A method, device and storage medium for doffing based on AMR trolley

Abstract

The present invention relates to a method, device and storage medium for doffing based on an AMR trolley, which is applied to the technical field of chemical fiber doffing, including: a solution of utilizing AMR to doff the wires, and a design of an implementation process for AMR doffing. The AMR has its own obstacle avoidance function, which avoids the mechanized and fixed doffing method of AGV, and is more flexible and efficient than AGV. At the same time, the present application dynamically calculates its own real-time position through the UWB base station signal and positioning algorithm deployed in the workshop. The positioning algorithm improves the existing commonly used positioning algorithms UWB and TOA algorithms, and reliably calculates the position information on the basis of reducing hardware costs through geometric constraints and least squares. At the same time, the dynamic weight method is used to suppress the interference of low-quality signals, and the TDOA and RSSI are combined to improve the robustness of the occlusion scene, thereby improving the accuracy of the AMR's own positioning during movement.

Description

Technical Field

[0001] The present invention relates to the technical field of chemical fiber doffing, and in particular to a doffing method, device and storage medium based on an AMR trolley. Background Art

[0002] The chemical fiber industry has a large production scale and complex process flow, requiring comprehensive control and management of the fiber drop project. At the same time, the chemical fiber industry has high requirements for production capacity, quality, cost, efficiency, etc. The current chemical fiber market is highly competitive and market demand is constantly changing. Chemical fiber companies need to respond quickly to market demand. Companies often enhance their competitiveness by improving the rationality and automation level of process flow. Therefore, customers hope to use an efficient, intelligent, and easy-to-operate fiber drop process scheduling system to achieve more accurate production planning and production process monitoring, thereby improving production efficiency and product quality and reducing costs.

[0003] Currently, most wire dropping processes use AGVs to take wires. However, only the mechanical part of the AGV that takes the wire from the winding machine has been designed. There is no proposal to use AGV to implement the entire wire dropping process, nor is there a positioning algorithm for the AGV during its movement in the entire workshop. In reality, AGVs cannot automatically avoid obstacles, and the environment in the wire dropping workshop is generally very complex, with people moving around and wire carriers placed randomly. As a result, AGVs cannot truly implement the wire dropping process efficiently and intelligently. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a wire dropping method, device and storage medium based on an AMR cart, so as to solve the problem in the prior art that only the AGV is used to take the wire from the winding machine and only its mechanical part is designed, and no proposal is made to use the AGV to implement the entire wire dropping process; at the same time, it solves the problem that there is no positioning algorithm for the AGV during the entire workshop movement, and in actual conditions the AGV cannot automatically avoid obstacles, and the general environment of the wire dropping workshop is very complex, with people moving around and the wire-carrying carts being placed randomly, resulting in the AGV not being able to truly realize the wire dropping process efficiently and intelligently.

[0005] According to a first aspect of an embodiment of the present invention, a wire doffing method based on an AMR trolley is provided, the method comprising:

[0006] The doffing winding machine to be doffed sends a call signal to the upper computer, and the call signal includes the production line number, winding machine position, spindle specifications, batch number and tube color;

[0007] After receiving the call signal, the host computer analyzes the call signal, obtains the coordinates of the wire taking point according to the position of the winding machine, and obtains the coordinates of the storage end point according to the production line number, the spindle specification, the batch number and the tube color;

[0008] The host computer obtains the initial position information, power, task queue and device status of all AMR robots in the current workshop;

[0009] 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;

[0010] After receiving the task instruction, the selected AMR robot dynamically calculates its real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, and sends it to the host computer;

[0011] 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 coordinates of the wire picking point 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 robot;

[0012] The selected AMR robot arrives at the coordinates of the wire taking point along the optimal path to take the wire;

[0013] After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate to store the wire ingot, completing the task.

[0014] Preferably,

[0015] The host computer selects the AMR robots that perform the wire dropping task based on the initial position information, power, task queue, and device status of all AMR robots, including:

[0016] The host computer selects one or more AMR robots whose power level is greater than a preset first power threshold, whose current task queue is less than a preset number of task queues, and whose device status is idle;

[0017] If there are multiple AMR robots, the AMR robot whose initial position is closest to the wire picking starting point coordinates is selected from the multiple AMR robots as the selected AMR robot.

[0018] Preferably,

[0019] After receiving the task instruction, the selected AMR robot dynamically calculates its real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, including:

[0020] Obtain the base station coordinates of the three base stations deployed in the workshop, obtain the time difference between the measured signals reaching any two receivers, obtain the RSSI measurement values of the three base stations, and obtain the signal-to-noise ratio (SNR) of the three base stations.

[0021] According to the signal-to-noise ratios (SNRs) of the three base stations, the SNR of each base station is divided by the sum of the SNRs of the three base stations to obtain the dynamic weight of each base station.

[0022] Obtaining a dynamic weight between any two base stations based on the dynamic weight of each base station;

[0023] Based on the base station coordinates of the three base stations, distance expressions from the selected AMR robot to the three base stations are obtained respectively;

[0024] The TDOA error between any two base stations is obtained based on the time difference between the measured signals reaching any two receivers, the dynamic weight between the two base stations, and the distance expression from the selected AMR robot to the corresponding two base stations.

[0025] Set the TDOA error weight coefficient, and obtain the TDOA overall error expression based on the TDOA error weight coefficient, the TDOA errors of any two base stations, and the dynamic weight between any two base stations;

[0026] Set the weight coefficient of RSSI error, reference signal strength and path loss index respectively;

[0027] The overall RSSI error expression is obtained based on the RSSI error weight coefficient, reference signal strength, path loss index, RSSI measurement values of the three base stations, and the distance expression from the selected AMR robot to the three base stations;

[0028] Adding the TDOA overall error expression and the RSSI overall error expression to obtain an optimization objective function, obtaining a residual vector expression based on the optimization objective function, and derivatizing the residual vector expression to obtain a Jacobian matrix expression;

[0029] Initialize the coordinates of the selected AMR robot to (x, y) (0, 0), and solve the residual vector and Jacobian matrix under the coordinates of the currently selected AMR robot;

[0030] Obtain the step size of the currently selected AMR robot's coordinates based on the solved residual vector and Jacobian matrix;

[0031] If the absolute value of the step length under the coordinates of the currently selected AMR robot is greater than or equal to the preset error threshold, the coordinates of the currently selected AMR robot are subtracted from the step length under the coordinates of the currently selected AMR robot to obtain the updated coordinates of the AMR robot;

[0032] Repeat the above steps until the absolute value of the step length solved based on the coordinates of the AMR robot after a certain update is less than the preset error threshold, or the preset number of iterations is reached, then output the current coordinates of the AMR robot selected at this time as the real-time position of the selected AMR robot.

[0033] Preferably,

[0034] During the process of the selected 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.

[0035] Preferably,

[0036] The selected AMR robot arrives at the wire picking point coordinates along the optimal path to pick up the wire, including:

[0037] The selected AMR robot moves along the optimal path. When the distance between the selected AMR robot and the winding machine is less than a preset distance threshold, the industrial camera arranged on the top is activated to identify the visual mark arranged on the wire connection port of the winding machine.

[0038] The selected AMR robot calculates the lateral and longitudinal deviations between its own robotic arm and the winding machine wire connection port based on visual mark recognition. The selected AMR robot approaches the winding machine wire connection port based on visual feedback, and adjusts its own posture through a servo motor to correct the deviation between its own robotic arm and the winding machine wire connection port, and takes out the fully wound wire ingot from the wire connection port through the robotic arm.

[0039] Preferably,

[0040] After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate for storing the wire ingot. The task of completing the task includes:

[0041] After the selected AMR robot successfully takes the wire, it continues to move along the optimal path to the storage end coordinates. After the selected AMR robot reaches the storage end coordinates, it confirms the target storage layer through RFID or visual recognition of the shelf number; the selected AMR robot places the full-roll wire ingot at the designated position through its own robotic arm. After confirming that the placement is completed, the selected AMR robot reports a task completion signal to the host computer, releases resources and returns to the standby area.

[0042] Preferably, it also includes:

[0043] 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;

[0044] The host computer sends a charging signal to the AMR robot. After receiving the charging signal, the AMR robot dynamically calculates its real-time position through the UWB base station signal and positioning algorithm deployed in the workshop and sends the position to the host computer.

[0045] The host computer selects the charging pile closest to the real-time position of the AMR robot and the coordinates of each charging pile imported in advance as the target charging pile, and generates the optimal charging route based on the real-time position of the AMR robot and the coordinates of the target charging pile and sends it to the AMR robot;

[0046] The AMR robot reaches the target charging pile for charging according to the optimal charging route.

[0047] According to a second aspect of an embodiment of the present invention, a wire doffing device based on an AMR trolley is provided, the device comprising:

[0048] Call module: used for the doffing winding machine to send a call signal to the upper computer. The call signal includes the production line number, winding machine location, spindle specifications, batch number and tube color;

[0049] Analysis module: used for analyzing the call signal after the host computer receives it, obtaining the coordinates of the wire taking point according to the position of the winding machine, and obtaining the coordinates of the storage end point according to the production line number, spindle specification, batch number and tube color;

[0050] AMR data acquisition module: used by the host computer to obtain the initial position information, power, task queue and device status of all AMR robots in the current workshop;

[0051] Task issuing module: used by the host computer to select the AMR robot that performs the wire dropping task based on the initial position information, power, task queue and device status of all AMR robots, and send task instructions to the selected AMR robot;

[0052] 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 deployed in the workshop and the positioning algorithm, and sends it to the host computer;

[0053] Optimal path planning module: After the host computer receives the real-time position of the selected AMR robot, it plans the optimal path from the current real-time position of the selected AMR robot to the coordinates of the wire picking point 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 robot;

[0054] Wire picking module: used for the selected AMR robot to reach the wire picking point coordinates along the optimal path to pick up the wire;

[0055] Storage module: After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate to store the wire ingot and complete the task.

[0056] According to a third aspect of an embodiment of the present invention, a storage medium is provided, which stores a computer program. When the computer program is executed by a main controller, it implements each step of the logistics equipment redesign method based on digital twins.

[0057] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0058] This application uses AMR to carry out the solution of doffing wire and designs the implementation process of AMR doffing wire. AMR has its own obstacle avoidance function, which avoids the mechanized and fixed doffing method of AGV, and 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. The positioning algorithm improves the existing commonly used 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, it suppresses the interference of low-quality signals through the dynamic weight method, and combines TDOA and RSSI to improve the robustness of occlusion scenes, thereby improving the accuracy of AMR's own positioning during movement.

[0059] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0061] Figure 1 1 is a flow chart showing a method for doffing wire based on an AMR trolley according to an exemplary embodiment;

[0062] Figure 2 is a schematic diagram illustrating the working principle of UWB according to another exemplary embodiment;

[0063] Figure 3 is a system schematic diagram of a wire doffing device based on an AMR trolley according to another exemplary embodiment;

[0064] In the figure: 1-call module, 2-analysis module, 3-AMR data acquisition module, 4-task issuing module, 5-position calculation module, 6-optimal path planning module, 7-wire collection module, 8-storage module. DETAILED DESCRIPTION

[0065] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0066] Example 1

[0067] Figure 1 FIG. 1 is a flow chart of a wire dropping method based on an AMR trolley according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0068] S1, the doffing winding machine to be doffed sends a call signal to the upper computer, and the call signal includes the production line number, winding machine location, spindle specifications, batch number and tube color;

[0069] S2, after receiving the call signal, the host computer analyzes the call signal, obtains the coordinates of the wire taking point according to the position of the winding machine, and obtains the coordinates of the storage end point according to the production line number, spindle specifications, batch number and tube color;

[0070] S3, the host computer obtains the initial position information, power, task queue and device status of all AMR robots in the current workshop;

[0071] 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;

[0072] S5, after receiving the task instruction, the selected AMR robot dynamically calculates its real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, and sends it to the host computer;

[0073] S6, after receiving the real-time position of the selected AMR robot, the host computer plans an optimal path from the current real-time position of the selected AMR robot to the winding machine position and then to the stored end point coordinates based on the pre-imported workshop map and obstacle distribution, and sends the optimal path to the selected AMR robot;

[0074] S7, the selected AMR robot arrives at the winding machine position along the optimal path to take the wire;

[0075] S8, after the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate to store the wire ingot, completing the task;

[0076] It is understandable that the present application receives in real time through the host computer a call signal from a doffing winder to be doffed, which contains information such as the production line number, winder location, ingot specifications, and target storage point; the host computer parses the signal content, determines the starting point (coordinates of the winding machine's wire receiving port) and end point (coordinates of the ingot storage shelf) of the wire removal, and dynamically allocates task priorities according to the production plan; the host computer determines the AMR to perform the task based on the real-time collected location, power, task queue, and device status (idle / busy / fault) of all AMRs, specifically including:

[0077] The host computer selects one or more AMR robots whose power level is greater than a preset first power threshold, whose current task queue is less than a preset number of task queues, and whose device status is idle based on the real-time power levels of all AMR robots; if there are multiple AMR robots, the AMR robot whose initial position is closest to the wire picking starting point coordinates is selected from the multiple AMR robots as the AMR robot to perform the task;

[0078] The host computer sends a task instruction to the selected AMR. After confirmation, the AMR locks the task and enters the execution state.

[0079] After receiving the task, the AMR dynamically calculates its own position using the UWB base station signal and positioning algorithm deployed in the workshop. Specifically, the following steps are performed:

[0080] Principle analysis:

[0081] UWB positioning technology:

[0082] Ultra-Wideband (UWB) technology is a wireless communication technology characterized by 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 and other aspects. The working principle of UWB technology is as follows: Figure 2As shown in the figure, communication and positioning are achieved by sending extremely short, high-bandwidth pulse signals. Since the time width of UWB pulse signals is extremely narrow, reaching the nanosecond level, it can achieve very high-precision distance measurement and positioning. Compared with positioning technologies such as magnetic tape, laser reflectors, Simultaneous Localization and Mapping (SLAM), and Global Positioning System (GPS), UWB technology has the following advantages:

[0083] High precision: UWB technology can achieve very high-precision distance measurement and positioning, with an accuracy of several millimeters or even higher, which is more accurate and reliable than other positioning technologies;

[0084] Anti-interference: UWB technology also has great advantages in anti-interference. Due to the large bandwidth of UWB signals, it can avoid interference with signals in other frequency bands. At the same time, the short pulse time of UWB signals can effectively reduce noise interference of signals.

[0085] Low power consumption: Compared with other positioning technologies, UWB technology consumes less power and can be widely used on low-power devices such as mobile devices;

[0086] No infrastructure required: Compared to laser reflectors and SLAM technologies, which require special infrastructure in the environment, UWB technology only requires integrating a UWB chip into a mobile device to achieve positioning, offering greater convenience and flexibility.

[0087] Wide range of applications: Compared with GPS technology, which requires a wide sky view and multiple satellites, UWB technology can achieve high-precision positioning in indoor and complex outdoor environments and has a wider range of applications;

[0088] In order to adapt to the complex doffing workshop environment in the chemical fiber industry, this application uses UWB communication technology for positioning.

[0089] Positioning algorithm:

[0090] The TDOA (Time Difference Of Arrival) algorithm determines the location of the signal source by measuring the time difference between the signal arriving at multiple receivers. The formula can be obtained:

[0091]

[0092] in Indicates that AMR reaches The distance between base stations can be found. It can be found that the position of AMR can be solved by adding one base station. However, since the clocks of different base stations may not be synchronized, even if the spatial constraints of solving unknowns are achieved by adding more base stations, it may not be accurate. Therefore, this application still adopts the method of 3 base stations, which can also reduce hardware costs.

[0093] In order to solve the 3D coordinates of the AMR with time difference, geometric constraints can be used. Assuming that the AMR is on the ground layer in space, in reality, the AMR is moving on the ground, that is, the Z coordinate of the AMR is set to 0, and the formula can be obtained:

[0094]

[0095] Then, the optimization objective function is set by the least squares method, and the formula is obtained:

[0096]

[0097] At the same time, since signal interference often occurs in workshop environments, this application dynamically assigns weights based on the signal-to-noise ratio (SNR) of base station signals to suppress interference from low-quality signals. The higher the weight, the greater the impact of the base station on positioning and the stronger the anti-interference ability. The weight formula is defined as follows:

[0098]

[0099] in, ; Then the least square objective function is weighted by the weight coefficient, and the weight coefficient is the weighted average of the two base stations:

[0100]

[0101] RSSI can provide distance attenuation information, making up for the shortcomings of TDOA in occlusion scenarios. By combining TDOA time difference and RSSI signal strength, a multi-dimensional positioning model is constructed to make positioning more accurate.

[0102] The logarithmic path loss model is set up as follows:

[0103]

[0104] in, is the reference distance The signal strength at is the path loss exponent, is Gaussian noise.

[0105] The joint optimization objective function is as follows:

[0106]

[0107] in, , is the theoretical time difference, The theoretical RSSI value

[0108] The optimization adopts the Levenberg-Marquardt (LM) algorithm, which is a combination of gradient descent method and Gauss-Newton method. It has fast convergence speed and can handle ill-conditioned Jacobian matrices.

[0109] Residual vector:

[0110]

[0111] Jacobian matrix: Calculate the partial derivative of the residual vector:

[0112]

[0113] step length :

[0114]

[0115] In summary, the specific calculation process of the positioning algorithm of this application is as follows:

[0116] Hyperparameter settings:

[0117] Base station coordinates: , , ;

[0118] Speed of Light: ;

[0119] Reference signal strength: ;

[0120] Path loss exponent: ;

[0121] Maximum number of iterations: ;

[0122] Convergence threshold: ;

[0123] α=0.6, β=0.4;

[0124] Measurements:

[0125] TDOA time difference: ;

[0126] RSSI measurement value: ;

[0127] SNR measurement value: ];

[0128] The measured value is the value obtained by measurement before calculation and is a known quantity;

[0129] Distance calculation expression:

[0130]

[0131]

[0132]

[0133] Calculate dynamic weight:

[0134] total_snr = SNR 1+ SNR 2+ SNR 3;

[0135] w 1= SNR 1 / total_snr

[0136] w 2= SNR 2 / total_snr

[0137] w 3= SNR 3 / total_snr

[0138]

[0139]

[0140]

[0141] TDOA error expression:

[0142] ;

[0143] ;

[0144] ;

[0145] ;

[0146] ;

[0147] Will and Add them together to get the optimization objective function:

[0148] α*error_tdoa + β*error_rssi;

[0149] It can be found that in the optimization objective function, the unknown quantity is , , , while according to , , the calculation expression shows that , , the reason for the unknown is that the real-time coordinates (x, y) of the current AMR robot are unknown, which is the parameter that the improved positioning algorithm of this application wants to obtain;

[0150] According to the optimization objective function, the residual vector

[0159] can be obtained: Taking the partial derivative of the residual vector can obtain the expression of the Jacobian matrix

[0160] :

[0161]

[0162] According to the expressions of the residual vector

[0163] and the Jacobian matrix , the expression of the step size

[0164] can be obtained:

[0165] Figure 3 ;

[0166] Since the unknown quantity is the real-time coordinates (x, y) of the current AMR robot, the AMR coordinates of this application are initialized: (x, y) = (0, 0);

[0167] Substituting (x, y) = (0, 0) into the above formula, the residual vector and the Jacobian matrix

[0168] at (x, y) = (0, 0) can be obtained. According to the solved residual vector and the Jacobian matrix

[0169] , the step size at (x, y) = (0, 0) can be obtained; if ||Δ|| < epsilon, it means that (x, y) = (0, 0) at this time is the real-time coordinates of the AMR robot required by this application, and the (x, y) = (0, 0) at this time is output. If ||Δ|| ≥ epsilon at this time, it means that (x, y) = (0, 0) at this time does not meet the requirements, then the (x, y) coordinates at this time are updated, and the coordinate update formula is:

[0170]

[0171] ; ​​​​

[0159] Then calculate the residual vector r and Jacobian matrix through the updated coordinates , and the step length at this time ; Determine again whether the updated coordinates meet the requirements. If so, output the solved AMR coordinates (x, y). If not, continue updating.

[0160] The positioning algorithm in this application improves the existing commonly used positioning algorithms UWB and TOA. It uses geometric constraints and least squares to reliably calculate position information while reducing hardware costs. It also suppresses interference from low-quality signals through a dynamic weighting method and combines TDOA and RSSI to improve robustness in occluded scenarios.

[0161] 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 position of the winding machine and then to the storage of the end point coordinates based on the pre-imported workshop map and obstacle distribution, and sends the optimal path to the AMR robot; during the AMR robot's movement 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 dynamic obstacles; when the distance between the AMR robot and the winding machine is less than the preset distance threshold, the industrial camera installed on the top is activated to identify the visual mark set at the wire connection port of the winding machine; AM The AMR robot calculates the lateral and longitudinal deviations between its own robotic arm and the winding machine's wire connection port based on visual marker recognition. Based on visual feedback, the AMR robot approaches the winding machine's wire connection port and adjusts its own posture via a servo motor to correct the deviation between its own robotic arm and the winding machine's wire connection port. It then uses its robotic arm to remove the fully wound wire ingot from the wire connection port. After successfully removing the wire, the AMR robot continues along the optimal path toward the storage destination coordinates. Upon reaching the storage destination coordinates, it uses RFID or visual recognition to identify the shelf number and confirm the target storage layer. The AMR robot then uses its own robotic arm to place the fully wound wire ingot in the designated location and verifies the placement status using a pressure sensor. Once placement is confirmed, the AMR robot reports a task completion signal to the host computer, releases resources, and returns to the standby area.

[0162] Based on the above positioning algorithm, this embodiment also discloses an automatic charging process of the AMR robot, which specifically includes:

[0163] The host computer monitors the real-time power of all AMR robots and determines whether the power of the AMR robot is less than the 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-mentioned 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 based on the real-time position of the AMR robot and the coordinates of the pre-imported charging piles. The host computer generates the optimal charging route based on 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 to the target charging pile along the optimal charging route.

[0164] Example 2

[0165] This embodiment also discloses a wire doffing device based on an AMR trolley, as shown in the attached figure. Figure 3 As shown, including:

[0166] Call module 1: used for the doffing winding machine to send a call signal to the upper computer. The call signal includes the production line number, winding machine location, spindle specifications, batch number and tube color;

[0167] Analysis module 2: used for analyzing the call signal after the host computer receives it, obtaining the coordinates of the wire taking point according to the position of the winding machine, and obtaining the coordinates of the storage end point according to the production line number, spindle specifications, batch number and tube color;

[0168] AMR data acquisition module 3: used by the host computer to obtain the initial position information, power, task queue and device status of all AMR robots in the current workshop;

[0169] Task issuing module 4: used by the host computer to select the AMR robot that performs the wire dropping task based on the initial position information, power, task queue and device status of all AMR robots, and send task instructions to the selected AMR robot;

[0170] Position calculation module 5: After the selected AMR robot receives the task instruction, it 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;

[0171] Optimal path planning module 6: After the host computer receives the real-time position of the selected AMR robot, it plans the optimal path from the current real-time position of the selected AMR robot to the coordinates of the wire picking point 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 robot;

[0172] Wire picking module 7: used for the selected AMR robot to reach the wire picking point coordinates along the optimal path to pick up the wire;

[0173] Storage module 8: After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate to store the wire ingot and complete the task.

[0174] Example 3:

[0175] This embodiment provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a host controller, each step in the above method is implemented;

[0176] It is understandable that the storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0177] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0178] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0179] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0180] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0181] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the 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.

[0182] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0183] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0184] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0185] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A doffing method based on an AMR trolley, characterized in that: The method comprises: The doffing winding machine to be doffed sends a call signal to the upper computer, and the call signal includes the production line number, winding machine position, spindle specifications, batch number and tube color; After receiving the call signal, the host computer analyzes the call signal, obtains the coordinates of the wire taking point according to the position of the winding machine, and obtains the coordinates of the storage end point according to the production line number, the specification of the silk spindle, the batch number and the tube color; The host computer obtains the initial position information, power, task queue and device status of all AMR robots in the current workshop; 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; After receiving the task instruction, the selected AMR robot dynamically calculates its 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 coordinates of the wire picking point 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 robot; The selected AMR robot arrives at the coordinates of the wire taking point along the optimal path to take the wire; After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate to store the wire ingot, completing the task.

2. The method according to claim 1, characterized in that The host computer selects the AMR robots that perform the wire dropping task based on the initial position information, power, task queue, and device status of all AMR robots, including: The host computer selects one or more AMR robots whose power level is greater than a preset first power threshold, whose current task queue is less than a preset number of task queues, and whose device status is idle; If there are multiple AMR robots, the AMR robot whose initial position is closest to the wire picking starting point coordinates is selected from the multiple AMR robots as the selected AMR robot.

3. The method according to claim 1, characterized in that After receiving the task instruction, the selected AMR robot dynamically calculates its real-time position through the UWB base station signal and positioning algorithm deployed in the workshop, including: Obtain the base station coordinates of the three base stations deployed in the workshop, obtain the time difference between the measured signals reaching any two receivers, obtain the RSSI measurement values of the three base stations, and obtain the signal-to-noise ratio (SNR) of the three base stations. According to the signal-to-noise ratios (SNRs) of the three base stations, the SNR of each base station is divided by the sum of the SNRs of the three base stations to obtain the dynamic weight of each base station. Obtaining a 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, distance expressions from the selected AMR robot to the three base stations are obtained respectively; The TDOA error between any two base stations is obtained based on the time difference between the measured signals reaching any two receivers, the dynamic weight between the two base stations, and the distance expression from the selected AMR robot to the corresponding two base stations. Set the TDOA error weight coefficient, and obtain the TDOA overall error expression based on the TDOA error weight coefficient, the TDOA errors of any two base stations, and the dynamic weight between any two base stations; Set the weight coefficient of RSSI error, reference signal strength and path loss index respectively; The overall RSSI error expression is obtained based on the RSSI error weight coefficient, reference signal strength, path loss index, RSSI measurement values of the three base stations, and the distance expression from the selected AMR robot to the three base stations; Adding the TDOA overall error expression and the RSSI overall error expression to obtain an optimization objective function, obtaining a residual vector expression based on the optimization objective function, and derivatizing the residual vector expression to obtain a Jacobian matrix expression; Initialize the coordinates of the selected AMR robot to (x, y) (0, 0), and solve the residual vector and Jacobian matrix under the coordinates of the currently selected AMR robot; Obtain the step size of the currently selected AMR robot's coordinates based on the solved residual vector and Jacobian matrix; If the absolute value of the step length under the coordinates of the currently selected AMR robot is greater than or equal to the preset error threshold, the coordinates of the currently selected AMR robot are subtracted from the step length under 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 length solved based on the coordinates of the AMR robot after a certain update is less than the preset error threshold, or the preset number of iterations is reached, then output the current coordinates of the AMR robot selected at this time as the real-time position of the selected AMR robot.

4. The method according to claim 3, characterized in that During the process of the selected 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.

5. The method according to claim 4, characterized in that The selected AMR robot arrives at the wire picking point coordinates along the optimal path to pick up the wire, including: The selected AMR robot moves along the optimal path. When the distance between the selected AMR robot and the winding machine is less than a preset distance threshold, the industrial camera arranged on the top is activated to identify the visual mark arranged on the wire connection port of the winding machine. The selected AMR robot calculates the lateral and longitudinal deviations between its own robotic arm and the winding machine wire connection port based on visual mark recognition. The selected AMR robot approaches the winding machine wire connection port based on visual feedback, and adjusts its own posture through a servo motor to correct the deviation between its own robotic arm and the winding machine wire connection port, and takes out the fully wound wire ingot from the wire connection port through the robotic arm.

6. The method according to claim 5, characterized in that After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate for storing the wire ingot. The task of completing the task includes: After the selected AMR robot successfully takes the wire, it continues to move along the optimal path to the storage end coordinates. After the selected AMR robot reaches the storage end coordinates, it confirms the target storage layer through RFID or visual recognition of the shelf number; the selected AMR robot places the full-roll wire ingot at the designated position through its own robotic arm. After confirming that the placement is completed, 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 Also 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 the AMR robot. After receiving the charging signal, the AMR robot dynamically calculates its real-time position through the UWB base station signal and positioning algorithm deployed in the workshop and sends the position to the host computer. The host computer selects the charging pile closest to the real-time position of the AMR robot and the coordinates of each charging pile imported in advance as the target charging pile, and generates the optimal charging route based on 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 reaches the target charging pile for charging according to the optimal charging route.

8. A doffing device based on an AMR trolley, characterized in that: The device comprises: Call module: used for the doffing winding machine to send a call signal to the upper computer. The call signal includes the production line number, winding machine location, spindle specifications, batch number and tube color; Analysis module: used for analyzing the call signal after the host computer receives it, obtaining the coordinates of the wire taking point according to the position of the winding machine, and obtaining the coordinates of the storage end point according to the production line number, spindle specification, batch number and tube color; AMR data acquisition module: used by the host computer to obtain the initial position information, power, task queue and device status of all AMR robots in the current workshop; Task issuing module: used by the host computer to select the AMR robot that performs the wire dropping task based on the initial position information, power, task queue and device status of all AMR robots, and send task instructions 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 deployed in the workshop and the positioning algorithm, 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, it plans the optimal path from the current real-time position of the selected AMR robot to the coordinates of the wire picking point 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 robot; Wire picking module: used for the selected AMR robot to reach the wire picking point coordinates along the optimal path to pick up the wire; Storage module: After the selected AMR robot successfully takes the wire, it continues along the optimal path to reach the storage end coordinate to store the wire ingot 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 of the wire dropping method based on the AMR car as described in any one of claims 1 to 7 is implemented.

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