Automatic luggage transport vehicle system in terminal building

Abstract

The invention discloses an automatic luggage transport vehicle system in an airport terminal, a transport vehicle can automatically navigate and automatically identify and track a tracing line, so that the movement of passengers from a security check-in entrance to a check-in counter becomes easier and more efficient, and the rapid and comfortable travel requirements of the passengers are met. Especially for old people or disabled people with mobility difficulties, the use of the transport vehicle not only relieves the burden of the old people or disabled people in the luggage transport process, but also enhances the independent travel ability of the old people or disabled people, and improves the travel comfort and confidence of the old people or disabled people. Besides, the automatic operation of the transport vehicle reduces the dependence on human resources, and reduces the need of manual intervention, thereby reducing the occurrence rate of human errors, and ensuring the safety and reliability of luggage transportation. The problems that traditional manual trolley pushing is low in speed, time-consuming and labor-consuming are solved.

Description

Technical Field

[0001] The invention belongs to the field of luggage transport vehicles, and in particular, relates to an automatic luggage transport vehicle system in a terminal building. Background Art

[0002] Visual line patrol technology is a method that uses cameras, image processing algorithms and other technologies to identify, track and locate lines. Visual line patrol technology uses cameras to obtain line images. During the computer processing of the image, the algorithm extracts the characteristic information of the line, such as color, shape, texture, etc. Then, line patrol is carried out based on this characteristic information to achieve automatic identification, tracking and positioning of the line. Compared with grayscale sensor line patrol technology that is easily affected by light, visual line patrol technology can process images under different lighting conditions, weather changes and complex environmental backgrounds. It has strong environmental adaptability and is more suitable for applications in complex scenarios such as airports.

[0003] Deep learning object detection is an important research direction in the field of computer vision. Its task is to automatically identify and locate specific targets (objects) in images or video frames and determine their categories. This technology relies on deep learning algorithms, especially convolutional neural networks (CNNs), to improve the accuracy and robustness of object detection by training models. YOLOv5 (You Only Look Once version 5) is a popular object detection algorithm and one of the latest versions of the YOLO series of algorithms. The algorithm was developed by researchers at the University of California, Berkeley, and performs well in real-time object detection tasks with its high speed and high accuracy.

[0004] Simultaneous Localization and Mapping (SLAM) is an important technology in the fields of robotics, computer vision, and autonomous driving. It refers to the technology of robots simultaneously positioning themselves and building environmental maps in unknown environments. SLAM technology allows robots or vehicles to estimate their position and posture in real time through their own sensors (such as cameras, lidar, inertial measurement units, etc.) without prior environmental information, and build a geometric map of the surrounding environment. In a dynamically changing environment such as an airport, robots need to rely on SLAM technology to achieve autonomous navigation and obstacle avoidance.

[0005] Beidou Navigation Satellite System is a global satellite navigation system independently developed by China. It can provide all-weather, all-day, high-precision positioning, navigation and timing services to global users, and can verify the correctness of the transport vehicle's route. Beidou + GPS dual-mode positioning can receive two satellite signals at the same time, thereby increasing the number of positioning satellites and improving positioning accuracy. Especially in complex environments, dual-mode positioning can effectively reduce signal interference and occlusion, and improve positioning accuracy.

[0006] Aircraft baggage check-in refers to the process of checking your luggage at the check-in counter before boarding, handing it over to the staff, and having the staff put it in the aircraft cargo hold. Due to the limited luggage storage space in the aircraft cabin, luggage that exceeds the specified size and weight needs to be transported as checked luggage.

[0007] Currently, luggage trolleys for airports need to be pushed manually, which is slow, time-consuming and laborious. Some people need comfortable and fast-moving means of transportation and carrying goods. Passengers usually need to move a long distance from the airport security checkpoint to the check-in counter. Most ordinary people hope to reach their destination quickly and effortlessly. In addition, when some passengers are unable to move independently (such as elderly people with limited mobility or people with disabilities), they need to rely on other manual forces to transport their luggage, which causes inconvenience to these passengers, affects their willingness to travel, and further affects their physical and mental health.

[0008] In view of this, the present invention is proposed. Summary of the invention

[0009] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide an automatic baggage transport vehicle system in a terminal building, which solves the problems raised in the above-mentioned background technology.

[0010] In order to solve the above technical problems, the basic concept of the technical solution adopted by the present invention is:

[0011] An automatic baggage transport vehicle system in a terminal building, including: for realizing automatic control of an airport transport vehicle, including: a transport vehicle and a transport vehicle control room located in the terminal building, the transport vehicle is equipped with a set of Mecanum wheels that can move freely in different directions, a display screen interactive component, and a control system, wherein the display screen interactive component is used for passengers to input flight number information, the control system is used to process the input information and control the movement of the transport vehicle, the control system includes a Beidou + GPS dual-mode positioning module, a visual line patrol module, a YOLOv5 target detection module, a SLAM module, and an ultrasonic ranging module, and the control system can command the movement and operation of the transport vehicle according to the data and information returned by each module;

[0012] The control system of the transport vehicle communicates with the control room via a wireless network, wherein the transport vehicle uploads the flight number information entered by the passenger to the control room in real time, and the control room returns the corresponding check-in counter number.

[0013] Optional, Beidou + GPS dual-mode positioning module is used to monitor the position of the transport vehicle in real time and feed the position information back to the control system. The control system performs real-time navigation and path planning based on the feedback data. The visual line patrol module is a high-definition camera installed at the bottom of the transport vehicle. The image data captured by the high-definition camera is transmitted to the control system for processing. The control system uses the visual line patrol algorithm to analyze the image to identify and track the tracking line. The YOLOv5 target detection module is used to identify the tracking line on the ground and guide the transport vehicle to resume the driving path when the visual line patrol module loses the tracking line. The detection results will be fed back to the control system in real time;

[0014] The SLAM module integrates laser radar and sensors, and then generates an environmental map in real time based on the feedback data from the laser radar and sensors, and exchanges data with the control system. Then, the control system performs dynamic path planning and obstacle avoidance based on the map information provided by the SLAM module;

[0015] The ultrasonic distance measurement module is used to measure the distance of obstacles in front of the transport vehicle in real time and feed the measurement results back to the control system. The control system determines whether to slow down or stop based on the distance information to avoid collision.

[0016] Optionally, when using the YOLOv5 target detection module to identify tracking lines on the ground, it is necessary to lay tracking lines used by the transport vehicle on the ground of the terminal, and each check-in counter has a different color. The transport vehicle uses a high-definition camera installed at the bottom and uses visual line patrol technology to identify tracking lines of the target color for tracking.

[0017] Optionally, when the transport vehicle captures the tracing line image through the visual line patrol module, first, use the OpenCV library to convert the image from the BGR color space to the HSV color space, and then change the HSV threshold according to the color of the tracing line to be tracked to isolate the tracing line. After obtaining the preliminary binary image, use operations such as dilation and erosion to remove noise and enhance the continuity of the line, and then detect the contour in the image. According to the centerline position determined by the centroid of the contour, the transport vehicle system adjusts the speed and rotation direction of each wheel according to the deviation between the centerline and the center of the vehicle based on the obtained centerline position information of the line.

[0018] Optionally, when controlling the Mecanum wheel, the Mecanum wheel calculates the degree of deviation between the current driving trajectory of the transport vehicle and the target tracking line according to the distance between the position of the center line of the line and the center point of the image, and then according to the coordination of the speed and direction of the four Mecanum wheels, the movement mode of the transport vehicle is simplified to the speed in the axis direction, the speed perpendicular to the axis direction, and the rotation speed, and then the PID algorithm is used to control these three parameters respectively, wherein the composition of PID is divided into proportional term P, integral term D, differential term D, P = K p ×e, I = K i ×∫e dt, Among them, K p , K i , K d are proportional, integral and differential gains respectively, e is the error, and t is time.

[0019] Optionally, when the YOLOv5 target detection module searches for the tracking line on the ground, first, the tracking line target to be identified is photographed at multiple angles under multiple lighting environments to establish a data set of a sufficient number of images, and the data set is divided into a training set, a validation set, and a test set. The captured images include images under normal lighting conditions, images under shadow conditions, images under strong light conditions, and images under low light conditions;

[0020] After obtaining the data set, use labelimg to calibrate each image and generate label data. After calibration, use the train.py script provided by YOLOv5 to train the model and use TensorBoard to monitor the training process in real time and view indicators such as the loss curve. After training, use the val.py script to verify the model and evaluate the performance of the model on the verification set. After verification, the model will be applied to the test set to detect the generalization ability of the model. After training, the fully verified model is deployed to the YOLOv5 target detection module of the transport vehicle for real-time target detection of the tracking line.

[0021] Optionally, when using LiDAR for simultaneous positioning and mapping, the LiDAR device emits laser beams to objects in the surrounding environment and receives laser beams reflected from these objects. Then, the time difference between the emission and reception of the laser beam is measured. Based on the speed of light and the measured time difference, the formula The distance between the target object and the LiDAR is calculated to generate a data set containing a large number of three-dimensional coordinate points, namely, a point cloud, where: Dis the distance between the target object and the laser radar, c is the speed of light, Δt is the time difference between the laser beam emitted from the laser radar and reflected back, f(t) is a function of time t, which represents the influence of environmental conditions on the speed of light at a specific time, g(t) is another function of time t, which represents the accuracy adjustment factor of the laser radar, taking into account the error of the system under different working conditions, and β is a constant, which reflects the nonlinear influence of environmental factors on the distance measurement accuracy;

[0022] In the current environment, local environment recognition is performed by comparing the point cloud data of the current environment with the data in the established map, and similar parts are found. The ICP algorithm is used to estimate the position of the robot in the map. The ICP algorithm minimizes the difference between the current data and the data in the map in an iterative manner to obtain the position. According to the position, the position and posture of the robot are adjusted to better align with the map. Then the point cloud data of the current environment is spliced ​​with the established map, and the map is updated based on the new data.

[0023] Optionally, while driving, the LiDAR continuously scans the area ahead to identify potential obstacles. If an obstacle is detected on the transporter's path and the distance between the obstacle and the transporter is less than or equal to 2 meters, the warning mechanism will be triggered immediately and the transporter will suspend its action. At this time, the transporter will issue a warning through the built-in voice broadcast system to remind nearby pedestrians or staff that the transporter cannot move forward and request to move the obstacle; and make a voice broadcast to request the obstacle to move its position. If the obstacle still hinders the movement of the transporter after a long time, the transporter will set the position 1m behind the obstacle as the navigation point and re-plan the driving path to bypass the obstacle and continue to move along the tracking line.

[0024] Optionally, the control system can grasp the operating status of the transport vehicle by receiving information from the Beidou + GPS dual-mode positioning module and analyzing the position information, and timely understand its current location and driving direction. If it is monitored that the transport vehicle deviates from the designated driving route by more than the preset threshold, the control room will quickly issue a warning and suspend the movement of the transport vehicle. After the transport vehicle is suspended, the staff will immediately intervene to conduct a fault check. The fault check includes a comprehensive evaluation of the vehicle's sensors, power system, control system and other key components to confirm whether there is a mechanical or electronic failure.

[0025] Optionally, if the inspection finds that the transport vehicle has a fault, the staff will arrange to send it to the manufacturer for repair to ensure that the problem is properly handled. At the same time, in order to avoid affecting the transportation task, the luggage in the vehicle will be quickly transferred to other available transport vehicles. If no fault is found after inspection, the staff will manually adjust the driving path of the transport vehicle and recalibrate its driving direction to return it to the designated tracking line.

[0026] After adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art. Of course, any product implementing the present invention does not necessarily need to achieve all the advantages described below at the same time:

[0027] The transport vehicle of the present invention can autonomously navigate, automatically identify and track the tracking line, making the movement of passengers from the security checkpoint to the check-in counter easier and more efficient, meeting the passengers' demand for fast and comfortable travel. Especially for the elderly or disabled people with limited mobility, the use of the transport vehicle not only reduces their burden during the luggage transportation process, but also enhances their ability to travel independently, and improves their comfort and self-confidence in travel. In addition, the automated operation of the transport vehicle reduces the dependence on human resources and the need for manual intervention, thereby reducing the incidence of human errors and ensuring the safety and reliability of luggage transportation. It solves the problem that the traditional manual push of the trolley is slow, time-consuming and labor-intensive.

[0028] The specific implementation modes of the present invention are further described in detail below in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The drawings described below are only some embodiments. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0030] In the figure:

[0031] Figure 1 A block diagram of the operating principle of a luggage transport vehicle provided in an embodiment of the present application is shown.

[0032] Figure 2 A principle block diagram of a visual line patrol technology provided in an embodiment of the present application is shown.

[0033] Figure 3 A principle block diagram of a route correction process using the YOLOv5 model target detection technology provided in an embodiment of the present application is shown.

[0034] Figure 4 A principle block diagram of an obstacle avoidance process using simultaneous localization and mapping (SLAM) technology provided in an embodiment of the present application is shown.

[0035] Figure 5 A schematic diagram of a luggage transport vehicle tracking route planning provided in an embodiment of the present application is shown.

[0036] It should be noted that these drawings and textual descriptions are not intended to limit the conceptual scope of the present invention in any way, but are intended to illustrate the concept of the present invention for those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0037] The present invention will now be described in further detail with reference to the accompanying drawings.

[0038] See also Figure 1-5 As shown, in this embodiment, an automatic baggage transport vehicle system in a terminal is provided, which is used to realize automatic control of an airport transport vehicle, including: a transport vehicle and a transport vehicle control room located in the terminal building, the transport vehicle is equipped with a set of Mecanum wheels that can move freely in different directions, a display screen interactive component, and a control system, wherein the display screen interactive component is used for passengers to input flight number information, the control system is used to process the input information and control the movement of the transport vehicle, the control system includes a Beidou + GPS dual-mode positioning module, a visual line patrol module, a YOLOv5 target detection module, a SLAM module, and an ultrasonic ranging module, and the control system can command the movement and operation of the transport vehicle according to the data and information returned by each module;

[0039] The control system of the transport vehicle communicates with the control room via a wireless network, wherein the transport vehicle uploads the flight number information entered by the passenger to the control room in real time, and the control room returns the corresponding check-in counter number.

[0040] In this embodiment, the Beidou + GPS dual-mode positioning module is used to monitor the position of the transport vehicle in real time and feed back the position information to the control system. The control system navigates and plans the path in real time based on the feedback data. The visual line patrol module is a high-definition camera installed at the bottom of the transport vehicle. The image data captured by the high-definition camera is transmitted to the control system for processing. The control system uses the visual line patrol algorithm to analyze the image to identify and track the tracking line. The YOLOv5 target detection module is used to identify the tracking line on the ground and guide the transport vehicle to resume the driving path when the visual line patrol module loses the tracking line. The detection results will be fed back to the control system in real time;

[0041] The SLAM module integrates laser radar and sensors, and then generates an environmental map in real time based on the feedback data from the laser radar and sensors, and exchanges data with the control system. Then, the control system performs dynamic path planning and obstacle avoidance based on the map information provided by the SLAM module;

[0042] The ultrasonic distance measurement module is used to measure the distance of obstacles in front of the transport vehicle in real time and feed the measurement results back to the control system. The control system determines whether to slow down or stop based on the distance information to avoid collision.

[0043] In this embodiment, when using the YOLOv5 target detection module to identify the tracking lines on the ground, it is necessary to lay the tracking lines used by the transport vehicle on the ground of the terminal building, and each check-in counter has a different color. The transport vehicle uses the high-definition camera installed at the bottom to use the visual line patrol technology to identify the tracking lines of the target color for tracking. Please refer to Figure 5The transport vehicles navigate through different colored tracking lines. The transport vehicles used by passengers park in the parking area near each security checkpoint, and the transport vehicles that have completed transportation are parked in the standby area. After the staff redistributes the transport vehicles, they will go to the parking areas of each security checkpoint to transport again. Different tracking lines try to minimize overlap and keep a distance to avoid congestion caused by the convergence of vehicles.

[0044] In this embodiment, when the transport vehicle captures the tracing line image through the visual line patrol module, first, the OpenCV library is used to convert the image from the BGR color space to the HSV color space, and then the HSV threshold is changed according to the color of the tracing line to be tracked to isolate the tracing line. After obtaining the preliminary binary image, operations such as dilation and erosion are used to remove noise and enhance the continuity of the line, and then the contour in the image is detected. According to the centerline position determined by the center of mass of the contour, the transport vehicle system adjusts the speed and rotation direction of each wheel according to the deviation between the centerline and the center of the vehicle based on the obtained centerline position information of the line.

[0045] In this embodiment, when controlling the Mecanum wheel, the Mecanum wheel calculates the degree of deviation between the current driving track of the transport vehicle and the target tracking line according to the distance between the position of the center line of the line and the center point of the image, and then according to the coordination of the speed and direction of the four Mecanum wheels, the movement mode of the transport vehicle is simplified into the speed in the axis direction, the speed in the direction perpendicular to the axis, and the rotation speed, and then the PID algorithm is used to control these three parameters respectively, wherein the composition of PID is divided into proportional term P, integral term D, differential term D, P = K p ×e, I = K i ×∫e dt, Among them, K p , K i , K d are proportional, integral and differential gains respectively, e is the error, and t is time.

[0046] In this embodiment, when the YOLOv5 target detection module searches for the tracking line on the ground, first, the tracking line target to be identified is photographed at multiple angles under multiple light environments to establish a data set of a sufficient number of images, and the data set is divided into a training set, a validation set, and a test set. The captured images include images under normal lighting conditions, images under shadow conditions, images under strong light conditions, and images under low light conditions;

[0047] After obtaining the data set, use labelimg to calibrate each image and generate label data. After calibration, use the train.py script provided by YOLOv5 to train the model and use TensorBoard to monitor the training process in real time and view indicators such as the loss curve. After training, use the val.py script to verify the model and evaluate the performance of the model on the verification set. After verification, the model will be applied to the test set to detect the generalization ability of the model. After training, the fully verified model is deployed to the YOLOv5 target detection module of the transport vehicle for real-time target detection of the tracking line.

[0048] In this embodiment, when using laser radar for synchronous positioning and mapping, the laser radar device transmits laser beams to objects in the surrounding environment and receives laser beams reflected from these objects. Then, the time difference required for the laser beam to be transmitted and received is measured. According to the speed of light and the measured time difference, the formula The distance between the target object and the LiDAR is calculated to generate a data set containing a large number of three-dimensional coordinate points, namely, a point cloud, where: D is the distance between the target object and the laser radar, c is the speed of light, Δt is the time difference between the laser beam emitted from the laser radar and reflected back, f(t) is a function of time t, which represents the influence of environmental conditions on the speed of light at a specific time, g(t) is another function of time t, which represents the accuracy adjustment factor of the laser radar, taking into account the error of the system under different working conditions, and β is a constant, which reflects the nonlinear influence of environmental factors on the distance measurement accuracy;

[0049] In the current environment, local environment recognition is performed by comparing the point cloud data of the current environment with the data in the established map, and similar parts are found. The ICP algorithm is used to estimate the position of the robot in the map. The ICP algorithm minimizes the difference between the current data and the data in the map in an iterative manner to obtain the position. According to the position, the position and posture of the robot are adjusted to better align with the map. Then the point cloud data of the current environment is spliced ​​with the established map, and the map is updated based on the new data.

[0050] In this embodiment, while driving, the laser radar continuously scans the area ahead to identify potential obstacles. If an obstacle is detected on the path of the transport vehicle, and the distance of the obstacle from the transport vehicle is less than or equal to 2 meters, the early warning mechanism will be triggered immediately, and the transport vehicle will suspend its action. At this time, the transport vehicle will issue a warning through the built-in voice broadcast system, reminding nearby pedestrians or staff that the transport vehicle cannot continue to move forward, and request to move the obstacle; and make a voice broadcast, requesting the obstacle to move its position. If the obstacle still hinders the movement of the transport vehicle after a long time, the transport vehicle will set the position 1m behind the obstacle as a navigation point, and re-plan the driving path to bypass the obstacle and continue to move along the tracking line. SLAM is a dynamically updated process. As the transport vehicle continues to explore the environment and collect new data, the map will be continuously updated. Through continuous iteration and optimization, the SLAM system can build high-quality maps and provide reliable positioning information for the transport vehicle.

[0051] In this embodiment, the control system can grasp the operating status of the transport vehicle by receiving information from the Beidou + GPS dual-mode positioning module and analyzing the position information, and timely understand its current location and driving direction. If it is monitored that the transport vehicle deviates from the designated driving route by more than a preset threshold, the control room will quickly issue a warning and suspend the movement of the transport vehicle. After the transport vehicle is stopped, the staff will immediately intervene to perform a fault check. The fault check includes a comprehensive evaluation of the vehicle's sensors, power system, control system and other key components to confirm whether there is a mechanical or electronic fault.

[0052] In this embodiment, if the inspection finds that the transport vehicle has a fault, the staff will arrange to send it to the manufacturer for repair to ensure that the problem is properly handled. At the same time, in order to avoid affecting the transportation task, the luggage in the vehicle will be quickly transferred to other available transport vehicles. If no fault is found after inspection, the staff will manually adjust the driving path of the transport vehicle and recalibrate its driving direction to return it to the designated tracking line.

[0053] Example 1: Transport vehicle operation process

[0054] In the first step, the transport vehicle control room assigns the transport vehicles to a specific parking area at the terminal security checkpoint, where the transport vehicles are on standby. This measure can effectively reduce the random parking of transport vehicles on the main channel, reduce interference with the flow of people, keep the road unobstructed, and thus improve management efficiency.

[0055] The beneficial effect of this technical solution is that the specific parking area helps to reduce the random parking of transport vehicles on the road, thereby avoiding interference with the flow of people and obstruction of transport vehicles. This helps to improve management efficiency and keep the road unobstructed.

[0056] In the second step, with the help of the staff, the passenger opens the latch of the transport vehicle, lowers the baffle, and places the luggage to be checked in (including overweight, special items, prohibited items, and dangerous goods, etc.) into the transport vehicle. Afterwards, the baffle is closed and the latch is re-inserted. This design facilitates the loading and unloading of luggage, and the presence of the baffle can effectively prevent the luggage from falling during transportation.

[0057] The beneficial effect of the technical solution is that the baffle with a latch can be lowered to the ground, making it convenient for luggage to be pushed up or slid down, and convenient for taking and placing luggage. At the same time, the existence of the baffle prevents luggage from falling during transportation.

[0058] In the third step, the passenger enters the flight number through the interactive component of the transporter's display screen. The information is then uploaded to the control room, which returns the corresponding check-in counter number based on the passenger's flight number. This centralized management method allows the control room to flexibly adjust counter allocation according to real-time conditions to ensure efficient use of resources. In addition, passengers can directly enter the flight number through the touch screen or voice recognition, avoiding cumbersome paper forms and manual communication, and the system's built-in data verification mechanism ensures the accuracy of the information.

[0059] The beneficial effect of this technical solution is that by centrally managing and allocating check-in counter resources, the control room can more flexibly adjust counter allocation according to real-time conditions to ensure the most effective use of resources. Passengers can easily enter information such as flight numbers on the display screen directly through touch screens or voice recognition, without the need to use additional paper forms or tedious communication with other staff. The system's built-in data verification mechanism can ensure the accuracy of passenger input information.

[0060] In the fourth step, the transport vehicle automatically goes to the designated check-in counter. The tracking line laid on the ground is about 2 cm wide, and each check-in counter has a tracking line of a corresponding color. The transport vehicle identifies the tracking line through the high-definition camera at the bottom and tracks it using the visual line patrol module. At the same time, the PID control algorithm ensures the stability of the transport vehicle during driving to prevent loss of control due to excessive speed. If the transport vehicle deviates from the route, the high-definition camera will slowly rotate in place and call the YOLOv5 target detection module to find the tracking line until the visual line patrol module re-identifies the line. If the tracking line cannot be captured for a long time, the transport vehicle will send a fault signal to the control room and wait for maintenance.

[0061] The transport vehicle also tracks its position in real time through the Beidou + GPS dual-mode positioning module, and the location information is transmitted to the control room for monitoring and scheduling. If the transport vehicle deviates too much from the designated route, the control room will issue a warning and suspend the vehicle's operation, and the staff will conduct a fault inspection on the transport vehicle. If a fault is found, the vehicle will be sent to the manufacturer for repair, and other transport vehicles will be arranged to handle the luggage in the vehicle; if there is no fault, the staff will manually adjust the vehicle route.

[0062] The transport vehicle uses laser radar for simultaneous localization and mapping (SLAM) to build a real-time map of the surrounding environment. If there is an obstacle on the forward route and the distance is less than 2 meters, the transport vehicle will pause and request to move the obstacle through voice broadcast. If the obstacle is not removed for a long time, the transport vehicle will set the position 1 meter behind it as a new navigation point to bypass the obstacle and continue to drive. This obstacle avoidance function effectively prevents collisions and ensures the safe and stable operation of the transport vehicle.

[0063] The beneficial effect of this technical solution is that the transport vehicle is equipped with advanced visual line-tracking technology, which can automatically identify and track the tracking line of a specific color without human intervention. This automation capability has greatly improved the automation level of airport logistics, making the baggage transportation process more efficient and reliable. During the vehicle's travel, the PID control algorithm is used for dynamic adjustment, and the speed and direction of the vehicle are adjusted through real-time feedback to ensure smooth and safe driving, greatly reducing the errors caused by human operation.

[0064] In addition, the Beidou + GPS dual-mode positioning module integrated in the transport vehicle provides high-precision location information and can monitor the location of the vehicle in real time. Through these data, the control room can accurately dispatch the transport vehicle and conduct real-time monitoring to ensure the smoothness of the logistics process. If the vehicle is detected to deviate from the designated driving route, the control room can immediately issue a warning and suspend the operation of the vehicle. This mechanism effectively prevents potential safety risks and ensures the safety and reliability of the transportation process. At the same time, the design of the transport vehicle has high flexibility and can automatically track according to the specific tracking lines of different check-in counters. This allows the transport vehicle to move freely within the airport and adapt to a variety of operational needs. At the same time, the obstacle avoidance function can not only effectively handle movable obstacles such as pedestrians or other vehicles, but also deal with immovable objects (such as fixed equipment or walls), demonstrating excellent environmental adaptability. Finally, by realizing automation and intelligence, manual intervention and waiting time in the transportation process are significantly reduced, thereby improving the overall operational efficiency of airport logistics. When the transport vehicle encounters a fault, the system will quickly handle the fault and arrange other vehicles to transfer the luggage in the vehicle in time to ensure the continuity and efficiency of the transportation task. This technical solution significantly improves the intelligence level of airport logistics and ensures the safety of transport vehicles, pedestrians and goods through high automation, precise monitoring, powerful fault detection and response capabilities, and efficient obstacle avoidance functions.

[0065] In the fifth step, after the transport vehicle arrives at the check-in counter, the staff takes the luggage out of the vehicle and obtains the corresponding passenger information through the interactive components of the control screen to check in the luggage. This digital interaction method reduces human errors, improves work efficiency, provides more convenient services, improves passenger experience, and avoids conflicts caused by poor communication.

[0066] The beneficial effects of this technical solution are: the automated and digital interaction method reduces the possibility of human errors, such as information entry errors or luggage matching errors, and improves work efficiency. It provides passengers with more convenient and efficient services and improves their travel experience. It avoids conflicts between passengers and staff due to service attitudes and communication methods.

[0067] Step 6: After the staff has checked in the baggage, the control room sends a return order to the transport vehicle, and the transport vehicle goes along the tracking line to a specific standby area to wait for new task assignments. The distance between different tracking lines is set to avoid congestion caused by vehicles converging.

[0068] The beneficial effect of this technical solution is that after the transportation task is completed, the transport vehicle will automatically return to the designated standby area. This design not only effectively avoids the accumulation of vehicles near the check-in counter, reduces traffic jams caused by too many vehicles, but also ensures the smooth operation of the entire airport. By centrally managing the standby area, the control room can monitor the status and location of each transport vehicle in real time, thereby improving the dispatch efficiency of the vehicle. The reasonable layout and planning of the standby area enables staff to quickly identify the number and status of standby vehicles, which is convenient for subsequent task allocation. This management mode ensures the efficiency of the logistics process, helps maintain the traffic order of the terminal road, and reduces the inconvenience of passengers caused by vehicle congestion. At the same time, the establishment of the standby area also provides convenient conditions for the maintenance and inspection of vehicles, ensuring that the transport vehicle is always in the best working condition. Through this efficient return and standby mechanism, the airport can better organize and allocate resources, achieve rapid response and flexibly respond to various transportation needs. Overall, this design not only improves transportation efficiency, but also improves the overall service quality of the airport, ensuring the convenience and safety of passengers during travel.

[0069] In the seventh step, the control room assigns the transport vehicle in the standby area to the new parking area at the security checkpoint. The transport vehicle arrives at the corresponding parking area according to the specific color tracking line on the ground and starts the next transport task. The efficient management of this process ensures the continuity of the transport task and the smooth operation of the overall logistics.

[0070] The beneficial effect of this technical solution is that through the remote dispatch and real-time monitoring system of the control room, the waiting time of the transport vehicle in the standby area and the time to find the parking area can be significantly reduced. This optimized process not only improves the overall transportation efficiency, but also ensures the rapid execution of the baggage transportation task. The control room uses an advanced dispatching system to obtain the status, location and task progress of each transport vehicle in real time, so as to make quick decisions based on real-time data. The staff in the control room can assign tasks to multiple transport vehicles at one time. This batch management and control method makes task allocation more efficient and avoids the delays that may be caused by the previous one-by-one allocation. The staff can flexibly adjust the tasks of each transport vehicle according to actual needs and the current status of the vehicle, making resource allocation more reasonable and ensuring that each transport vehicle can be used efficiently. In addition, real-time monitoring also makes the dispatching process transparent. The staff can check the driving route, task status and upcoming arrival time of each transport vehicle at any time, which is convenient for planning follow-up work in advance. This management mode not only improves work efficiency, but also strengthens the overall control of the transportation process, which helps to identify and solve potential problems in a timely manner, thereby improving service quality.

[0071] The present invention is not limited to the above-mentioned embodiments. Anyone should be aware that any structural changes made under the enlightenment of the present invention, and any technical solutions that are the same or similar to the present invention, fall within the protection scope of the present invention. The technology, shape, and structural parts not described in detail in the present invention are all well-known technologies.

Claims

1. An automatic baggage transport vehicle system in a terminal building, used to realize automatic control of airport transport vehicles, characterized in that: include: The transport vehicle and its control room located in the terminal building. The transport vehicle is equipped with a set of Mecanum wheels that can move freely in different directions, a display screen interactive component, and a control system. The display screen interactive component is used for passengers to input flight number information, and the control system is used to process the input information and control the movement of the transport vehicle. The control system includes a Beidou + GPS dual-mode positioning module, a visual line patrol module, a YOLOv5 target detection module, a SLAM module, and an ultrasonic ranging module. The control system can command the movement and operation of the transport vehicle based on the data and information sent back by each module; The control system of the transport vehicle communicates with the control room via a wireless network, wherein the transport vehicle uploads the flight number information entered by the passenger to the control room in real time, and the control room returns the corresponding check-in counter number.

2. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: The Beidou + GPS dual-mode positioning module is used to monitor the position of the transport vehicle in real time and feed back the position information to the control system. The control system navigates and plans the route in real time based on the feedback data. The visual line patrol module is a high-definition camera installed at the bottom of the transport vehicle. The image data captured by the high-definition camera is transmitted to the control system for processing. The control system uses the visual line patrol algorithm to analyze the image to identify and track the tracking line. The YOLOv5 target detection module is used to identify the tracking line on the ground and guide the transport vehicle to resume the driving path when the visual line patrol module loses the tracking line. The detection results will be fed back to the control system in real time. The SLAM module integrates laser radar and sensors, and then generates an environmental map in real time based on the feedback data from the laser radar and sensors, and exchanges data with the control system. Then, the control system performs dynamic path planning and obstacle avoidance based on the map information provided by the SLAM module; The ultrasonic ranging module is used to measure the distance of obstacles in front of the transport vehicle in real time and feed the measurement results back to the control system. The control system determines whether to slow down or stop based on the distance information to avoid collision.

3. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: When using the YOLOv5 target detection module to identify tracking lines on the ground, it is necessary to lay tracking lines used by the transport vehicle on the ground of the terminal, and each check-in counter has a different color. The transport vehicle uses a high-definition camera installed at the bottom and uses visual line patrol technology to identify tracking lines of the target color for tracking.

4. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: When the transport vehicle captures the tracing line image through the visual line patrol module, first, the OpenCV library is used to convert the image from the BGR color space to the HSV color space, and then the HSV threshold is changed according to the color of the tracing line to be tracked to isolate the tracing line. After obtaining the preliminary binary image, the dilation and erosion operations are used to remove noise and enhance the continuity of the line. Then the contour in the image is detected. According to the centerline position determined by the centroid of the contour, the transport vehicle system adjusts the speed and rotation direction of each wheel according to the deviation between the centerline and the center of the vehicle based on the obtained centerline position information of the line.

5. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: When controlling the Mecanum wheel, the Mecanum wheel calculates the deviation between the current driving trajectory of the transport vehicle and the target tracking line according to the distance between the position of the center line of the line and the center point of the image. Then, according to the coordination of the speed and direction of the four Mecanum wheels, the movement mode of the transport vehicle is simplified into the speed in the axis direction, the speed perpendicular to the axis direction, and the rotation speed. Then, the PID algorithm is used to control these three parameters respectively. Among them, the composition of PID is divided into proportional term P, integral term D, and differential term D, P = K p ×e, I = K i ×∫e dt, Among them, K p , K i , K d are proportional, integral and differential gains respectively, e is the error, and t is time.

6. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: When the YOLOv5 target detection module searches for the tracking lines on the ground, it first takes photos of the tracking line targets to be identified under various lighting conditions and angles to establish a data set with a sufficient number of images. The data set is divided into a training set, a validation set, and a test set. The images taken include images under normal lighting conditions, images under shadow conditions, images under strong light conditions, and images under low light conditions. After obtaining the data set, use labelimg to calibrate each image and generate label data. After calibration, use the train.py script provided by YOLOv5 to train the model and use TensorBoard to monitor the training process in real time and view the loss curve indicators. After training, use the val.py script to verify the model and evaluate the performance of the model on the verification set. After verification, the model will be applied to the test set to detect the generalization ability of the model. After training, the fully verified model is deployed to the YOLOv5 target detection module of the transport vehicle for real-time target detection of the tracking line.

7. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: When using LiDAR for simultaneous positioning and mapping, the LiDAR device emits laser beams to objects in the surrounding environment and receives the laser beams reflected from these objects. Then, the time difference between the emission and reception of the laser beam is measured. According to the speed of light and the measured time difference, the formula Calculate the distance between the target object and the laser radar to generate a data set containing a large number of three-dimensional coordinate points, namely, a point cloud, where D is the distance between the target object and the laser radar, c is the speed of light, Δt is the time difference between the laser beam being emitted from the laser radar and reflected back, f(t) is a function of time t, which represents the impact of environmental conditions on the speed of light at a specific time, g(t) is another function of time t, which represents the accuracy adjustment factor of the laser radar, taking into account the error of the system under different working conditions, and β is a constant that reflects the nonlinear impact of environmental factors on the distance measurement accuracy; In the current environment, local environment recognition is performed by comparing the point cloud data of the current environment with the data in the established map, and similar parts are found. The ICP algorithm is used to estimate the position of the robot in the map. The ICP algorithm minimizes the difference between the current data and the data in the map in an iterative manner to obtain the position. According to the position, the position and posture of the robot are adjusted to better align with the map. Then the point cloud data of the current environment is spliced ​​with the established map, and the map is updated based on the new data.

8. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: While driving, the laser radar continuously scans the area ahead to identify potential obstacles. If an obstacle is detected on the transport vehicle's path and the distance between the obstacle and the transport vehicle is less than or equal to 2 meters, the early warning mechanism will be triggered immediately and the transport vehicle will suspend its action. At this time, the transport vehicle will issue a warning through the built-in voice broadcast system to prompt nearby pedestrians or staff to move the obstacle. If the obstacle is not removed for a long time, the transport vehicle will set the position 1 meter behind the obstacle as a navigation point and re-plan the driving path to bypass the obstacle and continue to move along the tracking line.

9. The automatic baggage transport vehicle system in a terminal building according to claim 1, characterized in that: By receiving information from the Beidou + GPS dual-mode positioning module and analyzing the location information, the control system can grasp the operating status of the transport vehicle and promptly understand its current location and direction of travel. If it is detected that the transport vehicle deviates from the designated route by more than the preset threshold, the control room will quickly issue a warning and suspend the movement of the transport vehicle. After the transport vehicle is stopped, the staff will immediately intervene to conduct a fault check. The fault check includes a comprehensive evaluation of the vehicle's sensors, power system, control system and other key components to confirm whether there is a mechanical or electronic failure.

10. The automatic baggage transport vehicle system in a terminal building according to claim 9, characterized in that: If the inspection finds that the transport vehicle has a fault, the staff will arrange to send it to the manufacturer for repair to ensure that the problem is properly handled. At the same time, in order to avoid affecting the transportation task, the luggage in the vehicle will be quickly transferred to other available transport vehicles. If no fault is found after inspection, the staff will manually adjust the transport vehicle's driving path and recalibrate its driving direction to return it to the designated tracking line.

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