Luggage transportation path tracking and correcting system and method based on RFID and multi-sensor fusion

Abstract

The invention discloses a luggage transportation path tracking and correcting system and method based on RFID and multi-sensor fusion, and relates to the technical field of luggage sorting, and the method comprises the steps: obtaining the real-time data and final position of each luggage in the transportation process through arranging a plurality of sensors and RFID tags on each luggage transportation vehicle; according to the real-time data and the final position, utilizing a path planning method to avoid environmental obstacles, and planning a transportation path of each luggage transportation vehicle to obtain a first planned path; optimizing the first planning path through a local optimization method to obtain an optimized path; according to the optimized path, each luggage transport vehicle is controlled to move to a corresponding final position, so that each user arrives at the corresponding final position to extract the luggage; according to the invention, through combined use of the RFID tag and the plurality of sensors, comprehensive monitoring of the luggage transportation process is realized; by planning and optimizing the transportation path of the luggage transportation vehicle, the efficiency and safety of luggage transportation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of baggage sorting, and particularly to a baggage transportation path tracking and correction system and method based on the fusion of RFID and multi-sensors. Background Art

[0002] In a baggage transportation system, RFID tags are usually fixed on the baggage and connected to a baggage handling system through a reader. When the baggage passes through the reader, the information in the RFID tag will be automatically read and transmitted to the baggage handling system, thereby realizing the automatic identification of the baggage. However, currently, the baggage is generally transported collectively by a large transport vehicle to a specific sorting area for manual sorting. On the one hand, the stacked baggage may crush valuable items in the baggage. On the other hand, for users, they need to find their own baggage by themselves, which is prone to the risk of misappropriation, seriously reducing the user experience.

[0003] As disclosed in the Chinese patent with the authorization announcement number CN112258133B, a baggage tracking and identification system and method based on RFID technology includes a conveying mechanism, a logic control system, a baggage position detection device, and an RFID identification system. The conveying mechanism is used to convey the baggage with RFID tags attached; the logic control system is used to drive the conveying mechanism to convey the baggage, receive the position information obtained by the baggage position detection device, perform tracking control on the baggage, communicate with the RFID identification system in real time, and realize the automatic identification of the baggage barcode information; the baggage position detection device is used to detect the position information of the baggage on the conveying mechanism and send the position information to the logic control system; the RFID identification system is used to receive the baggage tracking signal transmitted by the logic control system, scan the RFID tag on the baggage, and complete the marking of the baggage tag information.

[0004] The above patent has the problems raised in this background art: only using RFID tags to track the transportation path of the baggage, the collected data is relatively single, and it is impossible to provide comprehensive and complete baggage transportation information, which may lead to inaccurate identification of the baggage transportation path. The traditional baggage transportation path is fixed, resulting in low baggage transportation efficiency. To solve the above problems, the present invention proposes a baggage transportation path tracking and correction system and method based on the fusion of RFID and multi-sensors. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the main object of the present invention is to provide a baggage transportation path tracking and correction system and method based on the fusion of RFID and multi-sensors, which can effectively solve the problems in the background art. The specific technical solutions of the present invention are as follows:

[0006] A baggage transportation path tracking and correction method based on the fusion of RFID and multi-sensors includes:

[0007] By setting multiple sensors and RFID tags on each luggage transport vehicle, real-time data and the end position of each piece of luggage during transportation are obtained;

[0008] According to the real-time data and the end position, using the path planning method to avoid environmental obstacles, the transportation path of each luggage transport vehicle is planned to obtain the first planned path;

[0009] Through the local optimization method, the first planned path is optimized to obtain the optimized path;

[0010] According to the optimized path, control each luggage transport vehicle to move to the corresponding end position, so that each user can reach the corresponding end position to pick up the luggage, where the end positions corresponding to each luggage transport vehicle are different.

[0011] Specifically, according to the real-time data and the end position, using the path planning method to avoid environmental obstacles, the transportation path of each luggage transport vehicle is planned to obtain the first planned path, including:

[0012] Use the grid division method to divide the action space of the luggage transport vehicle to obtain multiple space grids;

[0013] According to the positions of the obstacles in the action space, the space grids occupied by the obstacles are used as obstacle grids;

[0014] According to the end position, using the path planning method, within the non-obstacle grids, the transportation path of the luggage transport vehicle is planned to obtain the first planned path.

[0015] Specifically, the step of according to the end position, using the path planning method, within the non-obstacle grids, planning the transportation path of the luggage transport vehicle to obtain the first planned path includes:

[0016] Through the population optimization method, find the non-obstacle grids that the luggage transport vehicle needs to pass through to reach the end position to obtain multiple intermediate grids;

[0017] Use the path connection method to connect the intermediate grids to form the first planned path.

[0018] Specifically, the step of through the population optimization method, finding the non-obstacle grids that the luggage transport vehicle needs to pass through to reach the end position to obtain multiple intermediate grids includes:

[0019] Initialize the population, where each individual in the population represents a set of non-obstacle grids that the luggage transport vehicle needs to pass through to reach the end position;

[0020] According to the preset fitness function, calculate the fitness value of each individual;

[0021] According to the fitness value, individuals with fitness values greater than the preset fitness threshold are used as initial elite individuals;

[0022] Perform a mutation operation on the initial elite individuals to generate mutant individuals;

[0023] Perform a crossover operation on the mutant individuals and the initial elite individuals to generate crossover individuals;

[0024] Select the elite individuals of the next generation according to the fitness values corresponding to the crossover individuals;

[0025] Until the preset number of iterations is reached, the set of non-obstacle grids that the luggage transport vehicle corresponding to the individual with the optimal fitness value in the final elite individuals needs to pass through to reach the end position is used as the intermediate grid.

[0026] Specifically, using the path connection method to connect the intermediate grids to form the first planned path includes:

[0027] Starting from the current grid position of the luggage transport vehicle, search for adjacent grids in the intermediate grids, where the adjacent grids include adjacent grids in the four directions of up, down, left, and right;

[0028] According to the preset evaluation function, select the grid with the optimal evaluation result among the adjacent grids as the next grid;

[0029] Connect the grid center of the current grid with the grid center of the next grid;

[0030] Repeat the grid selection and connection process until the grid corresponding to the end position is connected to obtain the first planned path.

[0031] Specifically, using the local optimization method to optimize the first planned path to obtain the optimized path includes:

[0032] Set the safety distance threshold between the luggage transport vehicle and the obstacle boundary;

[0033] Using the local optimization method, delete the redundant grid nodes in the first planned path to obtain the optimized path.

[0034] Specifically, the setting of the safety distance threshold between the luggage transport vehicle and the obstacle boundary includes:

[0035] Calculate the shortest distance between the obstacle coordinates and the first planned path as the safety distance;

[0036] The sum of the circumradius of the obstacle and the length of the luggage transport vehicle is used as the safety distance threshold.

[0037] Specifically, using the local optimization method to delete the redundant grid nodes in the first planned path to obtain the optimized path includes:

[0038] Select three grid nodes A1, A2, and A3 in sequence starting from the end grid;

[0039] When the slopes of line A1A2 and line A2A3 are the same, the middle node A2 is a redundant node, and the middle node A2 is deleted;

[0040] When the slopes of line A1A2 and line A2A3 are different, when the safety distance between line A1A3 and the obstacle is greater than the safety distance threshold, the middle node A2 is deleted;

[0041] Repeat the screening process of redundant nodes until the middle node is the starting point of the luggage transport vehicle to obtain an optimized path.

[0042] Specifically, by setting multiple sensors and RFID tags on each luggage transport vehicle, real-time data and end positions of each luggage during transportation are obtained, including:

[0043] Perform outlier correction and filtering on the raw data collected by multiple sensors;

[0044] Perform data cleaning and data compression on the RFID tag data.

[0045] A luggage transport path tracking and correction system based on RFID and multi-sensor fusion, used to implement the luggage transport path tracking and correction method based on RFID and multi-sensor fusion, includes:

[0046] A data acquisition module, by setting multiple sensors and RFID tags on each luggage transport vehicle, obtains real-time data and end positions of each luggage during transportation;

[0047] A luggage transport path planning module, according to the real-time data and end positions, uses the path planning method to avoid environmental obstacles and plans the transport path of each luggage transport vehicle to obtain a first planned path;

[0048] A luggage transport path optimization module, through local optimization, optimizes the first planned path to obtain an optimized path;

[0049] A luggage transport module, according to the optimized path, controls each luggage transport vehicle to move to the corresponding end position, so that each user can reach the corresponding end position to pick up the luggage, where the end positions corresponding to each luggage transport vehicle are different.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] By combining the use of RFID tags and multiple sensors, the present invention realizes the comprehensive monitoring of the luggage transportation process, real-time monitors the position and status of the luggage, makes full use of the collected information from different data sources, and improves the accuracy and reliability of the data; through the path planning method, while avoiding obstacles, it can plan a relatively optimal transportation path to improve the luggage transportation efficiency; through the local optimization algorithm, the transportation path is further simplified, the luggage transportation time is further shortened, and a more efficient luggage transportation path is found; through the control system, the movement of each luggage transport vehicle is precisely controlled to make it travel along the optimized path to ensure that the luggage can accurately reach the end position, improving the reliability of the luggage transportation, thereby realizing the precise tracking and management of the luggage transportation, improving the efficiency and safety of the luggage transportation. In addition, each luggage transport vehicle carries a user's luggage, and since each end position is different, the end position can be numbered and the specific user can be informed by text message or other means, so that the user can directly go to the corresponding end position to wait for the luggage to arrive, avoiding the possibility of the user taking the wrong luggage. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flowchart of the working process of the luggage transportation path tracking and correction method based on RFID and multi-sensor fusion in Embodiment 1 of the present invention;

[0053] Figure 2 It is a schematic diagram of planning a path by the path connection method in Embodiment 1 of the present invention;

[0054] Figure 3 It is a schematic diagram of optimizing a path by the local optimization method in Embodiment 1 of the present invention;

[0055] Figure 4 It is a schematic diagram of the structure of the luggage transportation path tracking and correction system based on RFID and multi-sensor fusion in Embodiment 2 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0056] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be made in conjunction with the accompanying drawings of the specification.

[0057] Many specific details are set forth in the following description in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0058] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures or characteristics that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or selectively exclusive embodiments from other embodiments.

[0059] Embodiment 1

[0060] This embodiment provides a method for tracking and correcting the luggage transportation path based on the fusion of RFID and multiple sensors, as Figure 1 shown. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multiple sensors includes:

[0061] S101. By setting multiple sensors and RFID tags on each luggage transport vehicle, obtain the real-time data and the end position of each piece of luggage during transportation;

[0062] S102. According to the real-time data and the end position, use the path planning method to avoid environmental obstacles and plan the transportation path of each luggage transport vehicle to obtain the first planned path;

[0063] S103. Optimize the first planned path through the local optimization method to obtain the optimized path;

[0064] S104. According to the optimized path, control each luggage transport vehicle to move to the corresponding end position, so that each user can reach the corresponding end position to pick up the luggage, where the end positions corresponding to each luggage transport vehicle are different.

[0065] To achieve the intelligent transportation of luggage at the airport, place the passengers' luggage on the luggage transport vehicles, and by controlling the transportation trajectories of each luggage transport vehicle, achieve the rapid and accurate transportation of the luggage to the corresponding end positions. Specifically, the end positions are in a planned end area, and each end position is configured with a prominent identifier. For example, if the end position is a 0.3-square-meter rectangular waiting area grid, the luggage stops after moving to this rectangular waiting area grid. The user can receive the number of this rectangular waiting area grid through the mobile phone, and then reach this rectangular waiting area grid through the prominent identifier to pick up their own luggage.

[0066] In this embodiment, the RFID tag of each piece of luggage is attached to the corresponding luggage transport vehicle. Each RFID tag stores information about each piece of luggage, such as the luggage tag number, flight number, departure airport, arrival airport, takeoff and landing time, etc. By obtaining the flight information of the luggage, the end position of the luggage is obtained; multiple sensors are arranged on the luggage transport vehicle, such as position sensors, speed sensors, etc., to continuously monitor the dynamic information of the luggage, collect information such as the position and speed of the luggage, and send it to the background database; through the combined use of RFID tags and multiple sensors, comprehensive monitoring of the luggage transportation process can be achieved, ensuring the accuracy and integrity of the data. Through the data collected by multiple sensors, the transportation status of the luggage can be understood more carefully, providing data support for identifying the transportation path of the luggage.

[0067] Specifically, due to the complex airport environment with a large number of passengers and airport facilities, when the luggage transport vehicle moves in the airport, it is necessary to avoid these obstacles to ensure safe arrival at the destination; the present invention designs a path planning method that can effectively avoid obstacles in the airport environment, such as walls, pedestrians, and other luggage transport vehicles, etc., so as to ensure that the luggage transport vehicle arrives at the destination safely and smoothly. Currently, path planning algorithms include Dijkstra algorithm, A* algorithm, etc. The present invention creatively combines the genetic optimization algorithm and the A* algorithm to plan the driving path of the luggage transport vehicle. While avoiding obstacles, it can plan a better transportation path, obtain the first planned path, and improve the luggage transportation efficiency.

[0068] Specifically, on the basis of the first planned path, the local optimization method is used to further optimize the first planned path. Currently, local optimization methods include simulated annealing algorithm, ant colony algorithm, etc. In this embodiment, through the local optimization algorithm, on the premise of ensuring the safe distance between the luggage transport vehicle and the obstacles, the path is further simplified, the luggage transportation time is further shortened, and the luggage transportation efficiency is improved. By adjusting the path, a more efficient transportation path is found. The central control system accurately controls the movement of each luggage transport vehicle according to the optimized path. The control system will send instructions to the transport vehicle to guide it to drive according to the optimized path. At the same time, the position and status of the transport vehicle are continuously monitored through the real-time data of the sensors to ensure that it can accurately reach the corresponding end position, ensuring that the luggage can accurately reach the end position. By continuously monitoring the position and status of the transport vehicle, the reliability of luggage transportation can be improved.

[0069] The present invention realizes the comprehensive monitoring of the luggage transportation process through the combined use of RFID tags and multiple sensors, real-time monitors the position and status of the luggage, makes full use of the collected information from different data sources, and improves the accuracy and reliability of the data; through the path planning method, while avoiding obstacles, it can plan a relatively optimal transportation path to improve the luggage transportation efficiency; through the local optimization algorithm, the transportation path is further simplified to further shorten the luggage transportation time and find a more efficient luggage transportation path; through the control system, the movement of each luggage transport vehicle is precisely controlled to make it travel along the optimized path to ensure that the luggage can accurately reach the end position, improving the reliability of the luggage transportation, thereby realizing the precise tracking and management of the luggage transportation, improving the efficiency and safety of the luggage transportation. In addition, each luggage transport vehicle carries a user's luggage, and since each end position is different, the end positions can be numbered and identified, and the specific user can be informed by text message or other means, so that the user can directly go to the corresponding end position to wait for the luggage to arrive, avoiding the possibility of the user taking the wrong luggage.

[0070] Further, according to the real-time data and the end position, using the path planning method to avoid environmental obstacles, plan the transportation path of each luggage transport vehicle to obtain the first planned path, including:

[0071] S201. Divide the action space of the luggage transport vehicle using the grid division method to obtain multiple space grids;

[0072] S202. According to the positions of the obstacles in the action space, regard the space grids occupied by the obstacles as obstacle grids;

[0073] S203. According to the end position, use the path planning method to plan the transportation path of the luggage transport vehicle within the non-obstacle grids to obtain the first planned path.

[0074] In this embodiment, first, the movement space of the luggage transporter is divided by the grid division method into multiple grids of the same or similar size. Each grid represents a discrete space unit. By dividing the continuous space into multiple space grids through grid division, the complexity of the environment can be reduced and the efficiency of path planning can be improved. Based on the space grid division, according to the position information of the obstacles in the environment, the space grids occupied by the obstacles are marked as obstacle grids. By marking the obstacle grids, the positions of the obstacles in the movement space can be clearly represented. In path planning, the obstacle grids are impassable, and the transportation path of the luggage transporter needs to avoid the obstacle grids. For example, there are some fixed obstacles in the airport luggage transportation area, such as pillars, seats, etc. According to the position information of these obstacles, the space grids they occupy can be marked as obstacle grids. If a certain pillar is in the grid at the 3rd row and 5th column, then the grid coordinates corresponding to this grid are marked as obstacle grid coordinates, and the obstacle grid corresponding to the obstacle grid coordinates needs to be avoided during the path planning process.

[0075] In this embodiment, a radio frequency device is configured in each space grid, and the adjustable radio frequency range is the area defined in the space grid. In this way, the position of each piece of luggage can be determined through RFID radio frequency technology.

[0076] Specifically, after determining all the obstacle grids, according to the end position of the luggage, using the path planning method, the transportation path of the luggage transporter is planned within the non-obstacle grids to obtain an optimal path from the starting point to the end position. Through path planning, environmental obstacles can be avoided, and a shorter transportation path can be planned, reducing the time and cost of luggage transportation and improving the transportation efficiency. By combining the grid division method, obstacle grid marking, and path planning method, the precise planning of the transportation path of the luggage transporter can be realized, which not only improves the efficiency and accuracy of path planning but also ensures the safety and efficiency of luggage transportation.

[0077] Further, the step of planning the transportation path of the luggage transporter within the non-obstacle grids according to the end position by using the path planning method to obtain the first planned path includes:

[0078] S301. Through the population optimization method, find the non-obstacle grids that the luggage transporter needs to pass through to reach the end position to obtain multiple intermediate grids;

[0079] S302. Use the path connection method to connect the intermediate grids to form the first planned path.

[0080] In this embodiment, according to the starting point and ending point positions of the luggage transporter, the feasible path from the starting point to the ending point is searched by using the population optimization method. By evaluating the fitness of each feasible path, the path with high fitness is selected from multiple feasible paths, and the corresponding grids passed by it are used as intermediate grids. The population optimization method can search in the entire non-obstacle grid space to find the global optimal or approximate optimal path. This algorithm can be adjusted according to different fitness functions and constraint conditions to adapt to different transportation requirements and scenarios, and has high flexibility. For example, in the airport luggage transportation area, there is a starting point grid, an ending point grid, and several non-obstacle grids and obstacle grids. By using the population optimization method, multiple possible paths from the starting point to the ending point can be generated. By evaluating the fitness of each path, the optimal path can be selected as the intermediate grid sequence of the first planned path.

[0081] Specifically, using the path connection method, multiple intermediate grids are connected to form a continuous and smooth path. Through the A* algorithm with the four-neighborhood strategy, each grid point can only be connected to its four adjacent grid points above, below, left, and right. During the connection process, by calculating the evaluation function value of each intermediate grid to the ending point and combining the evaluation function value of the path already traveled, the optimal adjacent grid is selected for connection. Through the path connection method, the shortest path can be quickly found, improving the efficiency and flexibility of path planning. For example, there are three intermediate grid points C, D, and E, which are located between the starting point A and the ending point B respectively. Using the A* algorithm with the four-neighborhood strategy, these three intermediate grid points can be connected to form a path from A to B. The A* algorithm will calculate the estimated cost of each intermediate grid point to point B and combine the cost of the path already traveled, and select the path with the minimum cost for expansion. For example, if the cost from C to D is less than the cost from C to E, and the estimated cost from D to B is also smaller, then the algorithm will select the path C-D-B, and a shortest path from A to B can be obtained. By combining the population optimization method and the A* algorithm with the four-neighborhood strategy, the transportation path of the luggage transporter can be effectively planned to obtain a safe and efficient path.

[0082] Further, the method of finding the non-obstacle grids that the luggage transporter needs to pass through to reach the ending point position by using the population optimization method and obtaining multiple intermediate grids includes:

[0083] S401. Initialize the population, where each individual in the population represents a non-obstacle grid that the luggage transporter needs to pass through to reach the ending point position;

[0084] S402. Calculate the fitness value of each individual according to the preset fitness function;

[0085] S403, according to the fitness value, selecting a plurality of individuals with better fitness values as initial elite individuals;

[0086] S404, performing a mutation operation on the initial elite individuals to generate mutant individuals;

[0087] S405, performing a crossover operation on the mutant individual and the initial elite individual to generate a crossover individual;

[0088] S406, selecting elite individuals of the next generation according to the fitness values corresponding to the crossover individuals;

[0089] S407, until the preset number of iterations is reached, the non-obstacle grids that the luggage transport vehicle corresponding to the individual with the best fitness value among the final elite individuals needs to pass through to reach the terminal position are used as intermediate grids.

[0090] In this embodiment, individuals in the population are initialized according to the starting position and the end position of the luggage transport vehicle, and multiple transport paths are randomly generated. Each transport path includes multiple non-obstruction grids that need to be passed from the starting position to the end position. The fitness value of each individual is calculated according to a preset fitness function. In this embodiment, the fitness function is:

[0091]

[0092] In the formula, F is the fitness of each individual, L is the path length of each individual, j is the number of non-obstacle grids in the individual, and x n and x n+1 Indicates the x-axis coordinates of the nth and n+1th grids, y n and n+1 Indicates the y-axis coordinates of the nth and n+1th grids, S indicates the number of turning points in the individual planning path, α and β are the weights of L and S, and c is a parameter that can be set by yourself. According to the fitness function, the fitness value of each individual in the population is calculated, and the fitness threshold is set. Individuals with fitness values greater than the fitness threshold are selected as initial elite individuals. The selection formula for the initial elite individual set is as follows:

[0093] E0={x i 丨F i >F *}

[0094] In the formula, E0 represents the initial elite individual set, x i represents the state value of the i-th individual in the population, F i represents the fitness value of the i-th individual, F *Denote the fitness threshold. Select excellent individuals in the population according to the fitness threshold. By selecting elite individuals, the loss of excellent solutions during the algorithm optimization process can be prevented, and the quality of the optimization results can be improved. Add a small random perturbation to the initial elite individuals and perform a mutation operation to generate mutant individuals. The mutation formula is as follows:

[0095] x′ i = x i + ∈ × rand()

[0096] In the formula, x′ i is the state value of the mutant individual, ∈ is the mutation intensity, which can be set according to calculation needs, rand() is a random number uniformly distributed in the range [-1, 1]. By performing the mutation operation to obtain mutant individuals, the population diversity can be increased, and the algorithm can be prevented from falling into local optimal solutions. Perform a crossover operation on the mutant individuals and the initial elite individuals to generate crossover individuals. Through the crossover operation, the advantages of different individuals can be combined to generate better individuals, which is helpful for the optimization process of the algorithm. After the crossover operation, it is necessary to calculate the fitness value of the newly generated crossover individuals and select the elite individuals of the next generation according to the fitness value. By selecting the crossover individuals with higher fitness values as the elite individuals of the next generation, excellent solutions can be retained, and the convergence speed of the algorithm can be accelerated. The algorithm continuously iterates to update the population and elite individuals until the preset number of iterations is reached. After the iteration ends, select the individual with the optimal fitness value among the final elite individuals as the optimal solution, and use the non-obstacle grids that the baggage transport vehicle corresponding to the individual with the optimal fitness value among the final elite individuals needs to pass through to reach the end position as the intermediate grids. By continuously iterating and selecting excellent individuals, the algorithm can gradually approach the optimal solution and improve the quality of the optimization results.

[0097] Further, using the path connection method to connect the intermediate grids to form the first planned path includes:

[0098] S501. Starting from the current grid position of the baggage transport vehicle, search for adjacent grids in the intermediate grids, where the adjacent grids include adjacent grids in the four directions of up, down, left, and right;

[0099] S502. According to the preset evaluation function, select the grid with the optimal evaluation result among the adjacent grids as the next grid;

[0100] S503. Connect the grid center of the current grid with the grid center of the next grid;

[0101] S504. Repeat the grid selection and connection process until the grid corresponding to the end position is connected to obtain the first planned path.

[0102] In this embodiment, as Figure 2, the shaded grid is the obstacle grid. Starting from the current grid position of the luggage transport vehicle as the starting grid, it is necessary to reach the end grid in the upper right corner. Using the four-neighborhood A* algorithm, starting from the current grid, search for adjacent grids in the four directions of up, down, left, and right. These adjacent grids constitute the neighborhood of the current grid. Through four-neighborhood search, the complexity of the search can be reduced, and the continuity of the path between grids can be ensured.

[0103] Specifically, according to the preset evaluation function, select the grid with the optimal evaluation result among the adjacent grids as the next grid. The evaluation function in this embodiment is:

[0104] f(n) = g(n) + h(n)

[0105] In the formula, f(n) is the evaluation value, g(n) is the actual cost from the starting point to the current grid, and h(n) is the estimated cost from the current grid to the end grid. Specifically, the Euclidean distance is used for evaluation. By calculating the evaluation function values of each adjacent grid, select the grid with the smallest evaluation function value as the next grid. By selecting the grid with the smallest evaluation function value, the shortest path can be gradually selected; connecting the grid center points of the current grid and the next grid with line segments ensures the continuity of the path between grids and facilitates the movement of the luggage transport vehicle along the path.

[0106] Specifically, repeat steps S501 - S503, continuously select the optimal adjacent grids and connect them. Each selection is based on the neighborhood of the current grid and the evaluation function value. When reaching the end grid, stop the iteration. At this time, a complete path from the starting point to the end point has been formed. Through the path connection method, the integrity of the path from the starting point to the end point can be ensured. By gradually selecting the optimal grids and connecting them, the optimal path can be found; through the four-neighborhood search strategy and the selection of the evaluation function, an efficient and continuous path can be found for the luggage transport vehicle in the grid environment, as Figure 2 , through the path connection method, the driving path in the right figure is obtained as a better driving path.

[0107] Further, through the local optimization method, optimize the first planned path to obtain an optimized path, including:

[0108] S601. Set the safety distance threshold between the luggage transport vehicle and the obstacle boundary;

[0109] S602. Use the local optimization method to delete the redundant grid nodes in the first planned path to obtain an optimized path.

[0110] In this embodiment, in order to ensure that the luggage transport vehicle can safely avoid obstacles, a safety distance threshold is set. The safety distance threshold defines the minimum safety distance between the transport vehicle and the boundary of the obstacle, ensuring that no collision occurs due to being too close during driving and improving the safety during the operation of the luggage transport vehicle.

[0111] Specifically, the first planned path only considers the driving path between adjacent grids during the design process, resulting in a relatively long driving path. The local optimization method is to optimize the path by deleting redundant nodes or adjusting the node positions on the basis of the first planned path. By checking each grid node in the first planned path, it is determined whether the grid line formed by it meets the requirements of the safety distance threshold, and those redundant nodes that do not affect the path connectivity and are far from the obstacle are deleted, shortening the path length. By deleting redundant nodes, the path length can be significantly shortened, the transport efficiency can be improved, and the optimized path will be smoother, reducing the sharp turns of the luggage transport vehicle during driving. By optimizing the first planned path with the local optimization method, the safety and efficiency of the luggage transport path can be improved.

[0112] Furthermore, the setting of the safety distance threshold between the luggage transport vehicle and the boundary of the obstacle includes:

[0113] S701. Calculate the shortest distance between the obstacle coordinates and the first planned path as the safety distance;

[0114] S702. Take the sum of the radius of the circumscribed circle of the obstacle and the length of the luggage transport vehicle as the safety distance threshold.

[0115] In this embodiment, according to the accurate coordinates of the obstacle obtained by the sensor, by calculating the shortest distance between the obstacle and the planned path in real time, the real-time update and accuracy of the safety distance can be ensured. Taking this shortest distance as the safety distance, by calculating the shortest distance between the obstacle coordinates and the first planned path, considering the path direction and the position of the obstacle, a certain safety margin can be ensured between the transport vehicle and the obstacle.

[0116] Specifically, according to the shape and size of the obstacle, the radius of the circumscribed circle of the obstacle is determined, and the length of the luggage transport vehicle and the radius of the circumscribed circle of the obstacle are added to obtain the safety distance threshold. By setting the safety distance threshold, it can be ensured that the luggage transport vehicle always maintains a certain safety margin from the obstacle during driving, thereby improving the safety and efficiency of the transport process.

[0117] Furthermore, the use of the local optimization method to delete the redundant grid nodes in the first planned path to obtain the optimized path includes:

[0118] S801. Sequentially select three grid nodes A1, A2, and A3 starting from the end grid;

[0119] S802. When the slopes of the straight lines A1A2 and A2A3 are the same, the intermediate node A2 is a redundant node, and the intermediate node A2 is deleted.

[0120] S803. When the slopes of the straight lines A1A2 and A2A3 are different, when the safety distance between the straight line A1A3 and the obstacle is greater than the safety distance threshold, the intermediate node A2 is deleted.

[0121] S804. Repeat the screening process of redundant nodes until the intermediate node is the starting point of the luggage transport vehicle to obtain an optimized path.

[0122] In this embodiment, the local optimization method is used to further optimize the transport path of the luggage transport vehicle, delete the redundant nodes therein, and make the luggage transport more efficient. Starting from the end grid, three grid nodes A1, A2, and A3 are sequentially selected, and the straight lines A1A2 and A2A3 are respectively analyzed. When the slopes of the straight lines A1A2 and A2A3 are the same, it means that the node A2 is on the straight line A1A3 and has no contribution to the shape and length of the path. At this time, the node A2 is a redundant node, and deleting the redundant node can simplify the path and reduce the number of unnecessary nodes. When the slopes of the straight lines A1A2 and A2A3 are different, calculate the minimum distance between the straight line A1A3 and the obstacle. When the minimum distance is greater than the safety distance threshold, at this time A1A3 can meet the collision-free transport, and the intermediate node A2 can be deleted at this time to further shorten the length of the transport path.

[0123] Specifically, repeat steps S801 - S803, and gradually screen redundant nodes from the end point to the starting point. This process will continue until the intermediate node becomes the starting point of the luggage transport vehicle. At this time, the obtained path is the optimized path. By sequentially calculating, it is ensured that the entire path is optimized, and it remains efficient and safe from the starting point to the end point. By gradually optimizing, the complexity of global path re-planning is avoided. Using the local optimization method to delete the redundant grid nodes in the first planned path can simplify the path, improve efficiency and safety, and finally obtain the optimized path. On the premise of ensuring the safety of the luggage transport vehicle, the transport efficiency is improved. For example Figure 3 By optimizing the path in the left figure through the local optimization method, the path in the right figure is obtained, making the driving path of the luggage transport vehicle shorter.

[0124] Furthermore, by setting multiple sensors and RFID tags on each luggage transport vehicle, the real-time data and end position of each luggage during the transport process are obtained, including:

[0125] S901. Perform outlier correction and filtering processing on the original data collected by multiple sensors.

[0126] S902. Perform data cleaning and data compression on the RFID tag data.

[0127] In this embodiment, preprocess the original data collected by each sensor, including outlier correction and filtering. Detect outliers, find the outliers and determine their positions, including data loss, type error, timestamp error, etc. Through interpolation method, refer to the data before and after the abnormal data point to complete the correction of the abnormal data point, ensure the information is not missing, and maintain the continuity of the original collected data. Filter the corrected data to eliminate noise and interference, thereby improving the accuracy of the data.

[0128] Specifically, preprocess the RFID tag data, including data cleaning and data compression. A large amount of redundant data may be generated when the RFID technology performs data collection. Remove these redundant data through data cleaning. There may be a phenomenon of missed reading when the reader reads the RFID tag data. When it is found that there is a missed reading, fill in the data according to the data before and after to improve the integrity of the data. In order to reduce the storage and transmission overhead, the RFID tag data can be compressed. The RFID data can be compressed from the data layer to the logical area layer, thereby removing redundant data and having a certain tolerance for the data missed reading problem.

[0129] Embodiment 2

[0130] In this embodiment, as Figure 4 , provide a luggage transportation path tracking and correction system based on the fusion of RFID and multi-sensors, which is used to implement the luggage transportation path tracking and correction method based on the fusion of RFID and multi-sensors, including:

[0131] A data acquisition module, by setting multiple sensors and RFID tags on each luggage transport vehicle, acquires the real-time data and the end position of each luggage during transportation;

[0132] A luggage transportation path planning module, according to the real-time data and the end position, uses the path planning method to avoid environmental obstacles and plan the transportation path of each luggage transport vehicle to obtain the first planned path;

[0133] A luggage transportation path optimization module, through the local optimization method, optimizes the first planned path to obtain the optimized path;

[0134] A luggage transportation module, according to the optimized path, controls each luggage transport vehicle to move to the corresponding end position, so that each user arrives at the corresponding end position to pick up the luggage, where the end positions corresponding to each luggage transport vehicle are different.

[0135] In this embodiment, the data acquisition module includes an RFID tag, an RFID reader / writer, and multiple sensors. The RFID tag is used to store the unique identification information of the luggage, and the RFID reader / writer is used to read and write this information, read the user identity information and flight information corresponding to the luggage, so as to identify the destination location of the luggage. The multiple sensors include a position sensor, a humidity sensor, an acceleration sensor, etc., which are used to collect the environmental parameters and dynamic information during the luggage transportation process. Through the multiple sensors and the RFID tag, the real-time data and the destination location during the luggage transportation process are obtained.

[0136] Specifically, the luggage transportation path planning module includes a central processing unit and a path planning unit. The central processing unit is used to process and analyze the real-time data and the destination location information transmitted by the data acquisition module. The path planning unit, based on the real-time data and the destination location, uses the path planning method to avoid environmental obstacles and plan the transportation path for each luggage transport vehicle to obtain the first planned path. By dynamically programming the optimal path, the transportation efficiency is improved. The luggage transportation path optimization module includes an optimization algorithm processor and a communication interface. The optimization algorithm processor is used to execute the local optimization algorithm to refine and optimize the first planned path, delete redundant nodes, and avoid unnecessary turning and waiting. The communication interface is used to communicate with the luggage transportation path planning module and the luggage transportation module to improve the flexibility and response speed of the luggage transportation.

[0137] Specifically, the luggage transportation module includes a driving unit and a control unit. The driving unit includes a motor, a transmission device, etc., which are used to drive the movement of the luggage transport vehicle. The control unit includes a controller, a sensor interface, a communication interface, etc., which are used to receive the optimized path information and control the operation of the driving system, so that the luggage transport vehicle moves along the optimized path. During the movement, the environmental changes and the vehicle status are monitored in real time through sensors to ensure safe operation. These four modules together constitute a complete luggage transportation system, which realizes the high-efficiency, safety and reliability of luggage transportation through real-time data acquisition, path planning, path optimization and precise control.

[0138] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors, characterized in that including: By setting multiple sensors and RFID tags on each luggage transport vehicle, real-time data and the end position of each piece of luggage during transportation are obtained; According to the real-time data and the end position, using the path planning method to avoid environmental obstacles, the transportation path of each luggage transport vehicle is planned to obtain the first planned path; Through the local optimization method, the first planned path is optimized to obtain the optimized path; According to the optimized path, each luggage transport vehicle is controlled to move to the corresponding end position, so that each user can pick up the luggage at the corresponding end position, where the end positions corresponding to each luggage transport vehicle are different.

2. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 1, characterized in that According to the real-time data and the end position, using the path planning method to avoid environmental obstacles, the transportation path of each luggage transport vehicle is planned to obtain the first planned path, including: Using the grid division method to divide the action space of the luggage transport vehicle to obtain multiple space grids; According to the positions of the obstacles in the action space, the space grids occupied by the obstacles are used as obstacle grids; According to the end position, using the path planning method, within the non-obstacle grids, the transportation path of the luggage transport vehicle is planned to obtain the first planned path.

3. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 2, wherein The step of, according to the end position, using the path planning method, within the non-obstacle grids, planning the transportation path of the luggage transport vehicle to obtain the first planned path, includes: Through the population optimization method, find the non-obstacle grids that the luggage transport vehicle needs to pass through to reach the end position to obtain multiple intermediate grids; Using the path connection method to connect the intermediate grids to form the first planned path.

4. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 3, wherein The step of, through the population optimization method, finding the non-obstacle grids that the luggage transport vehicle needs to pass through to reach the end position to obtain multiple intermediate grids, includes: Initialize the population, where each individual in the population represents a set of non-obstacle grids that the luggage transport vehicle needs to pass through to reach the end position; According to the preset fitness function, calculate the fitness value of each individual; According to the fitness value, the individuals with fitness values greater than the preset fitness threshold are used as the initial elite individuals; Perform mutation operations on the initial elite individuals to generate mutant individuals; Perform crossover operations on the mutant individuals and the initial elite individuals to generate crossover individuals; According to the fitness values corresponding to the crossover individuals, select the elite individuals of the next generation; Until the preset number of iterations is reached, the set of non-obstacle grids that the luggage transport vehicle corresponding to the individual with the optimal fitness value in the final elite individuals needs to pass through to reach the end position is used as the intermediate grids.

5. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 3, characterized in that The step of, using the path connection method to connect the intermediate grids to form the first planned path, includes: Starting from the current grid position of the luggage transport vehicle, search for adjacent grids in the intermediate grids, where the adjacent grids include adjacent grids in the four directions of up, down, left, and right; According to the preset evaluation function, select the grid with the optimal evaluation result in the adjacent grids as the next grid; Connect the grid center of the current grid with the grid center of the next grid; Repeat the grid selection and connection process until the grid corresponding to the end position is connected to obtain the first planned path.

6. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 1, wherein Optimizing the first planned path by the local optimization method to obtain an optimized path, including: Setting a safety distance threshold between the luggage transport vehicle and the obstacle boundary; Using the local optimization method to delete redundant grid nodes in the first planned path to obtain an optimized path.

7. The method for tracking and correcting the luggage transportation path based on RFID and multi-sensor fusion according to claim 6, characterized in that The setting of the safety distance threshold between the luggage transport vehicle and the obstacle boundary includes: Calculating the shortest distance between the obstacle coordinates and the first planned path as the safety distance; Taking the sum of the circumradius of the obstacle and the length of the luggage transport vehicle as the safety distance threshold.

8. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 6, wherein The use of the local optimization method to delete redundant grid nodes in the first planned path to obtain an optimized path includes: Sequentially selecting three grid nodes A1, A2, and A3 starting from the end grid; When the slopes of the straight lines A1A2 and A2A3 are the same, the middle node A2 is a redundant node and the middle node A2 is deleted; When the slopes of the straight lines A1A2 and A2A3 are different, when the safety distance between the straight line A1A3 and the obstacle is greater than the safety distance threshold, the middle node A2 is deleted; Repeating the screening process of redundant nodes until the middle node is the starting point of the luggage transport vehicle to obtain an optimized path.

9. The method for tracking and correcting the luggage transportation path based on the fusion of RFID and multi-sensors according to claim 1, wherein Obtaining the real-time data and end position of each piece of luggage during transportation by setting multiple sensors and RFID tags on each luggage transport vehicle, including: Performing outlier correction and filtering on the raw data collected by multiple sensors; Performing data cleaning and data compression on the RFID tag data.

10. A luggage transportation path tracking and correction system based on the fusion of RFID and multiple sensors, characterized in that, For implementing the luggage transportation path tracking and correction method based on RFID and multi-sensor fusion according to any one of claims 1 to 9, including: A data acquisition module, which obtains the real-time data and end position of each piece of luggage during transportation by setting multiple sensors and RFID tags on each luggage transport vehicle; A luggage transportation path planning module, which plans the transportation path of each luggage transport vehicle according to the real-time data and end position, using a path planning method to avoid environmental obstacles to obtain a first planned path; A luggage transportation path optimization module, which optimizes the first planned path by the local optimization method to obtain an optimized path; A luggage transportation module, which controls each luggage transport vehicle to move to the corresponding end position according to the optimized path, so that each user arrives at the corresponding end position to pick up the luggage, where the end positions corresponding to each luggage transport vehicle are different.

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