CN106873603B - Zynq platform-based intelligent vehicle control system and control method for computer mouse - Google Patents
Zynq platform-based intelligent vehicle control system and control method for computer mouse Download PDFInfo
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Abstract
The invention discloses a Zynq platform-based intelligent vehicle control system and a control method for a computer mouse, which comprises the following steps: the system comprises six MMEMS inertial sensors, an infrared transmitting tube, an infrared receiving tube, a camera, a coding disc, a main controller, a full-bridge driving circuit, a micro motor, a wireless data transmission module and a human-computer interface module; the six-axis MMEMS inertial sensor, the infrared transmitting tube, the infrared receiving tube, the camera, the coding disc, the wireless data transmission module and the human-computer interface module are connected with the main controller; the main controller is connected with a full-bridge driving circuit, the full-bridge driving circuit is connected with a micro motor, and the micro motor is connected with an encoding disc. The advanced Zynq FPGA is adopted as a system control core, so that the performance of a computer mouse can be improved; the Zynq platform-based intelligent vehicle control method for the computer mouse comprises an algorithm for searching an unknown maze and an algorithm for solving an optimal path according to acquired maze information.
Description
Technical Field
The invention belongs to the technical field of intelligent robots, and particularly relates to a Zynq platform-based computer mouse intelligent vehicle control system and a control method.
Background
The father Shannon of the information theory not only first applies artificial intelligence to the aspect of playing chess by a computer, but also invents an electronic mouse which can automatically pass through a maze, namely a computer mouse, so as to prove that the computer can learn intelligently. The computer mouse is a small wheeled robot with artificial intelligence, which consists of an embedded microprocessor, a sensor and a motor. The computer mouse can be regarded as a small intelligent vehicle control system integrating multiple engineering subject knowledge, the problems of electronics, electricity, machinery, algorithms, computers and the like need to be considered during design, and the aspects of weight, speed, power consumption, sensing technology, gravity center, algorithms and the like need to be comprehensively considered in design. According to the computer mouse maze competition rules formulated by the international Institute of Electrical and Electronic Engineering (IEEE), a computer mouse needs to walk in an unknown maze consisting of 16 × 16 cells with the size of 18.5 × 18.5cm, search the information in the maze and find a path from a starting point to an end point of the maze, so as to reach the end point of the maze from the starting point at the fastest speed. In competition, a computer mouse needs to complete the solution of a maze path, and specifically comprises two tasks of searching an unknown maze and solving a shortest path. In the current computer mouse competition, an infrared distance measurement scheme is generally adopted to detect a wall, so that the wall is easily interfered by an external environment; the commonly adopted Flood-Fill maze search algorithm is to perform maze search and optimal path solution simultaneously, the Flood-Fill algorithm needs to be executed again every time a square is entered, and distance values of 256 maze squares need to be recalculated and updated every time. Obviously, especially in the initial stage of maze detection and under the condition of incomplete information, a great amount of meaningless calculation operations and data movement are carried out, and system resources are wasted.
In summary, the prior art has the following problems: the maze of the intelligent vehicle for the computer mouse is easy to be interfered by the external environment when searching, and has the problems of more resource consumption of a hardware system, long time consumption for solving the shortest path, easy falling into the local optimal solution and the like.
Disclosure of Invention
The invention aims to provide a Zynq platform-based computer mouse intelligent vehicle control system and a control method, and aims to solve the problems that a maze of a computer mouse intelligent vehicle is easily interfered by an external environment, more hardware system resources are consumed, the shortest path solving time is long, the computer mouse intelligent vehicle is easily trapped into a local optimal solution and the like in the conventional computer mouse intelligent vehicle searching process.
The invention is realized in this way, a computer mouse intelligent vehicle control system based on Zynq platform, the computer mouse intelligent vehicle control system based on Zynq platform includes:
the camera is used for acquiring a maze image; the Zynq FPGA platform-based main controller is used for carrying out image processing on the acquired maze images so as to identify all wall positions in the maze, solving an optimal path through an improved Flood-Fill algorithm, and controlling the micro motor to walk the maze at the highest speed and sprint according to the solved optimal path;
the coding disc is used for accurately controlling the rotating speed and the direction of the micro motor by acquiring the rotating angle of the shaft end of the micro motor and feeding the rotating angle back to the main controller;
the infrared transmitting tube and the infrared receiving tube are used for positioning correction to prevent the intelligent vehicle of the computer mouse from deviating, and are used for wall detection to prevent the intelligent vehicle of the computer mouse from touching the wall;
the six-axis MMEMS inertial sensor is used for feeding back accurate motion attitude information to the main controller so as to control the intelligent vehicle of the computer mouse to keep upright;
the human-computer interface module and the wireless data transmission module are used for debugging the intelligent vehicle for the computer mouse, the wireless data transmission module can be in wireless connection with the host or the mobile terminal in two modes of Wi-Fi and Bluetooth, the working state of the intelligent vehicle for the computer mouse and information of various sensors can be transmitted to the host or the mobile terminal in a wireless mode in real time, and the host or the mobile terminal can also control the intelligent vehicle for the computer mouse in real time.
The six-axis MMEMS inertial sensor, the infrared transmitting tube, the infrared receiving tube, the camera, the coding disc, the wireless data transmission module and the human-computer interface module are connected with the main controller;
the main controller is connected with a full-bridge driving circuit, the full-bridge driving circuit is connected with a micro motor, and the micro motor is connected with an encoding disc.
The invention also aims to provide a Zynq platform-based intelligent vehicle control method for the Zynq platform-based intelligent vehicle control system for the computer mouse, which comprises a search algorithm for an unknown maze and an algorithm for solving an optimal path according to acquired maze information;
the search algorithm of the unknown maze is as follows: firstly, acquiring a color image through a camera, then carrying out histogram equalization in an HSV color space, carrying out color separation and binarization according to the color of a maze wall, carrying out contour detection after denoising through morphological operation, then searching candidate marks according to the characteristic that the wall is a regular quadrangle, and storing the mark points in an anticlockwise sequence; finally, after correcting the distorted maze image collected by the camera through perspective transformation, counting the number of non-0 pixels of each small square, and extracting wall information by judging whether the small square is all non-0 pixels;
the optimal path solving algorithm comprises the following steps: firstly, according to the unknown maze search algorithm (obtaining the maze wall information and storing the wall information as a 16 x 16 array, the array stores the mutual communication condition of the 16 x 16 maze lattices in the maze drawing, then creating a queue, using the coordinate position of the target lattice of the maze as the initial value of the queue, then creating a 16 x 16 array, using the array to store the distance between each maze lattice and the target lattice or called a code value, initially setting all elements of the array to be 255, assigning the corresponding element of the target lattice in the array to be 0, then putting the target lattice into the queue, accessing the adjacent unfilled and connected lattices, filling the adjacent unfilled and connected lattices with a code value which is 1 greater than the code value of the preceding lattice, and putting the coordinate position into the queue, then judging whether the adjacent unfilled and connected lattices exist, if so, entering the next round of circulation, traversing the distance of each position from the target lattice by the method, obtaining a distance value coding table; and finally, sorting the grid code values in a descending order to obtain the optimal path from the starting grid to the target grid.
The invention also aims to provide the unmanned automobile provided with the Zynq platform-based computer mouse intelligent automobile control system.
The invention also aims to provide an industrial intelligent control system provided with the Zynq platform-based computer mouse intelligent vehicle control system.
According to the intelligent vehicle control system and the control method for the computer mouse based on the Zynq platform, the advanced ZYNQ is used as a system control core, so that the performance of the computer mouse can be improved; the classic Flood-Fill maze optimal path algorithm averagely needs 899557 times of operation, while the maze optimal path algorithm provided by the invention averagely only needs 788 times of operation, the solution efficiency of the faster maze optimal path algorithm provided by the invention is obviously improved, the operation times are reduced by more than 99%, the resource consumption of a computer mouse system in the solution process is greatly reduced, the execution time of the algorithm is effectively shortened, and the superiority of the faster maze optimal path algorithm provided by the invention is proved. The Zynq platform is adopted, and the high-efficiency real-time processing and parallel processing capacity of the FPGA is fully utilized.
Aiming at the defects that the labyrinth search of the existing intelligent vehicle for the computer mouse is easily interfered by the external environment, more hardware system resources are consumed, the shortest path solving time is long, and the existing intelligent vehicle for the computer mouse is easily trapped into the local optimal solution, the invention provides a control system and a control method which adopt a camera to collect labyrinth images, adopt an image processing method to carry out labyrinth search and adopt an improved Flood-Fill optimal path solving algorithm to calculate the optimal path. The control method separates maze search from optimal path solving, and the control system firstly utilizes a camera to collect maze images and then adopts a specific algorithm to obtain the wall information of the maze in an image processing mode, so as to replace the traditional mode of searching the maze through infrared detection to obtain the wall information of the maze. After the maze wall information is obtained, an improved Flood-Fill optimal path solving algorithm is adopted to obtain an optimal path. The improved Flood-Fill optimal path solving algorithm assumes that there is a "water source" at the Target cell (Target cell) of the maze and as the "Flood" flows in the maze, the wave front expands outward from the Target cell. The maze is circularly filled by calculating the distance between the Front square (Front cell) and the target square and adding 1 from the near distance value to the far distance value. When the wavefront finally reaches the starting grid of the maze, a flood fill algorithm is completed. It is the continuous movement of the front square and the updating of the distance value that finally obtains the distance value coding table from the target square to the adjacent square. According to the table, the grid values are sorted in descending order, and the optimal path from the starting grid to the target grid can be obtained.
Drawings
FIG. 1 is a schematic structural diagram of a Zynq platform-based intelligent vehicle control system for a computer mouse provided by the embodiment of the invention;
in the figure: 1. a six-axis MMEMS inertial sensor; 2. an infrared emission tube; 3. an infrared receiving tube; 4. a camera; 5. a code disc; 6. a main controller; 7. a full-bridge drive circuit; 8. a micro motor; 9. a wireless data transmission module; 10. and a human-machine interface module.
Fig. 2 and fig. 3 are flowcharts of a method for controlling a mouse intelligent vehicle based on a Zynq platform according to an embodiment of the invention; wherein fig. 2 is a flowchart of an unknown maze search algorithm, and fig. 3 is a flowchart of an optimal path solving algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following 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 following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the intelligent vehicle control system for a computer mouse based on the Zynq platform provided in the embodiment of the present invention includes: the system comprises a six-axis MMEMS inertial sensor 1, an infrared transmitting tube 2, an infrared receiving tube 3, a camera 4, a coding disc 5, a main controller 6, a full-bridge driving circuit 7, a micro motor 8, a wireless data transmission module 9 and a human-computer interface module 10.
The camera 4 is used for acquiring a maze image; the Zynq FPGA platform-based main controller is used for carrying out image processing on the acquired maze images so as to identify all wall positions in the maze, solving an optimal path through an improved Flood-Fill algorithm, and controlling the micro motor to walk the maze at the highest speed and sprint according to the solved optimal path;
the coding disc 5 is used for accurately controlling the rotating speed and the direction of the micro motor by acquiring the rotating angle of the shaft end of the micro motor and feeding the rotating angle back to the main controller;
the infrared transmitting tube 2 and the infrared receiving tube 3 are used for positioning correction to prevent the intelligent vehicle of the computer mouse from deviating and are used for wall detection to prevent the intelligent vehicle of the computer mouse from touching the wall;
the six-axis MMEMS inertial sensor 1 is used for feeding back accurate motion attitude information to the main controller so as to control the intelligent vehicle of the computer mouse to keep upright;
the human-computer interface module 10 and the wireless data transmission module 9 are used for debugging the intelligent vehicle for the computer mouse, the wireless data transmission module can be in wireless connection with the host or the mobile terminal in two modes of Wi-Fi and Bluetooth, the working state of the intelligent vehicle for the computer mouse and information of various sensors can be transmitted to the host or the mobile terminal in a wireless mode in real time, and the host or the mobile terminal can also control the intelligent vehicle for the computer mouse in real time.
The six-axis MMEMS inertial sensor 1, the infrared transmitting tube 2, the infrared receiving tube 3, the camera 4, the coding disc 5, the wireless data transmission module 9 and the human-computer interface module 10 are connected with the main controller 6.
The main controller 6 is connected with a full-bridge driving circuit 7, the full-bridge driving circuit 7 is connected with a micro motor 8, and the micro motor 8 is connected with the coding disc 5.
The embodiment of the invention provides a Zynq platform-based computer mouse intelligent vehicle control system with the highest speed: 4 m/s; designing the highest acceleration: 10m/s 2; adopt Xilinx Zynq-7000 series SoC chip XC7Z020 as the master control, possess: the dual-core ARM Cortex-A9 processor working at 667MHz has better performance than 1667 DMIPS; the programmable logic equivalent to the Artix-7 series FPGA can provide a powerful and flexible peripheral for a processor, even a soft-core processor is built, and a 28K logic unit and a large number of on-chip RAM and DSP units are shared; 128MB DDR3SDRAM and 32MB QSPI Flash ROM are loaded on board as program running and storage space; an extensible Wi-Fi module (optional) or a Bluetooth wireless serial port module (standard) for data transmission during debugging; two Faulhaber 1717T-006SR micro motors are adopted, and an IE2-512 coding disc is matched to provide accurate motion posture feedback; 4 pairs of high-sensitivity infrared transmitting tubes and receiving tubes are adopted for wall detection and positioning correction; an LSM330 six-axis (three-axis acceleration and three-axis angular velocity) MEMS inertial sensor is adopted to provide accurate motion attitude feedback; 4 tires for a Japan Beijing business Min-Z racing model vehicle are adopted, four-wheel drive is realized, driven wheels are not provided, friction pivot points are not provided, and the stability and the running efficiency are improved; two lithium polymer batteries with high discharge rate are connected in series, total 7.4V and 100mAh, and can continuously run for 20 minutes.
The motor control module: the Zynq platform controls the rotating speed of the motor by outputting PWM waves, and simultaneously can change the rotating direction of the motor by changing the polarity of the PWM waves.
A speed measuring module: the short-time internal rotation speed of the motor is approximately measured by using the encoding disc to be regarded as the instantaneous rotation speed of the motor, the PWM wave duty ratio is changed by using PID control to achieve the purpose of closed-loop speed regulation, and the situation that the speed is faster or slower is prevented from occurring.
A camera module: the camera is used for collecting image information, and the intelligent vehicle can identify a maze, automatically steer and automatically drive through a certain algorithm.
An attitude sensor module: a piece of six-axis MMEMS inertial sensor is adopted, and comprises 1 three-axis gyroscope and 1 three-axis accelerometer. Firstly, acquiring data of a gyroscope and an accelerometer to carry out attitude analysis, then fusing the data of the gyroscope and the accelerometer by using a Kalman filtering algorithm to obtain relatively real and stable attitude information, and finally controlling the intelligent vehicle of the computer mouse to keep upright by using a cascade PID. This cascade PID control system uses computer mouse intelligence car speed as outermost ring, and the automobile body angle is as the intermediate ring, uses automobile body angular velocity as innermost ring, and such control algorithm not only the parameter is adjusted easily, and ultimate actual effect also can be more stable, accurate.
As shown in fig. 2 and fig. 3, the method for controlling a mouse and computer intelligent vehicle based on a Zynq platform provided by the embodiment of the invention comprises the following steps:
the Zynq platform-based computer mouse intelligent vehicle control method provided by the embodiment of the invention comprises an algorithm for searching an unknown maze and an algorithm for solving an optimal path according to acquired maze information.
As shown in fig. 2, the search algorithm for the unknown maze: firstly, acquiring a color image through a camera, then carrying out histogram equalization in an HSV color space, carrying out color separation and binarization according to the color of a maze wall, carrying out contour detection after denoising through morphological operation, then searching candidate marks according to the characteristic that the wall is a regular quadrangle, and storing the mark points in a counterclockwise sequence. And finally, after correcting the distorted maze image collected by the camera through perspective transformation, counting the number of non-0 pixels of each small square, and extracting the wall information by judging whether the small square is a pixel which is not 0 at all.
The invention provides an optimal path solving algorithm, which separates maze search from optimal path solving based on an improved Flood-Fill optimal path solving algorithm, and the specific steps are shown in figure 3: first, wall information of the maze is obtained according to the above-mentioned search algorithm (shown in fig. 2) of the unknown maze, and the wall information is stored as a 16 × 16 array. The array stores the mutual communication condition (namely wall information) of the labyrinth grids of 16 multiplied by 16 in the maze pattern; a queue is then created and the coordinate position of the target tiles of the maze is used as the initial value for the queue. Then a 16 x 16 array is created, the array is used for storing the distance (or called as a coding value) between each maze check and the target check, all elements of the array are initially 255, and the corresponding element of the target check in the array is assigned to be 0; then, putting the target square in a queue, accessing adjacent unfilled (namely the coded value is not 255) connected squares, filling the connected squares with a coded value which is 1 greater than the coded value of the front square, and putting the coordinate positions of the connected squares in the queue; and then judging whether adjacent unfilled and connected squares exist or not, and entering the next round of circulation if the adjacent unfilled and connected squares exist. Traversing the distance between each position and the target square by the method to obtain a distance value coding table; and finally, sorting the grid code values in a descending order to obtain the optimal path from the starting grid to the target grid.
The invention also provides comprehensive tool software integrating computer mouse monitoring, controlling and function simulating to realize the following functions:
1 developing and monitoring function of running state data of computer mouse
A wifi module serial port is reserved on computer mouse hardware, key data of computer mouse operation, such as infrared distance measurement, gyroscope data, motor encoder data, images collected by a camera and the like, are uploaded to host computer software, and then distance information between the computer mouse and a maze wall, operation posture of the computer mouse and the like are analyzed. The relative setting parameters of the computer mouse can be adjusted by monitoring the operation of the computer mouse in real time.
2 function for controlling running state of computer mouse
And the PC terminal sends an instruction by using the upper computer software through a wifi serial port, so that the intelligent operation of the computer mouse is realized. For example, the computer mouse can be controlled to move forward, backward, turn left and right, stop immediately, return to the starting point, start sprinting and other basic functions by giving instructions.
3 function for verifying computer mouse algorithm
The traditional test method needs to continuously burn programs on the chip and actually run to verify the functional correctness of the chip. However, the Zynq program is slow in compiling speed, slow in configuration speed and slow in starting of a Linux embedded system, and if the traditional testing method is used, the efficiency is low, and the service life of a computer mouse is lost. In order to improve the debugging efficiency, the program is repeatedly programmed and the actual experiment is repeated without slight change of the program of the computer mouse. The algorithm of the computer mouse can be embedded into written upper computer software, and the actual effect of the computer mouse executing program is graphically simulated. And after the result is satisfied, burning the data into the chip, and carrying out actual test.
Compared with the traditional scheme, the invention has the following four advantages:
firstly, the method comprises the following steps: advanced ZYNQ is adopted as a system control core
Zynq is a built-in dual ARM Core-A9 MP Core FPGA heterogeneous architecture chip from Xilinx corporation. The strong performance of the Zynq chip and the flexibility of software and hardware collaborative design are very suitable for the actual requirements of the computer mouse labyrinth system design. A traditional FPGA is a programmable semi-custom circuit; the ARM is a RISC processor with low power consumption and strong function. The Zynq heterogeneous architecture of the FPGA and the ARM can better utilize the resources of the FPGA and the ARM at the same time. The Zynq heterogeneous architecture abstracts processing and separates control logic from processing logic. The control logic part is realized by using an on-chip SOC (processing System or ARM core); the processing logic (particularly the powerful real-time processing and parallel processing of FPGAs) part is implemented in the pl (programmable logic) part, which is connected via the AXI standard bus and facilitates standardized IPCORE packaging and usage.
Secondly, the method comprises the following steps: the invention provides a faster maze optimal path algorithm, and a maze optimal path search simulation platform is designed and developed by adopting Microsoft visual studio 2015. And respectively calculating the operation times required by the two algorithms in the process of solving the optimal path once through the simulation platform. A plurality of labyrinth maps adopted in the international and domestic computer mouse maze competition in recent years are used as test samples, and the test statistical data of each labyrinth map shows that the optimal path is solved once, the classical Flood-Fill labyrinth optimal path algorithm needs 899557 times on average, the faster labyrinth optimal path algorithm only needs 788 times on average, the solving efficiency of the faster labyrinth optimal path algorithm is obviously improved, the operation times are reduced by more than 99%, the computer mouse system resource consumption in the solving process is greatly reduced, the algorithm execution time is effectively shortened, and the superiority of the faster labyrinth optimal path algorithm provided by the invention is proved.
Thirdly, the method comprises the following steps: adding camera acquisition and fusing image processing
The maze information is acquired by using the method of image acquisition and processing of the camera, only a small space is needed, meanwhile, the interference of the external environment is avoided, and the defects of the infrared distance measurement scheme are perfectly overcome. The reason why the image processing is not adopted by the traditional computer mouse is that a large number of operations are needed, and the ARM processor is not free to process huge image data information of a complete 16-by-16 maze. And the Zynq platform is adopted, the high-efficiency real-time processing and parallel processing capacity of the FPGA is utilized, and the problem of calculation is completely avoided.
Fourthly: computer mouse mechanical structure with smaller and more stable design
The smart character: the volume of the computer mouse must be controlled within a certain size range. The size of a maze check of a computer mouse IEEE standard match is 18.5cm multiplied by 18.5cm, so that when the size of the volume of a computer mouse is designed, normal passing of the computer mouse is guaranteed, and the computer mouse can walk obliquely at 45 degrees in the face of continuous turning without touching the corner wall of the maze. When the computer mouse is obliquely driven, the moving space is very narrow, so that the smaller the volume, the better the moving space. The mechanical structure design that we pursued is the size of 1/4 to 1/3 of labyrinth grid to guarantee that the computer mouse has sufficient activity space in the various walking gestures in the maze.
Stability: the stability of the computer mouse structure directly determines the straight walking speed and the maximum turning speed of the computer mouse. If the gravity center of the whole framework of the computer mouse is too high or the position selection is not good, the speed cannot be too high when the computer mouse walks straight lines or turns, and the time for the computer mouse to search a maze and thrust towards a terminal point is directly influenced.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (3)
1. The utility model provides a computer mouse intelligence car control system based on Zynq platform which characterized in that, computer mouse intelligence car control system based on Zynq platform includes:
the camera is used for acquiring a maze image; the Zynq FPGA platform-based main controller is used for carrying out image processing on the acquired maze images so as to identify all wall positions in the maze, solving an optimal path through an improved Flood-Fill algorithm, and controlling the micro motor to walk the maze at the highest speed and sprint according to the solved optimal path;
the coding disc is used for accurately controlling the rotating speed and the direction of the micro motor by acquiring the rotating angle of the shaft end of the micro motor and feeding the rotating angle back to the main controller;
the infrared transmitting tube and the infrared receiving tube are used for positioning correction to prevent the intelligent vehicle of the computer mouse from deviating, and are used for wall detection to prevent the intelligent vehicle of the computer mouse from touching the wall;
the six-axis MMEMS inertial sensor is used for feeding back accurate motion attitude information to the main controller so as to control the intelligent vehicle of the computer mouse to keep upright;
the human-computer interface module and the wireless data transmission module are used for debugging the intelligent vehicle of the computer mouse, the wireless data transmission module can be wirelessly connected with the host or the mobile terminal in a Wi-Fi and Bluetooth mode, the working state of the intelligent vehicle of the computer mouse and the information of various sensors can be wirelessly transmitted to the host or the mobile terminal in real time, and the host or the mobile terminal can also control the intelligent vehicle of the computer mouse in real time;
the six-axis MMEMS inertial sensor, the infrared transmitting tube, the infrared receiving tube, the camera, the coding disc, the wireless data transmission module and the human-computer interface module are connected with the main controller;
the main controller is connected with a full-bridge driving circuit, the full-bridge driving circuit is connected with a micro motor, and the micro motor is connected with an encoding disc;
the Zynq platform-based computer mouse intelligent vehicle control method comprises a search algorithm for an unknown maze and an algorithm for solving an optimal path according to acquired maze information;
the search algorithm of the unknown maze is as follows: firstly, acquiring a color image through a camera, then carrying out histogram equalization in an HSV color space, carrying out color separation and binarization according to the color of a maze wall, carrying out contour detection after denoising through morphological operation, then searching candidate marks according to the characteristic that the wall is a regular quadrangle, and storing the mark points in an anticlockwise sequence; finally, after correcting the distorted maze image collected by the camera through perspective transformation, counting the number of non-0 pixels of each small square, and extracting wall information by judging whether the small square is all non-0 pixels;
the optimal path solving algorithm comprises the following steps: firstly, acquiring wall information of a maze according to a search algorithm of an unknown maze, and storing the wall information into a 16 x 16 array; the array stores the mutual communication condition of the labyrinth grids of 16 multiplied by 16 in the maze pattern; then, creating a queue, and taking the coordinate position of a target grid of the maze as an initial value of the queue; then a 16 x 16 array is created, the array is used for storing the distance between each maze lattice and the target square grid or is called as a code value, all elements of the array are initially 255, and the corresponding elements of the target square grid in the array are assigned to be 0; then, the target square grids are queued, adjacent unfilled and connected square grids are accessed, a coding value which is 1 greater than the coding value of the front square grid is filled in the target square grids, and the coordinate positions of the target square grids are queued; then judging whether adjacent unfilled and connected squares exist, if so, entering the next round of circulation, traversing the distance between each position and the target square by the method, and obtaining a distance value coding table; and finally, sorting the grid code values in a descending order to obtain the optimal path from the starting grid to the target grid.
2. An unmanned vehicle equipped with the Zynq platform-based computer mouse intelligent vehicle control system of claim 1.
3. An industrial intelligent control system for installing the Zynq platform-based computer mouse intelligent vehicle control system of claim 1.
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