Ground rail robot multi-degree-of-freedom control method, system, product and medium

Abstract

The invention discloses a multi-degree-of-freedom control method and system for a ground rail robot, a product and a medium, and relates to the field of control or adjustment systems. The method comprises the steps that characteristic quantities in carried object task data are extracted, and weight parameters, material parameters and position parameters of carried objects are obtained; matching track operation data of the ground track in a preset track database according to the weight parameters, the material parameters and the position parameters, extracting a target point in a track path of the ground track, calculating carrying path data of the robot on the ground track based on carrying parameters of the target point, and sending the carrying path data to the robot, and after the robot executes the carrying path data, real-time stress parameters and real-time position parameters of the robot are measured at the pickup point, the transition point and the placement point, the real-time stress parameters and the real-time position parameters are input into a preset evaluation model, track correction parameters and position correction parameters are obtained, and a correction instruction is sent to the ground track robot. By implementing the method, the carrying precision of the ground rail robot can be improved.

Description

Technical Field

[0001] The present application relates to the general field of control or regulation systems, and in particular to a multi-degree-of-freedom control method, system, product and medium for a ground rail robot. Background Art

[0002] With the continuous improvement of industrial automation, intelligent factories have widely adopted ground-track robot systems to complete material handling tasks. The ground-track robot system uses robots running on fixed tracks to achieve precise material handling and positioning, thereby improving production efficiency and reducing labor costs.

[0003] In the prior art, the ground-track robot system is usually controlled by a preset track motion path. Before the system is put into use, the staff will plan the robot's motion trajectory in advance according to the factory layout and production requirements, and fix the corresponding control parameters in the control system. The ground-track robot performs the handling task according to the preset track motion path, and performs positioning and path tracking through position sensors.

[0004] However, in the actual production process, due to the differences in weight and material of the transported materials, and the vibration and deformation caused by long-term operation of the ground rail, the preset track motion path is difficult to adapt to the dynamically changing working conditions. This fixed control method can easily cause the transported materials to shift or shake, affecting the transportation accuracy and may even cause material damage. Summary of the invention

[0005] The present application provides a multi-degree-of-freedom control method, system, product and medium for a rail robot, which are used to improve the handling accuracy of the rail robot.

[0006] In a first aspect, the present application provides a multi-degree-of-freedom control method for a ground rail robot, which is applied to a multi-degree-of-freedom control system of a ground rail robot, wherein the multi-degree-of-freedom control system of the ground rail robot comprises a ground rail robot and a ground rail, and the method comprises: obtaining task data of a transported object, extracting feature quantities in the task data of the transported object to obtain weight parameters, material parameters and position parameters of the transported object; matching track operation data of the ground rail in a preset track database according to the weight parameters, the material parameters and the position parameters, wherein the track operation data comprises track vibration parameters, speed parameters and displacement parameters; extracting the track path of the ground rail A target point is provided, and the transport path data of the ground rail robot on the ground rail is calculated based on the transport parameters of the target point, and the target point includes a pick-up point, a transition point and a placement point; the transport path data is sent to the ground rail robot, and after the ground rail robot executes the transport path data, the real-time force parameters and the real-time position parameters of the ground rail robot are measured at the pick-up point, the transition point and the placement point; the real-time force parameters and the real-time position parameters are input into a preset evaluation model to obtain a track correction parameter and a position correction parameter; and a correction instruction including the track correction parameter and the position correction parameter is sent to the ground rail robot.

[0007] By adopting the above technical solution, the weight, material and position parameters of the transported object are first obtained, and then the ground rail target point is extracted and the transport path data is calculated in combination with relevant parameters and sent to the robot. After the robot executes, its force and position parameters are collected and input into the preset evaluation model to obtain the correction parameters and then sent back to the robot. The parameters of the transported object provide a basis for matching the track operation data, and the calculated transport path data guides the robot movement. The correction parameters obtained based on the actual state of the robot can adjust its movement in time, effectively improving the transport accuracy and stability, and ensuring the high-quality completion of the transport task.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of extracting the target point in the track path of the ground rail, and calculating the transportation path data of the ground rail robot on the ground rail based on the transportation parameters of the target point, specifically includes: obtaining the initial positions of the pick-up point, the transition point and the placement point; adjusting the initial positions of the pick-up point, the transition point and the placement point according to the track vibration parameters to obtain the adjusted positions; calculating the motion trajectory of the ground rail robot between the points according to the displacement parameters; calculating the speed change sequence of the ground rail robot during the movement according to the speed parameters; converting the adjusted position, the motion trajectory and the speed change sequence into the ground rail robot control instruction format to generate the transportation path data.

[0009] By adopting the above technical solution, when calculating the handling path data, the initial position of the target point is first obtained, adjusted according to the track vibration parameters, and then the motion trajectory and speed change sequence are calculated using the displacement and speed parameters respectively. Finally, the format is converted to generate data. The adjustment of the track vibration parameters makes the target point position more reasonable and avoids deviations due to vibration. The displacement parameters ensure the accuracy of the robot's motion trajectory between points. The speed parameters ensure that the motion speed meets the requirements. The generated handling path data can better adapt to the actual situation of the track, making the robot movement more accurate and efficient, and improving the overall performance of the handling operation.

[0010] In combination with some embodiments of the first aspect, in some embodiments, before the step of inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter, the method further includes: collecting historical force parameters of the ground rail and historical position parameters of the ground rail robot during multiple operations; extracting the historical force parameters to obtain force characteristics, and extracting the historical position parameters to obtain position characteristics; training a neural network model based on the force characteristics and the position characteristics to obtain the preset evaluation model, the neural network model including a first subnetwork for generating track correction parameters and a second subnetwork for generating position correction parameters.

[0011] By adopting the above technical solution, the historical force and position parameters of the ground track and the robot are collected, and the features are extracted and then the neural network model is trained to obtain a preset evaluation model. The historical parameters provide a rich data basis for model training. By learning the rules and features in these data, the neural network model can accurately analyze the robot's motion state and generate appropriate correction parameters. The first subnetwork and the second subnetwork of the model are optimized for the track and position respectively, so that the obtained correction parameters are more targeted and effective, which enhances the correction ability of the robot's motion and ensures the stability and accuracy of the handling process.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the posture adjustment instruction including the posture adjustment amount and the execution speed to the ground rail robot, the method also includes: acquiring real-time image data of the ground rail robot, and identifying the posture parameters of the transported object from the real-time image data; when the posture parameter exceeds a preset posture threshold, calculating the posture adjustment amount according to the track correction parameter and the position correction parameter; determining the execution speed of the posture adjustment based on the weight parameter and the material parameter; and sending the posture adjustment instruction including the posture adjustment amount and the execution speed to the ground rail robot.

[0013] By adopting the above technical solution, the posture parameters of the transported object are obtained based on real-time image recognition. When the posture exceeds the threshold, the posture adjustment amount is calculated and the execution speed is determined according to the weight and material parameters. The track correction parameter reflects the deformation state of the track, and the position correction parameter reflects the position deviation of the robot. The two work together to produce the optimal posture adjustment amount. The execution speed matches the characteristics of the transported object. Heavy objects use low speed to ensure stability, and light objects use high speed to improve efficiency. The precise execution of the posture adjustment instruction keeps the transported object within a reasonable posture range at all times, avoiding the risk of damage caused by deviation and shaking. At the same time, the speed control considering the material characteristics ensures the safety and reliability of the adjustment process, and achieves high-quality control of the transport process.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of sending the posture adjustment instruction including the posture adjustment amount and the execution speed to the ground rail robot, the method also includes: when the posture adjustment instruction starts to be executed, collecting the real-time force parameters of the ground rail; when the real-time force parameter is greater than a preset safety threshold, sending a pause instruction to the ground rail robot so that the ground rail robot pauses posture adjustment; when the real-time force parameter recovers to within the preset safety threshold range, reducing the execution speed to the preset execution speed threshold.

[0015] By adopting the above technical solution, the stress parameters of the ground rail are monitored in real time when performing posture adjustment. If the safety threshold is exceeded, the adjustment is immediately suspended and waited for recovery. The stress parameters reflect the stress state of the entire system. Timely suspension can prevent excessive stress from causing damage to the ground rail and the robot. When the force returns to normal, the speed reduction execution mode is used to continue the adjustment, and the speed reduction effectively suppresses impact and vibration. The pause and speed reduction mechanism forms a complete safety protection system, and the execution state is intelligently adjusted to avoid the occurrence of dangerous working conditions.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter, the method further includes: determining a track vibration threshold based on the weight parameter and the material parameter; collecting real-time track vibration parameters of each track segment during the operation of the ground rail robot; comparing the real-time track vibration parameters with the maximum allowable track vibration threshold, marking the track segments that exceed the maximum allowable track vibration threshold, and obtaining marked track segments; and sending the position of the marked track segment to the target client.

[0017] By adopting the above technical solution, the track vibration threshold is determined according to the characteristics of the transported objects, and the vibration parameters of the track segment are collected and compared with the threshold to mark the over-limit segment. The vibration threshold is dynamically adjusted according to the material characteristics, and the weight and material parameters determine the maximum allowable vibration amplitude. The vibration status of each track segment is monitored in real time, and the location information of the over-limit segment is sent to the client in a timely manner. The track segment marking mechanism realizes the precise positioning of vibration anomalies, the dynamic threshold ensures the matching of the monitoring standard and the handling task, and the timely information feedback supports preventive maintenance, building a track monitoring system for active early warning and fault location.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter, the method further includes: using AR-assisted visual recognition technology to process the real-time image data to obtain environmental characteristic parameters; and adjusting the track correction parameter and the position correction parameter according to the environmental characteristic parameters.

[0019] By adopting the above technical solution, AR-assisted visual technology is used to process real-time images to obtain environmental features, and the orbit and position correction parameters are adjusted accordingly. AR technology superimposes virtual information on the real scene, enhances environmental perception, and accurately captures spatial geometric features, lighting conditions, and occlusion conditions. Environmental characteristics directly affect the calculation of correction parameters. Geometric features are used for orbital plane correction, lighting conditions guide attitude compensation, and occlusion information optimizes position adjustment. The parameter adjustment mechanism based on environmental characteristics establishes a dynamic association between the correction amount and the actual working conditions, improves the adaptability of the correction strategy to environmental changes, and achieves more accurate orbital motion control.

[0020] In a second aspect, an embodiment of the present application provides a multi-degree-of-freedom control system for a rail robot, the multi-degree-of-freedom control system for a rail robot comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, the one or more processors call the computer instructions to make the multi-degree-of-freedom control system of the rail robot execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on a multi-degree-of-freedom control system of a rail robot, the above-mentioned multi-degree-of-freedom control system of the rail robot executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions. When the above instructions are executed on a multi-degree-of-freedom control system of a rail robot, the multi-degree-of-freedom control system of the rail robot executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It can be understood that the multi-degree-of-freedom control system of the ground rail robot provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This application first obtains the weight, material and position parameters of the transported object, then extracts the ground rail target point and calculates the transport path data in combination with relevant parameters and sends it to the robot. After the robot executes, its force and position parameters are collected and input into the preset evaluation model to obtain the correction parameters and then send them back to the robot. The transported object parameters provide a basis for matching the track operation data, and the calculated transport path data guides the robot movement. The correction parameters obtained based on the actual state of the robot can adjust its movement in time, effectively improving the transport accuracy and stability, and ensuring the high-quality completion of the transport task.

[0025] 2. This application obtains the posture parameters of the transported object based on real-time image recognition, calculates the posture adjustment amount when the posture exceeds the threshold, and determines the execution speed based on the weight and material parameters. The track correction parameter reflects the deformation state of the track, and the position correction parameter reflects the position deviation of the robot. The two work together to produce the optimal posture adjustment amount. The execution speed is matched with the characteristics of the transported object. Heavy objects use low speed to ensure stability, and light objects use high speed to improve efficiency. The precise execution of the posture adjustment instruction ensures that the transported object always remains within a reasonable posture range, avoiding the risk of damage caused by deviation and shaking. At the same time, the speed control that takes into account the material characteristics ensures the safety and reliability of the adjustment process and achieves high-quality control of the handling process.

[0026] 3. This application monitors the force parameters of the ground rail in real time when performing posture adjustment. If the safety threshold is exceeded, the adjustment is immediately suspended and the robot is allowed to resume. The force parameters reflect the force state of the entire system. Timely suspension can prevent excessive stress from causing damage to the ground rail and the robot. When the force returns to normal, the adjustment is continued in the deceleration execution mode, and the speed reduction effectively suppresses impact and vibration. The pause and deceleration mechanism forms a complete safety protection system, and the execution state is intelligently adjusted to avoid dangerous working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1This is a flow chart of a multi-degree-of-freedom control method for a ground rail robot in an embodiment of the present application; Figure 2 This is another flowchart of the multi-degree-of-freedom control method for a ground rail robot in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of a multi-degree-of-freedom control system of a ground rail robot in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] In the final assembly workshop of an automobile manufacturing plant, various automobile parts of different weights need to be transported from the material area to the assembly line. Multiple ground rails are installed in the factory, and ground rail robots are responsible for transporting different materials such as engine assemblies (weighing up to 300kg), door assemblies (80kg), and instrument panel assemblies (50kg). Due to the large difference in material weight and diverse materials, and the uneven wear and partial deformation of the ground rails after three years of operation, materials often shake and position shift during transportation. Especially when transporting heavy engines, the position deviation caused by the vibration of the ground rails reaches ±15mm, which seriously affects the assembly accuracy. At the same time, light materials such as doors will also shake violently due to uneven tracks during high-speed transportation, increasing the risk of surface scratches, and the material loss caused by this exceeds 500,000 yuan per month.

[0032] A factory used the traditional fixed parameter control solution to deal with the above problems: a one-time calibration was performed when the ground rail was installed, the rail parameters and motion trajectory were solidified in the control system, and the same running speed (1.2m / s) and acceleration (0.5m / s²) were used for all materials. When carrying a 300kg engine, the robot strictly followed the preset trajectory, but because the deformation of the track was not taken into account, the engine shifted at the transition point. Workers had to manually adjust the engine position, which increased the assembly time by 30%. When carrying a 50kg instrument panel, the same low-speed running parameters were used, which affected the production rhythm. What's more serious is that when the track was deformed by 2mm, the control system could not be adjusted in time, causing the material to collide with the surrounding equipment, and 3 safety accidents occurred within a month.

[0033] After the factory adopts this application plan, it first identifies the characteristics of different materials through task data. When carrying a 300kg engine, the system automatically matches the track operation parameters, reduces the speed to 0.8m / s, and dynamically adjusts the operation trajectory according to the real-time vibration value of the track (1.5mm). When the force sensor detects an increase in force (800N) at the transition point, the system immediately calculates the correction amount and adjusts the robot posture to allow the engine to pass smoothly. For the 50kg instrument panel, considering its lighter weight, the system increases the speed to 1.5m / s. At the same time, the AR vision system monitors the surrounding environment in real time, and automatically optimizes the path planning when it identifies obstacles 1.2m away. During the entire handling process, the system continuously collects track segment data and immediately notifies maintenance personnel when the vibration of a certain section exceeds 2mm. Three months after implementation, material losses were reduced by 90% and assembly efficiency increased by 35%.

[0034] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a multi-degree-of-freedom control method for a rail robot in an embodiment of the present application.

[0035] S101, acquiring task data of a transported object, and extracting feature quantities in the task data of the transported object to obtain weight parameters, material parameters, and position parameters of the transported object.

[0036] Among them, the task data of the transported object represents the basic information and transport requirements of the object to be transported, including the basic attribute data of the object and the transport task parameters. The weight parameter refers to the mass of the transported object in kilograms. The material parameter is used to represent the material properties of the transported object, including hardness, elastic coefficient, etc. The position parameter represents the coordinate position and posture angle of the transported object in three-dimensional space. The feature quantity refers to the key numerical features extracted from the task data.

[0037] After receiving the handling task instruction, it is necessary to first obtain the basic data related to the task. Specifically, the system obtains the task data of the transported object through sensor collection or database query, and then uses the feature extraction algorithm to process the original data to extract three key parameters: weight, material, and position. The weight parameter is obtained by direct measurement of the weighing sensor, the material parameter is obtained by matching the preset material database, and the position parameter is obtained by measuring the visual positioning system.

[0038] In some embodiments, parameter acquisition and feature extraction can be achieved in the following ways: Optionally, first collect multi-angle images of the transported object through an industrial camera, use a deep learning algorithm to identify the type of the transported object, query the preset database to obtain standard weight and material parameters, and calculate the position parameters through a visual positioning algorithm; Optionally, use a weighing sensor to measure the weight in real time, use a force sensor to measure the contact force to obtain material properties, and use a laser radar scan to obtain three-dimensional position information. It is understandable that other sensing methods and algorithms can also be used to obtain the parameters of the transported object, which are not limited here.

[0039] S102, matching the track operation data of the ground rail in a preset track database according to the weight parameter, the material parameter and the position parameter, where the track operation data includes a track vibration parameter, a speed parameter and a displacement parameter.

[0040] The preset track database is a structured data set that stores the ground rail operation parameters. The track operation data represents the dynamic characteristic parameters of the ground rail under different working conditions. The track vibration parameters are used to represent the vibration characteristics of the track, including amplitude and frequency. The speed parameter refers to the movement speed limit of the ground rail robot. The displacement parameter represents the track deformation.

[0041] This step is performed after obtaining the basic parameters of the transported object, and is used to determine the control parameters of the track operation. Specifically, the system uses the weight, material and position parameters of the transported object as index conditions to search for matching historical operation records in the preset track database. The matching results are analyzed through the pattern recognition algorithm to extract the optimal track vibration parameters, running speed parameters and track displacement parameters as the control benchmark for this task.

[0042] In some embodiments, data matching can be achieved in the following ways: Optionally, a parameter matching method based on deep learning is used to build a neural network model to establish a mapping relationship between the transport parameters and the track parameters to achieve fast and accurate matching; Optionally, a rule-based expert system is used for parameter matching to calculate the optimal track operation parameters according to preset empirical rules. It is understandable that other intelligent algorithms can also be used to achieve parameter matching, which is not limited here.

[0043] S103, extracting a target point in the track path of the ground rail, and calculating the transportation path data of the ground rail robot on the ground rail based on the transportation parameters of the target point, wherein the target point includes a picking point, a transition point and a placement point.

[0044] Among them, the track path refers to the spatial trajectory curve of the ground rail robot. The target point represents the key position node in the track path. The pick-up point refers to the initial position where the robot grabs the object. The transition point is used to represent the intermediate turning position in the path. The placement point refers to the final target position of the object. The handling parameters include the spatial coordinates, posture angles and motion constraints of the target point. The handling path data refers to the complete data set that describes the robot's motion trajectory.

[0045] After obtaining the orbital operation parameters, it is necessary to plan a specific motion path. Specifically, the system first extracts the spatial coordinates of the pick-up point, transition point, and placement point from the orbital path. Then, based on the positional relationship and motion constraints of each target point, a trajectory planning algorithm is used to generate a smooth and continuous motion path. The robot's kinematic constraints, dynamic constraints, and environmental constraints are considered during the planning process to ensure the feasibility of the path. Finally, the planning results are converted into a standard path data format, including position sequence, velocity sequence, and acceleration sequence.

[0046] In some embodiments, path planning can be achieved in a variety of ways: optionally, first establish a cost function for path planning, including evaluation indicators such as path length, smoothness, and safety, then use the A* algorithm to search for the optimal path, and finally use cubic spline interpolation to generate a continuous trajectory; optionally, construct a virtual force field based on the artificial potential field method, obtain an obstacle avoidance path through iterative calculation, then use quintic polynomial interpolation to generate a velocity-continuous trajectory curve, and finally perform dynamic optimization to obtain the final path. It is understandable that other intelligent planning algorithms can also be used to achieve path generation, which is not limited here.

[0047] This step specifically includes: Obtaining initial positions of the pick-up point, the transition point, and the placement point; adjusting the initial positions of the pick-up point, the transition point and the placement point according to the track vibration parameter to obtain an adjusted position; Calculate the motion trajectory of the ground rail robot between various points according to the displacement parameter; Calculate the speed change sequence of the ground rail robot during the movement according to the speed parameter; The adjustment position, the motion trajectory and the speed change sequence are converted into a ground rail robot control instruction format to generate the transport path data.

[0048] Among them, the initial position represents the original coordinate value of the target point in the spatial coordinate system. The track vibration parameter refers to the vibration characteristic index of the ground rail during operation, including amplitude and frequency. The displacement parameter is used to indicate the position offset caused by the deformation of the ground rail. The speed parameter refers to the speed limit condition during the movement. The adjusted position represents the coordinates of the target point after vibration compensation. The motion trajectory refers to the motion path curve of the robot in space. The speed change sequence is used to describe the law of speed change during the movement. The control instruction format refers to the standard data format that can be recognized by the robot control system.

[0049] For the target point position adjustment and trajectory planning process, the specific implementation method is as follows: First, the initial three-dimensional coordinates of the pick-up point, transition point and placement point are obtained through the visual positioning system, and the coordinate value accuracy reaches 0.1mm. Then, the position compensation is performed based on the track vibration parameters. The compensation amount is calculated by the elastic deformation model. The compensated position can effectively suppress the vibration effect. Then, the continuous trajectory curve between each point is generated using the cubic spline interpolation method. The trajectory curve satisfies the position continuity and velocity continuity constraints. Based on the trajectory curve, the trapezoidal velocity planning algorithm is used to generate the velocity sequence, including the acceleration segment, the uniform velocity segment and the deceleration segment. The velocity value does not exceed the preset limit. Finally, the adjusted position data, trajectory curve parameters and velocity sequence are packaged into robot control instructions. The instruction format contains position instructions, velocity instructions and timing information. For example, the position instruction uses the {x, y, z, rx, ry, rz} format to represent the spatial position and posture, the velocity instruction uses the {vx, vy, vz, wx, wy, wz} format to represent the linear velocity and angular velocity, and the timing information uses the relative timestamp to represent the execution order.

[0050] S104, sending the transport path data to the ground rail robot, and after the ground rail robot executes the transport path data, measuring the real-time force parameters and real-time position parameters of the ground rail robot at the picking point, the transition point and the placement point.

[0051] Among them, the handling path data refers to the complete trajectory information describing the robot's movement. The real-time force parameters represent the force state of the robot during movement, including the force and torque components in three directions. The real-time position parameters refer to the actual spatial position and posture angle of the robot at key points. The data transmission adopts the industrial real-time communication protocol to ensure the real-time and reliability of command transmission.

[0052] This step is performed after the path planning is completed, and is used to realize path execution and status monitoring. Specifically, the system first sends the handling path data to the controller of the ground rail robot through industrial Ethernet. When the robot executes the path movement, force sensors and position sensors are installed at the pick-up point, transition point and placement point to collect the force state and position state of the robot in real time. After filtering and coordinate transformation, the sensor data forms the state parameters in a standard format for subsequent trajectory optimization.

[0053] In some embodiments, data transmission and status monitoring can be achieved in a variety of ways: Optionally, the path data is first packaged into a standard message format, and transmitted to the robot controller in real time via the EtherCAT bus, and then multi-threaded parallel acquisition of force sensor and position sensor data is started, and finally data synchronization and timestamp alignment are performed; Optionally, the OPCUA protocol is used to establish a data communication link, and path instructions are issued in real time and the execution status is fed back. At the same time, a distributed sensor network is used to collect state parameters, and a unified state estimate is obtained through a data fusion algorithm. It is understandable that other communication methods and status monitoring methods can also be used, which are not limited here.

[0054] S105, inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter.

[0055] Among them, the preset evaluation model represents a mathematical model used to evaluate the robot's motion state and generate a correction strategy. The real-time force parameter refers to the robot's current force state data. The real-time position parameter is used to represent the robot's spatial position and posture information. The orbit correction parameter represents the adjustment amount of the orbit motion parameter. The position correction parameter refers to the compensation amount for the robot's position and posture. The input of the evaluation model includes state parameters, the output includes correction parameters, and the intermediate process includes state evaluation and parameter optimization.

[0056] This step is performed after obtaining the real-time status of the robot and is used to generate a motion correction strategy. Specifically, the system preprocesses the collected force parameters and position parameters, including data standardization, outlier detection, and feature extraction. The processed data is then input into a preset evaluation model, which analyzes the stability and accuracy of the motion state based on a machine learning algorithm and calculates the optimal correction parameters. The evaluation model considers multiple evaluation indicators, including position accuracy, posture stability, dynamic response characteristics, etc., and obtains a balanced correction strategy through multi-objective optimization.

[0057] In some embodiments, state evaluation and parameter optimization can be achieved in a variety of ways: optionally, first build an evaluation model based on a deep neural network, use historical data for offline training, establish a mapping relationship between state parameters and correction parameters, then input the current state in real time for online reasoning, and finally optimize the correction parameters through a back propagation algorithm; optionally, use a fuzzy logic control method to build an evaluation system, design a fuzzy rule base to describe expert experience, obtain a preliminary correction amount through fuzzy reasoning, and then use a genetic algorithm to globally optimize the correction parameters. It is understandable that other intelligent algorithms can also be used to achieve state evaluation and parameter optimization, which is not limited here.

[0058] Before step 105, the method further includes the following steps: Collect historical force parameters of the ground rail during multiple operations and historical position parameters of the ground rail robot; Extract the historical force parameters to obtain force characteristics, and extract the historical position parameters to obtain position characteristics; A neural network model is trained based on the force characteristics and the position characteristics to obtain the preset evaluation model, and the neural network model includes a first sub-network for generating track correction parameters and a second sub-network for generating position correction parameters.

[0059] Among them, the historical force parameters represent the mechanical data recorded by the ground rail system in previous operations, including force components in three directions (Fx, Fy, Fz) and torque components in three directions (Mx, My, Mz). The historical position parameters refer to the six-degree-of-freedom posture data of the robot during actual movement, including position coordinates (x, y, z) and Euler angles (α, β, γ). The force characteristics are used to represent the statistical and dynamic characteristics of the force data. The position characteristics represent the spatial distribution characteristics and timing characteristics of the position data. The neural network model is a deep learning model that maps input features to correction parameters. The first subnetwork maps the force characteristics to track corrections (δx, δy, δz). The second subnetwork maps the position characteristics to position corrections (Δx, Δy, Δz, Δα, Δβ, Δγ).

[0060] The establishment process of the preset evaluation model mainly includes three stages: data collection, feature extraction and model training. First, the ATIF / TSensor six-dimensional force / torque sensor array is installed at the key nodes of the ground rail system, with a sampling frequency of 1kHz, a force measurement range of ±1000N, a torque measurement range of ±100Nm, and a resolution of 0.1N / 0.01Nm; at the same time, the LeicaAT960 laser tracker is used to record the robot position, with a sampling frequency of 100Hz and a measurement accuracy of ±0.02mm / m. In the load range of 10kg to 100kg, different working conditions are set with a step size of 10kg. Each working condition is tested for 100 cycles, each test lasts for 10 minutes, and a total of 120 hours of operation data are obtained. The raw data is low-pass filtered at 50Hz to remove high-frequency noise, the 3σ criterion is used to eliminate outliers, and linear interpolation is used to fill in missing values. Then, the historical force data is feature extracted, and statistical features such as mean μF, standard deviation σF, skewness SF, and kurtosis KF are calculated. The main frequency f0 and its amplitude A0, subharmonic frequency f1, f2 and amplitude A1, A2 are obtained through FFT transformation. The short-time Fourier transform is calculated to obtain the time-frequency feature matrix T(f, t); the spatial distribution features such as position mean μP, variance matrix ΣP, main direction vector v1, v2, v3, and motion features such as velocity sequence V(t), acceleration sequence a(t), and angular velocity sequence ω(t) are calculated for the historical position data. Finally, a two-branch neural network is constructed. The first branch contains 4 convolutional layers and 2 fully connected layers to process 48-dimensional force features, and the second branch contains 1 LSTM layer and 2 fully connected layers to process 36-dimensional position features. The two branches are fused through the attention mechanism and connected to 3 fully connected layers to generate track correction and position correction parameters respectively. The network was trained using a composite loss function that included mean square error loss, parameter regularization term, and smoothness constraint term. The stochastic gradient descent method was used for optimization. The learning rate adopted a cosine annealing strategy and was trained for 200 rounds on 80% of the data. The final model achieved performance indicators of less than 0.1mm average error in orbit correction and less than 0.05mm / 0.01° average error in position correction on the test set.

[0061] S106: Send a correction instruction including the track correction parameter and the position correction parameter to the ground rail robot.

[0062] The correction instruction refers to a control command containing track correction parameters and position correction parameters. Track correction parameters refer to the adjustment amount of track motion characteristics. Position correction parameters are used to indicate the compensation amount for the robot position and posture. The instruction is sent using a real-time communication protocol to ensure the timeliness of the correction. The ground track robot refers to the executor of the correction instruction.

[0063] This step is performed after the correction parameters are generated to achieve motion correction. Specifically, the system first encapsulates the trajectory correction parameters and position correction parameters into control instructions in a standard format, including the correction amount, execution timing, and safety constraints. The correction instructions are then sent to the controller of the ground rail robot through the industrial real-time network. After receiving the instructions, the controller parses and verifies them to ensure that the correction parameters are within a safe range, and then executes the correction action according to the predetermined strategy to achieve real-time adjustment of the robot's motion.

[0064] In some embodiments, the generation and sending of correction instructions can be achieved in a variety of ways: optionally, the correction parameters are first converted into control quantities in the robot joint space, motion instructions in a standard format are generated, and then sent to the robot controller via real-time Ethernet, and finally the instruction execution status is monitored and the correction effect is recorded; optionally, a hierarchical control architecture is adopted, correction strategies are generated at the upper layer, converted into correction instructions executable by the underlying controller, and the real-time nature of the instructions is ensured through a multi-buffer mechanism, while an emergency processing mechanism is established to handle communication anomalies. It is understandable that other control methods can also be used to achieve motion correction, which is not limited here.

[0065] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the multi-degree-of-freedom control method for the rail robot in an embodiment of the present application.

[0066] S201, sending a correction instruction including the track correction parameter and the position correction parameter to the ground rail robot.

[0067] It can be understood that this step is similar to step S106 and will not be described in detail here.

[0068] S202: Acquire real-time image data of the ground rail robot, and identify posture parameters of the transported object from the real-time image data.

[0069] Real-time image data refers to the image information of the transported object collected in real time by the industrial camera installed on the ground rail robot, including RGB color images or depth images. Posture parameters are a set of parameters that describe the position and direction of the transported object in three-dimensional space, including translation parameters (x, y, z coordinates) and rotation parameters (roll, pitch, yaw angles).

[0070] This step is specifically implemented in the following way: First, the industrial camera continuously collects images of the transported object at a preset frequency (such as 30 frames per second). Then, the computer vision algorithm is used to pre-process the image, including image denoising, distortion correction, etc. Next, the target detection algorithm (such as FasterR-CNN) is used to locate the position of the transported object in the image and extract its contour features. Finally, based on the spatial distribution of the contour feature points, the PnP algorithm is used to solve the six-degree-of-freedom posture parameters of the transported object. The entire processing process is carried out in real time to ensure that the latest posture information is obtained.

[0071] S203: When the attitude parameter exceeds a preset attitude threshold, a attitude adjustment amount is calculated according to the trajectory correction parameter and the position correction parameter.

[0072] The preset attitude threshold is determined according to the accuracy requirements of the handling task. The position deviation threshold is ±5mm in the x, y, and z directions, and the angle deviation threshold is ±2° in roll, pitch, and yaw. The track correction parameters include the deformation, vibration amplitude, and stiffness coefficient of each track section, which are measured in real time by sensors on the track. The position correction parameters include position compensation, attitude compensation, and dynamic response characteristics, which are calculated by the control system based on historical operation data.

[0073] When calculating the attitude adjustment, first establish a deformation model based on the track state, taking into account the elastic deformation and temperature deformation of the track. At the same time, establish a position constraint model, including the robot arm kinematic constraints and workspace constraints. Combine these two models and use the least squares method to calculate the optimal adjustment to return the attitude parameters to the threshold range. The calculation results include the displacement adjustment in three directions (Δx, Δy, Δz) and the angle adjustment in three directions (Δroll, Δpitch, Δyaw). Finally, verify whether the adjustment meets the robot arm motion range limit.

[0074] S204: Determine an execution speed of posture adjustment based on the weight parameter and the material parameter.

[0075] In addition to the mass of the object being transported, weight parameters also include the center of gravity and moment of inertia. Material parameters describe the physical properties of the object being transported, including material type, hardness, surface friction coefficient, elastic modulus, and impact resistance. The execution speed is divided into translation speed and rotation speed, which need to be reasonably set according to the characteristics of the object being transported to ensure the safety of the adjustment process.

[0076] The speed determination process uses a fuzzy control method: the weight parameter is normalized to the range of 0-1, and the material characteristics are comprehensively quantified as a safety factor. According to empirical rules, the heavier the weight and the more fragile the material, the lower the execution speed; the lighter the weight and the stronger the material, the higher the execution speed. The linear speed calculation formula is v=vmax×(1-w)×h, and the angular speed calculation formula is ω=ωmax×(1-w)×h, where vmax and ωmax are the maximum allowable speeds, w is the normalized weight value, and h is the material safety factor. The calculated speed must also meet the acceleration constraint, and a smooth speed trajectory is generated through speed planning, which is finally converted into a robot joint speed command.

[0077] S205: Sending a posture adjustment instruction including the posture adjustment amount and the execution speed to the ground rail robot.

[0078] The posture adjustment instruction is a data packet, which contains the displacement adjustment amount (Δx, Δy, Δz), the angle adjustment amount (Δroll, Δpitch, Δyaw) and the corresponding execution speed parameters (vx, vy, vz, ωroll, ωpitch, ωyaw). The ground rail robot includes a motion control unit, a communication unit and an actuator. The motion control unit is responsible for parsing instructions and generating motion trajectories. The communication unit uses industrial Ethernet for data transmission. The actuator includes a servo motor and a driver.

[0079] This step sends the posture adjustment command to the motion control unit of the ground rail robot through industrial Ethernet. The motion control unit first converts the adjustment amount and speed parameters into motion parameters in the joint space, including the target position and speed of each joint. Then, trajectory planning is performed based on the fifth-order polynomial to generate the position, velocity and acceleration curves of each joint to ensure smooth and continuous motion trajectory. The trajectory planning results are converted into servo control instructions and sent to the servo drivers of each joint in real time through the EtherCAT bus. After receiving the instructions, the servo driver controls the servo motor to perform posture adjustment actions according to the planned trajectory. The entire communication process uses the real-time Ethernet protocol with a communication cycle of 1ms to ensure the real-time and synchronization of the instructions.

[0080] S206: When the attitude adjustment instruction starts to be executed, real-time force parameters of the ground rail are collected.

[0081] Real-time force parameters refer to the force and torque that the ground rail bears during the movement of the robot, including the force components (Fx, Fy, Fz) and torque components (Mx, My, Mz) in the three directions of x, y, and z. A six-dimensional force / torque sensor array is installed on the ground rail. The sampling frequency of each sensor is 1kHz, the measurement range is ±1000N for force and ±100Nm for torque, and the measurement accuracy is 0.1% of the full scale.

[0082] This step starts the data acquisition of the force / torque sensor at the same time as the posture adjustment instruction starts to execute. The sensor converts the analog signal into a digital signal and transmits it to the data acquisition unit through the CAN bus. The data acquisition unit filters and transforms the raw data to obtain the six-dimensional force / torque data in the ground rail base coordinate system. In order to improve data reliability, the data of multiple sensors are weighted and fused, and the weight coefficient is determined according to the distance from the sensor to the point of action. The processed force parameters are updated in real time at a frequency of 100Hz for subsequent safety monitoring.

[0083] S207: When the real-time force parameter is greater than a preset safety threshold, a pause instruction is sent to the ground rail robot, so that the ground rail robot pauses posture adjustment.

[0084] The preset safety thresholds are force and torque limits determined according to the load-bearing capacity and structural strength of the ground rail, including the safety thresholds of force (Fx_max, Fy_max, Fz_max) and the safety thresholds of torque (Mx_max, My_max, Mz_max). The pause command is an emergency control command that contains deceleration parameters and stop mode parameters. The pause process of the ground rail robot needs to ensure smooth deceleration to avoid shock caused by sudden stops.

[0085] This step first compares the six components of the real-time force parameters with the corresponding safety thresholds. When any component exceeds the safety threshold, the control system immediately generates a pause command, which contains the maximum allowable deceleration (2m / s²) and the slow stop mode flag. The pause command is sent to the motion control unit of the ground rail robot via industrial Ethernet. After receiving the command, the control unit immediately stops the current trajectory planning and regenerates the deceleration trajectory. The deceleration trajectory uses a quadratic curve to ensure that each joint decelerates smoothly until it stops. During the deceleration process, the servo drive maintains torque output to prevent the robot from falling out of control. When the robot stops completely, the servo system enters the position holding mode and waits for subsequent processing instructions.

[0086] S208: When the real-time force parameter is restored to within the preset safety threshold range, the execution speed is reduced to a preset execution speed threshold.

[0087] The preset safety threshold range refers to the safety interval of force and torque limits, including the safety interval of force ([0.7×Fx_max, 0.8×Fx_max], [0.7×Fy_max, 0.8×Fy_max], [0.7×Fz_max, 0.8×Fz_max]) and the safety interval of torque ([0.7×Mx_max, 0.8×Mx_max], [0.7×My_max, 0.8×My_max], [0.7×Mz_max, 0.8×Mz_max]). The preset execution speed threshold is the speed reduction ratio of the original execution speed. The linear speed threshold is 0.6 times the original speed, and the angular speed threshold is 0.5 times the original speed.

[0088] The control system continuously monitors the six-dimensional force / torque data, and recalculates the execution speed when it detects that all components have returned to the safe range. The new linear velocity value v_new=0.6×v_original, and the new angular velocity value ω_new=0.5×ω_original. The control system generates a motion command containing the new speed parameters and sends it to the ground rail robot via industrial Ethernet. After receiving the command, the motion control unit uses a trapezoidal speed curve to generate an acceleration trajectory to gradually accelerate the robot from a stationary state to the new execution speed. The acceleration process uses a fixed acceleration (1m / s²) to ensure a smooth transition. After the robot reaches the new speed, it continues to perform the remaining posture adjustment actions.

[0089] S209: Determine a track vibration threshold based on the weight parameter and the material parameter.

[0090] Track vibration thresholds include displacement amplitude thresholds (unit: mm) and acceleration amplitude thresholds (unit: m / s²). Weight parameters include the mass value of the transported object (unit: kg) and mass distribution parameters. Material parameters include the elastic modulus (unit: GPa), damping ratio, and seismic performance index of the material. These parameters together determine the dynamic response characteristics of the ground rail system during the transport process.

[0091] This step first calculates the baseline vibration threshold based on the weight parameter, the displacement amplitude baseline value A_base=10 / m, the acceleration amplitude baseline value a_base=20 / m, where m is the mass of the transported object (kg). Then the correction factor is calculated based on the material parameters, the correction factor k=E×ζ / E_ref, where E is the elastic modulus of the material, ζ is the damping ratio, and E_ref is the reference elastic modulus (taken as 200GPa). The final displacement amplitude threshold A_limit=A_base×k, and the acceleration amplitude threshold a_limit=a_base×k. The calculation of the vibration threshold takes into account the mass effect and material properties of the transported object, which is used for subsequent vibration monitoring.

[0092] S210. During the operation of the ground rail robot, real-time track vibration parameters of each track segment are collected.

[0093] Real-time track vibration parameters include displacement amplitude (unit: mm), acceleration amplitude (unit: m / s²), main vibration frequency (unit: Hz) and vibration waveform characteristics. Track segment refers to the physical segment in the ground rail system, each segment is 2 meters long. Each track segment is equipped with an acceleration sensor and a displacement sensor. The sampling frequency of the acceleration sensor is 2kHz and the range is ±50m / s²; the sampling frequency of the displacement sensor is 1kHz and the range is ±10mm.

[0094] The sensor array monitors the track vibration in real time, and the collected raw data is amplified and filtered by the signal conditioning circuit. The data acquisition unit digitizes the processed signal and calculates the vibration parameters. The displacement amplitude is directly measured by the displacement sensor, the acceleration amplitude is measured by the acceleration sensor, and the main vibration frequency is obtained by FFT analysis of the acceleration signal. The vibration waveform characteristics are obtained by extracting the time domain and frequency domain characteristics of the signal. The processed vibration parameters are updated at a frequency of 50Hz to form a vibration status data stream for each track segment. These data are used to evaluate the dynamic performance of the track and early warning analysis.

[0095] S211. Compare the real-time track vibration parameter with the maximum allowable track vibration threshold, mark the track segment that exceeds the maximum allowable track vibration threshold, and obtain a marked track segment.

[0096] Real-time track vibration parameters include displacement amplitude (A), acceleration amplitude (a), and main vibration frequency (f). The maximum allowable track vibration thresholds include displacement amplitude threshold (A_limit), acceleration amplitude threshold (a_limit), and frequency range threshold (f_range). The track segment is marked with a binary flag (status_flag) to record the vibration limit status, including the track segment number (section_id), the limit parameter type (exceed_type), and the limit level (exceed_level).

[0097] The control system evaluates the vibration parameters of each track segment at a frequency of 50 Hz. The evaluation process first calculates the displacement amplitude ratio RA=A / A_limit, the acceleration amplitude ratio Ra=a / a_limit, and the frequency deviation ratio Rf=|f-f_center| / f_range, where f_center is the expected vibration frequency. When any ratio is greater than 1, the status_flag of the corresponding track segment is set to 1, and the exceed_type (1 indicates displacement exceeds the limit, 2 indicates acceleration exceeds the limit, and 3 indicates frequency exceeds the limit) and exceed_level (equal to the maximum exceedance ratio) are recorded. The marking information forms a structured data packet, which contains four fields: section_id, status_flag, exceed_type, and exceed_level, for subsequent early warning processing.

[0098] S212: Send the position of the marked track segment to the target client.

[0099] The location information of the marked track segment includes the starting coordinates (start_pos), ending coordinates (end_pos) and center coordinates (center_pos) of the track segment. The target client includes the field control terminal and the remote monitoring terminal, which communicate using the TCP / IP protocol and the data transmission uses the JSON format. The client has the functions of data reception, parsing and display.

[0100] The control system packages the position information and vibration parameters of the marked track segment into a JSON data packet. The data packet structure includes: basic track segment information (section_info), position information (position_info), vibration parameters (vibration_info) and exceed information (exceed_info). The data packet is sent to the target client via industrial Ethernet, and the transmission protocol uses TCP / IP to ensure the reliability of data transmission. After receiving the data packet, the client performs JSON parsing, extracts the track segment position information and vibration parameters, and displays the position and status of the exceeded track segment in a graphical manner on the human-machine interface. The display content includes the position of the track segment in three-dimensional space, the real-time value of the vibration parameter, and the color identification of the degree of exceedance.

[0101] S213: Using AR-assisted visual recognition technology to process the real-time image data to obtain environmental characteristic parameters.

[0102] AR-assisted visual recognition technology is a technical system that integrates augmented reality and computer vision, including a camera calibration module (used to establish a camera internal and external parameter model), a feature extraction module (to extract key image features), and a spatial registration module (to achieve virtual-real registration). Real-time image data includes RGB images (resolution 4096×3072 pixels, frame rate 30fps) and depth images (resolution 1280×720 pixels, depth accuracy 0.1mm). Environmental feature parameters include three categories: spatial geometric features (plane equations, edge equations, three-dimensional coordinates of corner points), lighting conditions (average brightness value, contrast value, main light source direction vector), and occlusion conditions (occlusion area boundary point set, occlusion object size parameters).

[0103] The image processing flow adopts a multi-stage serial processing method: in the first stage, the RGB image is preprocessed, Gaussian filtering is used to remove noise, histogram equalization is used to enhance image contrast, and distortion correction is performed based on camera calibration parameters. In the second stage, SIFT feature points are extracted from the processed image. Each feature point contains position coordinates, scale, direction and 128-dimensional feature descriptor. In the third stage, the feature points are matched with the depth image, the three-dimensional coordinates of the feature points are constructed, and feature point cloud data is formed. In the fourth stage, the ICP algorithm is used for point cloud registration, and the position matrix T and rotation matrix R of the camera in the world coordinate system are iteratively calculated. In the fifth stage, environmental features are extracted based on the registration results: the plane equation group Ax+By+Cz+D=0 is fitted by the RANSAC algorithm; the edge is detected by the Canny operator, and the straight line equation group is fitted; the feature corner point coordinates are obtained by Harris corner detection. In the sixth stage, the image brightness distribution is analyzed, the average brightness value L_avg and the contrast value C are calculated, and the main light source direction vector is determined by brightness gradient analysis. In the seventh stage, the discontinuity of the depth image is used to detect the occluded area, extract the occluded boundary point set {(x_i, y_i, z_i)}, and calculate the bounding box parameters of the occluded object.

[0104] S214: Adjust the trajectory correction parameter and the position correction parameter according to the environmental characteristic parameter.

[0105] The orbit correction parameters include orbit geometry correction (δx, δy, δz) and attitude correction (δroll, δpitch, δyaw). The position correction parameters include spatial position compensation (Δx, Δy, Δz) and attitude compensation (Δroll, Δpitch, Δyaw). The adjustment of these parameters is dynamically updated based on the environmental characteristic parameters.

[0106] The adjustment process is divided into two parts: orbit correction and position correction. The orbit correction first uses the detected plane features to establish the ground reference plane, calculates the deviation between the actual orbit plane and the reference plane, and obtains the orbit geometry correction: δx=k1×(x_actual-x_ref), δy=k1×(y_actual-y_ref), δz=k1×(z_actual-z_ref), where k1 is the correction coefficient (value 0.8), (x_actual, y_actual, z_actual) are the actual plane parameters, and (x_ref, y_ref, z_ref) are the reference plane parameters. The attitude correction is calculated by edge features: δroll=k2×(θx_actual-θx_ref), δpitch=k2×(θy_actual-θy_ref), δyaw=k2×(θz_actual-θz_ref), where k2 is the attitude correction coefficient (value 0.6), and θ represents the inclination angle in each direction. The position correction adjusts the compensation amount according to the occlusion condition: when occlusion is detected, the position compensation amount increases the safety margin according to the size of the occluding object, Δx=Δx_base+d_safe, Δy=Δy_base+d_safe, Δz=Δz_base+d_safe, where d_safe is the safety distance (value 100mm). At the same time, the lighting conditions affect the posture compensation: in areas with uneven lighting (contrast C>0.7), the posture compensation amount is increased to improve stability, Δroll=k3×Δroll_base, Δpitch=k3×Δpitch_base, Δyaw=k3×Δyaw_base, where k3 is the lighting compensation coefficient (value 1.2).

[0107] The following describes the multi-degree-of-freedom control system of the ground rail robot in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the structure of a physical device of a multi-degree-of-freedom control system of a rail robot in an embodiment of the present application.

[0108] It should be noted that Figure 3 The structure of the multi-degree-of-freedom control system of the ground rail robot shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0109] like Figure 3As shown, the multi-degree-of-freedom control system of the ground rail robot includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0110] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0111] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0112] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0113] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0114] Specifically, the multi-degree-of-freedom control system of the rail robot in this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the multi-degree-of-freedom control method of the rail robot provided in the above embodiment is implemented.

[0115] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the multi-degree-of-freedom control system of the ground rail robot described in the above embodiment; or may exist independently without being assembled into the multi-degree-of-freedom control system of the ground rail robot. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the multi-degree-of-freedom control system of the ground rail robot, the multi-degree-of-freedom control system of the ground rail robot implements the multi-degree-of-freedom control method of the ground rail robot provided in the above embodiment.

[0116] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0117] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0118] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A multi-degree-of-freedom control method for a ground rail robot, characterized in that: Applied to a multi-degree-of-freedom control system of a ground rail robot, the multi-degree-of-freedom control system of the ground rail robot comprises a ground rail robot and a ground rail, and the method comprises: Acquire the task data of the transported object, extract the characteristic quantities in the task data of the transported object to obtain the weight parameter, material parameter and position parameter of the transported object; Matching the track operation data of the ground rail in a preset track database according to the weight parameter, the material parameter and the position parameter, wherein the track operation data includes a track vibration parameter, a speed parameter and a displacement parameter; Extracting target points in the track path of the ground rail, and calculating the transportation path data of the ground rail robot on the ground rail based on the transportation parameters of the target points, wherein the target points include a pick-up point, a transition point, and a placement point; The transport path data is sent to the ground rail robot, and after the ground rail robot executes the transport path data, the real-time force parameters and real-time position parameters of the ground rail robot are measured at the pick-up point, the transition point and the placement point; Inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter; A correction instruction including the track correction parameter and the position correction parameter is sent to the ground rail robot.

2. The method according to claim 1, characterized in that The step of extracting a target point in the track path of the ground rail and calculating the transportation path data of the ground rail robot on the ground rail based on the transportation parameters of the target point specifically includes: Obtaining initial positions of the pick-up point, the transition point, and the placement point; adjusting the initial positions of the pick-up point, the transition point and the placement point according to the track vibration parameter to obtain an adjusted position; Calculate the motion trajectory of the ground rail robot between various points according to the displacement parameters; Calculating a speed change sequence of the ground rail robot during movement according to the speed parameter; The adjustment position, the motion trajectory and the speed change sequence are converted into a ground rail robot control instruction format to generate the transport path data.

3. The method according to claim 1, characterized in that Before the step of inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter, the method further includes: Collect historical force parameters of the ground rail during multiple operations and historical position parameters of the ground rail robot; Extracting the historical force parameters to obtain force characteristics, and extracting the historical position parameters to obtain position characteristics; A neural network model is trained based on the force characteristics and the position characteristics to obtain the preset evaluation model, wherein the neural network model includes a first subnetwork for generating track correction parameters and a second subnetwork for generating position correction parameters.

4. The method according to claim 1, characterized in that: After the step of sending the correction instruction including the track correction parameter and the position correction parameter to the ground rail robot, the method further includes: Acquiring real-time image data of the ground rail robot, and identifying posture parameters of the transported object from the real-time image data; When the attitude parameter exceeds a preset attitude threshold, calculating an attitude adjustment amount according to the trajectory correction parameter and the position correction parameter; determining an execution speed of posture adjustment based on the weight parameter and the material parameter; A posture adjustment instruction is sent to the ground rail robot, wherein the posture adjustment instruction includes the posture adjustment amount and the execution speed.

5. The method according to claim 4, characterized in that After the step of sending the posture adjustment instruction including the posture adjustment amount and the execution speed to the ground rail robot, the method further includes: When the attitude adjustment instruction starts to be executed, real-time force parameters of the ground rail are collected; When the real-time force parameter is greater than a preset safety threshold, a pause instruction is sent to the ground rail robot so that the ground rail robot pauses posture adjustment; When the real-time force parameter is restored to within the preset safety threshold range, the execution speed is reduced to a preset execution speed threshold.

6. The method according to claim 1, characterized in that After the step of inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter, the method further includes: determining a rail vibration threshold based on the weight parameter and the material parameter; During the operation of the ground rail robot, real-time track vibration parameters of each track segment are collected; Comparing the real-time orbit vibration parameter with the maximum allowable orbit vibration threshold, marking the orbit segment exceeding the maximum allowable orbit vibration threshold, and obtaining a marked orbit segment; The position of the marked track segment is sent to the target client.

7. The method according to claim 4, characterized in that After the step of inputting the real-time force parameter and the real-time position parameter into a preset evaluation model to obtain a track correction parameter and a position correction parameter, the method further includes: The real-time image data is processed using AR-assisted visual recognition technology to obtain environmental characteristic parameters; The trajectory correction parameter and the position correction parameter are adjusted according to the environmental characteristic parameter.

8. A multi-degree-of-freedom control system for a ground-rail robot, characterized in that: The multi-degree-of-freedom control system of the rail robot includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program codes, the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the multi-degree-of-freedom control system of the rail robot to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction runs on a rail robot multi-degree-of-freedom control system, the rail robot multi-degree-of-freedom control system executes the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product runs on a rail robot multi-degree-of-freedom control system, the rail robot multi-degree-of-freedom control system is enabled to execute the method according to any one of claims 1 to 7.

Images