borders of the track can be more effectively found out by using gray image processing; the image is not binarized. The image processing basic sequence is as follows: and preprocessing the acquired image information, and processing the special track elements by fitting a central line on the boundary. Such as crossroads, cross road blocks, rings, running lines, etc.
More specifically, in the intelligent vehicle control system based on camera detection, the serial PID control of the steering and speed regulation of the software system, for the steering of the trolley, a position PID is adopted, differential control is carried out by using the PID, and the differential control is substituted into the motor incremental PID to realize the serial PID control of the motor. Tests show that the method is used for regulating the speed stably. The real-time speed of the trolley is obtained by adopting an incremental encoder, which is equivalent to the dynamic real-time acquisition of interference information to a feedback system of a motor, and the interference information is combined with a PID control algorithm to form basic composite control of interference observation compensation. External interference is effectively inhibited, and a system model is more ideal.
More specifically, in the camera detection-based intelligent vehicle control system, our software system design is mainly performed under the IAR Embedded Workbench IDE platform, which is a very efficient Integrated Development Environment (IDE), enabling users to fully and efficiently develop and manage Embedded application engineering. As a development platform, the system has more perfect characteristics.
Referring to the overall design scheme of the control system shown in fig. 1, the control system comprises four parts, namely a sensor acquisition part, a signal processing part, a control algorithm part and an execution mechanism part, wherein an MK60FX512VLQ15 (32-bit) single chip microcomputer of Enzhipu semiconductor company is used as a core processor, a CMOS gray camera MT9V032 image acquisition module is used as a main part, an ultrasonic ranging module and an electromagnetic induction device are used for assisting in acquiring road information, a gray degree group is directly processed through the image processing algorithm to identify track elements, and an expected speed is obtained; meanwhile, an incremental encoder is adopted to obtain the real-time speed of the trolley, and then the closed-loop control of the RN380 motor of the trolley and the steering control of the S3010 steering engine are realized by utilizing a PID algorithm. Finally, the whole control system design can effectively ensure that the trolley stably and rapidly runs on the track.
Referring to fig. 2, the design and manufacturing process of the control system of the smart car described herein can be divided into 6 parts: selection and installation of components, design and manufacture of hardware circuits, vehicle model assembly, software control algorithm and strategy, hardware system improvement, test function and improved control system. Through discussion and test, the automatic control system of the trolley selects a singlechip MK60FX512VLQ15 as a core, a CMOS gray camera MT9V032 as an image acquisition module and an HC-SR04 ultrasonic ranging module as auxiliary judgment; an RN380 motor, a Futaba S3010 steering engine and the like are used as actuating mechanisms and matched with a battery and a corresponding driving circuit; on the basis of a hardware circuit, a series of calculations are performed by using road detection information and trolley operation parameter information through a software control algorithm; finally, the trolley can automatically identify the path, react to different types of tracks and stably and quickly run on the tracks.
More specifically, in the design and manufacture of the hardware circuit in the control system design and manufacture process, after the schematic diagram of the hardware circuit is completed, the unqualified circuit board may cause large signal interference, and the phenomenon of excessive heat generation occurs. Under the premise of satisfying the functions, it is also very important to design a PCB board having good circuit performance and heat dissipation performance. The basic flow of designing the PCB by adopting the AltiumDesigner comprises the following steps: setting a PCB design environment (grid point size, board layer parameters, wiring parameters and the like), planning a circuit board (determining a frame), importing a schematic diagram, packaging parts and distributing the parts (suggesting an interactive layout).
On the way of drawing the intelligent vehicle PCB, the PCB main control board is concise and compact, the space is saved, the use quantity of components and parts is reduced as much as possible, the area of the PCB is reduced, and the PCB is light and small as much as possible, so that the weight of the whole vehicle is reduced, and the gravity center position of a model vehicle is lowered. Among these needs to be noted are:
the GND of the power device and the control device should be isolated to prevent the former from interfering with the latter, and a 0 ohm resistor may be used; the filter capacitor is close to a module needing filtering as much as possible, the pins which are not used are not suspended, and the 10K resistor is used for pulling up or pulling down, so that the electromagnetic interference can be reduced.
In the aspect of layout, the same module is as close as possible, and the interactive layout is convenient.
In terms of wiring, the wires of the upper layer and the lower layer are preferably vertical (for example, the upper layer is integrally and transversely wired, and the lower layer is integrally and vertically wired), so that signal interference can be reduced, and the wiring is more convenient; the wiring line widths of the various lines are different, with the power supply line and the ground line being thicker.
In the aspect of structure, the arrangement of components and the routing of a circuit are attractive and neat, and the trolley can be well balanced by adopting a symmetrical structure as much as possible.
Referring to the circuit structure of the power supply module in fig. 3, the power supply of all hardware circuits is provided by 18650 lithium batteries (7.2V, 2000mA), and the required supply voltage and current are different because the circuit system is composed of different module circuits. Therefore, a plurality of voltage stabilizing circuits are required to be designed. Mainly comprises the following steps:
voltage of 3.3V: mainly for MK60FX512VLQ15(K60) singlechip, CMOS camera MT9V032 provides voltage.
Voltage of 5V: ultrasonic sensor, incremental encoder and interface circuit such as OLED display, pilot lamp.
Voltage of 6V: the working voltage is mainly provided for the S3010 steering engine.
Voltage of 7.2V: the lithium battery is mainly used for motor driving, and the voltage at two ends of the lithium battery can be directly used.
The 3.3V part is a core part and is easy to interfere, and a voltage stabilizing chip can be added independently, so that the mutual interference among modules is reduced, and the noise is reduced.
Referring to the general flow chart of software system design in fig. 5, the operation of the system needs to be initialized first, and then some module parameters are adjusted. The initialization sequence is as follows: the method comprises the steps of clock initialization, encoder quadrature decoding initialization, steering engine and motor PID parameter initialization, camera initialization, steering engine and motor initialization, key initialization, interrupt initialization and LCD initialization. And then, a problem inevitably occurs when the image is collected and processed by adopting a camera to extract road information, namely, the influence of camera distortion on the track information is caused. The generated correction table is imported into the MK60FX512VLQ15 core processor, and the acquired image is corrected. The default acquisition of the CMOS grayscale camera MT9V032 is a 188 × 120 grayscale image, wherein the width is 188 and the height is 120. The information content of one gray image is far larger than that of a black-and-white image, and if proper image processing is used, the judgment of the track elements is more accurate; meanwhile, through testing, under the condition of uneven sunlight, the left and right borders of the track can be more effectively found out by using gray image processing; the image is not binarized. The image processing basic sequence is as follows: and preprocessing the acquired image information, and processing the special track elements by fitting a central line on the boundary. This is followed by image information preprocessing, which requires some basic information of the image to be extracted and processed before the image is subjected to boundary fitting to the centerline. Firstly, the size of the collected image is determined to be 188 x 120 by default, but the collected and processed data is overlarge, and the trolley can move in time only by looking far, so that the 188 x 80 resolution is finally selected. Then, the image is compressed to reduce the image load of the single chip microcomputer, and the even number is halved, so that the image array is changed from image [80] [188] to image [80] [94 ]. Since the processing of the grayscale image is used, a transition edge threshold is required to determine the black and white boundaries to determine the course boundary. After testing, the black is 0 and the white is 255, and it is more appropriate to take 20 as the boundary jump edge threshold.
More specifically, in the general flow chart of software system design, the centerline fitting method is shown in fig. 4, and the image processing adopts a method of extracting the edge of the racetrack, and the basic idea is as follows:
1. and directly scanning the images of the three lines at the bottom in a traversing manner, and extracting the boundary according to the jumping edge.
2. The width of the track is determined, and the effective track edge is extracted within the width of the track, so that the interference which is not within the width range can be effectively filtered.
3. And searching the boundary of the current line near the boundary position of the previous line by using the continuity of the track.
4. And after recording the boundaries of the whole left and right tracks, summing and dividing by two to obtain a central line array.
5. Since the camera may distort the missing line, the line can be repaired by a two-point method.
6. In the aspect of weight distribution, lines are easy to lose at a distance, the bottom data can cause car lag response, and therefore the weight occupied by the middle array is large, and the upper weight and the lower weight are small.
More specifically, in the general flow chart of software system design, the special track elements include straight and curved tracks, crossroads, crossing roadblocks, circular rings, start lines, and the like. Wherein it is already possible to drive on straight and curved roads and crossroads by extracting the neutral line; and starting the electromagnetic induction device to seek according to the electromagnetic guide line when the image is not normally acquired.
Aiming at the transverse roadblock, a method for identifying the roadblock is adopted, because the roadblock has colors, the gray value of the roadblock is obviously different from that of a white track, the roadblock can be identified by adopting a longitudinal line searching mode, and an ultrasonic ranging sensor is used for assisting in judgment, so that the stability is improved. After the judgment is successful, the open loop control can be used for driving a fixed angle to go out, and then the fixed angle is reversely driven to return; the closer to the obstacle, the larger the angle. Aiming at the ring, the width of the track before the tangent point of the ring is mainly identified is changed from large to small and then is changed from small to large, and the line is supplemented after the ring is judged. And aiming at the starting line, a transverse line searching mode is adopted, if more than 3 left and right boundaries are searched, the starting line can be judged, and departure and parking are controlled.
Referring to fig. 6, the control method and flow chart of the actuator, in the serial PID control of steering and speed regulation of the software system, for the steering of the trolley, a position PID is adopted, and differential control is performed by using the current PID, and the differential control is substituted into the motor incremental PID, so that the serial PID control of the motor is realized. Tests show that the method is used for regulating the speed stably. The real-time speed of the trolley is obtained by adopting an incremental encoder, which is equivalent to the dynamic real-time acquisition of interference information to a feedback system of a motor, and the interference information is combined with a PID control algorithm to form basic composite control of interference observation compensation. External interference is effectively inhibited, and a system model is more ideal.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all the modifications and equivalents of the technical spirit of the present invention to any simple modifications of the above embodiments are within the scope of the technical solution of the present invention.
Claims (5)
1. The intelligent vehicle control device based on the gray-scale camera detection is characterized in that the intelligent vehicle control device based on the gray-scale camera detection is provided with a sensor acquisition device for information acquisition;
the signal processing device is connected with the sensor acquisition device and is used for analyzing and processing the acquired information;
the singlechip is connected with the signal processing device and is used for carrying out control algorithm on the operation data;
and the actuating mechanism is connected with the singlechip and used for executing the data generated by the singlechip.
2. The intelligent vehicle control device based on grayscale camera detection of claim 1, wherein the sensor acquisition device includes:
the image acquisition module is used for directly processing the gray number group by adopting a gray camera through an image processing algorithm to identify the track elements and obtain the expected speed;
the ultrasonic ranging module acquires the real-time speed of the trolley by adopting an incremental encoder;
and the electromagnetic induction device assists in acquiring road information.
3. The intelligent vehicle control device based on grayscale camera detection of claim 2, wherein the grayscale camera is installed behind the steering engine, the electromagnetic induction device is installed in front of the steering engine, and the ultrasonic ranging module is installed in front of the steering engine.
4. The intelligent vehicle control device based on the gray-scale camera detection as claimed in claim 1, wherein a power module for providing required power supply for each module of the system is further installed in the intelligent vehicle control device based on the gray-scale camera detection, and a voltage regulating device and a voltage stabilizing device are installed in the power module.
5. The intelligent vehicle control device based on the gray-scale camera detection as claimed in claim 1, wherein a driving device is installed in the actuator, the driving device is connected with a driving circuit through a driving module, and the driving circuit is connected with a driving motor.
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