Intelligent carrying trolley path search optimization method, device, equipment and medium

Abstract

The application relates to an intelligent carrying trolley path search optimization method, device, equipment and medium, the method comprises the following steps: taking an initial planning path scheme as an initial solution of a simulated annealing algorithm, perturbing the initial solution to generate a new planning path scheme; determining the difference between the target function value corresponding to the new planning path scheme and the target function value of the current planning path scheme, replacing the current planning path scheme with the new planning path scheme according to the difference by adopting a Metropolis criterion in the simulated annealing algorithm; determining the current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, judging whether the current temperature is lower than a preset temperature threshold, and if so, selecting the planning path scheme with the shortest path from all iteration results as the final planning path scheme. The application can accurately and quickly eliminate the path scheme that causes the trolley to fall into a cycle and search for the path scheme that can complete the specified task most quickly.

Description

Technical Field

[0001] The present application relates to the field of production control, and in particular to a method for optimizing the path search of an intelligent transport vehicle, a corresponding device, an electronic device, and a computer-readable storage medium. Background Art

[0002] In the information age, intelligent manufacturing has become a key development trend in industry transformation and upgrading. Against the backdrop of the rapid development of the intelligent manufacturing industry, intelligent logistics has received increasing attention, with workshop logistics and distribution, as a key component, receiving significant attention. In intelligent manufacturing workshops, facility layout and resource allocation are crucial aspects of discrete manufacturing system planning and design. During the layout design and logistics distribution phases of intelligent manufacturing workshop systems, AGV path selection and distribution efficiency are important system performance indicators. Most other domestic research on system facility layout focuses on optimizing transportation costs within the logistics distance matrix. Few studies have considered multi-path selection in manufacturing workshops, which includes path planning, speed changes, and the selection of the optimal path.

[0003] During factory layout, to maximize production capacity within a limited workshop area, AGV tracks are arranged in a circular pattern, with the rings of varying sizes nested within one another. This multi-ring, nested, closed-loop layout allows for more flexible, scientific, economical, and executable scheduling of production resources. During the manufacturing system layout design and resource allocation phase, product output rate and average production cycle are crucial system performance indicators. As production support facilities, the operational efficiency of AGVs significantly impacts workshop output rate and average production cycle. In AGV path planning research, the vast majority of scholars focus on obstacle avoidance, optimal path selection, and collision avoidance. These studies are conducted in relatively simple environments, and few studies have examined complex, real-world workshop environments. The path planning problem in a multi-task, multi-ring, nested, closed-loop workshop presents a unique problem. The rationality of the planning directly impacts the overall workshop logistics speed, playing a key role in controlling workshop logistics costs and the various waiting costs associated with congestion. However, research examining this type of path planning as part of the optimization objective is rare.

[0004] Currently, the paths generated in multi-ring nested closed-loop workshops may fall into multiple loops, which are prone to "0"-shaped loops or "6"-shaped loops, increasing the running time of intelligent transport vehicles (AGVs), the waiting time of machining centers, and the path congestion time, thereby wasting transportation costs.

[0005] In summary, the path generated by the existing technology in a multi-ring nested closed-loop workshop may fall into multiple loops, and it is easy to fall into a "0"-shaped loop or a "6"-shaped loop, which increases the running time of the intelligent transport vehicle, the waiting time of the machining center and the path congestion time, thereby causing waste of transportation costs and other problems. This application makes corresponding explorations to solve this problem. Summary of the Invention

[0006] The purpose of this application is to solve the above problems and provide an intelligent transport vehicle path search optimization method, corresponding device, electronic device and computer-readable storage medium.

[0007] In order to meet the various objectives of this application, this application adopts the following technical solutions:

[0008] An intelligent transport vehicle path search optimization method proposed to meet one of the purposes of this application is applied to a multi-ring nested closed-loop workshop, including:

[0009] In response to the intelligent transport vehicle path search optimization instruction, obtain the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track, and the target track in the multi-ring nested closed-loop workshop, wherein the unconnected track set represents the tracks that are not selected after the tracks in the track set being connected are connected;

[0010] The greedy algorithm strategy in the preset path self-generation algorithm is used to generate an initial planning path plan for the intelligent transport vehicle according to the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track and the target track;

[0011] Using the initial planned path solution as an initial solution of a simulated annealing algorithm, and perturbing the initial solution to generate a new planned path solution;

[0012] Determine a difference between an objective function value corresponding to the new planned path solution and an objective function value of the current planned path solution, and replace the current planned path solution with the new planned path solution based on the difference using the Metropolis criterion in the simulated annealing algorithm;

[0013] The current temperature of the simulated annealing algorithm is determined based on the preset rapid temperature drop function, and it is judged whether the current temperature is lower than the preset temperature threshold. If so, the planned path solution with the shortest path among all iterative results is selected as the final planned path solution to complete the search optimization of the intelligent transport vehicle path.

[0014] Optionally, the step of using a greedy algorithm strategy in a preset path self-generation algorithm to generate an initial planned path plan for the intelligent transport vehicle based on the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track, and the target track includes:

[0015] Determine the passable track set R, the track set being connected S, and the unconnected track set S in the multi-ring nested closed loop workshop 0 , connected orbit set S', initial orbit a0 and target orbit a n , wherein the unconnected track set S 0 Represents the set of unselected orbits after S is turned on, and the initial orbit a0 is the initial orbit x 23 , the target track a n is the target orbit x 22 ;

[0016] Determine the target track x of the intelligent transport vehicle 22 , from the initial track x in the parking lot 23 Initially, add a0 to the set of tracks being connected S;

[0017] From the initial orbit x 23 Start, select the next track according to the greedy algorithm, and select a reachable and unvisited track by checking the accessibility relationship of the tracks;

[0018] For each selected track a n , a strong trade-off is made. If one of the tracks x1 and x7 is selected as a1, the other track will enter the unconnected track set S 0 , to determine the initial unconnected track set S 0 1. Repeat this process until the set of connected tracks S contains all target tracks x i 、x j 、x k 、x 22 , at this time, the path S1 is generated;

[0019] The completed path S1 is stored in the connected track set S', and the unconnected track set S generated during the path generation process is stored in the connected track set S'. 0 Deposit into the no-call table;

[0020] From the initial unconnected track set S 0 1 selects the first track as the new starting point, repeats the above path generation process, generates a new path S2, stores the completed path S2 in the connected track set S', and records the new unconnected track set S 02. Continue until all unconnected elements in a set are found to be consistent with the previously recorded initial unconnected track set S 0 1, the planned path plan that meets the current task of the intelligent transport vehicle is generated to determine the initial planned path plan of the intelligent transport vehicle.

[0021] Optionally, the step of using the initial planned path solution as an initial solution of a simulated annealing algorithm and perturbing the initial solution to generate a new planned path solution includes:

[0022] Determine an initial planning path plan for the intelligent transport vehicle, and use the initial planning path plan as an initial solution of the simulated annealing algorithm;

[0023] A conversion operator is used to perform an exchange operation, a reversal operation, an insertion operation or a partial exchange on the initial solution to generate a new planning path solution.

[0024] Optionally, the step of determining a difference between an objective function value corresponding to the new planned path solution and an objective function value of the current planned path solution, and replacing the current planned path solution with the new planned path solution based on the difference using the Metropolis criterion in the simulated annealing algorithm includes:

[0025] The expression of the Metropolis criterion is:

[0026]

[0027] Among them, f(l) represents the objective function of the current planning path plan, f(l′) represents the objective function of the new planning path plan, n is the scale factor of the objective function, T is the current temperature, when f(l′)-f(l)<0, the objective function value of the new planning path plan is lower, and the new planning path plan is directly accepted; when f(l′)-f(l)>0, the probability is used to calculate the objective function of the new planning path plan. Accept the new planned path plan.

[0028] Optionally, the step of determining a rapid temperature drop function of a simulated annealing algorithm comprises:

[0029] Determine the initial temperature, number of iterations, and temperature drop coefficient during the annealing process of the simulated annealing algorithm;

[0030] Calculating and determining a first product between the number of iterations and the temperature drop coefficient, and calculating and determining a first sum value between the first product and a value of 1;

[0031] determining a temperature at a time when the number of iterations is k based on a first ratio between the initial temperature and the first sum value to determine a rapid temperature drop function;

[0032] The steps of determining the initial temperature of the simulated annealing algorithm include:

[0033] Determine the objective function of the current planned path plan, the objective function of the new planned path plan, and the preset acceptance probability;

[0034] Calculating and determining the natural logarithm value of the acceptance probability;

[0035] Calculating and determining a first difference between the objective function of the new planned path solution and the objective function of the current planned path solution;

[0036] A second ratio between the first difference and the natural logarithm of the acceptance probability is calculated and determined, and a negative value of the second ratio is used as an initial temperature of the simulated annealing algorithm.

[0037] Optionally, after the step of generating an initial planned path plan for the intelligent transport vehicle, the following steps are included:

[0038] Invoking a preset improved A* algorithm, and optimizing the initial planned path solution based on the improved A* algorithm;

[0039] The optimized initial planning path solution is used as the initial solution of the simulated annealing algorithm.

[0040] Optionally, after determining the current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, determining whether the current temperature is lower than a preset temperature threshold, and if so, selecting the shortest planning path solution from all iteration results as the final planning path solution, the method further includes:

[0041] Based on the preset speed adaptation algorithm, the intelligent transport vehicle is adapted to its speed to avoid collision with other intelligent transport vehicles;

[0042] Calculate and determine the safe speed and braking acceleration of the intelligent transport vehicle to ensure it can stop safely before encountering an obstacle;

[0043] According to the power acceleration and current speed of the intelligent transport vehicle, the speed adjustment window is determined to dynamically update the feasible speed range;

[0044] For multiple intelligent transport vehicles, predict the displacement of other intelligent transport vehicles and adjust the obstacle avoidance strategy to reduce collisions;

[0045] Based on the forward movement and expansion radius of other intelligent transport vehicles, the distance evaluation function is updated to ensure that obstacles can be accurately avoided in actual path planning.

[0046] An intelligent transport vehicle path search and optimization device provided for another purpose of the present application includes:

[0047] a data acquisition module configured to respond to an intelligent transport vehicle path search optimization instruction and acquire a passable track set, a track set being connected, a non-connected track set, a connected track set, an initial track, and a target track within a multi-ring nested closed-loop workshop, wherein the non-connected track set represents a track set that has not been selected after accessing a track in the track set being connected;

[0048] An initial path determination module is configured to generate an initial planned path plan for the intelligent transport vehicle based on the set of passable tracks, the set of tracks being connected, the set of unconnected tracks, the set of connected tracks, the initial track, and the target track using a greedy algorithm strategy in a preset path self-generation algorithm;

[0049] A new planning path determination module is configured to use the initial planning path solution as an initial solution of a simulated annealing algorithm and perturb the initial solution to generate a new planning path solution;

[0050] a path updating module configured to determine a difference between an objective function value corresponding to a new planned path solution and an objective function value of a current planned path solution, and replace the current planned path solution with the new planned path solution based on the difference using the Metropolis criterion in a simulated annealing algorithm;

[0051] The search optimization module is configured to determine the current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, and to determine whether the current temperature is lower than a preset temperature threshold. If so, the planned path solution with the shortest path among all iterative results is selected as the final planned path solution to complete the search optimization of the intelligent transport vehicle path.

[0052] An electronic device provided to meet another purpose of the present application includes a central processing unit and a memory, wherein the central processing unit is used to call and run a computer program stored in the memory to execute the steps of the intelligent transport vehicle path search optimization method described in the present application.

[0053] A computer-readable storage medium is provided to meet another purpose of the present application, which stores a computer program implemented according to the intelligent transport vehicle path search optimization method in the form of computer-readable instructions. When the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

[0054] Compared with the prior art, the present application addresses the problem that the paths generated by the prior art in multi-ring nested closed-loop workshops may fall into multiple loops, which are prone to falling into "0"-shaped loops or "6"-shaped loops, increasing the running time of the intelligent transport vehicle, the waiting time of the machining center, and the path congestion time, thereby causing waste of transportation costs. The present application has, but is not limited to, the following beneficial effects:

[0055] First, a new path search method is innovatively proposed. On the basis of the initial planning path generated by the path self-generation algorithm (Feasible path Self Generation Algorithm, FSGA), the simulated annealing (SA) algorithm is integrated for optimization, so that all path schemes meeting the task requirements can be generated, the path schemes causing the trolley to fall into a cycle can be accurately and quickly eliminated, the running speed of the intelligent trolley is combined, and the fastest path scheme to complete the specified task can be accurately and quickly searched out;

[0056] Second, the speed self-adaptation of the path self-generation algorithm (Feasible path Self Generation Algorithm, FSGA): the intelligent trolley is adjusted by the path self-generation algorithm (Feasible path Self Generation Algorithm, FSGA), so that the speed self-adaptation realizes intelligent obstacle avoidance, dynamically adjusts the speed, and avoids the congestion caused by the simultaneous operation of multiple intelligent trolleys;

[0057] Third, the path self-generation algorithm integrates the improved A* algorithm: based on all the path schemes generated by the path self-generation algorithm (Feasible path Self Generation Algorithm, FSGA), the fastest path is searched, which is an indispensable key step of the path self-generation algorithm (Feasible path Self Generation Algorithm, FSGA) and can accurately and quickly search out the optimal path scheme to complete the specified task. BRIEF DESCRIPTION OF DRAWINGS

[0058] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0059] Figure 1 A schematic diagram of a path generated in the embodiment of the present application in a "0" type loop;

[0060] Figure 2 A schematic diagram of a path generated in the embodiment of the present application in a "6" type loop;

[0061] Figure 3 A flowchart of the path search optimization method of the intelligent trolley in the embodiment of the present application;

[0062] Figure 4 A schematic diagram of the facility layout of a multi-loop nested closed loop workshop in the embodiment of the present application;

[0063] Figure 5Schematic diagram of the accessible relationship between the various tracks of the multi-ring nested closed loop workshop in the embodiment of the present application;

[0064] Figure 6 A schematic diagram of the congestion phenomenon of the intelligent transport vehicle in the embodiment of the present application;

[0065] Figure 7 This is a schematic diagram of the deadlock phenomenon of the intelligent transport vehicle in the embodiment of the present application;

[0066] Figure 8 This is a functional block diagram of the intelligent transport vehicle path search and optimization device in an embodiment of the present application;

[0067] Figure 9 Schematic diagram of the structure of the computer device in the embodiment of the present application. DETAILED DESCRIPTION

[0068] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and are not to be construed as limiting the present application.

[0069] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0070] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0071] It will be understood by those skilled in the art that the terms "client," "terminal," and "terminal device" as used herein include both devices that are wireless signal receivers, i.e., devices that only have wireless signal receivers without transmission capabilities, and devices that have receiving and transmitting hardware capable of two-way communication over a two-way communication link. Such devices may include: cellular or other communication devices such as personal computers and tablet computers, which have single-line displays, multi-line displays, or cellular or other communication devices without multi-line displays; PCS (Personal Communications Service), which may combine voice, data processing, fax, and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / Intranet access, a web browser, a notepad, a calendar, and / or a GPS (Global Positioning System) receiver; and conventional laptop and / or palmtop computers or other devices, which have and / or include a radio frequency receiver. As used herein, the terms "client," "terminal," or "terminal device" may be portable, transportable, or installed in a vehicle (air, sea, and / or land), or may be adapted and / or configured to operate locally and / or in a distributed manner at any other location on Earth and / or in space. As used herein, the terms "client," "terminal," or "terminal device" may also refer to a communication terminal, an Internet terminal, or a music / video playback terminal, such as a PDA, an MID (Mobile Internet Device), and / or a mobile phone with music / video playback capabilities, or may include a smart TV, a set-top box, or other device.

[0072] The hardware referred to by names such as "server", "client", and "service node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. Computer programs are stored in its memory, and the central processing unit loads the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0073] It should be noted that the concept of "server" in the present application can also be extended to the case of a server cluster. According to the principle of network deployment understood by those skilled in the art, the servers should be logically divided, and in physical space, these servers can be independent of each other but can be called through an interface, or can be integrated into a physical computer or a computer cluster. Those skilled in the art should understand this variation and should not be restricted by the implementation of the network deployment of the present application.

[0074] One or more technical features of the present application, unless explicitly specified, can be deployed on a server for implementation and accessed by a client remotely calling an online service interface provided by the server, or can be directly deployed and run on a client for implementation.

[0075] The neural network model referred to or possibly referred to in the present application, unless explicitly specified, can be deployed on a remote server and remotely called by a client, or can be deployed on a client with sufficient device capability for direct calling. In some embodiments, when it runs on a client, its corresponding intelligence can be obtained through transfer learning to reduce the requirement for client hardware running resources and avoid excessive occupation of client hardware running resources.

[0076] The various data involved in the present application, unless explicitly specified, can be remotely stored on a server or stored on a local terminal device, as long as it is suitable for being called by the technical solutions of the present application.

[0077] Those skilled in the art should know that the various methods of the present application, although based on the same concept and described to present commonality among them, are independently executable unless otherwise specified. Similarly, for each embodiment disclosed in the present application, it is based on the same inventive concept, so the same concept is understood to be equivalent, and although the concept is expressed differently, it is only for convenience and is appropriately transformed.

[0078] Unless it is explicitly stated that the embodiments disclosed in the present application are mutually exclusive, the technical features involved in each embodiment can be combined flexibly to construct new embodiments, as long as such combination does not deviate from the spirit of the present application and can meet the needs of the prior art or solve some deficiencies in the prior art. For this variation, those skilled in the art should know.

[0079] Please refer to Figure 1 and Figure 2The Feasible Path Self-Generation Algorithm (FSGA) proposed in this application can generate all paths that meet the task requirements, but these paths are not always efficient. The generated paths may fall into multiple loops, increasing the running time of the automated guided vehicle (AGV), the waiting time of the machining center, and the path congestion time, thereby wasting transportation costs. It is easy to fall into a "0" loop or a "6" loop, specifically including:

[0080] (1) Please refer to Figure 1 ,like Figure 1 As shown in the figure, the generated path repeatedly returns to the starting point of the road, forming a "0"-shaped loop. The AGV selects a starting track among the available tracks and then repeats the initial plan, falling into a loop. After multiple cycles, it exits and forms a complete path plan. Although this plan meets the task requirements and can reach the target track, it is inefficient.

[0081] (2) Please refer to Figure 2 ,like Figure 2 As shown in the figure, the generated path repeatedly returns to the road, forming a "6"-shaped loop. When choosing a track, the AGV selects a track that has already been selected in the track plan. It then repeats the initial plan, falling into a loop. After multiple cycles, it exits and forms a complete path plan. This also meets the task requirements and can reach the target track, but it increases the time cost.

[0082] Based on the above example scenarios, please refer to Figure 3 In one embodiment, the intelligent transport vehicle path search and optimization method of the present application is applied to a multi-ring nested closed loop workshop, including:

[0083] Step S10: Responding to the intelligent transport vehicle path search optimization instruction, obtaining a passable track set, a track set being connected, a non-connected track set, a connected track set, an initial track, and a target track in a multi-ring nested closed-loop workshop, wherein the non-connected track set represents tracks that have not been selected after being connected to the tracks in the track set being connected;

[0084] See also Figure 4 , the facility layout of the multi-ring nested closed loop workshop is as follows Figure 4As shown, the intelligent transport cart path search optimization method of the present application generates all path plans that meet the task requirements in a multi-ring nested closed-loop workshop and then selects the optimal path plan. Considering the actual production workshop layout, some channel spaces are narrow and can only accommodate one intelligent transport cart (AGV cart); some spaces are wide and can accommodate two intelligent transport carts (AGV carts) at the same time. The present application increases the track complexity during the construction of the workshop model. Some aisles in the workshop support two-way passage of intelligent transport carts (AGV carts), while some only allow one-way passage and prevent collisions when the cart turns, causing accidents. All tracks can only go straight by default and cannot turn around on the track to change the direction of operation. The path plan goal is to generate a set of tracks that receive assigned tasks, start from the parking lot, pass through the designated processing center, and return to the parking lot.

[0085] The computer terminal device can respond to the intelligent transport vehicle path search optimization instruction and obtain the passable track set R, the track set being connected S, and the unconnected track set S in the multi-ring nested closed loop workshop. 0 , connected orbit set S', initial orbit a0 and target orbit a n , wherein the unconnected track set S 0 Represents the set of tracks that are not selected after S is turned on.

[0086] Step S20: Generate an initial planned path plan for the intelligent transport vehicle based on the set of passable tracks, the set of tracks being connected, the set of unconnected tracks, the set of connected tracks, the initial track, and the target track using a greedy algorithm strategy in a preset path self-generation algorithm;

[0087] After obtaining the passable track set, the track set being connected, the track set not connected, the track set connected, the initial track, and the target track in the multi-ring nested closed-loop workshop, a greedy algorithm strategy in the preset path self-generation algorithm is used to generate an initial planning path plan for the intelligent transport vehicle based on the passable track set, the track set being connected, the track set not connected, the track set connected, the initial track, and the target track;

[0088] Furthermore, the step of using a greedy algorithm strategy in a preset path self-generation algorithm to generate an initial planned path plan for the intelligent transport vehicle according to the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track, and the target track includes:

[0089] Step S201: Determine the passable track set R, the track set S being connected, and the unconnected track set S in the multi-ring nested closed loop workshop. 0 , connected orbit set S', initial orbit a0 and target orbit an , wherein the unconnected track set S 0 Represents the set of unselected orbits after S is turned on, and the initial orbit a0 is the initial orbit x 23 , the target track a n is the target orbit x 22 ;

[0090] Step S202: Determine the target track x of the intelligent transport vehicle. 22 , from the initial track x in the parking lot 23 Initially, add a0 to the set of tracks being connected S;

[0091] From the initial orbit x 23 Start, select the next track according to the greedy algorithm, and select a reachable and unvisited track by checking the accessibility relationship of the tracks;

[0092] Step S203: For each selected track a n , a strong trade-off is made. If one of the tracks x1 and x7 is selected as a1, the other track will enter the unconnected track set S 0 , to determine the initial unconnected track set S 0 1. Repeat this process until the set of connected tracks S contains all target tracks x i 、x j 、x k 、x 22 , at this time, the path S1 is generated;

[0093] Step S204: store the completed path S1 into the connected track set S', and store the unconnected track set S generated during the path generation process 0 Deposit into the no-call table;

[0094] Step S205: From the initial unconnected track set S 0 1 selects the first track as the new starting point, repeats the above path generation process, generates a new path S2, stores the completed path S2 in the connected track set S', and records the new unconnected track set S 0 2. Continue until all unconnected elements in a set are found to be consistent with the previously recorded initial unconnected track set S 0 1, the planned path plan that meets the current task of the intelligent transport vehicle is generated to determine the initial planned path plan of the intelligent transport vehicle.

[0095] Specifically, see Figure 5, the Feasible path Self Generation Algorithm (FSGA) adopts a greedy algorithm strategy, each time starting from the initial track x 23 Start towards the unvisited track until all target tracks are expanded; define x to represent the passable track of the workshop intelligent transport trolley (AGV trolley), R to represent the passable track set of the workshop intelligent transport trolley (AGV trolley), the track set being connected S is used to store the track set being connected, and the track set not being connected S is used to store the track set being connected. 0 It is used to store the unselected tracks after the track of S is connected. The connected track set S' is used to store the connected tracks. a represents a single track element stored in each set, a0 represents the initial track, and a n represents the target track, such as Figure 4 As shown, the car can only leave the parking lot through track 23 and enter the parking lot through track 22, so the initial track, target track and final track are determined. The expression of the initial track, target track and the track set being connected is expressed as follows:

[0096] x n ∈R,n=1,2,…,23,

[0097] a0=x 23 , a n =x 22 ,

[0098] S={a0,a1……a n-1 , a n},

[0099] exist Figure 4 The dotted arrows in the middle represent the connectable relationship between the tracks. For example, the track 23 can be connected to track 1 or track 7. Figure 5 As shown in the figure, two unidirectional orbits on the same channel are not connected to each other and are defined as conjugate orbits. A conjugate orbit table is established, and a total of 7 pairs of conjugate orbits (4, 5), (6, 7), (8, 9), (10, 11), (12, 13), (16, 17), and (18, 19) are stored in the conjugate orbit table.

[0100] Furthermore, the car receives the assigned task, assuming that the machining center is at x i 、x j 、x k On the track, the current intelligent transport vehicle (AGV vehicle) target track is x i 、x j 、x k , starting from the parking lot through the initial track x 23Enter the multi-ring nested closed loop workshop and make a strong choice of the next access track. For each selected track a n , a strong trade-off is made. If one of the tracks x1 and x7 is selected as a1, the other track will enter the unconnected track set S 0 , repeat this process until the set of orbits being connected S contains all target orbits x i 、x j 、x k 、x 22 At this time, the path S1 is generated and the complete path S1 is stored in the connected set S' table, and the new unconnected set S generated during the connection process is 0 The first set of complete paths is generated by storing the latest row in the unconnected table. The expression is as follows:

[0101]

[0102]

[0103]

[0104] Furthermore, from the unconnected track set S 0 Select the first track as the new starting point, repeat the above path generation process to generate a new path S2, store the completed path S2 in the connected track set S', and record the new unconnected track set S 0 2. Continue until all unconnected elements in a set are found to be consistent with the previously recorded initial unconnected track set S 0 1. The planned path plan that meets the current task of the intelligent transport vehicle is generated to determine the initial planned path plan of the intelligent transport vehicle;

[0105] More specifically, we then start from the initial unconnected track set S 0 1Select the first element a1 0 As a1 of path S2, repeat the above steps, reselect a new connected track, generate a complete path S2 and store it in the connected set S'. In the process of selecting a new connected track, a new unconnected track S will be generated. 0 2. S 0 2. Store the latest row in the unconnected table until a set of all unconnected elements and S appears. 0 1 is exactly the same, and the path plan that meets the current task is generated to determine the initial planning path plan of the intelligent transport vehicle, that is, when S 0 n =S 0 1, terminate the path generation method.

[0106] In some embodiments, after the step of generating an initial planned path plan for the intelligent transport vehicle, the following steps are included:

[0107] Step S2001: calling a preset improved A* algorithm, and optimizing the initial planned path solution based on the improved A* algorithm;

[0108] Step S2002: Using the optimized initial planning path solution as the initial solution of the simulated annealing algorithm.

[0109] The algorithm flow of the improved A* algorithm specifically includes:

[0110] (1) Initialization: Create two lists: open list and close list, which are used to store nodes to be explored and nodes that have been explored; add the starting node to the open list; set the initial dynamic weight factor and intersection priority. (Assume that the intersection priority is determined by traffic flow. The smaller the traffic flow, the higher the priority; set the new path weight to be the new path length and the old path weight to be the old path length. The shorter the path length, the smaller the weight). Because the intelligent transport vehicle (AGV vehicle) can usually only move in four directions: up, down, left, and right; Manhattan distance refers to the sum of the horizontal and vertical distances from the current point m to the target point F, that is, |h(m)|=|Xm-XF|+|Ym-YF|. By continuously updating the f value of the intermediate point m and selecting the point with the smallest f value for exploration, the A* algorithm can gradually search for the optimal solution, that is, the shortest path from the starting point to the target point. In this process, all the searched points are the process points of the shortest path.

[0111] (2) Initialize the path planning map and environmental information: perform a cyclic search. When the open list is not empty, perform the following steps: select the node c with the smallest f(m) from the open list as the current node, and move c to the close list; if c is the target node, stop searching and output the optimal path result; otherwise, traverse the adjacent nodes m of the current node c: set g(m) = g(c) + d(c, m), and the parent node of m is c; if the adjacent node m is in the open list, when g(m) > g(c) + d(c, m), update g(m) = g(c) + d(c, m), and set the parent node of m to c;

[0112] (3) If m is not in the closelist, calculate the dynamic weight of m: adjust the weight according to factors such as obstacle density, path congestion, and intersection priority.

[0113] (4) If m is not in the open list, add m to the open list, update its parent node to c, and calculate g(m), h(m), and f(m); if m is in the open list, new_weight = distance(new_path), old_weight = distance(old_path)

[0114] (5) Output the optimal path: When the target node appears in the open list, the search ends. Output the node grid sequence in the order in which they enter the closed list. This sequence is the optimal path.

[0115] (6) Real-time path update: Update the path planning map and environmental information according to real-time environmental changes; when the environment changes, re-plan the path and re-execute the A* algorithm; update the dynamic weight factor and intersection priority according to the new path information.

[0116] Here, speed adaptation is taken into consideration, and corresponding distance adaptation is added to timely update the dynamic weight, further ensuring that the system is always in a high-quality solution: the path adaptation formula is used to adjust the f value of node m: adjusted_f(m) = f(m) / w, where adjusted_f(m) is the adjusted f value of node m, f(m) is the original f value of node m, and w is the dynamic weight factor of node m.

[0117] This improved A* algorithm process meticulously considers path planning and path updates, particularly in optimizing paths under real-time environmental changes. The algorithm incorporates dynamic node weights, intersection priorities, and real-time path updates to achieve more flexible and efficient path planning in dynamic environments. Furthermore, an adaptive path formula adjusts the node's f-value, further ensuring the system maintains a high-quality solution, avoiding multiple nested closed loops and finding a faster path. These meticulous designs efficiently enable path planning and real-time adjustments for automated guided vehicles (AGVs).

[0118] Based on the above steps, an optimized initial planning path solution can be determined, and the optimized initial planning path solution is used as the initial solution of the simulated annealing algorithm.

[0119] Step S30: using the initial planned path solution as an initial solution of a simulated annealing algorithm, and perturbing the initial solution to generate a new planned path solution;

[0120] After generating an initial planned path plan for the intelligent transport vehicle, using the initial planned path plan as an initial solution of a simulated annealing algorithm, and perturbing the initial solution to generate a new planned path plan;

[0121] Further, the step of perturbing the initial planning path scheme as the initial solution of the simulated annealing algorithm to generate a new planning path scheme comprises:

[0122] Step S301, determining an initial planning path scheme of the intelligent carrying trolley, taking the initial planning path scheme as the initial solution of the simulated annealing algorithm;

[0123] Step S302, performing exchange operation, reversal operation, insertion operation or local exchange on the initial solution by using a conversion operator to generate a new planning path scheme.

[0124] Step S40, determining the difference between the objective function value corresponding to the new planning path scheme and the objective function value of the current planning path scheme, and replacing the current planning path scheme with the new planning path scheme according to the difference by using the Metropolis criterion in the simulated annealing algorithm;

[0125] After perturbing the initial solution to generate a new planning path scheme, the difference between the objective function value corresponding to the new planning path scheme and the objective function value of the current planning path scheme is determined, and the current planning path scheme is replaced with the new planning path scheme according to the difference by using the Metropolis criterion in the simulated annealing algorithm;

[0126] Further, the expression of the Metropolis criterion is:

[0127]

[0128] Wherein, f(l) represents the objective function of the current planning path scheme, f(l') represents the objective function of the new planning path scheme, n is the scale factor of the objective function, T is the current temperature, when f(l')-f(l)<0, the objective function value of the new planning path scheme is lower, and the new planning path scheme is directly accepted; when f(l')-f(l)>0, the new planning path scheme is accepted with a probability of exp(-(f(l')-f(l)) / nT). The new planning path scheme is accepted.

[0129] Step S50, determining the current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, judging whether the current temperature is lower than a preset temperature threshold, if so, selecting the planning path scheme with the shortest path among all iteration results as the final planning path scheme to complete the search optimization of the intelligent carrying trolley path.

[0130] After the current planning path scheme is replaced by the new planning path scheme according to the difference value by using the Metropolis criterion in the simulated annealing algorithm, a current temperature of the simulated annealing algorithm is calculated based on a preset rapid temperature drop function, and it is determined whether the current temperature is lower than a preset temperature threshold value; if so, a planning path scheme with the shortest path is selected from all iteration results as a final planning path scheme to complete the search optimization of the intelligent carrying trolley path.

[0131] The step of determining the rapid temperature drop function of the simulated annealing algorithm comprises:

[0132] In step S501, an initial temperature, an iteration number and a temperature drop coefficient in an annealing process of the simulated annealing algorithm are determined.

[0133] In step S502, a first product between the iteration number and the temperature drop coefficient is calculated and determined, and a first sum value between the first product and a numerical value 1 is calculated and determined.

[0134] In step S503, a temperature when the iteration number is k is determined based on a first ratio value between the initial temperature and the first sum value, so as to determine the rapid temperature drop function.

[0135] Specifically, the expression of the rapid temperature drop function is as follows:

[0136]

[0137] In the formula, k is the iteration number, a is the temperature drop coefficient in the annealing process, T k is the temperature when the iteration number is k.

[0138] Further, the step of determining the initial temperature of the simulated annealing algorithm comprises:

[0139] In step S5011, an objective function of the current planning path scheme, an objective function of the new planning path scheme and a preset acceptance probability are determined.

[0140] In step S5012, a natural logarithm value of the acceptance probability is calculated and determined.

[0141] In step S5013, a first difference value between the objective function of the new planning path scheme and the objective function of the current planning path scheme is calculated and determined.

[0142] In step S5014, a second ratio value between the first difference value and the natural logarithm value of the acceptance probability is calculated and determined, and a negative value of the second ratio value is taken as the initial temperature of the simulated annealing algorithm.

[0143] The initial temperature is set to a sufficiently large value to ensure a stronger global search capability. According to the Metropolis criterion, the initial temperature of the simulated annealing algorithm can be expressed as:

[0144]

[0145] Where f(l′) represents the objective function of the new planned path plan, f(l) represents the objective function of the current planned path plan, and P0 is the preset acceptance probability.

[0146] In some embodiments, the calculation formula of the objective function increment is expressed as follows:

[0147] Δf=f(l′)-f(l)

[0148] Where l represents the initial path plan, l′ represents the new planned path plan, f(l) represents the objective function of the current planned path plan, f(l′) represents the objective function of the new planned path plan, and Δf represents the increment of the objective function.

[0149] In order to ensure accurate definition and judgment of the state space when falling into a loop, this application adopts the construction of appropriate one-dimensional serial number identification and two-dimensional serial number identification, where the one-dimensional serial number identification is a continuous positive integer serial number as shown in Table 1, and the two-dimensional identification is a matrix-like axis pattern as shown in Table 2;

[0150] Table 1 One-dimensional serial coordinate system

[0151] 1 6 11 16 21 2 7 12 17 22 3 8 13 18 23 4 9 14 19 24 5 10 15 20 25

[0152] Table 2 Two-dimensional serial number coordinate system

[0153] (1，1) (1，2) (1，3) (1，4) (1，5) (2，1) (2，2) (2，3) (2，4) (2，5) (3，1) (3，2) (3，3) (3，4) (3，5) (4，1) (4，2) (4，3) (4，4) (4，5) (5，1) (5，2) (5，3) (5，4) (5，5)

[0154] The specific conversion relationship is as follows:

[0155]

[0156]

[0157] N=(j-1)*n+i,

[0158] In this formula, n is the dimension of the coordinate system, N is the serial number in the one-dimensional coordinate system, i and j are the row and column pairs in the two-dimensional coordinate system, mod is the remainder function, and ceil is the integer function.

[0159] The method further comprises the following steps: determining a current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, determining whether the current temperature is lower than a preset temperature threshold, and if so, selecting a planning path solution with the shortest path among all iteration results as a final planning path solution.

[0160] Step S1100: Adapting the speed of the intelligent transport vehicle to avoid collision with other intelligent transport vehicles based on a preset speed adaptation algorithm;

[0161] Step S1200: Calculate and determine the safe speed and braking acceleration of the intelligent transport vehicle to ensure that it can stop safely before encountering an obstacle;

[0162] Step S1300: Determine a speed adjustment window based on the power acceleration and current speed of the intelligent transport vehicle to dynamically update a feasible speed range;

[0163] Step S1400: For multiple intelligent transport carts, predict the displacement of other intelligent transport carts and adjust the obstacle avoidance strategy to reduce collisions;

[0164] Step S1500: Based on the forward movement and expansion radius of other intelligent transport vehicles, the distance evaluation function is updated to ensure that obstacles can be accurately avoided in actual path planning.

[0165] Specifically, if Figure 6 As shown in FIG, the Feasible Path Self-Generation Algorithm (FSGA) proposed in this application solves the multi-ring nested closed-loop workshop path planning problem. Multiple carts are dynamically scheduled at the same time. There are situations where multiple carts need to go to the same processing center at the same time and need to pass through the same track, causing congestion. Congestion will cause unnecessary cart operation costs and time costs, directly affecting the speed of the overall workshop logistics and the overall production efficiency of the workshop. Figure 7 As shown in the figure, since the current intelligent transport vehicle (AGV) and other intelligent transport vehicles (AGVs) have to enter each other's paths, a deadlock phenomenon occurs, which cannot be resolved by the priority method and traffic rules method.

[0166] In order to solve the above congestion and deadlock problems, a speed adaptation scheme for an intelligent transport vehicle (AGV) is proposed. The speed adaptation algorithm includes:

[0167] (1) The steps for setting the safe speed are as follows: Set the safe speed so that the car can stop before colliding with an obstacle. P(v) is the shortest distance between the car and the obstacle on its trajectory, and V a This is the range of motion of the AGV without colliding with the nearest obstacle. The acceleration during braking is V b , indicating the range:

[0168]

[0169] (2) Considering the dynamic acceleration of the car, the search space is sampled into a dynamic window, and only the speed that can be reached with the current acceleration is retained. Let t be the time interval, V a is the actual speed, then the motion range of the dynamic window is:

[0170]

[0171] (3) Final search space:

[0172] V r =V s ∩V a ∩V d , ③

[0173] Among them, V s It is the limit of the robot's maximum speed and minimum speed.

[0174] In the multi-task multi-loop nested closed loop of multiple intelligent transport carts (AGV carts), it is possible to safely avoid sudden obstacles (AGV carts themselves) without affecting the overall route of each intelligent transport cart (AGV cart) in the collaborative operation mode of multiple intelligent transport carts (AGV carts). Considering the environment in which multiple intelligent transport carts (AGV carts) are operating, when one of the intelligent transport carts (AGV carts) is taken as the research object, the other intelligent transport carts (AGV carts) are equivalent to moving obstacles. Considering that the displacement of the dynamic intelligent transport cart (AGV cart) at the next moment is very likely to cause the research object to fail to avoid obstacles in time and cause conflicts, a forward prediction strategy is proposed to predict the position information of the obstacles at the next moment to reduce the collision conflicts between intelligent transport carts (AGV carts). Therefore, among the m intelligent transport carts (AGV carts), S ij It represents the distance between the i-th and j-th intelligent transport vehicles (AGVs). When the research object is the intelligent transport vehicle (AGV) No. 1, the distance between the No. 1 intelligent transport vehicle (AGV) and other intelligent transport vehicles (AGVs) is considered, which is expressed as follows:

[0175] min{T12, T13…T1m}, ④

[0176] That is, the minimum value in the set is selected to determine the research object and its nearest dynamic obstacle.

[0177] At this point, P(v) cannot simply consider the safe distance between the research object and the nearest obstacle, because the two intelligent transport vehicles (AGVs) are moving, equivalent to moving obstacles. The safe distance at this moment is the safe distance for the research object at this moment. When the dynamic obstacle moves forward at the next moment, a collision may occur. To avoid conflicts, the distance evaluation function is improved:

[0178] P′(v)=P(v i )+P*(v j ), ⑤

[0179] In formula ①, P(v j ) represents the obstacle distance evaluation function of the research object at the current moment. In the speed window, at this moment, all obstacles are static. However, in fact, the intelligent transport vehicles (AGV vehicles) are all moving and planning their paths towards their respective target points. Their positions and speeds are changing all the time. At this time, it is necessary to add P*(v j ), P*(v j ) represents the forward moving distance of the obstacle, which avoids the situation that although the forward predicted distance of the research object is feasible at the previous moment, the obstacle is also moving forward. Considering the forward moving distance of the obstacle can well predict the appropriate obstacle distance evaluation function at the next moment. In the speed window of the research object, the position of the obstacle at the next moment can be predicted in advance, thereby avoiding unnecessary collision problems. On the other hand, the intelligent transport vehicle (AGV vehicle) cannot be too close to the obstacle during driving, which is prone to collision. At this time, if the actual radius r of the obstacle is, the collision judgment area of ​​the obstacle is expanded, and the range of its radius expansion is set as the coefficient When calculating the distance value of P′(v), instead of calculating the distance from the center of the mobile robot to the center of the obstacle, we calculate the distance from the center of the mobile robot to the center of the obstacle, and then subtract the expansion radius of the obstacle. This will provide a more reliable safe speed range. Substituting the improved distance evaluation function into formula ①, the speed window from the obstacle changes from Va* to Va′, and the improved speed search space is Vr′, which is expressed as follows:

[0180]

[0181] V′ r =V s ∩V′ a ∩V d , ⑦

[0182] From the above steps, we can see that after the Feasible Path Self-Generation Algorithm (FSGA) generates all path solutions that meet the task requirements, the simulated annealing (SA) algorithm is incorporated to determine whether the generated path solutions are cyclic paths. If they are cyclic paths, they are eliminated, and the speed is adaptively adjusted among the remaining path solutions to form a complete fastest path search algorithm.

[0183] As can be seen from the above embodiments, compared with the prior art, the present application addresses the problem that the paths generated by the prior art in a multi-ring nested closed-loop workshop may fall into multiple loops, which are prone to falling into "0"-shaped loops or "6"-shaped loops, increasing the operating time of the intelligent transport vehicle, the waiting time of the machining center, and the path congestion time, thereby causing waste of transportation costs. The present application has, but is not limited to, the following beneficial effects:

[0184] First, a new path search method is innovatively proposed. Based on the initial planning path generated by the Feasible Path Self-Generation Algorithm (FSGA), it is optimized by incorporating the simulated annealing (SA) algorithm. This method can generate all path plans that meet the task requirements and accurately and quickly eliminate path plans that cause the vehicle to get stuck in loops. Combined with the operating speed of the intelligent transport vehicle, it can accurately and quickly search for the path plan that will complete the specified task the fastest.

[0185] Second, the Feasible Path Self-Generation Algorithm (FSGA) uses speed adaptation to adjust the speed of the vehicle's intelligent transport trolleys, enabling them to adapt to intelligent obstacle avoidance and dynamically adjust speed to prevent congestion caused by multiple intelligent transport trolleys operating simultaneously.

[0186] Third, the path self-generation algorithm is integrated into the improved A* algorithm: searching for the fastest path among all path solutions generated by the Feasible path Self-Generation Algorithm (FSGA) is an indispensable key step in the path self-generation algorithm (FSGA), which can accurately and quickly search for the optimal path solution to complete a specified task.

[0187] See also Figure 8, one of the purposes of the present application is to provide an intelligent carrying trolley path search optimization device, which comprises a data acquisition module 1100, an initial path determination module 1200, a new planning path determination module 1300, a path update module 1400 and a search optimization module 1500. Wherein, the data acquisition module 1100 is arranged to respond to the intelligent carrying trolley path search optimization instruction, and to acquire the passable track set in the multi-loop nested closed loop workshop, the track set being connected, the unconnected track set, the connected track set, the initial track and the target track. The unconnected track set represents the track set which is not selected after being connected to the track set being connected. The initial path determination module 1200 is arranged to use the greedy algorithm strategy in the preset path self-generation algorithm to generate the initial planning path scheme of the intelligent carrying trolley according to the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track and the target track. The new planning path determination module 1300 is arranged to take the initial planning path scheme as the initial solution of the simulated annealing algorithm, and to disturb the initial solution to generate a new planning path scheme. The path update module 1400 is arranged to determine the difference between the target function value corresponding to the new planning path scheme and the target function value of the current planning path scheme, and to replace the current planning path scheme with the new planning path scheme according to the difference by using the Metropolis criterion in the simulated annealing algorithm. The search optimization module 1500 is arranged to calculate and determine the current temperature of the simulated annealing algorithm based on the preset rapid temperature drop function, to judge whether the current temperature is lower than the preset temperature threshold, and if so, to select the planning path scheme with the shortest path among all the iteration results as the final planning path scheme to complete the search optimization of the intelligent carrying trolley path.

[0188] Based on any embodiment of the present application, please refer to Figure 9 Another embodiment of the present application also provides an electronic device, which can be realized by a computer device, such as Figure 9As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a computer-readable storage medium, a memory, and a network interface connected via a system bus. Among them, the computer-readable storage medium of the computer device stores an operating system, a database, and computer-readable instructions, and the database may store a control information sequence. When the computer-readable instructions are executed by the processor, the processor can implement an intelligent transport trolley path search optimization method. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute the intelligent transport trolley path search optimization method of the present application. The network interface of the computer device is used to connect and communicate with the terminal. Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0189] In this embodiment, the processor is used to execute Figure 8 The memory stores the program code and various data required to execute the specific functions of each module and its submodule in the intelligent transport vehicle path search and optimization device. The network interface is used to transmit data between user terminals or servers. The memory in this embodiment stores the program code and data required to execute all modules / submodules in the intelligent transport vehicle path search and optimization device of this application. The server can call the server's program code and data to execute the functions of all submodules.

[0190] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the intelligent transport vehicle path search optimization method described in any embodiment of the present application.

[0191] The present application also provides a computer program product, including a computer program / instruction, which, when executed by one or more processors, implements the steps of the intelligent transport vehicle path search optimization method described in any embodiment of the present application.

[0192] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments of the present application can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the method. The aforementioned storage medium can be a computer-readable storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0193] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

[0194] In summary, this application innovatively proposes a new path search method. Based on the Feasible Path Self Generation Algorithm (FSGA) to generate the initial planning path, it incorporates the simulated annealing (SA) algorithm for optimization, so that it can generate all path plans that meet the task requirements, and can accurately and quickly eliminate path plans that cause the vehicle to fall into a loop. Combined with the operating speed of the intelligent transport vehicle, it can accurately and quickly search for the path plan that completes the specified task the fastest.

Claims

1. An intelligent transport vehicle path search optimization method, applied to a multi-ring nested closed loop workshop, characterized in that: include: In response to the intelligent transport vehicle path search optimization instruction, obtain the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track, and the target track in the multi-ring nested closed-loop workshop, wherein the unconnected track set represents the tracks that are not selected after the tracks in the track set being connected are connected; The greedy algorithm strategy in the preset path self-generation algorithm is used to generate the initial planning path plan of the intelligent transport vehicle according to the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track and the target track, which includes: Determine the set of accessible tracks in a multi-ring nested closed-loop workshop , the track collection being connected , Unconnected track collection , connected track collection , initial orbit and target orbit , wherein the unconnected track set Characterization connection After the unselected track set, the initial track The initial orbit , the target orbit Target orbit ; Determine the target track of the intelligent transport vehicle , the initial track from the parking lot Start, Add to the collection of tracks being connected middle; From the initial track Start, select the next track according to the greedy algorithm, and select a reachable and unvisited track by checking the accessibility relationship of the tracks; For each selected track , make a strong trade-off, if from the track and tracks Choose one as , then the other track will enter the unconnected track set , to determine the initial unconnected track set , repeat this process until the set of tracks being connected is Include all target tracks , then the path Generation completed; The path to be completed Save to the connected track collection In the process of path generation, the set of unconnected tracks will be generated Deposit into the no-call table; From the initial unconnected track collection Select the first track as the new starting point and repeat the above path generation process to generate a new path , the completed path Save to the connected track collection and record the new unconnected track set , continue until all unconnected elements in a set are found to be consistent with the initial unconnected track set recorded previously Similarly, a planned path plan that meets the current task of the intelligent transport vehicle is generated to determine the initial planned path plan of the intelligent transport vehicle; Using the initial planned path solution as an initial solution of a simulated annealing algorithm, and perturbing the initial solution to generate a new planned path solution; Determine a difference between an objective function value corresponding to the new planned path solution and an objective function value of the current planned path solution, and replace the current planned path solution with the new planned path solution based on the difference using the Metropolis criterion in the simulated annealing algorithm; The current temperature of the simulated annealing algorithm is determined based on the preset rapid temperature drop function, and it is judged whether the current temperature is lower than the preset temperature threshold. If so, the planned path solution with the shortest path among all iterative results is selected as the final planned path solution to complete the search optimization of the intelligent transport vehicle path.

2. The intelligent transport vehicle path search and optimization method according to claim 1, characterized in that: The step of using the initial planned path solution as an initial solution of a simulated annealing algorithm and perturbing the initial solution to generate a new planned path solution comprises: Determine an initial planning path plan for the intelligent transport vehicle, and use the initial planning path plan as an initial solution of the simulated annealing algorithm; A conversion operator is used to perform an exchange operation, a reversal operation, an insertion operation or a partial exchange on the initial solution to generate a new planning path solution.

3. The intelligent transport vehicle path search and optimization method according to claim 1, characterized in that: The step of determining a difference between an objective function value corresponding to a new planned path solution and an objective function value of a current planned path solution, and replacing the current planned path solution with the new planned path solution based on the difference using the Metropolis criterion in a simulated annealing algorithm includes: The expression of the Metropolis criterion is: ， in, Represents the objective function of the current planning path solution, represents the objective function of the new planning path solution, n is the scale factor of the objective function, T is the current temperature, when When , the objective function value of the new planning path solution is lower, and the new planning path solution is directly accepted; when When the probability Accept the new planned path plan.

4. The intelligent transport vehicle path search and optimization method according to claim 1, characterized in that: The steps for determining a rapid temperature drop function for a simulated annealing algorithm include: Determine the initial temperature, number of iterations, and temperature drop coefficient during the annealing process of the simulated annealing algorithm; Calculating and determining a first product between the number of iterations and the temperature drop coefficient, and calculating and determining a first sum value between the first product and a value of 1; determining a temperature at a time when the number of iterations is k based on a first ratio between the initial temperature and the first sum value to determine a rapid temperature drop function; The steps of determining the initial temperature of the simulated annealing algorithm include: Determine the objective function of the current planned path plan, the objective function of the new planned path plan, and the preset acceptance probability; Calculating and determining the natural logarithm value of the acceptance probability; Calculating and determining a first difference between the objective function of the new planned path solution and the objective function of the current planned path solution; A second ratio between the first difference and the natural logarithm of the acceptance probability is calculated and determined, and a negative value of the second ratio is used as an initial temperature of the simulated annealing algorithm.

5. The intelligent transport vehicle path search and optimization method according to claim 1, characterized in that: After the steps of generating the initial planning path plan for the intelligent transport vehicle include: Invoking a preset improved A* algorithm, and optimizing the initial planned path solution based on the improved A* algorithm; The optimized initial planning path solution is used as the initial solution of the simulated annealing algorithm.

6. The intelligent transport vehicle path search and optimization method according to any one of claims 1 to 5, characterized in that: The method further comprises the following steps: determining a current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, determining whether the current temperature is lower than a preset temperature threshold, and if so, selecting a planning path solution with the shortest path among all iteration results as a final planning path solution. The intelligent transport vehicle is adapted to its speed based on a preset speed adaptation algorithm to avoid collisions with other intelligent transport vehicles; Calculate and determine the safe speed and braking acceleration of the intelligent transport vehicle to ensure it can stop safely before encountering an obstacle; According to the power acceleration and current speed of the intelligent transport vehicle, the speed adjustment window is determined to dynamically update the feasible speed range; For multiple intelligent transport vehicles, predict the displacement of other intelligent transport vehicles and adjust the obstacle avoidance strategy to reduce collisions; Based on the forward movement and expansion radius of other intelligent transport vehicles, the distance evaluation function is updated to ensure that obstacles can be accurately avoided in actual path planning.

7. An intelligent transport vehicle path search and optimization device, characterized in that: include: a data acquisition module configured to respond to an intelligent transport vehicle path search optimization instruction and acquire a passable track set, a track set being connected, a non-connected track set, a connected track set, an initial track, and a target track within a multi-ring nested closed-loop workshop, wherein the non-connected track set represents a track set that has not been selected after accessing a track in the track set being connected; The initial path determination module is configured to use a greedy algorithm strategy in a preset path self-generation algorithm to generate an initial planning path plan for the intelligent transport vehicle based on the passable track set, the track set being connected, the unconnected track set, the connected track set, the initial track, and the target track, which includes: Determine the set of accessible tracks in a multi-ring nested closed-loop workshop , the track collection being connected , Unconnected track collection , connected track collection , initial orbit and target orbit , wherein the unconnected track set Characterization connection After the unselected track set, the initial track The initial orbit , the target orbit Target orbit ; Determine the target track of the intelligent transport vehicle , the initial track from the parking lot Start, Add to the collection of tracks being connected middle; From the initial track Start, select the next track according to the greedy algorithm, and select a reachable and unvisited track by checking the accessibility relationship of the tracks; For each selected track , make a strong trade-off, if from the track and tracks Choose one as , then the other track will enter the unconnected track set , to determine the initial unconnected track set , repeat this process until the set of tracks being connected is Include all target tracks , then the path Generation completed; The path to be completed Save to the connected track collection In the process of path generation, the set of unconnected tracks will be generated Deposit into the no-call table; From the initial unconnected track collection Select the first track as the new starting point and repeat the above path generation process to generate a new path , the completed path Save to the connected track collection and record the new unconnected track set , continue until all unconnected elements in a set are found to be consistent with the initial unconnected track set recorded previously Similarly, a planned path plan that meets the current task of the intelligent transport vehicle is generated to determine the initial planned path plan of the intelligent transport vehicle; A new planning path determination module is configured to use the initial planning path solution as an initial solution of a simulated annealing algorithm and perturb the initial solution to generate a new planning path solution; a path updating module configured to determine a difference between an objective function value corresponding to a new planned path solution and an objective function value of a current planned path solution, and replace the current planned path solution with the new planned path solution based on the difference using the Metropolis criterion in a simulated annealing algorithm; The search optimization module is configured to determine the current temperature of the simulated annealing algorithm based on a preset rapid temperature drop function, and to determine whether the current temperature is lower than a preset temperature threshold. If so, the planned path solution with the shortest path among all iterative results is selected as the final planned path solution to complete the search optimization of the intelligent transport vehicle path.

8. An electronic device comprising a central processing unit and a memory, characterized in that: The central processing unit is configured to call and run a computer program stored in the memory to execute the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that It stores a computer program implemented according to the method described in any one of claims 1 to 6 in the form of computer-readable instructions, and when the computer program is called and executed by a computer, the steps included in the corresponding method are executed.

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