Navigation obstacle avoidance intelligent control system and method applied to golf cart

Abstract

The invention relates to the field of navigation control, in particular to a navigation obstacle avoidance intelligent control system and method applied to a golf trolley, and the system comprises a camera shooting platform module, an obstacle recognition module, a passing planning module, a posture positioning module and a speed control module, the obstacle recognition module is used for recognizing shelters and judging the types of the shelters, the passing planning module is used for planning the optimal advancing direction of the golf trolley, the posture positioning module is used for outputting the current coordinates of the golf trolley and correcting advancing deviation, and the speed control module is used for controlling the advancing speed of the golf trolley in real time. According to the invention, the accuracy of obstacle avoidance decision can be improved, more complex cooperative obstacle avoidance and adaptive learning functions can be realized, the maintenance cost of vehicles and court facilities can be reduced, the overall operation efficiency of a court can be improved, the accident risk can be reduced, and more intelligent court traffic management can be realized.

Description

Technical Field

[0001] The present invention relates to the field of navigation control, and in particular to an intelligent navigation and obstacle avoidance control system and method applied to a golf bag cart. Background Art

[0002] A golf bag cart is a small electric vehicle specially designed for use on golf courses. It is mainly used to help golfers carry golf bags, clubs and other equipment conveniently on the course. When the user needs equipment, the golf bag cart can sense and follow the user, delivering the golf equipment to the golfer, providing the user with a better sports experience.

[0003] Golf courses are characterized by complex terrain, numerous obstacles such as people, machinery, balls, and tee balls, all of which are constantly moving. Golf carts often have their routes obstructed, forcing them to stop and wait until obstacles clear, delaying their arrival. Some golf carts have radar or infrared capabilities that can detect obstacles and automatically navigate around them, but these electronic devices are expensive and susceptible to interference, making them difficult to navigate in areas with heavy traffic and unsuitable for complex golf courses.

[0004] In addition, the braking effect of the golf cart is different and the driving speed is difficult to control. When the obstacle is in an irregular movement process, the golf cart is likely to hit the obstacle at a high speed, causing equipment damage and personal injury. When selecting a path, it is necessary to detour a long distance to avoid the moving obstacle, which reduces the golf cart's addressing efficiency and obstacle avoidance flexibility. Summary of the Invention

[0005] The purpose of the present invention is to provide a navigation obstacle avoidance intelligent control system and method for a golf bag cart to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a navigation and obstacle avoidance intelligent control system applied to a golf bag car, comprising: a camera platform module, an obstacle recognition module, a traffic planning module, a posture positioning module and a speed control module;

[0007] The camera platform module is used to set a platform on the top of the golf bag car, the front edge of the platform is perpendicular to the travel direction of the golf bag car and is provided with a scale, and two cameras are installed on the platform so that the main optical axis vectors of the two cameras are parallel to the travel direction vector of the golf bag car, and the line between the two cameras is parallel to the front edge of the platform, so that at least one scale unit of the scale is within the shooting range of the two cameras;

[0008] The obstacle recognition module is used to control the camera to capture images in the direction of the golf bag car's travel, obtain a first image and a second image from the two cameras respectively, use an image segmentation algorithm to separate the obstruction part in the image, use the golf bag car's current position as the origin, and measure the distance of each point of the obstruction based on the length of the scale captured in the image and the installation distance of the camera, and give the spatial coordinates of the point. The coordinates of all points constitute an obstruction model, and the obstruction model is sent to the sample database to identify the type of obstruction;

[0009] The passage planning module is used to determine the direction of travel of the golf bag car based on the user's satellite positioning coordinates, identify impassable obstacles in a semicircular area centered on the travel direction vector, screen the spaces between the obstacle edges whose length is greater than the width of the golf bag car, generate passable sectors, verify all passable sectors, select the sector closest to the user's positioning after the golf bag car passes as the planned sector, and control the golf bag car to move forward with the center of the sector as the direction;

[0010] The posture positioning module is used to establish a positioning model based on the forward direction and real-time speed of the golf cart, input the time parameter into the model, output the current coordinates of the golf cart, correct the travel offset, and measure the distance again with the current coordinates as the center at fixed intervals to update the distance between the golf cart and each obstacle;

[0011] The speed control module is used to control the travel speed of the car in real time according to the sector radius, the distance between the car and the obstacle, and the braking parameters of the car. When the sector radius is smaller than the passing radius during the forward process, the car is braked; when the travel deviation exceeds the braking torque, the car is decelerated, and the forward direction and speed of the car are re-planned.

[0012] Furthermore, the camera platform module includes: an edge positioning unit and a dual-camera imaging unit;

[0013] The edge positioning unit is used to build a platform on the top of the golf bag cart, the platform is parallel to the ground, and a scale is provided on the front edge;

[0014] The dual-camera imaging unit is used to fix two cameras on the platform and detect obstacles using computer vision and binocular moment measurement principles.

[0015] Furthermore, the obstacle recognition module includes: an image segmentation unit and a sample comparison unit;

[0016] The image segmentation unit is used to determine the occlusion in the captured image using an image segmentation algorithm, and the image segmentation algorithm includes: K-means, FCN, DeepLab, R-CNN and U-Net algorithms;

[0017] The sample comparison unit is used to compare the features of the obstruction with the features in the sample library. If the comparison result is an inaccessible or unrecognizable sample, it is determined to be an obstacle.

[0018] Furthermore, the traffic planning module includes: a signal transmission unit, a sector identification unit and a path selection unit;

[0019] The signal transmission unit is used to receive the user's positioning information and determine the direction and distance between the user and the golf bag car;

[0020] The sector identification unit is used to calculate the distance between the golf bag car and each obstacle, and identify the passable sector of the golf bag car;

[0021] The path selection unit is used to screen the passable sectors and plan the shortest route.

[0022] Furthermore, the posture positioning module includes: a posture modeling unit and a parameter positioning unit;

[0023] The posture modeling unit is used to establish a posture positioning model of the ball bag car and return the current coordinates of the ball bag car in real time;

[0024] The parameter positioning unit is used to correct the travel offset, update the obstacle position, and determine the distance between the obstacle and the golf cart.

[0025] Furthermore, the speed control module includes: a speed planning unit, a dynamic feedback unit and a vehicle braking unit;

[0026] The speed planning unit is used to determine the speed of the golf bag car according to the minimum distance between the golf bag car and the obstacle and the vehicle braking distance;

[0027] The dynamic feedback unit is used to update the obstacle position and re-plan the speed and direction of the golf cart;

[0028] The vehicle braking unit is used for emergency braking when it is detected that the distance between the obstacle and the vehicle is lower than a threshold value, and assisting the vehicle in decelerating.

[0029] A navigation and obstacle avoidance intelligent control method for a golf bag cart comprises the following steps:

[0030] Step S1. A platform is provided on top of the golf cart, with a scale marked on the front of the platform. Two cameras are mounted in parallel on the platform so that the principal optical axes of the cameras are aligned with the direction of travel of the golf cart, and at least one scale unit of the scale is within the camera's shooting range;

[0031] Step S2. Two cameras capture images of the area in front of the golf cart. An image segmentation algorithm is used to separate the obstruction from the image. Based on the length of the scale in the image and the camera's shooting range, the binocular imaging principle is used to measure the distance to the obstruction and obtain the coordinates of each point on the edge of the obstruction.

[0032] Step S3. Input the coordinates and grayscale of each point on the edge of the occluder into the sample database, mark the occluders identified as impassable or unrecognizable as obstacles, and select the space between adjacent obstacle edges where the length is greater than the width of the golf cart to generate a passable sector;

[0033] Step S4. Determine the speed of the golf cart according to the distance between the cart and the nearest obstacle, use the traversable sector with the maximum effective travel speed as the planning sector, and use the direction of the angle bisector of the planned sector as the direction of travel of the golf cart;

[0034] Step S5. Determine the current coordinates of the golf cart based on its forward direction and real-time speed, correct the travel offset in real time, update the obstacle position, perform emergency braking when the distance between the obstacle and the vehicle is detected to be lower than the threshold, and re-plan the forward direction.

[0035] Furthermore, step S1 includes:

[0036] Step S11. A platform is provided on top of the golf cart, with the platform surface parallel to the ground. Two cameras are mounted on the platform in parallel, with the principal optical axes of the cameras aligned with the direction of travel of the golf cart, and the distances between the two cameras and the centerline of the platform are equal. The platform is made of a transparent material and does not obstruct the camera's shooting aperture.

[0037] Step S12. A ruler is set at the front edge of the platform, with the zero scale of the ruler aligned with the center of the line connecting the two cameras, so that at least one scale unit in the ruler is within the shooting range of the two cameras. A visual processing chip is set under the platform to receive pictures taken by the cameras and upload them to the information platform for processing.

[0038] Furthermore, step S2 includes:

[0039] Step S21. Two cameras capture images in front of the golf cart to obtain a first image and a second image, respectively. An image segmentation algorithm is used to determine occluders in the captured images. The image segmentation algorithm includes K-means, FCN, DeepLab, R-CNN, and U-Net algorithms.

[0040] Step S22: Based on the length of the scale in the image and the camera's shooting range, the distance between each point on the edge of the obstruction and the golf bag is calculated using the binocular imaging principle:

[0041]

[0042] Where z1 represents the horizontal distance between the point and the ball bag, x1 and x2 represent the horizontal coordinates of the midpoint in the first and second images respectively, f represents the focal length of the camera, B represents the distance between the two cameras, and a represents the compression ratio of the scale, a = L / A, where L is the scale scale in the image and A is the pixel width of the scale scale.

[0043] Since the heights of the two cameras are the same, y1 = y2, and the vertical distance between the point and the bag is z2 = a·y1-h, where y1 and y2 represent the vertical coordinates of the midpoints in the first and second images, respectively;

[0044] Step S23: Measure the distance of each point on the edge of the obstruction to determine the horizontal and vertical distances to the golf bag car, establish a coordinate system with the golf bag car as the zero point, and obtain the coordinates of each point on the edge of the obstruction.

[0045] Furthermore, step S3 includes:

[0046] Step S31: Input the coordinates and grayscale of each point on the edge of the occluder into the sample database and compare them with the existing obstacle samples in the sample database. If the comparison result is an inaccessible or unrecognizable sample, the occluder is determined to be an obstacle.

[0047] Step S32. Based on the user's satellite positioning coordinates, identify impassable obstacles in a semicircular area centered on the direction of travel, where the direction of travel is from the golf cart coordinates to the user coordinates. Filter out spaces between obstacles whose width is greater than the golf cart width, and generate a passable sector with the golf cart coordinates as the vertex and the line connecting the obstacle edges as the chord.

[0048] Furthermore, step S4 includes:

[0049] Step S41. Determine the speed of the golf cart according to the distance between the golf cart and the nearest obstacle:

[0050]

[0051] Among them, v represents the speed of the golf cart, v max represents the maximum speed of the golf cart, T represents the distance between the golf cart and the obstacle, u represents the braking deceleration of the golf cart, T0 is the reserved deceleration length, and v0 is the preset speed reduction;

[0052] Step S42. Calculate the effective travel speed VR of each passable sector, where VR = v·cosθ, where θ represents the angle between the sector angle bisector and the travel direction, select the passable sector with the largest effective travel speed as the planning sector, and use the angle bisector direction of the planning sector ball as the forward direction of the ball bag car.

[0053] Furthermore, step S5 includes:

[0054] Step S51. Analyze the driving signal of the golf cart, and update the coordinates of the golf cart and the positions of obstacles in the planned sector in real time based on the coordinates of the golf cart before movement, the direction of movement of the golf cart, and the real-time speed of the golf cart, and correct the offset between the direction of movement of the golf cart and the direction of the angle bisector of the planned sector ball;

[0055] Step S52. When the width between obstacles in the planned sector is smaller than the width of the golf cart, stop the movement of the golf cart, and the camera takes pictures again to identify the passable sectors around the current position of the golf cart, and re-plan the direction of the golf cart. At the same time, emergency braking is performed when it is detected that the distance between the obstacle and the vehicle is lower than the threshold.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. This invention installs dual cameras on the top of the golf cart to identify the location of obstacles. It uses an image segmentation algorithm to segment the obstructions from the image, and then feeds the obstruction features in the two images into a sample database to identify the obstacle type to determine whether the golf cart can pass. This can improve the accuracy of obstacle avoidance decisions and realize more complex collaborative obstacle avoidance and adaptive learning functions.

[0058] 2. The present invention can measure the distance to obstacles in the direction of the golf cart's advance, use the cart's current position pointing to the destination coordinates as the direction of advance, determine all passable sectors in the cart's direction of advance based on the obstacle ranging results, and select the passable sector with the smallest deviation angle to advance, thereby intelligently planning the shortest route, reducing detour time, and shortening the time it takes for the golf cart to reach the user's location, which can reduce the maintenance cost of vehicles and stadium facilities and improve the overall operational efficiency of the stadium.

[0059] 3. The present invention can establish a posture positioning model based on the forward direction and speed of the golf cart, and control the travel speed of the cart in real time according to the sector radius, the distance between the vehicle and the obstacle, and the braking parameters of the golf cart. During the forward process, the cart brakes according to the position of the obstacle, leaving a braking distance for the golf cart, preventing the golf cart from colliding with moving obstacles, reducing the risk of accidents, and realizing more intelligent stadium traffic management. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0061] Figure 1 This is a structural diagram of an intelligent navigation and obstacle avoidance control system for a golf bag cart according to the present invention;

[0062] Figure 2 The present invention is a schematic diagram of the steps of a navigation obstacle avoidance intelligent control method applied to a golf bag cart. DETAILED DESCRIPTION

[0063] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0064] See also Figure 1 , the present invention provides a technical solution: a navigation obstacle avoidance intelligent control system applied to a golf bag car, comprising: a camera platform module, an obstacle recognition module, a traffic planning module, a posture positioning module and a speed control module;

[0065] The camera platform module is used to set a platform on the top of the golf bag car, the front edge of the platform is perpendicular to the travel direction of the golf bag car and is provided with a scale, and two cameras are installed on the platform so that the main optical axis vectors of the two cameras are parallel to the travel direction vector of the golf bag car, and the line between the two cameras is parallel to the front edge of the platform, so that at least one scale unit of the scale is within the shooting range of the two cameras;

[0066] The camera platform module includes: an edge positioning unit and a dual-camera imaging unit;

[0067] The edge positioning unit is used to build a platform on the top of the golf bag cart, the platform is parallel to the ground, and a scale is provided on the front edge;

[0068] The dual-camera imaging unit is used to fix two cameras on the platform and detect obstacles using computer vision and binocular moment measurement principles.

[0069] The obstacle recognition module is used to control the camera to capture images in the direction of the golf bag car's travel, obtain a first image and a second image from the two cameras respectively, use an image segmentation algorithm to separate the obstruction part in the image, use the golf bag car's current position as the origin, and measure the distance of each point of the obstruction based on the length of the scale captured in the image and the installation distance of the camera, and give the spatial coordinates of the point. The coordinates of all points constitute an obstruction model, and the obstruction model is sent to the sample database to identify the type of obstruction;

[0070] The obstacle recognition module includes: an image segmentation unit and a sample comparison unit;

[0071] The image segmentation unit is used to determine the occlusion in the captured image using an image segmentation algorithm, and the image segmentation algorithm includes: K-means, FCN, DeepLab, R-CNN and U-Net algorithms;

[0072] The sample comparison unit is used to compare the features of the obstruction with the features in the sample library. If the comparison result is an inaccessible or unrecognizable sample, it is determined to be an obstacle.

[0073] The passage planning module is used to determine the direction of travel of the golf bag car based on the user's satellite positioning coordinates, identify impassable obstacles in a semicircular area centered on the travel direction vector, screen the spaces between the obstacle edges whose length is greater than the width of the golf bag car, generate passable sectors, verify all passable sectors, select the sector closest to the user's positioning after the golf bag car passes as the planned sector, and control the golf bag car to move forward with the center of the sector as the direction;

[0074] The traffic planning module includes: a signal transmission unit, a sector identification unit and a path selection unit;

[0075] The signal transmission unit is used to receive the user's positioning information and determine the direction and distance between the user and the golf bag car;

[0076] The sector identification unit is used to calculate the distance between the golf bag car and each obstacle, and identify the passable sector of the golf bag car;

[0077] The path selection unit is used to screen the passable sectors and plan the shortest route.

[0078] The posture positioning module is used to establish a positioning model based on the forward direction and real-time speed of the golf cart, input the time parameter into the model, output the current coordinates of the golf cart, correct the travel offset, and measure the distance again with the current coordinates as the center at fixed intervals to update the distance between the golf cart and each obstacle;

[0079] The posture positioning module includes: a posture modeling unit and a parameter positioning unit;

[0080] The posture modeling unit is used to establish a posture positioning model of the ball bag car and return the current coordinates of the ball bag car in real time;

[0081] The parameter positioning unit is used to correct the travel offset, update the obstacle position, and determine the distance between the obstacle and the golf cart.

[0082] The speed control module is used to control the travel speed of the car in real time according to the sector radius, the distance between the car and the obstacle, and the braking parameters of the car. When the sector radius is smaller than the passing radius during the forward process, the car is braked; when the travel deviation exceeds the braking torque, the car is decelerated, and the forward direction and speed of the car are re-planned.

[0083] The speed control module includes: a speed planning unit, a dynamic feedback unit and a vehicle braking unit;

[0084] The speed planning unit is used to determine the speed of the golf bag car according to the minimum distance between the golf bag car and the obstacle and the vehicle braking distance;

[0085] The dynamic feedback unit is used to update the obstacle position and re-plan the speed and direction of the golf cart;

[0086] The vehicle braking unit is used for emergency braking when it is detected that the distance between the obstacle and the vehicle is lower than a threshold value, and assisting the vehicle in decelerating.

[0087] like Figure 2 As shown, a navigation obstacle avoidance intelligent control method applied to a golf bag cart includes the following steps:

[0088] Step S1. A platform is provided on top of the golf cart, with a scale marked on the front of the platform. Two cameras are mounted in parallel on the platform so that the principal optical axes of the cameras are aligned with the direction of travel of the golf cart, and at least one scale unit of the scale is within the camera's shooting range;

[0089] Step S1 includes:

[0090] Step S11. A platform is provided on top of the golf cart, with the platform surface parallel to the ground. Two cameras are mounted on the platform in parallel, with the principal optical axes of the cameras aligned with the direction of travel of the golf cart, and the distances between the two cameras and the centerline of the platform are equal. The platform is made of a transparent material and does not obstruct the camera's shooting aperture.

[0091] Step S12. A ruler is set at the front edge of the platform, with the zero scale of the ruler aligned with the center of the line connecting the two cameras, so that at least one scale unit in the ruler is within the shooting range of the two cameras. A visual processing chip is set under the platform to receive pictures taken by the cameras and upload them to the information platform for processing.

[0092] Step S2. Two cameras capture images of the area in front of the golf cart. An image segmentation algorithm is used to separate the obstruction from the image. Based on the length of the scale in the image and the camera's shooting range, the binocular imaging principle is used to measure the distance to the obstruction and obtain the coordinates of each point on the edge of the obstruction.

[0093] Step S2 includes:

[0094] Step S21. Two cameras capture images in front of the golf cart to obtain a first image and a second image, respectively. An image segmentation algorithm is used to determine occluders in the captured images. The image segmentation algorithm includes K-means, FCN, DeepLab, R-CNN, and U-Net algorithms.

[0095] Step S22: Based on the length of the scale in the image and the camera's shooting range, the distance between each point on the edge of the obstruction and the golf bag is calculated using the binocular imaging principle:

[0096]

[0097] Where z1 represents the horizontal distance between the point and the ball bag, x1 and x2 represent the horizontal coordinates of the midpoint in the first and second images respectively, f represents the focal length of the camera, B represents the distance between the two cameras, and a represents the compression ratio of the scale, a = L / A, where L is the scale scale in the image and A is the pixel width of the scale scale.

[0098] Since the heights of the two cameras are the same, y1 = y2, and the vertical distance between the point and the bag is z2 = a·y1-h, where y1 and y2 represent the vertical coordinates of the midpoints in the first and second images, respectively;

[0099] Step S23: Measure the distance of each point on the edge of the obstruction to determine the horizontal and vertical distances to the golf bag car, establish a coordinate system with the golf bag car as the zero point, and obtain the coordinates of each point on the edge of the obstruction.

[0100] Step S3. Input the coordinates and grayscale of each point on the edge of the occluder into the sample database, mark the occluders identified as impassable or unrecognizable as obstacles, and select the space between adjacent obstacle edges where the length is greater than the width of the golf cart to generate a passable sector;

[0101] Step S3 includes:

[0102] Step S31: Input the coordinates and grayscale of each point on the edge of the occluder into the sample database and compare them with the existing obstacle samples in the sample database. If the comparison result is an inaccessible or unrecognizable sample, the occluder is determined to be an obstacle.

[0103] Step S32. Based on the user's satellite positioning coordinates, identify impassable obstacles in a semicircular area centered on the direction of travel, where the direction of travel is from the golf cart coordinates to the user coordinates. Filter out spaces between obstacles whose width is greater than the golf cart width, and generate a passable sector with the golf cart coordinates as the vertex and the line connecting the obstacle edges as the chord.

[0104] Step S4. Determine the speed of the golf cart according to the distance between the cart and the nearest obstacle, use the traversable sector with the maximum effective travel speed as the planning sector, and use the direction of the angle bisector of the planned sector as the direction of travel of the golf cart;

[0105] Step S4 includes:

[0106] Step S41. Determine the speed of the golf cart according to the distance between the golf cart and the nearest obstacle:

[0107]

[0108] Among them, v represents the speed of the golf cart, v max represents the maximum speed of the golf cart, T represents the distance between the golf cart and the obstacle, u represents the braking deceleration of the golf cart, T0 is the reserved deceleration length, and v0 is the preset speed reduction;

[0109] Step S42. Calculate the effective travel speed VR of each passable sector, where VR = v·cosθ, where θ represents the angle between the sector angle bisector and the travel direction, select the passable sector with the largest effective travel speed as the planning sector, and use the angle bisector direction of the planning sector ball as the forward direction of the ball bag car.

[0110] Step S5. Determine the current coordinates of the golf cart based on its forward direction and real-time speed, correct the travel offset in real time, update the obstacle position, perform emergency braking when the distance between the obstacle and the vehicle is detected to be lower than the threshold, and re-plan the forward direction.

[0111] Step S5 includes:

[0112] Step S51. Analyze the driving signal of the golf cart, and update the coordinates of the golf cart and the positions of obstacles in the planned sector in real time based on the coordinates of the golf cart before movement, the direction of movement of the golf cart, and the real-time speed of the golf cart, and correct the offset between the direction of movement of the golf cart and the direction of the angle bisector of the planned sector ball;

[0113] Step S52. When the width between obstacles in the planned sector is smaller than the width of the golf cart, stop the movement of the golf cart, and the camera takes pictures again to identify the passable sectors around the current position of the golf cart, and re-plan the direction of the golf cart. At the same time, emergency braking is performed when it is detected that the distance between the obstacle and the vehicle is lower than the threshold.

[0114] Example: The pixel coordinates of the obstacle point in front of the golf cart in camera 1 and camera 2 are (200, 100) and (400, 100) respectively. The focal length of the camera is 2m. The 0.1m scale in the ruler occupies 100 pixels. The distance between the cameras is 0.5m. Then the horizontal distance between the obstacle point and the golf cart is 25m, and the vertical distance is 0.1m.

[0115] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0116] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An intelligent control method for navigation and obstacle avoidance applied to a golf bag cart, characterized in that: The method comprises the following steps: Step S1. A platform is provided on top of the golf cart, with a scale marked on the front of the platform. Two cameras are mounted in parallel on the platform so that the principal optical axes of the cameras are aligned with the direction of travel of the golf cart, and at least one scale unit of the scale is within the camera's shooting range; Step S2. Two cameras capture images of the area in front of the golf cart. An image segmentation algorithm is used to separate the obstruction from the image. Based on the length of the scale in the image and the camera's shooting range, the binocular imaging principle is used to measure the distance to the obstruction and obtain the coordinates of each point on the edge of the obstruction. Step S3. Input the coordinates and grayscale of each point on the edge of the occluder into the sample database, mark the occluders identified as impassable or unrecognizable as obstacles, and select the space between adjacent obstacle edges where the length is greater than the width of the golf cart to generate a passable sector; Step S4. Determine the speed of the golf cart according to the distance between the cart and the nearest obstacle, use the traversable sector with the maximum effective travel speed as the planning sector, and use the direction of the angle bisector of the planned sector as the direction of travel of the golf cart; Step S5. Determine the current coordinates of the golf cart based on its forward direction and real-time speed, correct the travel offset in real time, update the obstacle position, perform emergency braking when the distance between the obstacle and the vehicle is detected to be lower than the threshold, and re-plan the forward direction.

2. The intelligent control method for navigation and obstacle avoidance of a golf bag vehicle according to claim 1, characterized in that: Step S1 includes: Step S11. A platform is provided on top of the golf cart, with the platform surface parallel to the ground. Two cameras are mounted on the platform in parallel, with the principal optical axes of the cameras aligned with the direction of travel of the golf cart, and the distances between the two cameras and the centerline of the platform are equal. The platform is made of a transparent material and does not obstruct the camera's shooting aperture. Step S12. A ruler is set at the front edge of the platform, with the zero scale of the ruler aligned with the center of the line connecting the two cameras, so that at least one scale unit in the ruler is within the shooting range of the two cameras. A visual processing chip is set under the platform to receive pictures taken by the cameras and upload them to the information platform for processing.

3. The intelligent control method for navigation and obstacle avoidance of a golf bag vehicle according to claim 2, characterized in that: Step S2 includes: Step S21. Two cameras capture images in front of the golf cart to obtain a first image and a second image, respectively. An image segmentation algorithm is used to determine occluders in the captured images. The image segmentation algorithm includes K-means, FCN, DeepLab, R-CNN, and U-Net algorithms. Step S22: Based on the length of the scale in the image and the camera's shooting range, the distance between the point on the edge of the obstruction and the center of the golf bag is calculated using the binocular imaging principle: Where z1 represents the horizontal distance between the point and the ball bag, x1 and x2 represent the horizontal coordinates of the midpoint in the first and second images respectively, f represents the focal length of the camera, B represents the distance between the two cameras, and a represents the compression ratio of the scale, a = L / A, where L is the scale scale in the image and A is the pixel width of the scale scale. Since the heights of the two cameras are the same, y1 = y2, and the vertical distance between the point and the bag is z2 = a·y1-h, where y1 and y2 represent the vertical coordinates of the midpoints in the first and second images, respectively; Step S23: Measure the distance of each point on the edge of the obstruction to determine the horizontal and vertical distances to the golf bag car, establish a coordinate system with the golf bag car as the zero point, and obtain the coordinates of each point on the edge of the obstruction.

4. The intelligent control method for navigation and obstacle avoidance of a golf bag vehicle according to claim 3, characterized in that: Step S3 includes: Step S31: Input the coordinates and grayscale of each point on the edge of the occluder into the sample database and compare them with the existing obstacle samples in the sample database. If the comparison result is an inaccessible or unrecognizable sample, the occluder is determined to be an obstacle. Step S32. Based on the user's satellite positioning coordinates, identify impassable obstacles within a semicircular area centered in the direction of travel, where the direction of travel is from the cart coordinates to the user coordinates. Select spaces between obstacles that are wider than the cart width, and generate a traversable sector with the cart coordinates as vertices and the line connecting the obstacle edges as chords. Step S4 includes: Step S41. Determine the speed of the golf cart according to the distance between the golf cart and the nearest obstacle: Among them, v represents the speed of the golf cart, v max represents the maximum speed of the golf cart, T represents the distance between the golf cart and the obstacle, u represents the braking deceleration of the golf cart, T0 is the reserved deceleration length, and v0 is the preset speed reduction; Step S42. Calculate the effective travel speed VR of each passable sector, where VR = v·cosθ, where θ represents the angle between the sector angle bisector and the travel direction, select the passable sector with the largest effective travel speed as the planning sector, and use the angle bisector direction of the planning sector ball as the forward direction of the ball bag car.

5. The intelligent control method for navigation and obstacle avoidance applied to a golf bag vehicle according to claim 4, characterized in that: Step S5 includes: Step S51. Analyze the driving signal of the golf cart, and update the coordinates of the golf cart and the positions of obstacles in the planned sector in real time based on the coordinates of the golf cart before movement, the direction of movement of the golf cart, and the real-time speed of the golf cart, and correct the offset between the direction of movement of the golf cart and the direction of the angle bisector of the planned sector ball; Step S52. When the width between obstacles in the planned sector is smaller than the width of the golf cart, stop the movement of the golf cart, and the camera takes pictures again to identify the passable sectors around the current position of the golf cart, and re-plan the direction of the golf cart. At the same time, emergency braking is performed when it is detected that the distance between the obstacle and the vehicle is lower than the threshold.

6. A navigation and obstacle avoidance intelligent control system for a golf bag car, characterized in that: The system includes the following modules: camera platform module, obstacle recognition module, traffic planning module, posture positioning module and speed control module; The camera platform module is used to set a platform on the top of the golf bag car, the front edge of the platform is perpendicular to the travel direction of the golf bag car and is provided with a scale, and two cameras are installed on the platform so that the main optical axis vectors of the two cameras are parallel to the travel direction vector of the golf bag car, and the line between the two cameras is parallel to the front edge of the platform, so that at least one scale unit of the scale is within the shooting range of the two cameras; The obstacle recognition module is used to control the camera to capture images in the direction of the golf bag car's travel, obtain a first image and a second image from the two cameras respectively, use an image segmentation algorithm to separate the obstruction part in the image, use the golf bag car's current position as the origin, and measure the distance of each point of the obstruction based on the length of the scale captured in the image and the installation distance of the camera, and give the spatial coordinates of the point. The coordinates of all points constitute an obstruction model, and the obstruction model is sent to the sample database to identify the type of obstruction; The passage planning module is used to determine the direction of travel of the golf bag car based on the user's satellite positioning coordinates, identify impassable obstacles in a semicircular area centered on the travel direction vector, screen the spaces between the obstacle edges whose length is greater than the width of the golf bag car, generate passable sectors, verify all passable sectors, select the sector closest to the user's positioning after the golf bag car passes as the planned sector, and control the golf bag car to move forward with the center of the sector as the direction; The posture positioning module is used to establish a positioning model based on the forward direction and real-time speed of the golf cart, input the time parameter into the model, output the current coordinates of the golf cart, correct the travel offset, and measure the distance again with the current coordinates as the center at fixed intervals to update the distance between the golf cart and each obstacle; The speed control module is used to control the travel speed of the car in real time according to the sector radius, the distance between the car and the obstacle, and the braking parameters of the car. When the sector radius is smaller than the passing radius during the forward process, the car is braked; when the travel deviation exceeds the braking torque, the car is decelerated, and the forward direction and speed of the car are re-planned.

7. The intelligent navigation and obstacle avoidance control system for a golf bag vehicle according to claim 6, characterized in that: The camera platform module includes: an edge positioning unit and a dual-camera imaging unit; The edge positioning unit is used to build a platform on the top of the golf bag cart, the platform is parallel to the ground, and a scale is provided on the front edge; The dual-camera imaging unit is used to fix two cameras on the platform and detect obstacles using computer vision and binocular moment measurement principles; The obstacle recognition module includes: an image segmentation unit and a sample comparison unit; The image segmentation unit is used to determine the occlusion in the captured image using an image segmentation algorithm, and the image segmentation algorithm includes: K-means, FCN, DeepLab, R-CNN and U-Net algorithms; The sample comparison unit is used to compare the features of the obstruction with the features in the sample library. If the comparison result is an inaccessible or unrecognizable sample, it is determined to be an obstacle.

8. The intelligent navigation and obstacle avoidance control system for a golf bag vehicle according to claim 7, characterized in that: The traffic planning module includes: a signal transmission unit, a sector identification unit and a path selection unit; The signal transmission unit is used to receive the user's positioning information and determine the direction and distance between the user and the golf bag car; The sector identification unit is used to calculate the distance between the golf bag car and each obstacle, and identify the passable sector of the golf bag car; The path selection unit is used to screen the passable sectors and plan the shortest route.

9. The intelligent navigation and obstacle avoidance control system for a golf bag vehicle according to claim 8, characterized in that: The posture positioning module includes: a posture modeling unit and a parameter positioning unit; The posture modeling unit is used to establish a posture positioning model of the ball bag car and return the current coordinates of the ball bag car in real time; The parameter positioning unit is used to correct the travel offset, update the obstacle position, and determine the distance between the obstacle and the golf cart.

10. The intelligent navigation and obstacle avoidance control system for a golf bag vehicle according to claim 9, characterized in that: The speed control module includes: a speed planning unit, a dynamic feedback unit and a vehicle braking unit; The speed planning unit is used to determine the speed of the golf bag car according to the minimum distance between the golf bag car and the obstacle and the vehicle braking distance; The dynamic feedback unit is used to update the obstacle position and re-plan the speed and direction of the golf cart; The vehicle braking unit is used for emergency braking when it is detected that the distance between the obstacle and the vehicle is lower than a threshold value, and assisting the vehicle in decelerating.

Images