Doffing method and device based on AMR trolley and storage medium

Abstract

The invention relates to a yarn falling method and device based on an AMR trolley and a storage medium, and is applied to the technical field of chemical fiber yarn falling. The method comprises the steps that an AMR is used for doffing and yarn falling, the implementation process of yarn falling and yarn falling of the AMR is designed, the AMR has the obstacle avoidance function, the mechanical and fixed yarn falling mode of an AGV is avoided, and the method and device are more flexible and efficient than the AGV; meanwhile, the real-time position of the system is dynamically calculated through a UWB base station signal deployed in a workshop and a positioning algorithm, the positioning algorithm improves existing common positioning algorithms UWB and TOA, and position information is reliably calculated on the basis of reducing hardware cost through geometric constraint and least square. And meanwhile, the interference of low-quality signals is suppressed through a dynamic weight method, the robustness of a shielding scene is improved by combining TDOA and RSSI, and the self-positioning accuracy in the AMR moving process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical fiber filament dropping, and particularly relates to a filament dropping method, device and storage medium based on an AMR cart. Background Art

[0002] The chemical fiber industry has a large production scale and complex technological processes, and comprehensive control and management of the filament dropping process are required. At the same time, the chemical fiber industry has high requirements for production capacity, quality, cost, efficiency, etc. Currently, the chemical fiber market is highly competitive and market demands are constantly changing. Chemical fiber enterprises need to quickly respond to market demands, and enterprises often enhance their competitiveness by improving the rationality and automation level of technological processes. Therefore, customers hope to use an efficient, intelligent and easy-to-operate filament dropping process scheduling system to achieve more accurate production plans and production process monitoring, thereby improving production efficiency and product quality and reducing costs.

[0003] Currently, most of the filament dropping processes use AGVs for wire taking, but only the mechanical part of the AGV for taking wire from the winding machine has been designed. There is no proposal to use AGVs to implement the entire filament dropping process, let alone a positioning algorithm for the AGV during its movement in the entire workshop. Moreover, in actual situations, AGVs cannot automatically avoid obstacles, and the environment in the filament dropping workshop is generally very complex, with personnel walking around and filament-carrying carts placed randomly, resulting in AGVs not being able to truly achieve the filament dropping process efficiently and intelligently. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a filament dropping method, device and storage medium based on an AMR cart to solve the problems in the prior art that only the mechanical part of the AGV for taking wire from the winding machine has been designed, and there is no proposal to use AGVs to implement the entire filament dropping process; at the same time, to solve the problem that there is no positioning algorithm for the AGV during its movement in the entire workshop, and in actual situations, AGVs cannot automatically avoid obstacles, and the environment in the filament dropping workshop is generally very complex, with personnel walking around and filament-carrying carts placed randomly, resulting in AGVs not being able to truly achieve the filament dropping process efficiently and intelligently.

[0005] According to the first aspect of the embodiments of the present invention, a filament dropping method based on an AMR cart is provided, and the method includes: The winding machine to be filament-dropped sends a call signal to the host computer, and the call signal includes the production line number, the position of the winding machine, the specification of the silk ingot, the batch number, and the tube color; After receiving the call signal, the host computer parses the call signal, obtains the wire-taking point coordinates according to the position of the winding machine, and obtains the storage end coordinates according to the production line number, the specification of the silk ingot, the batch number, and the tube color; The host computer obtains the initial position information, battery level, task queue, and device status of all AMR robots in the current workshop; The host computer selects the AMR robots that will perform the wire dropping task based on the initial position information, battery level, task queue, and device status of all AMR robots, and sends task instructions to the selected AMR robots; After receiving the task instructions, the selected AMR robots dynamically calculate their real-time positions through the UWB base station signals deployed in the workshop and positioning algorithms, and send them to the host computer; After receiving the real-time positions of the selected AMR robots, the host computer plans the optimal path from the current real-time positions of the selected AMR robots to the wire picking point coordinates and then to the storage end point coordinates based on the pre-imported workshop map and obstacle distribution, and sends the optimal path to the selected AMR robots; The selected AMR robots reach the wire picking point coordinates along the optimal path to pick up wire; After successfully picking up wire, the selected AMR robots continue to reach the storage end point coordinates along the optimal path to store the wire ingots and complete the task.

[0006] Preferably, The host computer selects the AMR robots that will perform the wire dropping task based on the initial position information, battery level, task queue, and device status of all AMR robots, including: The host computer selects one or more AMR robots with a battery level greater than a preset first battery threshold, a current task queue less than a preset task queue quantity, and a device status of idle; If there are multiple AMR robots, the AMR robot with the initial position closest to the wire picking starting point coordinates is selected from the multiple AMR robots as the selected AMR robot.

[0007] Preferably, After receiving the task instructions, the selected AMR robots dynamically calculate their real-time positions through the UWB base station signals deployed in the workshop and positioning algorithms, including: Obtain the base station coordinates of three base stations deployed in the workshop respectively, obtain the time difference of the measurement signal reaching any two receivers respectively, obtain the RSSI measurement values of the three base stations respectively, and obtain the signal-to-noise ratio SNR of the three base stations respectively; According to the signal-to-noise ratio SNR of the three base stations, divide the signal-to-noise ratio SNR of each base station by the sum of the signal-to-noise ratios SNR of the three base stations to obtain the dynamic weight of each base station; Obtain the dynamic weight between any two base stations based on the dynamic weight of each base station; Based on the base station coordinates of the three base stations, obtain the distance expressions of the selected AMR robots to the three base stations respectively; The TDOA error between any two base stations is obtained according to the time difference of the measurement signal arriving at any two receivers, the dynamic weight between the two base stations, and the distance expressions of the selected AMR robot to the corresponding two base stations; Set the weight coefficient of the TDOA error, and obtain the overall TDOA error expression according to the weight coefficient of the TDOA error, the TDOA error between any two base stations, and the dynamic weight between any two base stations; Respectively set the weight coefficient of the RSSI error, the reference signal strength, and the path loss exponent; According to the weight coefficient of the RSSI error, the reference signal strength, the path loss exponent, the RSSI measurement values of the three base stations, and the distance expressions of the selected AMR robot to the three base stations, obtain the overall RSSI error expression; Add the overall TDOA error expression and the overall RSSI error expression to obtain the optimization objective function, obtain the residual vector expression based on the optimization objective function, and take the derivative of the residual vector expression to obtain the Jacobian matrix expression; Initialize the coordinates of the selected AMR robot as (x, y) to be (0, 0), and solve the residual vector and the Jacobian matrix at the coordinates of the currently selected AMR robot; Obtain the step size at the coordinates of the currently selected AMR robot according to the solved residual vector and the Jacobian matrix; If the absolute value of the step size at the coordinates of the currently selected AMR robot is greater than or equal to the preset error threshold, subtract the step size at the coordinates of the currently selected AMR robot from the coordinates of the currently selected AMR robot to obtain the updated coordinates of the AMR robot; Repeat the above steps until the absolute value of the step size solved according to the coordinates of the AMR robot updated at a certain time is less than the preset error threshold, or, the preset number of iterations is reached, and output the current coordinates of the currently selected AMR robot as the real-time position of the selected AMR robot.

[0008] Preferably, During the process of the selected AMR robot traveling along the optimal path, dynamic obstacles on the optimal path are detected by a lidar and an ultrasonic sensor, and the optimal path is dynamically adjusted to avoid and bypass the dynamic obstacles.

[0009] Preferably, The selected AMR robot taking wire along the optimal path to the wire taking point coordinates includes: The selected AMR robot travels along the optimal path. When the distance between the selected AMR robot and the winder is less than a preset distance threshold, an industrial camera set on the top is activated to identify the visual marker set at the wire connection port of the winder; The selected AMR robot calculates the lateral and longitudinal deviations between its own robotic arm and the wire connection port of the winder based on the visual marker recognition. The selected AMR robot approaches the wire connection port of the winder according to the visual feedback, and adjusts its own pose through the servo motor to correct the deviation between its own robotic arm and the wire connection port of the winder, and takes out the full spool of wire ingot from the wire connection port through the robotic arm.

[0010] Preferably, After the selected AMR robot successfully picks up the wire, it continues to travel along the optimal path to the storage end coordinate for storing the wire ingot. The tasks completed include: After the selected AMR robot successfully picks up the wire, it continues to travel along the optimal path towards the storage end coordinate. After the selected AMR robot reaches the storage end coordinate, it identifies the shelf number through RFID or vision, and confirms the target storage layer; the selected AMR robot places the full spool of wire ingot at the designated position through its own robotic arm. After confirming that the placement is complete, the selected AMR robot reports a task completion signal to the host computer, releases the resources and returns to the standby area.

[0011] Preferably, it further includes: The host computer determines whether the power of all AMR robots is less than a preset second power threshold according to the power of all AMR robots obtained. If the power of any AMR robot is less than the preset second power threshold; The host computer sends a charging signal to this AMR robot. After receiving the charging signal, this AMR robot dynamically calculates its own real-time position through the UWB base station signal deployed in the workshop and the positioning algorithm and sends it to the host computer; The host computer selects the charging pile that is the closest to the real-time position of this AMR robot and has a charging position as the target charging pile according to the real-time position of this AMR robot and the coordinates of each charging pile pre-imported. The host computer generates an optimal charging route according to the real-time position of this AMR robot and the coordinates of the target charging pile and sends it to the AMR robot; The AMR robot reaches the target charging pile for charging according to the optimal charging route.

[0012] According to the second aspect of the embodiments of the present invention, a wire dropping device based on an AMR trolley is provided. The device includes: Call module: used for the unwinding winder to be dropped to send a call signal to the host computer. The call signal includes the production line number, the position of the winder, the wire ingot specification, the batch number, and the tube color; Parsing module: After the host computer receives a call signal, it parses the call signal, obtains the wire-taking point coordinates according to the position of the winding machine, and obtains the storage end coordinates according to the production line number, wire ingot specification, batch number, and tube color; AMR data acquisition module: It is used for the host computer to obtain the initial position information, power, task queue, and equipment status of all AMR robots in the current workshop; Task distribution module: It is used for the host computer to select the AMR robot that executes the wire dropping task according to the initial position information, power, task queue, and equipment status of all AMR robots, and send a task instruction to the selected AMR robot; Position calculation module: After the selected AMR robot receives the task instruction, it dynamically calculates its own real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, and sends it to the host computer; Optimal path planning module: After the host computer receives the real-time position of the selected AMR robot, based on the pre-imported workshop map and obstacle distribution, it plans the optimal path from the current real-time position of the selected AMR robot to the wire-taking point coordinates and then to the storage end coordinates, and sends the optimal path to the selected AMR robot; Wire-taking module: It is used for the selected AMR robot to reach the wire-taking point coordinates along the optimal path to take wire; Storage module: After the selected AMR robot successfully takes the wire, it continues to reach the storage end coordinates along the optimal path to store the wire ingot and complete the task.

[0013] According to the third aspect of the embodiments of the present invention, a storage medium is provided. The storage medium stores a computer program, and when the computer program is executed by a main controller, it implements each step in the above-mentioned digital twin-based logistics equipment redesign method.

[0014] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: This application uses AMR for the solution of bobbin dropping and wire dropping, and designs the implementation process of AMR wire dropping and bobbin dropping. AMR has a built-in obstacle avoidance function, avoiding the mechanized and fixed wire dropping method of AGV, which is more flexible and efficient than AGV; at the same time, this application dynamically calculates its own real-time position through the UWB base station signal and positioning algorithm deployed in the workshop. This positioning algorithm improves the existing common positioning algorithms UWB and TOA algorithms. Through geometric constraints and least squares, it reliably calculates the position information on the basis of reducing hardware costs. At the same time, the method of dynamic weight is used to suppress the interference of low-quality signals, and combines TDOA and RSSI to improve the robustness of the occlusion scenario and improve the accuracy of the self-positioning of AMR during the moving process.

[0015] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are incorporated herein and constitute a part of this specification, showing embodiments consistent with the present invention and, together with the specification, are used to explain the principles of the present invention.

[0017] Figure 1 is a schematic flowchart of a wire dropping method based on an AMR cart shown according to an exemplary embodiment; Figure 2 is a schematic diagram of the working principle of UWB shown according to another exemplary embodiment; Figure 3 is a schematic system diagram of a wire dropping device based on an AMR cart shown according to another exemplary embodiment; In the drawings: 1 - Call module, 2 - Parsing module, 3 - AMR data acquisition module, 4 - Task distribution module, 5 - Position calculation module, 6 - Optimal path planning module, 7 - Wire taking module, 8 - Storage module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0019] Embodiment 1 Figure 1 is a schematic flowchart of a wire dropping method based on an AMR cart shown according to an exemplary embodiment, as Figure 1 shown, the method includes: S1. The bobbin winder to be wire dropped sends a call signal to the host computer, and the call signal includes the production line number, the position of the winder, the wire bobbin specification, the batch number, and the tube color; S2. After receiving the call signal, the host computer parses the call signal, obtains the wire taking point coordinates according to the position of the winder, and obtains the storage end coordinates according to the production line number, the wire bobbin specification, the batch number, and the tube color; S3. The host computer obtains the initial position information, power, task queue, and equipment status of all AMR robots in the current workshop; S4. The host computer selects an AMR robot to perform the wire dropping task based on the initial position information, power, task queue, and device status of all AMR robots, and sends a task instruction to the selected AMR robot; S5. After receiving the task instruction, the selected AMR robot dynamically calculates its own real-time position through the UWB base station signals deployed in the workshop and the positioning algorithm, and sends it to the host computer; S6. After receiving the real-time position of the selected AMR robot, the host computer plans the optimal path from the current real-time position of the selected AMR robot to the winding machine position and then to the storage end coordinate based on the pre-imported workshop map and obstacle distribution, and sends the optimal path to the selected AMR robot; S7. The selected AMR robot reaches the winding machine position along the optimal path to pick up the wire; S8. After successfully picking up the wire, the selected AMR robot continues to reach the storage end coordinate along the optimal path to store the wire ingot and completes the task; It can be understood that in this application, the host computer receives the call signal sent by the unwinding and winding machine to be wire-dropped in real time. The call signal includes information such as the production line number, winding machine position, wire ingot specification, and target storage point, etc. The host computer analyzes the signal content, determines the wire-taking starting point (coordinate of the wire connection port of the winding machine) and the end point (coordinate of the wire ingot storage shelf), and dynamically assigns task priorities according to the production plan. The host computer determines the AMR to execute the task based on the positions, power, task queues, and device status (idle / busy / fault) of all AMRs collected in real time, specifically including: The host computer selects one or more AMR robots whose power is greater than a preset first power threshold, the current task queue is less than the preset task queue quantity, and the device status is idle according to the real-time power of all AMR robots; if there are multiple AMR robots, the AMR robot with the initial position closest to the wire-taking starting point coordinate is selected from the multiple AMR robots as the AMR robot to execute the task; The host computer sends a task instruction to the selected AMR. After the AMR confirms, it locks the task and enters the execution state; After receiving the task, the AMR dynamically calculates its own position through the UWB base station signals deployed in the workshop and the positioning algorithm, specifically including: Principle analysis: UWB positioning technology: Ultra-Wideband (UWB) technology is a wireless communication technology. Its characteristics are short pulse duration, large bandwidth, and low power density. Compared with traditional wireless communication technologies, UWB technology has great advantages in data transmission rate, precise positioning, anti-interference, etc. The working principle of UWB technology is as shown in the appendix Figure 2As shown, communication and positioning are achieved by sending extremely short and high-bandwidth pulse signals. Since the time width of UWB pulse signals is extremely narrow, reaching the nanosecond level, very high-precision distance measurement and positioning can be achieved. Compared with positioning technologies such as magnetic stripe tapes, laser reflectors, Simultaneous Localization And Mapping (SLAM), and Global Positioning System (GPS), UWB technology has the following advantages: High precision: UWB technology can achieve very high-precision distance measurement and positioning, with an accuracy that can reach a few millimeters or even higher, making it more accurate and reliable than other positioning technologies; Anti-interference: UWB technology also has great advantages in anti-interference. Since the bandwidth of UWB signals is very large, interference with signals in other frequency bands can be avoided. At the same time, the short pulse time of UWB signals can effectively reduce signal noise interference; Low power consumption: Compared with other positioning technologies, UWB technology has lower power consumption and can be widely applied to low-power devices such as mobile devices; No need for infrastructure: Compared with laser reflectors and SLAM technology that require special infrastructure to be arranged in the environment, UWB technology only needs to integrate a UWB chip on a mobile device to achieve positioning, with stronger convenience and flexibility; Wide application range: Compared with GPS technology that requires an open sky view and support from multiple satellites, UWB technology can achieve high-precision positioning in indoor and complex outdoor environments, with a wider application range; In order to adapt to the complex environment of the winding workshop in the chemical fiber industry, this application uses UWB communication technology for positioning.

[0020] Positioning algorithm: The TDOA (Time Difference Of Arrival) algorithm determines the position of the signal source by measuring the time difference of the signal arriving at multiple receivers, and the formula can be obtained:

[0021] where represents the distance from the AMR to the th base station. It can be found that adding one base station can solve the position of the AMR. However, since the clocks of different base stations may not be synchronized, even adding the number of base stations to achieve the spatial constraint of solving unknowns may not be accurate. Therefore, this application still uses the method of three base stations, which can also reduce the hardware cost.

[0022] To solve the 3D coordinates of the AMR with a time difference, geometric constraints can be imposed. Assuming the AMR is on the ground layer of the space, in actual situations, the AMR travels on the ground, that is, setting the Z coordinate of the AMR to 0, the formula can be obtained:

[0023] Then, by using the least squares method to set the optimization objective function, the formula can be obtained:

[0024] At the same time, since signal interference often occurs in the workshop environment suit, this application dynamically allocates weights according to the signal-to-noise ratio (SNR) of the base station signals to suppress the interference of low-quality signals. The higher the weight of the base station, the greater the impact on positioning and the stronger the anti-interference ability. The weight formula is defined as follows:

[0025] Where, ; Then, by weighting the least squares objective function with the weight coefficient, the weight coefficient takes the average weight of two base stations:

[0026] RSSI can provide distance attenuation information to make up for the deficiencies of TDOA in occlusion scenarios. By combining the TDOA time difference and the RSSI signal strength, a multi-dimensional positioning model is constructed to make the positioning more accurate.

[0027] The logarithmic path loss model is set as follows:

[0028] Where, is the reference distance at the signal strength, is the path loss exponent, is the Gaussian noise.

[0029] Thus, the jointly optimized objective function is obtained as follows:

[0030] Where, , is the theoretical time difference, is the theoretical RSSI value The optimization uses the Levenberg-Marquardt (LM) algorithm. LM is a combination of the gradient descent method and the Gauss-Newton method, with a fast convergence speed and the ability to handle ill-conditioned Jacobian matrices; Residual vector:

[0031] Jacobian matrix: Calculate the partial derivatives of the residual vector:

[0032] Step size :

[0033] In summary, the specific calculation process of the positioning algorithm of this application is as follows: Hyperparameter setting: Base station coordinates: , , ; Speed of light: ; Reference signal strength: ; Path loss exponent: ; Maximum number of iterations: ; Convergence threshold: ; α = 0.6, β = 0.4; Measured values: TDOA time difference: ; RSSI measured value: ; SNR measured value: ; The measured values are the values measured before calculation and are known quantities; Distance calculation expression:

[0034]

[0035]

[0036] Calculate the dynamic weight: total_snr = SNR 1 + SNR 2 + SNR 3; w 1 = SNR 1 / total_snr w 2 = SNR 2 / total_snr w 3 = SNR 3 / total_snr

[0037]

[0038]

[0039] TDOA error expression: ; ; ; ; ; Add and to obtain the optimized objective function: α * error_tdoa + β * error_rssi; It can be found that in the optimized objective function, the unknowns are , , , and according to the , , calculation expressions, , , The reason for being unknown is that the real-time coordinates (x, y) of the current AMR robot are unknown, which is exactly the parameter that the improved positioning algorithm of this application wants to obtain; According to the optimized objective function, the expression of the residual vector r can be obtained: ; Taking the partial derivative of the residual vector r can obtain the expression of the Jacobian matrix :

[0040] According to the expressions of the residual vector r and the Jacobian matrix , the expression of the step size can be obtained: ; Since the unknowns are the real-time coordinates (x, y) of the current AMR robot, the AMR coordinates are initialized in this application: (x, y) = (0, 0); Substitute (x, y) = (0, 0) into the above formula, and the residual vector r at (x, y) = (0, 0) and the Jacobian matrix , according to the solved residual vector r and the Jacobian matrix , the step size at (x, y) = (0, 0) can be obtained ; if ||Δ|| < epsilon, it indicates that the current (x, y) = (0, 0) is the real-time coordinate of the AMR robot required by this application, and the current (x, y) = (0, 0) is output. If ||Δ|| ≥ epsilon at this time, it means that the current (x, y) = (0, 0) does not meet the requirements, then the (x, y) coordinates at this time are updated, and the coordinate update formula is: ; Then calculate the residual vector r and the Jacobian matrix through the updated coordinates, as well as the step size at this time ; Again, judge whether the updated coordinates meet the requirements. If they meet, output the solved AMR coordinates (x, y). If they do not meet, continue to update.

[0041] The positioning algorithm of this application improves the existing commonly used positioning algorithms UWB and TOA algorithms. Based on geometric constraints and least squares, it reliably calculates the position information while reducing hardware costs. At the same time, it suppresses the interference of low-quality signals through the method of dynamic weights, and combines TDOA and RSSI to improve the robustness of the occlusion scenario; After receiving the real-time position of the AMR robot performing the task, the upper computer plans the optimal path from the current real-time position of the AMR robot to the winding machine position and then to the storage end coordinate based on the pre-imported workshop map and obstacle distribution, and sends the optimal path to the AMR robot; during the process of the AMR robot moving along the optimal path, it detects dynamic obstacles on the optimal path through lidar and ultrasonic sensors, and dynamically adjusts the optimal path to avoid and bypass dynamic obstacles; when the distance between the AMR robot and the winding machine is less than the preset distance threshold, an industrial camera set on the top is activated to identify the visual mark set at the wire connection port of the winding machine; the AMR robot calculates the lateral and longitudinal deviations between its own robotic arm and the wire connection port of the winding machine according to the visual mark recognition, and the AMR robot approaches the wire connection port of the winding machine according to the visual feedback, and adjusts its own pose through the servo motor to correct the deviation between its own robotic arm and the wire connection port of the winding machine, and takes out the full spool of wire from the wire connection port through the robotic arm; after the AMR robot successfully takes the wire, it continues to move along the optimal path towards the storage end coordinate. After reaching the storage end coordinate, it identifies the shelf number through RFID or vision, confirms the target storage layer, and the AMR robot places the full spool of wire at the specified position through its own robotic arm. After verifying the placement status through the pressure sensor and confirming that the placement is completed, the AMR robot reports a task completion signal to the upper computer, releases resources and returns to the standby area; Based on the above positioning algorithm, this embodiment also discloses the automatic charging process of the AMR robot, which specifically includes: The host computer monitors the real-time power of all AMR robots and determines whether the power of an AMR robot is less than a preset second power threshold (it should be noted that the second power threshold is less than the above-mentioned first power threshold). If the power of any AMR robot is less than the preset second power threshold, the host computer sends a charging signal to the AMR robot. After receiving the charging signal, the AMR robot calculates its own real-time position through the above positioning algorithm and sends it to the host computer. The host computer selects the charging pile closest to the real-time position of the AMR robot and with a charging position as the target charging pile according to the real-time position of the AMR robot and the coordinates of each charging pile pre-imported, and generates an optimal charging route according to the real-time position of the AMR robot and the coordinates of the target charging pile and sends it to the AMR robot. The AMR robot automatically charges along the optimal charging route to the target charging pile.

[0042] Embodiment 2 This embodiment also discloses a wire dropping device based on an AMR trolley, as shown in the appendix Figure 3 shown, including: Call module 1: Used for the bobbin winder to be wire-dropped to send a call signal to the host computer. The call signal includes the production line number, the position of the winder, the wire ingot specification, the batch number, and the tube color; Parsing module 2: Used for the host computer to parse the call signal after receiving the call signal, obtain the wire-taking point coordinates according to the position of the winder, and obtain the storage end coordinates according to the production line number, wire ingot specification, batch number, and tube color; AMR data acquisition module 3: Used for the host computer to acquire the initial position information, power, task queue, and equipment status of all AMR robots in the current workshop; Task distribution module 4: Used for the host computer to select an AMR robot to execute the wire dropping task according to the initial position information, power, task queue, and equipment status of all AMR robots, and send a task instruction to the selected AMR robot; Position calculation module 5: Used for the selected AMR robot to dynamically calculate its own real-time position through the UWB base station signal and positioning algorithm deployed in the workshop after receiving the task instruction, and send it to the host computer; Optimal path planning module 6: Used for the host computer to plan the optimal path from the current real-time position of the selected AMR robot to the wire-taking point coordinates and then to the storage end coordinates based on the pre-imported workshop map and obstacle distribution after receiving the real-time position of the selected AMR robot, and send the optimal path to the selected AMR robot; Wire taking module 7: It is used for the selected AMR robot to reach the wire taking point coordinates along the optimal path to take wire; Storage module 8: After the selected AMR robot successfully takes wire, it continues to reach the storage end coordinates along the optimal path to store the wire ingots and complete the task.

[0043] Embodiment 3: This embodiment provides a storage medium, which stores a computer program. When the computer program is executed by the main controller, each step in the above method is implemented; It can be understood that the above-mentioned storage medium can be a read-only memory, a magnetic disk or an optical disc, etc.

[0044] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content of other embodiments.

[0045] It should be noted that in the description of the present invention, terms such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "a plurality of" means at least two.

[0046] Any process or method description shown in the flowchart or described in other ways herein can be understood as representing a module, segment or part of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of the present invention belong.

[0047] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0048] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0049] In addition, in each of the embodiments of the present invention, the functional units can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0050] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, or the like.

[0051] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0052] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A filament dropping method based on an AMR vehicle, characterized in that, The method includes: The bobbin winder to be thread-fed sends a call signal to the host computer, and the call signal includes the production line number, the position of the winder, the silk ingot specification, the batch number, and the tube color; After receiving the call signal, the host computer parses the call signal, obtains the wire-taking point coordinates according to the position of the winder, and obtains the storage end coordinates according to the production line number, the silk ingot specification, the batch number, and the tube color; The host computer obtains the initial position information, power, task queue, and equipment status of all AMR robots in the current workshop; The host computer selects an AMR robot to perform the wire-feeding task according to the initial position information, power, task queue, and equipment status of all AMR robots, and sends a task instruction to the selected AMR robot; After receiving the task instruction, the selected AMR robot dynamically calculates its own real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, and sends it to the host computer; After receiving the real-time position of the selected AMR robot, the host computer plans the optimal path from the current real-time position of the selected AMR robot to the wire-taking point coordinates and then to the storage end coordinates based on the pre-imported workshop map and obstacle distribution, and sends the optimal path to the selected AMR robot; The selected AMR robot reaches the wire-taking point coordinates along the optimal path to take wire; After successfully taking wire, the selected AMR robot continues to reach the storage end coordinates along the optimal path to store the silk ingot and completes the task.

2. The method according to claim 1, wherein The host computer selects an AMR robot to perform the wire-feeding task according to the initial position information, power, task queue, and equipment status of all AMR robots, including: The host computer selects one or more AMR robots whose power is greater than a preset first power threshold, the current task queue is less than a preset task queue number, and the equipment status is idle; If there are multiple AMR robots, the AMR robot with the initial position closest to the wire-taking starting point coordinates is selected from the multiple AMR robots as the selected AMR robot.

3. The method according to claim 1, wherein After receiving the task instruction, the selected AMR robot dynamically calculates its own real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, including: Obtaining the base station coordinates of three base stations deployed in the workshop respectively, obtaining the time difference of the measurement signal reaching any two receivers respectively, obtaining the RSSI measurement values of the three base stations respectively, and obtaining the signal-to-noise ratio SNR of the three base stations respectively; According to the signal-to-noise ratio SNR of the three base stations, dividing the signal-to-noise ratio SNR of each base station by the sum of the signal-to-noise ratios SNR of the three base stations to obtain the dynamic weight of each base station; Obtaining the dynamic weight between any two base stations based on the dynamic weight of each base station; Based on the base station coordinates of the three base stations, obtaining the distance expressions of the selected AMR robot to the three base stations respectively; The TDOA error between any two base stations is obtained based on the time difference of the measurement signal arriving at any two receivers, the dynamic weight between the two base stations, and the distance expressions of the selected AMR robot to the corresponding two base stations; Set the weight coefficient of the TDOA error, and obtain the overall TDOA error expression according to the weight coefficient of the TDOA error, the TDOA error between any two base stations, and the dynamic weight between any two base stations; Set the weight coefficient of the RSSI error, the reference signal strength, and the path loss exponent respectively; Obtain the overall RSSI error expression according to the weight coefficient of the RSSI error, the reference signal strength, the path loss exponent, the RSSI measurement values of the three base stations, and the distance expressions of the selected AMR robot to the three base stations; Add the overall TDOA error expression and the overall RSSI error expression to obtain the optimization objective function, obtain the residual vector expression based on the optimization objective function, and take the derivative of the residual vector expression to obtain the Jacobian matrix expression; Initialize the coordinates of the selected AMR robot as (x, y) to be (0, 0), and solve the residual vector and the Jacobian matrix at the coordinates of the currently selected AMR robot; Obtain the step size at the coordinates of the currently selected AMR robot according to the solved residual vector and the Jacobian matrix; If the absolute value of the step size at the coordinates of the currently selected AMR robot is greater than or equal to the preset error threshold, subtract the step size at the coordinates of the currently selected AMR robot from the coordinates of the currently selected AMR robot to obtain the updated coordinates of the AMR robot; Repeat the above steps until the absolute value of the step size solved according to the coordinates of the AMR robot updated at a certain time is less than the preset error threshold, or the preset number of iterations is reached, then output the current coordinates of the currently selected AMR robot as the real-time position of the selected AMR robot.

4. The method according to claim 3, wherein During the process of the selected AMR robot traveling along the optimal path, dynamic obstacles on the optimal path are detected by a lidar and an ultrasonic sensor, and the optimal path is dynamically adjusted to avoid and bypass the dynamic obstacles.

5. The method according to claim 4, wherein The selected AMR robot taking wire at the wire-taking point coordinates along the optimal path includes: The selected AMR robot travels along the optimal path. When the distance between the selected AMR robot and the winder is less than the preset distance threshold, an industrial camera set on the top is started to identify the visual mark set at the wire connection port of the winder; The selected AMR robot calculates the lateral and longitudinal deviations between its own robotic arm and the wire connection port of the winder according to the visual mark recognition, the selected AMR robot approaches the wire connection port of the winder according to the visual feedback, and adjusts its own pose through a servo motor to correct the deviation between its own robotic arm and the wire connection port of the winder, and takes out the full spool of wire from the wire connection port through the robotic arm.

6. The method according to claim 5, wherein After the selected AMR robot successfully picks up the wire, it continues to move along the optimal path to the storage end coordinates for storing the wire ingot. The tasks completed include: After the selected AMR robot successfully picks up the wire, it continues to move along the optimal path towards the storage end coordinates. After the selected AMR robot reaches the storage end coordinates, it identifies the shelf number through RFID or visual recognition and confirms the target storage layer; the selected AMR robot places the full spool of wire ingot at the designated position through its own robotic arm. After confirming that the placement is complete, the selected AMR robot reports a task completion signal to the host computer, releases resources, and returns to the standby area.

7. The method according to claim 6, characterized in that It also includes: The host computer determines whether the power of all AMR robots is less than a preset second power threshold based on the power of all AMR robots obtained. If the power of any AMR robot is less than the preset second power threshold; The host computer sends a charging signal to this AMR robot. After receiving the charging signal, this AMR robot dynamically calculates its own real-time position through the UWB base station signal deployed in the workshop and the positioning algorithm and sends it to the host computer; The host computer selects the charging pile closest to the real-time position of this AMR robot and with a charging position as the target charging pile based on the real-time position of this AMR robot and the coordinates of each charging pile pre-imported. The host computer generates an optimal charging route based on the real-time position of this AMR robot and the coordinates of the target charging pile and sends it to the AMR robot; The AMR robot reaches the target charging pile according to the optimal charging route for charging.

8. A filament dropping device based on an AMR vehicle, characterized in that, The device includes: Call module: used for the bobbin winder waiting for wire dropping to send a call signal to the host computer. The call signal includes the production line number, winder position, wire ingot specification, batch number, and tube color; Parsing module: used for the host computer to parse the call signal after receiving it, obtain the wire picking point coordinates according to the winder position, and obtain the storage end coordinates according to the production line number, wire ingot specification, batch number, and tube color; AMR data acquisition module: used for the host computer to obtain the initial position information, power, task queue, and device status of all AMR robots in the current workshop; Task distribution module: used for the host computer to select the AMR robot to perform the wire dropping task according to the initial position information, power, task queue, and device status of all AMR robots, and send a task instruction to the selected AMR robot; Position calculation module: used for the selected AMR robot to dynamically calculate its own real-time position through the UWB base station signal deployed in the workshop and the positioning algorithm after receiving the task instruction, and send it to the host computer; Optimal path planning module: used for the host computer to plan the optimal path from the current real-time position of the selected AMR robot to the wire picking point coordinates and then to the storage end coordinates based on the pre-imported workshop map and obstacle distribution after receiving the real-time position of the selected AMR robot, and send the optimal path to the selected AMR robot; Wire taking module: used for the selected AMR robot to reach the wire taking point coordinates along the optimal path to take wire; Storage module: used for the selected AMR robot to continue to reach the storage end coordinates along the optimal path to store the wire ingots after successfully taking wire, and complete the task.

9. A storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by the main controller, each step in a wire dropping method based on an AMR cart as described in any one of claims 1-7 is implemented.

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