Python-based laser AGV simulation test method and device

Abstract

The invention discloses a Python-based laser AGV simulation test method and device, and the method comprises the steps: reading a map data file through a server, analyzing the map data, and setting a test scene and test parameters; a task instruction is sent to the AGV through the server side, a laser navigation interface is called to enable the AGV to execute a simulation test task, and the moving track and state information of the AGV are obtained, remotely monitored and displayed through a B / S architecture-based user service interface set by the client side; and based on the running track and the state information of the AGV, executing a performance optimization strategy on the AGV through the server, and performing fault early warning on the AGV. Through the method provided by the invention, the accuracy and real-time performance of path planning of the AGV in actual operation can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of AGV trajectory control, and in particular to a Python-based laser AGV simulation test method and device. Background Art

[0002] Laser Automated Guided Vehicle (AGV) is an intelligent robot that can autonomously navigate, avoid obstacles, and perform material handling tasks. It is widely used in factories and warehouses in modern industry. Existing laser AGVs usually rely on advanced technologies such as laser navigation to achieve autonomous driving and efficient path planning. Before actual deployment, necessary simulation tests are performed on laser AGVs to verify the performance and stability of laser AGVs and help discover potential problems with laser AGVs.

[0003] However, the existing laser AGV simulation test methods have the following shortcomings: the real-time performance of data transmission is poor, and it is difficult to accurately simulate high-frequency navigation control requirements; the efficiency of path planning and obstacle avoidance algorithms in complex scenarios still needs to be improved; the remote control and monitoring functions are not perfect, and lack flexible interfaces and ease of use. Therefore, a laser AGV simulation test method is needed to solve the above problems. Summary of the invention

[0004] In a first aspect, an embodiment of the present invention provides a Python-based laser AGV simulation test method, comprising: Read the map data file through the server and parse the map data, set the test scene and test parameters; Send task instructions to the AGV through the server, call the laser navigation interface to make the AGV perform simulation test tasks, obtain and remotely monitor and display the running track and status information of the AGV through the user service interface based on the B / S architecture set by the client; Based on the running track and the status information of the AGV, the server executes a performance optimization strategy for the AGV and issues a fault warning to the AGV; Wherein, the UDP communication protocol is used to transmit data between the server, the client and the AGV.

[0005] Preferably, the test scenarios include: simple straight line scenarios, complex curve scenarios, dynamic obstacle scenarios, and multi-AGV collaborative scenarios; the test parameters include: the starting position of the AGV, the target position of the AGV, and obstacle avoidance rules; Among them, the obstacle avoidance rules include: static obstacle avoidance rules, dynamic obstacle avoidance rules, single vehicle control rules, and safe distance rules.

[0006] Preferably, calling the laser navigation interface includes: obtaining the current position of the AGV through a position update method and a position monitoring method, and obtaining the running track of the AGV based on a path planning method according to the current position of the AGV.

[0007] Preferably, obtaining the running trajectory of the AGV based on the path planning method includes: Determine the waypoints of the AGV during its travel, and divide the travel path of the AGV into multiple paths; Selecting two of the path points as control points of the Bezier curve, and obtaining a Bezier curve path based on a third-order Bezier curve formula; Dividing the Bezier curve into a plurality of equally divided points according to the length of the Bezier curve path and the traveling speed of the AGV on the Bezier curve path; Calculate the tangent line of the equally divided points according to the coordinates of the equally divided points, and obtain the orientation angle of the AGV at each of the equally divided points; The Bezier curve path is smoothed.

[0008] Preferably, multiple AGVs are supported to execute simulation test tasks in parallel, and the test tasks include: driving path test, obstacle avoidance ability test, and real-time response ability test.

[0009] In a second aspect, an embodiment of the present invention provides a Python-based laser AGV simulation test device, comprising: Data parsing module: used to read map data files through the server and parse map data, set test scenarios and test parameters; Simulation test module: used to send task instructions to the AGV through the server, and call the laser navigation interface to make the AGV perform simulation test tasks; Monitoring and display module: used to obtain and remotely monitor and display the running track and status information of the AGV through a user service interface based on a B / S architecture set by a client; Performance optimization module: used to optimize the performance of the AGV through the server based on the running track and the status information of the AGV.

[0010] Data communication module: used to complete data transmission between the server, the client and the AGV using the UDP communication protocol.

[0011] The beneficial effects of the present invention are as follows: 1. Efficient communication: Use UDP protocol for data transmission to ensure real-time and efficient communication and meet high-frequency navigation control requirements.

[0012] 2. High running path accuracy: The Bezier curve method can generate a smooth motion trajectory and improve the path planning accuracy of the AGV.

[0013] 3. Ability to realize remote monitoring and control: B / S architecture allows users to remotely monitor the operating status of AGV at any location, improving the usability and flexibility of the AGV system.

[0014] 4. System stability: The experiment verified the stability of the AGV system in complex environments and was able to achieve the simultaneous operation of multiple AGVs.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A schematic diagram of a Python-based laser AGV simulation test method flow chart provided in an embodiment of the present invention; Figure 2 A schematic diagram of the test environment deployment of a Python-based laser AGV simulation test method provided in an embodiment of the present invention; Figure 3 A document schematic diagram of a laser navigation interface of a Python-based laser AGV simulation test method provided by an embodiment of the present invention; Figure 4 A schematic structural diagram of a Python-based laser AGV simulation test device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] To facilitate understanding of this embodiment, Figure 1 A Python-based laser AGV simulation test method disclosed in an embodiment of the present invention is introduced in detail. Example 1

[0021] S1: Read the map data file through the server and parse the map data, set the test scene and test parameters.

[0022] The map data file is an XML format file used to store map data. The map data is stored in a dictionary and can be parsed by readFile.py. The map data includes but is not limited to path point coordinates, path point ID, route information, and AMR information. Specifically, route information includes: control point coordinates, length, and maximum speed; AMR information includes: read and write variable IP, read and write variable port, laser navigation IP, and laser navigation port.

[0023] Preferably, the test scenarios include: simple straight line scenarios, complex curve scenarios, dynamic obstacle scenarios, and multi-AGV collaborative scenarios; the test parameters include: the starting position of the AGV, the target position of the AGV, and the obstacle avoidance rules; Among them, obstacle avoidance rules include: static obstacle avoidance rules, dynamic obstacle avoidance rules, bicycle control rules, and safe distance rules.

[0024] Specifically, in the test scenarios, the simple straight line scenario is used to test the basic navigation function of the AGV. In this scenario, the AGV travels in a straight line from the starting point to the end point; the complex curve scene is used to test the path planning and obstacle avoidance capabilities of the AGV. Multiple curves and intersections are set in this scenario; the dynamic obstacle scene is used to test the real-time obstacle avoidance and path replanning capabilities of the AGV. In this scenario, dynamic obstacles are set on the AGV's driving path; multi-AGV collaborative scenario: used to test the scheduling and conflict resolution capabilities of the AGV system. In this scenario, multiple AGVs work collaboratively in the same area.

[0025] Specifically, in the obstacle avoidance rules, the static obstacle avoidance rules include: during the driving process, the AGV needs to use sensors to detect whether there are static obstacles in front of it. If detected, it will stop immediately or bypass it.

[0026] Dynamic obstacle avoidance rules include: During the driving process, the AGV needs to predict possible future collision points by calculating the motion trajectory and speed of dynamic obstacles in real time, and take obstacle avoidance measures in advance, including deceleration, turning, or stopping and waiting.

[0027] Single-vehicle control rules include: setting single-vehicle control in the same column. When different AGVs need to enter the same column, the AGV assigned the task later needs to avoid the AGV assigned the task earlier at the waiting point. Among them, single-vehicle control rules are applicable to multi-AGV collaborative scenarios.

[0028] Safety distance rules include: During driving, AGV needs to maintain a certain safety distance from dynamic obstacles and static obstacles to prevent collisions caused by sensor errors or control delays.

[0029] It should be noted that the AGV is an initialized laser AGV intelligent robot in the SimulatedAGV instance.

[0030] S2: Send task instructions to AGV through the server, call the laser navigation interface to make AGV perform simulation test tasks, obtain and remotely monitor and display the operation trajectory and status information of AGV through the user service interface based on B / S architecture set by the client.

[0031] In this embodiment, the server is built through the Java-based Spark framework and has a RESTful API Web service interface; when the server reads or writes data from the AGV, the struct module is used to parse and package the data in the sub-throttling form to ensure the accuracy and integrity of the data.

[0032] The status information of the AGV includes, but is not limited to: the location information of the AGV, the real-time speed of the AGV, the fork arm information of the AGV, the operating status of the AGV, and the navigation status of the AGV.

[0033] like Figure 2 As shown, the server operating system is set to Windows 10, the application software is PyCharm, and the database system is MySQL; the client operating system is set to Windows 10, and the application software is Google Chrome.

[0034] Among them, the UDP communication protocol is used to complete the data transmission between the server, client and AGV.

[0035] In this embodiment, the UDP communication protocol is generated based on the socket library of Python software. The UDP communication protocol can define fields including protocol authorization code, message type, communication sequence number, command code, and can identify and parse commands. The advantage of using the UDP communication protocol to transmit data is that it can achieve simple and efficient real-time data communication and increase the stability of the AGV system.

[0036] Preferably, calling the laser navigation interface includes: obtaining the current position of the AGV through a position update method and a position monitoring method, and obtaining the running track of the AGV based on a path planning method according to the current position of the AGV.

[0037] The method for updating the position includes: when the AGV starts to travel, after combining factors including the equally divided points of the travel route, the control points, the change of the navigation state, etc., the coordinates of all points on the travel route are calculated according to the specified path, and the current position of the AGV is updated at a predetermined time interval. The user service interface based on the B / S architecture is generated based on HTML files, CSS files and JavaScript files.

[0038] The position monitoring method includes: continuously monitoring the position information from the AGV through variable reading and writing and navigation control.

[0039] Among them, variable reading and writing include: reading variables and writing variables. Reading variables include but are not limited to: alarm information, AGV status, AGV fork arm height, AGV fork arm status, battery status. Writing variables include but are not limited to: setting fork arm height, setting area, server heartbeat, remote shutdown, pull-in, re-issuing navigation, setting control status.

[0040] Navigation control includes but is not limited to: manual-automatic switching, manual positioning of AGV, obtaining the current position of AGV, navigation control operations, and querying the running status and / or navigation status of AGV. Obtaining the current position of AGV includes: if the path point ID is empty, the current position is the AGV parking point by default. Navigation control operations include: starting navigation, canceling navigation, pausing navigation, continuing navigation, creating navigation tasks and pausing navigation.

[0041] Specifically, querying the running status of the AGV includes but is not limited to: querying the X coordinate of the current position of the AGV, the Y coordinate of the current position of the AGV, and the heading angle of the current position of the AGV. Querying the navigation status of the AGV includes the following operations: reaching the navigation point task, waiting, heading to the navigation point, paused, completed, failed, exited, and waiting for the door opening / closing operation. The navigation status of the AGV is determined based on the operation in the navigation control.

[0042] Furthermore, the running trajectory of the AGV is obtained based on the path planning method, including: S201: Determine the waypoints of the AGV during its driving process, and divide the driving path of the AGV into multiple paths.

[0043] S202: Select two path points as control points of the Bezier curve, and obtain the Bezier curve path based on a third-order Bezier curve formula.

[0044] Specifically, S202 includes: setting adjacent waypoints as the starting point and the end point of each path, selecting two waypoints as the control points of the Bezier curve under the condition of ensuring the smoothness of the curve, and drawing the entire Bezier curve path based on the third-order Bezier curve formula. The third-order Bezier curve formula is:

[0045] In the formula, is the coordinate of a point on the third-order Bezier curve; , , , are the coordinates of the control points; t is a parameter with a value range of , where 0 represents the starting point , 1 indicates the end point .

[0046] S203: Divide the Bezier curve into a plurality of equally divided points according to the length of the Bezier curve path and the traveling speed of the AGV on the Bezier curve path.

[0047] The faster the travel speed, the more equal points there are. The formula for equal points is:

[0048] In the formula, n is the number of equally divided points, i is the i-th equally divided point, is the parameter value of the Bezier curve of the i-th equally divided point, and t is the parameter of the Bezier curve, which is used to control the equally divided points of the Bezier curve.

[0049] S204: Calculate the tangent lines of the equally divided points according to the coordinates of the equally divided points, and obtain the orientation angle of the AGV at each equally divided point.

[0050] The orientation angle of the AGV at each equally divided point is the angle between the tangent line at the equally divided point and the horizontal axis. The formula for the tangent line at the equally divided point is:

[0051] Where t is the parameter of the Bezier curve, which is used to control the equally divided points on the Bezier curve; is the direction vector of the tangent line at the bisector point; and They are the x-coordinate and y-coordinate of the equally divided points on the Bezier curve respectively; and They are the rates of change of equally divided points on the Bezier curve in the x direction and y direction respectively.

[0052] In this embodiment, the value range of the parameter t of the Bezier curve is [0, 1]. By continuously adjusting t, the number of turns and the driving distance of the AGV can be significantly reduced, thereby improving the operating efficiency.

[0053] AGV's orientation angle at each equally divided point The formula is:

[0054] S205: Smoothing the Bezier curve path.

[0055] Among them, smoothing the Bezier curve path can ensure the continuity and smoothness of the AGV's motion trajectory, thereby improving the navigation efficiency and accuracy of the AGV.

[0056] In this embodiment, when the navigation control operation of "start navigation" is performed on the AGV, the following steps are performed: by obtaining the specified path and path point ID, the map data file is read according to each specified path, and the path length and maximum speed of the specified path are calculated; the running time is calculated by the path length and the maximum speed to obtain the equal-division points, and the control point coordinates are obtained through the specified path; the LineUtils tool class is called to calculate the control points and arcs on the Bezier curve through the control point coordinates and the equal-division points; When the operations of "going to the navigation point" and "completed" are performed to query the navigation status of the AGV, the following steps are performed: if the navigation control operation is "continue navigation", further determine whether there are any path points that have not been passed; if there are any path points that have not been passed, the navigation status of the AGV is "going to the navigation point"; if there are no path points that have not been passed, the navigation status of the AGV is "completed"; The user service interface is set to refresh every 3 seconds. If the running time is less than 3 seconds, the value of the equal-division point is fixed to 1; otherwise, the value of the equal-division point is 1 / 3 of the running time.

[0057] In this embodiment, the document diagram of the laser navigation interface is as follows: Figure 3 Preferably, multiple AGVs are supported to execute simulation test tasks in parallel, and the test tasks include: driving path test, obstacle avoidance test, and system real-time response capability test.

[0058] The driving path test is used to verify the driving ability of the AGV in different scenarios and different driving paths and the accuracy of the path planning algorithm, and to evaluate whether the AGV can accurately and efficiently drive according to the predetermined driving path, thereby ensuring that the AGV can stably and reliably complete the material handling task in actual applications. The driving path includes but is not limited to straight lines, curves, and intersections.

[0059] The obstacle avoidance test is used to test the obstacle avoidance ability and reaction speed of the AGV when encountering static obstacles and / or dynamic obstacles, as well as to evaluate the sensor accuracy of the AGV, the effectiveness and real-time performance of the obstacle avoidance algorithm, to ensure that the AGV can respond quickly to avoid collisions when encountering static obstacles and / or dynamic obstacles.

[0060] The system real-time response capability test is used to test the real-time response speed and stability of the AGV system, ensuring that the AGV can maintain an efficient and stable operating state in actual applications and improve the reliability and availability of the overall system. The system real-time response capability test includes: by simulating high-frequency navigation control requirements and real-time data processing scenarios, evaluating whether the AGV system can accurately process large amounts of data in a short period of time and respond in a timely manner.

[0061] S3: Based on the running trajectory and status information of the AGV, the performance optimization strategy is implemented for the AGV, and fault warning is issued for the AGV.

[0062] The status information includes, but is not limited to: the speed of the AGV, the acceleration of the AGV, and the steering angle of the AGV.

[0063] Fault warning for AGV includes: when a component of the AGV fails or component performance deteriorates, a warning is issued in a timely manner through the user service interface, and the possible cause of the failure and the corresponding solution are displayed, so that maintenance personnel can quickly locate and repair the problem.

[0064] Preferably, the performance optimization strategies include: path planning optimization, obstacle avoidance strategy optimization, speed control optimization, energy management optimization, and collaborative operation optimization.

[0065] Among them, path planning optimization includes identifying deficiencies or redundancies in path planning by analyzing the AGV's running trajectory. For example, if the AGV frequently turns or detours in a specific area, it may be necessary to optimize the path planning in that area to reduce unnecessary turns and driving distances, thereby improving operating efficiency and accuracy.

[0066] The obstacle avoidance strategy optimization includes adjusting the obstacle avoidance strategy according to the state information of the AGV during the obstacle avoidance process. For example, if the AGV exhibits poor real-time response capability in a dynamic obstacle scenario, the obstacle avoidance algorithm applied to the AGV system can be optimized to enable the AGV to predict and respond to changes in dynamic obstacles more quickly.

[0067] Speed ​​control optimization includes real-time adjustment and control of the AGV's speed by analyzing the speed changes of the AGV on different path segments. For example, when the AGV is running in a complex curve or narrow passage, the speed of the AGV can be appropriately reduced to ensure the safety of the AGV; and when the AGV is running in an open or straight area, the speed of the AGV can be increased to improve the operating efficiency of the AGV.

[0068] Energy management optimization includes reducing the energy consumption of AGVs based on the energy consumption data and status information of AGVs. Reducing the energy consumption of AGVs includes adjusting the power output of AGVs and reducing the idle time and waiting time of AGVs.

[0069] Collaborative operation optimization includes, in multi-AGV collaborative scenarios, optimizing collaborative operation strategies by analyzing the running tracks and status information of each AGV. Among them, optimizing collaborative operation strategies includes: adjusting the scheduling order and path planning of AGVs to reduce conflicts and waiting time; and improving the overall operating efficiency and stability of the multi-AGV system by establishing an information sharing and coordination mechanism.

[0070] In this embodiment, the above technical solution is implemented based on Python language. In combination with the above technical solution, the advantages of this application also include: 1. Support cross-platform operation: Users can complete the simulation test of AGV through the browser without installing specific client software, and support multiple operating systems and devices.

[0071] 2. Strong scalability: It is easy to add new functional modules or upgrade functions without making extensive modifications to the client.

[0072] 3. High security: User data and system security can be protected through security measures such as SSL / TLS encryption and permission control on the server side. Example 2

[0073] Combination Figure 4 , a Python-based laser AGV simulation test device, comprising: Data parsing module: used to read map data files through the server and parse map data, set test scenarios and test parameters; Simulation test module: used to send task instructions to AGV through the server, and call the laser navigation interface to make AGV perform simulation test tasks; Monitoring and display module: used to obtain and remotely monitor and display the running track and status information of AGV through the user service interface based on B / S architecture set by the client; Performance optimization module: used to optimize the performance of AGV through the server based on the running trajectory and status information of AGV.

[0074] Data communication module: used to complete data transmission between the server, client and AGV using the UDP communication protocol.

[0075] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A Python-based laser AGV simulation test method, characterized in that: include: Read the map data file through the server and parse the map data, set the test scene and test parameters; Send task instructions to the AGV through the server, call the laser navigation interface to make the AGV perform simulation test tasks, obtain and remotely monitor and display the running track and status information of the AGV through the user service interface based on the B / S architecture set by the client; Based on the running track and the status information of the AGV, the server executes a performance optimization strategy for the AGV and issues a fault warning to the AGV; Wherein, the UDP communication protocol is used to transmit data between the server, the client and the AGV.

2. According to a Python-based laser AGV simulation test method according to claim 1, it is characterized in that: The test scenarios include: simple straight line scenarios, complex curve scenarios, dynamic obstacle scenarios, and multi-AGV collaborative scenarios; the test parameters include: the starting position of the AGV, the target position of the AGV, and obstacle avoidance rules; Among them, the obstacle avoidance rules include: static obstacle avoidance rules, dynamic obstacle avoidance rules, single vehicle control rules, and safe distance rules.

3. According to a Python-based laser AGV simulation test method according to claim 1, it is characterized in that: Calling the laser navigation interface includes: obtaining the current position of the AGV through a position update method and a position monitoring method, and obtaining the running track of the AGV based on a path planning method according to the current position of the AGV.

4. The Python-based laser AGV simulation test method according to claim 3 is characterized in that: Acquiring the running trajectory of the AGV based on the path planning method includes: Determine the waypoints of the AGV during its travel, and divide the travel path of the AGV into multiple paths; Selecting two of the path points as control points of the Bezier curve, and obtaining a Bezier curve path based on a third-order Bezier curve formula; Dividing the Bezier curve into a plurality of equally divided points according to the length of the Bezier curve path and the traveling speed of the AGV on the Bezier curve path; Calculate the tangent line of the equally divided points according to the coordinates of the equally divided points, and obtain the orientation angle of the AGV at each of the equally divided points; The Bezier curve path is smoothed.

5. The Python-based laser AGV simulation test method according to claim 1, characterized in that: Support multiple AGVs to execute simulation test tasks in parallel, and the test tasks include: driving path test, obstacle avoidance ability test, and real-time response ability test.

6. A Python-based laser AGV simulation test device, characterized in that: include: Data parsing module: used to read map data files through the server and parse map data, set test scenarios and test parameters; Simulation test module: used to send task instructions to the AGV through the server, and call the laser navigation interface to make the AGV perform simulation test tasks; Monitoring and display module: used to obtain and remotely monitor and display the running track and status information of the AGV through a user service interface based on a B / S architecture set by a client; Performance optimization module: used to optimize the performance of the AGV through the server based on the running track and the status information of the AGV; Data communication module: used to complete data transmission between the server, the client and the AGV using the UDP communication protocol.

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