Unmanned vehicle intelligent scheduling and path-finding online method and device, and medium

Abstract

The embodiment of the invention discloses an unmanned vehicle intelligent scheduling and road-finding online method and device and a medium, and the method comprises the steps: obtaining the state information sent by a vehicle end, and carrying out the analysis of the state information according to the type of a vehicle, and obtaining the state of the vehicle; performing priority ranking on unexecuted tasks according to the task data, and determining a target task with a first priority; according to the vehicle state, calculating planned path information and time cost for the vehicle to arrive at each task node when the vehicle completes the target task; according to the time cost, determining a target vehicle for executing the target task, and sending a scheduling instruction for executing the target task to the target vehicle; and controlling the target vehicle to find a road line according to the scheduling instruction. According to the method for selecting the way-finding line according to the time consumed by the task, the vehicle can be on line at any position and can participate in task execution after being on line, and the task execution efficiency is high.

Description

Technical Field

[0001] The present specification relates to the technical field of vehicle control, and in particular to a method, device and medium for intelligent dispatching and route finding of unmanned vehicles. Background Art

[0002] In the field of unmanned driving, vehicle path planning is generally done by the vehicle itself relying on high-precision maps for autonomous planning. This approach has many disadvantages. For example, if a path is temporarily banned or the path has heavy traffic, the vehicle will not change its route in real time, which will affect the vehicle's traffic efficiency and may even cause a collision risk. If it is handled solely by the vehicle, the vehicle's computing pressure will increase. It is necessary to obtain and maintain all vehicle data on all path states in real time, and it is necessary to calculate the optimal solution in real time. In addition, the calculation results between vehicles will cause unpredictable conflicts.

[0003] Unmanned driving in industrial scenarios also requires the coordination of unmanned vehicles, unmanned equipment, manned vehicles, and workers. The beginning of communication between unmanned vehicles and control systems can be called going online. Currently, unmanned vehicles may go online at a fixed location or randomly select a path from the current location or go online by manually specifying a path. However, this method cannot achieve effective unified management and control, and the efficiency of executing tasks is low. Summary of the invention

[0004] One or more embodiments of the present specification provide a method, device and medium for intelligent scheduling and route finding of unmanned vehicles, which are used to solve the following technical problems: In the prior art, the efficiency of executing tasks by autonomously planning paths by unmanned vehicles is low.

[0005] One or more embodiments of this specification adopt the following technical solutions:

[0006] One or more embodiments of this specification provide a method for intelligent dispatching and route finding of unmanned vehicles, the method comprising:

[0007] Obtaining operation plan information and employee information published by the power plant;

[0008] Determine target constraints according to the operation plan information and the employee information, and generate an integer linear programming model according to the target constraints;

[0009] Generate a feasible rotation plan based on the integer linear programming model;

[0010] Selecting the optimal rotation scheme from the feasible rotation schemes using a genetic algorithm;

[0011] Based on the optimal rotation scheme and the feasible rotation scheme, an optimal rotation table and a reference rotation table are displayed.

[0012] Furthermore, generating an integer linear programming model according to the target constraint condition includes:

[0013] The target constraint conditions include soft constraint conditions and hard constraint conditions;

[0014] Selecting items that must be met from the operation plan information and the employee information as hard constraints;

[0015] Selecting the remaining constraints from the operation plan information and the employee information as soft constraints;

[0016] The soft constraints are used as variables of the linear integer programming model, and the objective function of the linear integer programming model is established according to the hard constraints.

[0017] Furthermore, the determining of target constraints according to the operation plan information and the employee information, and generating an integer linear programming model according to the target constraints, further includes:

[0018] Hard constraints must include at least one of the following: personnel availability, special personnel constraints, qualifications, and rotation rules;

[0019] The soft constraint conditions include at least one of the following: job priority constraint, similarity balance, and shift constraint.

[0020] Furthermore, generating an integer linear programming model according to the target constraint condition includes:

[0021] The weight of the hard constraint condition is not less than 6, the weight of the soft constraint condition is not greater than 4, and the total weight is 10.

[0022] Furthermore, generating a feasible rotation scheme based on the integer linear programming model includes:

[0023] The integer linear programming model is solved to obtain a feasible solution as the feasible rotation scheme.

[0024] Furthermore, the adopting of a genetic algorithm to select an optimal rotation scheme from the feasible rotation schemes includes:

[0025] Randomly generate individuals that meet the strong constraint conditions to form an initial population;

[0026] Calculating the fitness of individuals in the initial population according to the target constraint condition;

[0027] A roulette wheel selection algorithm is used to select individuals with better fitness from the initial population, and a crossover operation and a mutation operation are performed on the initial population;

[0028] Determine whether the population satisfies the termination rule, wherein the termination rule includes that the number of iterations exceeds a certain value, or the fitness of the individual does not improve after X consecutive iterations, or the running time exceeds a certain time T;

[0029] When the termination rule is not met, the individual fitness is compared and adjusted through the above selection, crossover and mutation operations, and the initial population is updated until the updated population meets the termination rule, thereby generating an optimal rotation scheme.

[0030] Furthermore, the randomly generated individuals satisfying the strong constraint conditions to form an initial population include:

[0031] An initial matrix is ​​randomly generated, with the set of all employees as the rows of the initial matrix and the set of all shift days as the columns of the initial matrix.

[0032] Furthermore, the step of calculating the fitness of individuals in the initial population according to the target constraint condition includes:

[0033] Determine target indicators, wherein the target indicators include the shortest maximum completion time indicator of the planned task and the lowest delay loss traversal representative individual performance;

[0034] The fitness score corresponding to the individual is determined according to whether the target indicator is met.

[0035] One or more embodiments of this specification provide an unmanned vehicle intelligent dispatching and route finding online device, characterized in that the device includes:

[0036] at least one processor; and,

[0037] a memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0039] Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status;

[0040] Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority;

[0041] Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task;

[0042] Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle;

[0043] The target vehicle is controlled to find its way online according to the dispatch instruction.

[0044] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to:

[0045] Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status;

[0046] Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority;

[0047] Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task;

[0048] Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle;

[0049] The target vehicle is controlled to find its way online according to the dispatch instruction.

[0050] At least one of the above technical solutions adopted in the embodiments of this specification can achieve the following beneficial effects: the present invention selects a method for finding a route and going online according to the time consumed by the task, and the vehicle can go online at any location, and can participate in the execution of the task after going online, and the efficiency of executing the task is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of this specification or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. In the drawings:

[0052] Figure 1 A flowchart of an unmanned vehicle intelligent dispatching and route finding method provided in an embodiment of this specification;

[0053] Figure 2 A schematic diagram of the architecture of an unmanned vehicle intelligent dispatching and route finding method provided in an embodiment of this specification;

[0054] Figure 3A schematic diagram of an unmanned vehicle searching for nearby feasible paths provided in an embodiment of this specification. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0056] The embodiments of this specification provide a method for intelligent dispatching and route finding of unmanned vehicles. It should be noted that the execution subject in the embodiments of this specification can be a server or any device with data processing capabilities. Figure 1 A flowchart of an unmanned vehicle intelligent dispatching and route finding method provided in an embodiment of this specification.

[0057] like Figure 1 As shown, the method mainly includes the following steps:

[0058] Step S101, obtaining status information sent by the vehicle end, and parsing the status information according to the type of the vehicle to obtain the vehicle status.

[0059] The vehicle sends vehicle status information to the system at a fixed frequency (10-30Hz) through UDP, TCP or message middleware. The system receives the information and parses it according to the vehicle type.

[0060] The vehicle uploaded data includes but is not limited to speed, turning angle, path ID, path location, number of path finding paths, path finding path, current task information, vehicle coordinates, vehicle status information, and vehicle error information.

[0061] After receiving the vehicle message, the system will mark the vehicle as online and record the last communication time of the vehicle. If the vehicle fails to synchronize with the system for 5 seconds (configurable), the system will consider the vehicle offline and mark it as offline.

[0062] The online vehicle system will synchronize status information to the vehicle at a fixed frequency (10-30Hz). The status information synchronized by the system to the vehicle includes but is not limited to the status of the autonomous driving remote driving mode, remote driving command information (only effective in remote driving mode), vehicle relative speed limit, vehicle mission instructions and other information.

[0063] When the vehicle is not currently performing a task, the vehicle is determined to be in an idle state, and when the vehicle is currently performing a task, the vehicle is determined to be in a task state.

[0064] Step S102 , prioritizing the unexecuted tasks according to the task data, and determining a target task with the highest priority.

[0065] Tasks are created manually or by other business systems through interfaces. Task data includes: vehicle key value (identification information of a certain vehicle type or a specified vehicle), task priority, task execution mode (immediate execution, scheduled execution), scheduled execution time, post-task processing method (clear, retain, loop execution), instruction set and (each instruction includes: target path, target area, target action, target carrying parameters).

[0066] Each lifecycle system prioritizes the unexecuted tasks and determines the target task with the highest priority.

[0067] Then, the corresponding vehicle model is obtained according to the task vehicle key value (a certain vehicle type or a specified vehicle ID) to determine whether the vehicle model can perform the target task.

[0068] Step S103, calculating, based on the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task.

[0069] This step may specifically include the following steps:

[0070] (1) If the vehicle is executing the current task, the end point of the current task is used as the starting point of the target task, and a path from the starting point to the node in the target task is planned, and the required time cost is calculated, where the time cost is the estimated time to execute the target task and the estimated remaining execution time of the current task;

[0071] (2) If the vehicle is not currently executing a task and has uploaded the path ID, the location of the vehicle in the uploaded path is used as the starting point of the target task, and the path from the starting point to the node in the target task is planned, and the required time cost is calculated;

[0072] (3) If the vehicle is waiting to go online after completing the route finding, the path with the minimum relative time cost for executing the target task is selected from the route finding path list as the starting point for calculating the route and time cost.

[0073] (4) Currently, if the vehicle cannot plan a feasible path to any task node in the target task, the time cost of the vehicle to perform the target task is set to infinity.

[0074] Step S104: determining a target vehicle for executing the target task according to the time cost, and sending a scheduling instruction for executing the target task to the target vehicle.

[0075] Traverse the vehicle list and find the vehicle with the minimum time cost to perform the target task. If the vehicle can perform the target task, then the vehicle is used as the target vehicle and the target task is assigned to the target vehicle.

[0076] If the vehicle cannot perform the target task, the vehicle selection logic of the target task is skipped, that is, the vehicle selection logic of the next task is executed.

[0077] Step S105, controlling the target vehicle to find a route and go online according to the dispatch instruction.

[0078] The system will notify the target vehicle to perform the target task through downlink messages and continue to interact with the target vehicle on tasks. If the target vehicle is in the path-finding completion state, the system will send the optimal target path for performing the target task to the target vehicle and notify the target vehicle to go online on the target path. The target vehicle will continue to perform the task after going online on the path.

[0079] In summary, the present invention selects a method for path finding and going online according to the time consumed by the task. The vehicle can go online at any location and participate in the execution of the task after going online, and the efficiency of executing the task is high.

[0080] The embodiment of this specification also provides an unmanned vehicle intelligent dispatching and route finding online device, such as Figure 2 As shown, the device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to:

[0081] Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status;

[0082] Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority;

[0083] Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task;

[0084] Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle;

[0085] The target vehicle is controlled to find its way online according to the dispatch instruction.

[0086] One or more embodiments of this specification provide a non-volatile computer storage medium storing computer executable instructions, wherein the computer executable instructions are configured to:

[0087] Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status;

[0088] Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority;

[0089] Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task;

[0090] Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle;

[0091] The target vehicle is controlled to find its way online according to the dispatch instruction.

[0092] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0093] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0094] The devices and media provided in the embodiments of this specification correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0095] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0096] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0100] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0101] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0103] The above description is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of this specification shall be included in the scope of the claims of this specification.

Claims

1. An unmanned vehicle intelligent dispatching and route finding method, characterized in that: The method comprises: Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status; Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority; Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task; Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle; The target vehicle is controlled to find its way online according to the dispatch instruction.

2. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 1, characterized in that: The step of parsing the status information according to the type of the vehicle to obtain the vehicle status includes: When the status information sent by the vehicle is obtained, the vehicle is marked as online and the last communication time of the vehicle is recorded; When no updated status information sent by the vehicle is received within a preset time period after the last communication time, marking the vehicle as offline; When the vehicle is in an online state, the state information is parsed to obtain the vehicle type, vehicle identification, and path information of the vehicle; According to the vehicle identification and the path information, it is determined whether the vehicle is in an idle state or a mission state.

3. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 2 is characterized in that: Determining, according to the vehicle identification and the path information, whether the vehicle is in an idle state or a task state includes: Determining a current path of the vehicle and a current position in the current path according to the path information; determining whether the vehicle is in an idle state according to a current position of the vehicle on the current path; When the vehicle is in an idle state, obtaining a feasible path searched by the vehicle at the current position; When the vehicle is performing a task, it is in a task state.

4. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 3 is characterized in that: According to the vehicle state, the planned path information and time cost of the vehicle to reach each task node when the vehicle completes the target task are calculated, including: When the vehicle is in an idle state, the current position of the vehicle is determined to be the starting point of the target task, and according to the feasible path searched by the vehicle, the planned path information from the starting point to the task node of the target task is obtained, and the time cost required for the vehicle to perform the target task is determined; When the vehicle state is a task state, the task end point of the current task being performed by the vehicle is determined as the starting point of the target task. Then, based on the path information of the location of the starting point, the planned path information from the starting point to the task node of the target task is obtained, and the time cost required for the vehicle to perform the target task is determined.

5. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 4 is characterized in that: Determining the time cost required for the vehicle to perform the target task includes: When the planned path information is obtained, the target task execution time required by the vehicle based on the planned path information is obtained; If the vehicle is in an idle state, the target task execution time is used as the time cost; If the vehicle is in a task state, the target task execution time and the time required to execute the current task are added together as the time cost; When the planned path information cannot be obtained, the time cost is set to be infinite.

6. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 2, characterized in that: When calculating the planned path information and time cost of the vehicle to reach each task node according to the vehicle state, the method further includes: According to the vehicle type, determining whether the vehicle is suitable for executing the target task with the first priority; When the judgment result is yes, the planned path information and time cost of the vehicle reaching each task node when the vehicle completes the target task are calculated according to the vehicle state.

7. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 1, characterized in that: Determining a target vehicle for executing the target task according to the time cost, and sending a scheduling instruction for executing the target task to the target vehicle, including: Selecting a vehicle with the lowest time cost from a preset vehicle list as the target vehicle, and generating a scheduling instruction for executing the target task according to the planned path information of the target vehicle; The dispatch instruction is sent to the target vehicle, wherein the dispatch instruction includes a target path.

8. The method for intelligent dispatching and route finding of unmanned vehicles according to claim 7, characterized in that: Controlling the target vehicle to find a route according to the dispatch instruction includes: The target vehicle is controlled to travel on a target path according to the dispatch instruction.

9. An unmanned vehicle intelligent dispatching and route finding online device, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status; Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority; Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task; Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle; The target vehicle is controlled to find its way online according to the dispatch instruction.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Acquire status information sent by the vehicle end, and parse the status information according to the type of the vehicle to obtain the vehicle status; Prioritize the unexecuted tasks according to the task data and determine the target task with the highest priority; Calculating, according to the vehicle state, the planned path information and time cost for the vehicle to reach each task node when the vehicle completes the target task; Determining a target vehicle for performing the target task according to the time cost, and sending a scheduling instruction for performing the target task to the target vehicle; The target vehicle is controlled to find its way online according to the dispatch instruction.

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