A virtual joystick-based control method and system for a forked mobile robot

Abstract

The application relates to the technical field of human-computer interaction, and proposes a forked mobile robot control method and system based on a virtual rocker, the method comprising the following steps: performing touch coordinate configuration on virtual rocker interaction to obtain a positioning virtual control area; performing differential comparison on real-time touch point coordinates of a user based on the center reference coordinates of the positioning virtual control area to obtain vector parameters, wherein the vector parameters comprise a control amplitude and a control direction; performing slight touch offset discrimination on the vector parameters, and performing adaptive nonlinear compensation correction on the discriminated slight touch offset to obtain optimized vector parameters; responding to a virtual emergency stop signal, and clearing and locking the optimized vector parameters to obtain interlocked vector parameters; performing speed and direction stepless coupling assignment on the interlocked vector parameters to obtain integrated vector parameters; and generating a composite control instruction according to the integrated vector parameters; and the application can improve the efficiency of forked mobile robot control.

Description

Technical Field

[0001] This invention relates to the field of human-computer interaction technology, and in particular to a method and system for controlling a fork-type mobile robot based on a virtual joystick. Background Technology

[0002] Traditional forklift mobile robots generally use hydraulic cylinders combined with physical handles for remote control. The hydraulic cylinder drive mode has inherent defects such as sealing aging and leakage, nonlinear power response, and insufficient control precision. The physical handle relies on mechanical stroke to output control signals. Under long-term high-frequency operation, the internal transmission structure will continue to wear and jam. Hardware procurement and regular replacement and maintenance will result in continuous high costs. Currently, there is no complete set of mobile virtual joystick control solutions adapted to the electric cylinder drive system. Conventional simple virtual touch control does not have origin drift compensation, jitter filtering, and nonlinear adaptive correction logic. Slight touch disturbances will continuously output invalid control commands, making it impossible to achieve continuous stepless fine control of speed and steering. At the same time, it lacks an independent closed-loop emergency stop interlock control mechanism. After an emergency stop is triggered, the control parameters cannot be forcibly locked and returned to zero, which poses a safety hazard of robot accidental start.

[0003] Existing hydraulic cylinder-driven forklift mobile robot physical handle control solutions suffer from high hardware losses, insufficient control smoothness, and inadequate safety protection capabilities. Wireless virtual joystick stepless control and safety interlocking technology adapted to electric cylinder drive architecture is currently a gap in the industry. Therefore, how to improve the smoothness, accuracy, ease of operation, and operational safety level of remote wireless control of electric cylinder-driven forklift mobile robots has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method and system for controlling a fork-type mobile robot based on a virtual joystick, in order to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a method for controlling a fork-type mobile robot based on a virtual joystick, comprising:

[0006] D1. Configure the touch coordinates for the virtual joystick interaction of the fork-type mobile robot to obtain the positioning virtual control area of ​​the fork-type mobile robot.

[0007] D2. Based on the center reference coordinates of the positioning virtual control area, perform differential comparison on the real-time touch point coordinates of the fork-type mobile robot to obtain the vector parameters of the fork-type mobile robot, wherein the vector parameters include the control amplitude and control direction.

[0008] D3. The vector parameters are identified by minute touch offsets, and the identified minute touch offsets are adaptively nonlinearly compensated and corrected to obtain the optimized vector parameters of the fork-type mobile robot.

[0009] D4. Respond to the virtual emergency stop signal of the forklift mobile robot and clear and lock the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot;

[0010] D5. Assign velocity and orientation stepless coupling values ​​to the interlocking vector parameters to obtain the integrated vector parameters of the fork-type mobile robot;

[0011] D6. Generate composite control commands for the fork-type mobile robot based on the integrated vector parameters.

[0012] In a preferred embodiment, configuring the touch coordinates for the virtual joystick interaction of the forklift mobile robot to obtain the positioning virtual control area of ​​the forklift mobile robot includes:

[0013] Obtain the virtual joystick interaction of the fork-type mobile robot;

[0014] The original touch coordinates of the virtual joystick interaction are normalized in coordinate space to obtain the normalized touch coordinates of the virtual joystick interaction.

[0015] The normalized touch coordinates are extracted by origin reference matching to obtain the calibration origin offset of the normalized touch coordinates;

[0016] Based on the calibration origin offset, the normalized touch coordinates are compensated for origin drift to obtain the compensated touch coordinates.

[0017] The compensated touch coordinates are subjected to coordinate boundary clamping constraints to obtain the positioning virtual control area of ​​the fork-type mobile robot.

[0018] In a preferred embodiment, the user's real-time touch point coordinates of the fork-type mobile robot are differentially compared based on the center reference coordinates of the positioning virtual control area to obtain the vector parameters of the fork-type mobile robot. These vector parameters include the control amplitude and control direction, and include:

[0019] The center of gravity of the virtual control area is extracted to obtain the center reference coordinates of the fork-type mobile robot.

[0020] Based on the central reference coordinates, the user's real-time touch point coordinates of the fork-type mobile robot are decomposed by difference to obtain the lateral offset component and longitudinal offset component of the fork-type mobile robot.

[0021] The directional polarity of the lateral offset component is determined to obtain the control direction of the fork-type mobile robot;

[0022] The longitudinal offset component is normalized and calibrated to obtain the control range of the fork-type mobile robot.

[0023] The control direction and the control amplitude are integrated into the vector parameters of the fork-type mobile robot.

[0024] In a preferred embodiment, the step of identifying minute touch offsets in the vector parameters and performing adaptive nonlinear compensation correction on the identified minute touch offsets to obtain optimized vector parameters for the fork-type mobile robot includes:

[0025] The vector parameters are subjected to low-pass filtering to obtain a filtered data stream of the vector parameters, and the minute touch offset characteristics of the vector parameters are obtained based on the filtered data stream.

[0026] Based on a preset noise filtering threshold range, the minute touch offset features are filtered and divided to obtain the invalid jitter component or the effective control component of the minute touch offset features. The invalid jitter component is the amount of minute accidental touch interference, and the effective control component is the amount of active touch control offset.

[0027] The effective control components are subjected to piecewise gain mapping to obtain the intermediate parameters of the fork-type mobile robot.

[0028] Based on the current operating state of the forklift mobile robot, the intermediate parameters are dynamically phase-adjusted to obtain the preliminary compensation vector of the forklift mobile robot;

[0029] The preliminary compensation vector is superimposed with the vector parameters, and the superimposed vector is subjected to smoothness constraints to obtain the optimized vector parameters of the fork-type mobile robot.

[0030] In a preferred embodiment, the step of performing piecewise gain mapping on the effective control components to obtain the intermediate parameters of the forklift robot includes:

[0031] Obtain the control amplitude in the vector parameters, and determine the nonlinear mapping range of the effective control component based on the control amplitude;

[0032] Based on the nonlinear mapping interval, adaptive weight allocation is performed on the effective control components to obtain dynamic compensation coefficients;

[0033] Based on the dynamic compensation coefficient, the amplitude of the effective control component is corrected to obtain the intermediate parameter.

[0034] In a preferred embodiment, the dynamic compensation coefficient is calculated using the following formula:

[0035] ;

[0036] in, The dynamic compensation coefficient is... The preset maximum gain adjustment factor. The preset nonlinear intensity adjustment factor, The control range, This is the median reference value of the nonlinear mapping interval.

[0037] In a preferred embodiment, the step of responding to the virtual emergency stop signal of the forklift robot and zeroing and locking the optimized vector parameters to obtain the interlocking vector parameters of the forklift robot includes:

[0038] In response to the virtual emergency stop signal of the fork-type mobile robot, the output data channel of the optimized vector parameters is forcibly blocked to obtain the no-output truncation parameters of the fork-type mobile robot.

[0039] Logical verification is performed on the associated safety loop state without output truncation parameters to obtain the loop safety confirmation signal of the fork-type mobile robot;

[0040] Based on the loop safety confirmation signal, the zero-output cutoff parameter is controlled by zeroing to obtain the locking state maintenance parameter of the fork mobile robot.

[0041] By marking the locking state holding parameters with anti-rebound markers, the interlocking vector parameters of the fork-type mobile robot are obtained.

[0042] In a preferred embodiment, the step of assigning velocity-azimuth stepless coupling values ​​to the interlocking vector parameters to obtain the integrated vector parameters of the forklift mobile robot includes:

[0043] The interlocking vector parameters are analyzed by component analysis to obtain the velocity scalar parameters and azimuth scalar parameters of the interlocking vector parameters;

[0044] The speed scalar parameter is continuously mapped to the speed range to obtain the speed assignment data of the fork-type mobile robot.

[0045] Based on the azimuth scalar parameter, the velocity assignment data is subjected to azimuth projection mapping to obtain the azimuth assignment data of the fork-type mobile robot.

[0046] The velocity assignment data and the orientation assignment data are dynamically fused to obtain the integrated vector parameters of the fork-type mobile robot.

[0047] In a preferred embodiment, generating the composite control commands for the forklift robot based on the integrated vector parameters includes:

[0048] The integrated vector parameters are analyzed in the parameter domain to obtain the velocity component parameters and azimuth component parameters of the integrated vector parameters;

[0049] By binding the velocity component parameters and the orientation component parameters with control primitives, an integrated control primitive carrier for the fork-type mobile robot is obtained.

[0050] The integrated control element carrier is filled with control words to obtain the original control word sequence of the fork-type mobile robot;

[0051] A checksum is attached to the original control word sequence to obtain a checksum-enabled control word sequence.

[0052] Based on the control word sequence with check, the control word sequence with check is packaged into instruction frames to obtain the composite control instructions for the fork-type mobile robot.

[0053] To address the aforementioned problems, the present invention also provides a fork-type mobile robot control system based on a virtual joystick, the system comprising:

[0054] The touch area calibration module is used to configure the touch coordinates for the virtual joystick interaction of the fork-type mobile robot to obtain the positioning virtual control area of ​​the fork-type mobile robot.

[0055] The touch vector calculation module is used to perform differential comparison of the real-time touch point coordinates of the user of the fork-type mobile robot based on the center reference coordinates of the positioning virtual control area, so as to obtain the vector parameters of the fork-type mobile robot, the vector parameters including the control amplitude and control direction;

[0056] An offset adaptive correction module is used to identify minute touch offsets in the vector parameters and perform adaptive nonlinear compensation correction on the identified minute touch offsets to obtain the optimized vector parameters of the fork-type mobile robot.

[0057] An emergency stop interlocking module is used to respond to the virtual emergency stop signal of the forklift mobile robot and to clear and lock the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot.

[0058] The velocity-azimuth coupling assignment module is used to perform velocity-azimuth stepless coupling assignment on the interlocking vector parameters to obtain the integrated vector parameters of the fork-type mobile robot;

[0059] The composite instruction generation module is used to generate composite control instructions for the fork-type mobile robot based on the integrated vector parameters.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] 1. This invention constructs a standardized virtual control area through coordinate normalization, origin drift compensation, and boundary clamping. It completes vector parameter calculation by extracting the center of gravity and decomposing the independent lateral and longitudinal offset components through differential decomposition. Combined with low-pass filtering, offset discrimination, piecewise nonlinear gain mapping, and dynamic phase adjustment, it achieves adaptive correction of touch jitter. It can output continuous, smooth, and abrupt optimized vector parameters, which are adapted to the high-precision and linear power output characteristics of electric cylinder driven forklift mobile robots. It fully realizes stepless continuous control of driving speed and steering direction throughout the entire process. At the same time, it completes wireless soft control deployment through the WeChat mini-program carrier, eliminating the need for physical handle hardware structure and simplifying the overall control hardware configuration system.

[0062] 2. This invention establishes a complete virtual emergency stop interlock closed-loop control process, including channel blocking, safety circuit logic verification, parameter zeroing locking, and anti-rebound marking. Then, it performs stepless coupling assignment of velocity and orientation values ​​on the vector parameters after safety interlocking. After parameter parsing, control element binding, and instruction packetization with verification codes, standardized composite control commands are generated and sent to the electric cylinder drive execution unit. Multiple safety verification mechanisms block abnormal operation command outputs, stably matching the electric cylinder response control logic, comprehensively improving the smoothness, accuracy, ease of operation, and overall machine safety protection level of remote wireless control. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating a method for controlling a fork-type mobile robot based on a virtual joystick, according to an embodiment of the present invention.

[0064] Figure 2 A functional block diagram of a fork-type mobile robot control system based on a virtual joystick, provided in an embodiment of the present invention;

[0065] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0066] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0067] This application provides a method for controlling a fork-type mobile robot based on a virtual joystick. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for controlling a fork-type mobile robot based on a virtual joystick can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0068] Reference Figure 1 The diagram shown is a flowchart illustrating a fork-type mobile robot control method based on a virtual joystick, according to an embodiment of the present invention. In this embodiment, the fork-type mobile robot control method based on a virtual joystick includes:

[0069] D1. Configure the touch coordinates for the virtual joystick interaction of the fork-type mobile robot to obtain the positioning virtual control area of ​​the fork-type mobile robot.

[0070] In this embodiment of the invention, configuring the touch coordinates for the virtual joystick interaction of the fork-type mobile robot to obtain the positioning virtual control area of ​​the fork-type mobile robot includes:

[0071] Obtain the virtual joystick interaction of the fork-type mobile robot;

[0072] The original touch coordinates of the virtual joystick interaction are normalized in coordinate space to obtain the normalized touch coordinates of the virtual joystick interaction.

[0073] The normalized touch coordinates are extracted by origin reference matching to obtain the calibration origin offset of the normalized touch coordinates;

[0074] Based on the calibration origin offset, the normalized touch coordinates are compensated for origin drift to obtain the compensated touch coordinates.

[0075] The compensated touch coordinates are subjected to coordinate boundary clamping constraints to obtain the positioning virtual control area of ​​the fork-type mobile robot.

[0076] Collect all touch interaction signals generated by the virtual joystick interface on the terminal page, fully capture all interactive behaviors generated by the user's finger touching the virtual joystick interface, fully retain the original touch point information corresponding to the virtual joystick interaction, and fully output the virtual joystick interaction of the fork-type mobile robot.

[0077] A standard coordinate space of uniform size is defined, and all the horizontal and vertical position information contained in the original touch coordinates are mapped into the standard coordinate space. This unifies the value range of all original touch coordinates, eliminates the differences in coordinate values ​​caused by different device screen sizes and screen resolutions, and generates normalized touch coordinates for virtual joystick interaction after complete mapping and conversion.

[0078] Iterate through all point data included in the normalized touch coordinates, filter the reference coordinate points generated when the user is stationary without any operation, compare the position of the selected reference coordinate points with the theoretical origin preset in the standard coordinate space point by point, calculate the difference in horizontal position and the difference in vertical position between the two sets of coordinate points, and combine the two sets of differences to form the calibration origin offset of the normalized touch coordinates.

[0079] The calibration origin offset includes horizontal and vertical differences. The two sets of differences are superimposed on the horizontal and vertical coordinate values ​​corresponding to each point of the normalized touch coordinates to offset the positional deviation between the reference coordinate point and the theoretical origin, eliminate the origin drift phenomenon accumulated by long-term touch operation, and generate the compensated touch coordinates of the normalized touch coordinates after the difference superposition calculation is completed for all points.

[0080] The virtual joystick interface is set to allow touch points to stay at horizontal and vertical limits. Each point within the compensated touch coordinates is checked for boundary. If the point coordinates exceed the horizontal limit, the point coordinates are locked to the horizontal limit position. If the point coordinates exceed the vertical limit, the point coordinates are locked to the vertical limit position. After all points have completed the boundary locking process, the positioning virtual control area of ​​the fork-type mobile robot is generated.

[0081] The beneficial effects are that the entire coordinate processing flow can unify the touch coordinate output standard of different terminal devices, completely eliminate the origin drift problem caused by differences in device hardware and long-term touch operation, limit the effective control range through boundary constraints, avoid invalid coordinate data input beyond the control range from the source, ensure that the subsequent vector parameter calculation process is carried out based on a stable and standardized positioning virtual control area, and continuously improve the accuracy and stability of virtual joystick control coordinate acquisition for fork mobile robots.

[0082] D2. Based on the center reference coordinates of the positioning virtual control area, perform differential comparison on the real-time touch point coordinates of the fork-type mobile robot to obtain the vector parameters of the fork-type mobile robot, wherein the vector parameters include the control amplitude and control direction.

[0083] In this embodiment of the invention, the user's real-time touch point coordinates of the fork-type mobile robot are differentially compared based on the center reference coordinates of the positioning virtual control area to obtain the vector parameters of the fork-type mobile robot. These vector parameters include the control amplitude and control direction, and include:

[0084] The center of gravity of the virtual control area is extracted to obtain the center reference coordinates of the fork-type mobile robot.

[0085] Based on the central reference coordinates, the user's real-time touch point coordinates of the fork-type mobile robot are decomposed by difference to obtain the lateral offset component and longitudinal offset component of the fork-type mobile robot.

[0086] The directional polarity of the lateral offset component is determined to obtain the control direction of the fork-type mobile robot;

[0087] The longitudinal offset component is normalized and calibrated to obtain the control range of the fork-type mobile robot.

[0088] The control direction and the control amplitude are integrated into the vector parameters of the fork-type mobile robot.

[0089] The system fully traverses all valid coordinate points within the virtual control area, extracts the lateral and longitudinal position data corresponding to each coordinate point, adds up the lateral position data of all points to obtain the average lateral position, adds up the longitudinal position data of all points to obtain the average longitudinal position, and merges the average lateral and average longitudinal positions to form the geometric center point of the virtual control area. This center point is directly used as the central reference coordinate of the forklift mobile robot.

[0090] The system retrieves the lateral and longitudinal position values ​​carried by the central reference coordinates, and simultaneously retrieves the lateral and longitudinal position values ​​contained in the user's real-time touch point coordinates generated by the user's action on the virtual joystick at the current moment. The lateral position difference is obtained by subtracting the lateral value of the central reference coordinates from the real-time touch point lateral value, and the longitudinal position difference is obtained by subtracting the longitudinal value of the central reference coordinates from the real-time touch point longitudinal value. The two sets of independent differences are stored separately to form the lateral offset component and longitudinal offset component of the fork-type mobile robot.

[0091] Read the pure numerical result of the lateral offset component, determine the robot's right turning direction if the value is greater than zero, determine the stationary state with no lateral offset if the value is equal to zero, and determine the robot's left turning direction if the value is less than zero. Record the determined direction information to form the control direction of the forklift robot.

[0092] The maximum allowable offset distance of 0.5 in the longitudinal direction of the positioning virtual control area is selected as a unified reference standard. The longitudinal difference corresponding to the longitudinal offset component is converted with the reference standard. The longitudinal offset distances of different lengths are uniformly converted into a fixed numerical range [0,1]. After the conversion, the control amplitude of the fork mobile robot in a standardized numerical form is obtained.

[0093] Retrieve the complete orientation information of the control direction and the standardized numerical information of the control amplitude that have already been generated, bind and store the two types of information in the same data carrier, complete the integrated combination and encapsulation of the two types of data, and generate vector parameters of the fork mobile robot that simultaneously contain the two types of information of control amplitude and control direction.

[0094] The beneficial effects are that stable and unbiased central reference coordinates are obtained by extracting the center of gravity of the region, and independent lateral and longitudinal offset data are separated by difference decomposition. The direction polarity determination and amplitude normalization calibration are carried out separately. The split processing method can avoid mutual interference between direction information and amplitude information, ensuring unbiased control direction recognition and standardized control amplitude output. The combined vector parameter data has clear logic and can provide accurate and layered original control data support for subsequent offset correction processes.

[0095] D3. The vector parameters are identified by minute touch offsets, and the identified minute touch offsets are adaptively nonlinearly compensated and corrected to obtain the optimized vector parameters of the fork-type mobile robot.

[0096] In this embodiment of the invention, the step of identifying minute touch offsets in the vector parameters and performing adaptive nonlinear compensation correction on the identified minute touch offsets to obtain the optimized vector parameters of the fork-type mobile robot includes:

[0097] The vector parameters are subjected to low-pass filtering to obtain a filtered data stream of the vector parameters, and the minute touch offset characteristics of the vector parameters are obtained based on the filtered data stream.

[0098] Based on a preset noise filtering threshold range, the minute touch offset features are filtered and divided to obtain the invalid jitter component or the effective control component of the minute touch offset features. The invalid jitter component is the amount of minute accidental touch interference, and the effective control component is the amount of active touch control offset.

[0099] The effective control components are subjected to piecewise gain mapping to obtain the intermediate parameters of the fork-type mobile robot.

[0100] Based on the current operating state of the forklift mobile robot, the intermediate parameters are dynamically phase-adjusted to obtain the preliminary compensation vector of the forklift mobile robot;

[0101] The preliminary compensation vector is superimposed with the vector parameters, and the superimposed vector is subjected to smoothness constraints to obtain the optimized vector parameters of the fork-type mobile robot.

[0102] The step of performing piecewise gain mapping on the effective control components to obtain the intermediate parameters of the fork-type mobile robot includes:

[0103] Obtain the control amplitude in the vector parameters, and determine the nonlinear mapping range of the effective control component based on the control amplitude;

[0104] Based on the nonlinear mapping interval, adaptive weight allocation is performed on the effective control components to obtain dynamic compensation coefficients;

[0105] Based on the dynamic compensation coefficient, the amplitude of the effective control component is corrected to obtain the intermediate parameter.

[0106] The formula for calculating the dynamic compensation coefficient is as follows:

[0107] ;

[0108] in, The dynamic compensation coefficient is... The preset maximum gain adjustment factor. The preset nonlinear intensity adjustment factor, The control range, This is the median reference value of the nonlinear mapping interval.

[0109] The system continuously receives complete vector parameters output in a continuous time sequence. It samples the control direction data and control amplitude data stored in each frame of vector parameters and performs low-pass filtering on the first-order exponent. It continuously retains data content with smooth time changes and directly removes numerical content with instantaneous changes between single frames. It outputs all data content after smooth filtering in chronological order and completely combines all time-series data to form a filtered data stream of vector parameters. It compares the difference in numerical changes between adjacent time-series points in the filtered data stream frame by frame and extracts a set of small fluctuation data with the difference amplitude maintained in the range of 0 to 0.02. This set is uniformly recorded as the small touch offset feature of the vector parameters.

[0110] The system retrieves a pre-set noise filtering threshold range of [0, 0.02]. It then compares the value of each set of fluctuation data within the micro-touch offset feature with the upper and lower boundaries of the threshold range. When all the fluctuation data in a single set fall within the threshold range, the set of data is classified as micro-touch interference and marked as an invalid jitter component of the micro-touch offset feature. When the fluctuation data in a single set exceeds the threshold range boundary, the set of data is classified as active touch control offset and marked as an effective control component of the micro-touch offset feature. This completes the classification and division of all micro-touch offset feature data.

[0111] Extract the complete control amplitude values ​​stored within the vector parameters, and divide the current control amplitude range into three independent nonlinear mapping intervals: low amplitude interval [0, 0.3], medium amplitude interval [0.3, 0.7], and high amplitude interval [0.7, 1]. Each independent data segment corresponds to a set of dedicated nonlinear mapping intervals. The current effective control component is completely assigned to the matched dedicated nonlinear mapping interval for storage, thus completing the locking of the nonlinear mapping interval corresponding to the effective control component. The median reference value γ of each interval is set to 0.15, 0.5, and 0.85 respectively.

[0112] Read the interval attribute information corresponding to the matched nonlinear mapping interval, and assign corresponding numerical weights according to different offset positions within the interval. The closer the offset distance is to the ends of the interval, the higher the weight value is assigned, and the closer the offset distance is to the center of the interval, the lower the weight value is assigned. After all points have completed the weight assignment, the dynamic compensation coefficients adapted to the current nonlinear mapping interval are generated.

[0113] The maximum gain adjustment factor corresponding to the dynamic compensation coefficient is fixedly entered and stored during the deployment phase of the virtual joystick interaction function, maintaining a constant value of 0.4 throughout the process. The nonlinear intensity adjustment factor corresponding to the dynamic compensation coefficient is fixed at 2.5, and its value does not change with touch operation. The control amplitude is taken from the amplitude information stored in the vector parameters generated after differential comparison of the virtual control area. The median reference value of the nonlinear mapping interval is taken from the fixed reference value corresponding to the geometric midpoint of the nonlinear mapping interval divided according to the control amplitude. All parameters adopt a completely consistent coordinate displacement dimension to ensure that the dimensions of all parameters are completely matched and consistent when participating in the conversion process. Example calculation: Take α=0.4, β=2.5, γ=0.5, A=0.7, and substitute them into the calculation. =2.5 0.2 = 0.5 (0.5) = 0.4621, =1+0.4 0.4621 = 1.1841.

[0114] Based on the continuous and smooth transformation characteristics of hyperbolic tangent operation, a constant basic compensation base is used as the conversion benchmark. The upper limit of the overall compensation intensity is controlled by the maximum gain adjustment factor. The smooth change rate of the transformation process is changed by the nonlinear intensity adjustment factor. The position difference between the control amplitude and the median reference value of the nonlinear mapping interval is used as the transformation input material to complete the overall conversion. Finally, a dynamic compensation coefficient that adapts to the current effective control component offset is output. The function of allocating differentiated compensation weights for different control amplitude intervals is fully realized. The entire conversion process uses the coordinate displacement dimension uniformly to carry out the entire conversion process, eliminating the conversion deviation caused by dimension mismatch.

[0115] When the value of the control amplitude is less than the median reference value of the nonlinear mapping interval, the converted position difference is negative data. After processing with hyperbolic tangent, a negative transformation result is obtained. After superimposing the basic compensation base, a dynamic compensation coefficient with a smaller specification is output. When the value of the control amplitude is equal to the median reference value of the nonlinear mapping interval, the converted position difference is zero data. After processing with hyperbolic tangent, a zero-value transformation result is obtained. A dynamic compensation coefficient with a specification equal to the basic compensation base is output. When the value of the control amplitude is greater than the median reference value of the nonlinear mapping interval, the converted position difference is positive data. After processing with hyperbolic tangent, a positive transformation result is obtained. After superimposing the basic compensation base, a dynamic compensation coefficient with a larger specification is output. All parameters involved in the conversion maintain a unified coordinate displacement dimension throughout the entire change stage, and the change trend is continuous without abrupt breaks.

[0116] The dynamic compensation coefficients are fully read and synchronously converted with the original offset values ​​carried by the effective control components. The dynamic compensation coefficients are used to fully correct the overall magnitude of the original offset values ​​of the effective control components. After the amplitude correction is completed, the intermediate parameters of the fork mobile robot are generated.

[0117] The system collects real-time complete operating status information of the forklift mobile robot, including various status indicators such as stationary driving, constant speed driving, acceleration and deceleration driving. It retrieves the corresponding exclusive phase adjustment rules according to the current operating status indicators, and adjusts the overall time offset of the offset data carried by the intermediate parameters according to the exclusive phase adjustment rules. After the adjustment is completed, the initial compensation vector of the forklift mobile robot is generated.

[0118] The system reads all the data of the unprocessed raw vector parameters and simultaneously retrieves all the data of the generated preliminary compensation vector. It then performs numerical superposition on the corresponding control direction and control amplitude data within the two sets of data. After superposition, the superimposed vector is obtained. The system continuously monitors the numerical change amplitude between adjacent frames within the continuous time sequence of the superimposed vector. A smoothing constraint threshold of 0.03 is set. When the numerical change amplitude between adjacent frames exceeds the fixed smoothing standard, the numerical difference between the two frames is reduced by a smoothing attenuation coefficient of 0.5. After all time-series frames have completed the smoothness constraint processing, the optimized vector parameters of the fork mobile robot are generated.

[0119] The beneficial effects are as follows: by using low-pass filtering, irregular small-amplitude touch jitter in the original vector parameters is completely removed; by relying on a fixed noise threshold, interference data and user-active control data are accurately distinguished; independent nonlinear mapping intervals are divided for effective control components and adaptive weights are assigned to complete amplitude correction; the compensation phase is adjusted in combination with the robot's real-time running status; and finally, the original vector is superimposed and smoothed and constrained, thus eliminating the robot's jerking problem caused by slight finger tremors throughout the process; the compensation intensity is dynamically matched according to different control amplitudes, taking into account both the dual requirements of low-speed fine control and high-speed stable driving; and the fluctuation amplitude of the output optimized vector parameters is stable and controllable, which greatly improves the smoothness of the remote touch-controlled driving trajectory of the forklift mobile robot.

[0120] D4. Respond to the virtual emergency stop signal of the forklift mobile robot and clear and lock the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot;

[0121] In this embodiment of the invention, the step of responding to the virtual emergency stop signal of the forklift mobile robot and clearing and locking the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot includes:

[0122] In response to the virtual emergency stop signal of the fork-type mobile robot, the output data channel of the optimized vector parameters is forcibly blocked to obtain the no-output truncation parameters of the fork-type mobile robot.

[0123] Logical verification is performed on the associated safety loop state without output truncation parameters to obtain the loop safety confirmation signal of the fork-type mobile robot;

[0124] Based on the loop safety confirmation signal, the zero-output cutoff parameter is controlled by zeroing to obtain the locking state maintenance parameter of the fork mobile robot.

[0125] By marking the locking state holding parameters with anti-rebound markers, the interlocking vector parameters of the fork-type mobile robot are obtained.

[0126] The system monitors all signal commands sent from the virtual joystick interface of the WeChat mini program in real time. Once it detects a virtual emergency stop signal for the fork-type mobile robot triggered by a user's operation, it immediately cuts off all data transmission links for the optimized vector parameters and terminates the continuous transmission of optimized vector parameters to the subsequent velocity and orientation coupling assignment stage. After the blocking operation is executed, all optimized vector parameters to be output stop flowing. At this time, only the parameter carrier with no output capability after the channel is blocked remains. The data content carried inside this carrier is uniformly recorded as the non-output truncated parameters of the fork-type mobile robot.

[0127] The system retrieves all safety loop associated status identifiers bound within the no-output truncation parameter, reads the hardware connectivity status, signal feedback status, and braking mechanism readiness status of each safety loop one by one, and checks each status identifier against the safety operation standard according to the safety judgment logic. Only when all status identifiers corresponding to all safety loops meet the standard conditions set by the safety judgment logic is a valid feedback signal for passing the judgment generated. If any safety loop status identifier does not meet the standard conditions, the verification process continues to loop continuously. After all verification items pass the standard check, a loop safety confirmation signal for the forklift mobile robot is output.

[0128] The complete receiving loop confirms the effective judgment information of the safe transmission of the signal. For the control amplitude data and control direction data stored in the internal parameter without output truncation, the zeroing process is performed uniformly. Both types of data are reset to the reference value of 0, which represents no control action. At the same time, the parameter locking control mechanism is activated to restrict any changes in the parameter value after zeroing. This prevents external touch operation and equipment data fluctuations from modifying the zeroing parameters. After the zeroing process and locking control are completed, the locking state parameters of the fork mobile robot with fixed values ​​throughout the process are formed.

[0129] Read all the data content that is fixed to zero in the locked state parameter, and attach a special anti-rebound identification mark to the entire set of parameter data. This mark will continue to follow the parameter to the next process. After the subsequent process recognizes the mark, it will refuse to receive any new touch offset data, and there will be no instantaneous rebound output of the control vector when the emergency stop state is released. After completing the anti-rebound mark binding operation, the interlocking vector parameters of the fork mobile robot are generated.

[0130] The beneficial effects are that after the virtual emergency stop signal is triggered, the parameter output channel is directly cut off. Combined with a multi-level safety loop logic verification mechanism, the parameter zeroing and locking are performed only after the braking conditions are fully met. An additional anti-rebound mark limits the instantaneous rebound output of parameters, constructing a complete closed-loop emergency stop safety interlock logic. This blocks the issuance of control commands from the source of data transmission, avoids the safety hazard of the robot moving unexpectedly in the emergency stop state, and greatly improves the operational safety level of remote wireless control of the forklift mobile robot.

[0131] D5. Assign velocity and orientation stepless coupling values ​​to the interlocking vector parameters to obtain the integrated vector parameters of the fork-type mobile robot;

[0132] In this embodiment of the invention, the step of assigning velocity-azimuth stepless coupling values ​​to the interlocking vector parameters to obtain the integrated vector parameters of the forklift mobile robot includes:

[0133] The interlocking vector parameters are analyzed by component analysis to obtain the velocity scalar parameters and azimuth scalar parameters of the interlocking vector parameters;

[0134] The speed scalar parameter is continuously mapped to the speed range to obtain the speed assignment data of the fork-type mobile robot.

[0135] Based on the azimuth scalar parameter, the velocity assignment data is subjected to azimuth projection mapping to obtain the azimuth assignment data of the fork-type mobile robot.

[0136] The velocity assignment data and the orientation assignment data are dynamically fused to obtain the integrated vector parameters of the fork-type mobile robot.

[0137] The system fully reads all data information stored within the interlocking vector parameters, separates the control amplitude data and control direction data carried within the parameters, extracts the independent data related to the control amplitude as the velocity scalar parameter of the interlocking vector parameters, and extracts the independent data related to the control direction as the azimuth scalar parameter of the interlocking vector parameters. The two types of data are stored separately and marked for distinction, thus realizing the complete component analysis of the interlocking vector parameters.

[0138] The minimum and maximum permissible operating speeds of the forklift mobile robot are retrieved to form a complete speed range. A continuous conversion rule is established that corresponds one-to-one between the speed scalar parameter value range and the vehicle speed range. According to the conversion rule, each set of speed scalar parameter values ​​is uniformly converted to the value range corresponding to the actual driving speed of the vehicle. There are no numerical segmentation breaks in the conversion process, and the numerical conversion is maintained continuously without any discontinuities. After all scalar values ​​have been converted, the speed assignment data of the forklift mobile robot is generated.

[0139] Extract the complete travel orientation information recorded within the orientation scalar parameter. Using the robot's forward reference orientation as a fixed projection reference plane, project and assign the travel speed values ​​contained in the speed assignment data according to the travel orientation corresponding to the orientation scalar parameter. This makes the speed values ​​match the corresponding travel direction to form a speed data set with directional attributes. The entire set of speed data after projection assignment is uniformly recorded as the orientation assignment data of the forklift mobile robot.

[0140] The system synchronously reads complete velocity and orientation assignment data, binds the velocity values ​​and orientation attribute data that correspond one-to-one within the two sets of data to the same set of data units, and updates the temporal changes of the two sets of data in real time. When the value of either set of data changes, the other set of data is adjusted synchronously. The speed information and orientation information are kept synchronized and matched throughout the process. After all data units have completed the bidirectional linkage binding process, the integrated vector parameters of the fork mobile robot are generated.

[0141] The beneficial effects are that by separating speed and orientation data through component analysis, speed range conversion is completed using a continuous mapping method without breaks to achieve stepless speed adjustment. By relying on orientation projection mapping, the speed value is accurately matched with the direction of travel. Then, the two types of data are dynamically fused to form a unified integrated vector parameter, ensuring that the speed and steering are synchronously linked and output without delay. This eliminates the problem of asynchronous speed changes and steering actions during operation and continuously optimizes the motion continuity of the virtual joystick control of the fork-type mobile robot.

[0142] D6. Generate composite control commands for the fork-type mobile robot based on the integrated vector parameters.

[0143] In this embodiment of the invention, generating the composite control command for the forklift robot based on the integrated vector parameters includes:

[0144] The integrated vector parameters are analyzed in the parameter domain to obtain the velocity component parameters and azimuth component parameters of the integrated vector parameters;

[0145] By binding the velocity component parameters and the orientation component parameters with control primitives, an integrated control primitive carrier for the fork-type mobile robot is obtained.

[0146] The integrated control element carrier is filled with control words to obtain the original control word sequence of the fork-type mobile robot;

[0147] A checksum is attached to the original control word sequence to obtain a checksum-enabled control word sequence.

[0148] Based on the control word sequence with check, the control word sequence with check is packaged into instruction frames to obtain the composite control instructions for the fork-type mobile robot.

[0149] The entire data content stored within the integrated vector parameters is read completely. The driving speed-related data and the travel orientation-related data carried within the parameters are separated. The independent driving speed-related data is extracted and marked as the speed component parameters of the integrated vector parameters, and the independent travel orientation-related data is extracted and marked as the orientation component parameters of the integrated vector parameters. The two types of data are stored separately and their complete time sequence information is retained, thus completing the complete parameter domain parsing of the integrated vector parameters.

[0150] The standardized minimum control data unit inside the device is retrieved as the control element. All data contents of the parsed velocity component parameters and all data contents of the orientation component parameters are synchronously written into the same control element, so that a single control element can carry both velocity and orientation information at the same time. All time-series data corresponding to the two types of component parameters are uniformly collected and bound. After binding, an integrated control element carrier for the fork-type mobile robot carrying complete control information is generated.

[0151] Read all the speed and orientation data stored inside the integrated control unit carrier. According to the fixed data storage format specified by the robot's underlying driver program, fill each set of data inside the carrier into the corresponding control word storage unit in sequence. Strictly follow the preset data arrangement order to complete the filling operation line by line. After filling, all the continuously arranged control word storage units are combined to form the original control word sequence of the fork mobile robot.

[0152] Read all control word data contained in the original control word sequence, read all values ​​in the sequence according to the device's data verification operation rules to participate in the operation, generate a unique corresponding verification identifier data as a verification code, and fix the generated verification code at the end of the original control word sequence to form an inseparable whole data string with all control word data. After the concatenation operation is completed, a control word sequence with verification is generated from the original control word sequence.

[0153] The complete sequence of control words with check is read, and according to the frame structure division rules specified in the robot wireless communication transmission protocol, the sequence of control words with check is split and loaded into the standard communication frame. The frame header and frame tail identifiers required for the communication frame are filled in synchronously. After the single frame data encapsulation is completed, it is combined into a complete set of instruction data packets according to the continuous transmission rules. The standardized communication data packet with complete encapsulation and packaging is the composite control instruction of the forklift mobile robot.

[0154] The beneficial effects are that the vector parameters are decomposed and integrated in layers and bound to a unified control unit, the control words are standardized and a check code is added to ensure the integrity of data transmission, and composite control commands are generated by packetizing according to the communication protocol. This ensures that the control commands sent to the forklift mobile robot have a unified format and verifiable data, avoids robot malfunctions caused by data loss or corruption during wireless transmission, and achieves standardized and secure transmission of control data.

[0155] like Figure 2 The diagram shown is a functional block diagram of a fork-type mobile robot control system based on a virtual joystick, according to an embodiment of the present invention.

[0156] The virtual joystick-based fork-type mobile robot control system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the virtual joystick-based fork-type mobile robot control system 100 may include a touch area calibration module 101, a touch vector calculation module 102, an offset adaptive correction module 103, an emergency stop interlocking module 104, a speed-orientation coupling assignment module 105, and a composite instruction generation module 106. The module described in this invention can also be called a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0157] In this embodiment, the functions of each module / unit are as follows:

[0158] The touch area calibration module 101 is used to configure the touch coordinates of the virtual joystick interaction of the fork mobile robot to obtain the positioning virtual control area of ​​the fork mobile robot.

[0159] The touch vector calculation module 102 is used to perform differential comparison of the real-time touch point coordinates of the user of the fork mobile robot based on the center reference coordinates of the positioning virtual control area, so as to obtain the vector parameters of the fork mobile robot, the vector parameters including the control amplitude and control direction.

[0160] The offset adaptive correction module 103 is used to identify minute touch offsets in the vector parameters and perform adaptive nonlinear compensation correction on the identified minute touch offsets to obtain the optimized vector parameters of the fork-type mobile robot.

[0161] The emergency stop interlocking module 104 is used to respond to the virtual emergency stop signal of the forklift mobile robot and to clear and lock the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot.

[0162] The velocity-orientation coupling assignment module 105 is used to perform velocity-orientation stepless coupling assignment on the interlocking vector parameters to obtain the integrated vector parameters of the fork-type mobile robot.

[0163] The composite instruction generation module 106 is used to generate composite control instructions for the fork-type mobile robot based on the integrated vector parameters.

[0164] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0165] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0167] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0168] This application embodiment can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling a fork-type mobile robot based on a virtual joystick, characterized in that, The method includes: D1. Configure the touch coordinates for the virtual joystick interaction of the fork-type mobile robot to obtain the positioning virtual control area of ​​the fork-type mobile robot. D2. Based on the center reference coordinates of the positioning virtual control area, perform differential comparison on the real-time touch point coordinates of the fork-type mobile robot to obtain the vector parameters of the fork-type mobile robot, wherein the vector parameters include the control amplitude and control direction. D3. The vector parameters are identified by minute touch offsets, and the identified minute touch offsets are adaptively nonlinearly compensated and corrected to obtain the optimized vector parameters of the fork-type mobile robot. D4. Respond to the virtual emergency stop signal of the forklift mobile robot and clear and lock the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot; D5. Assign velocity and orientation stepless coupling values ​​to the interlocking vector parameters to obtain the integrated vector parameters of the fork-type mobile robot; D6. Generate composite control commands for the fork-type mobile robot based on the integrated vector parameters.

2. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 1, characterized in that, The configuration of touch coordinates for the virtual joystick interaction of the forklift mobile robot to obtain the positioning virtual control area of ​​the forklift mobile robot includes: Obtain the virtual joystick interaction of the fork-type mobile robot; The original touch coordinates of the virtual joystick interaction are normalized in coordinate space to obtain the normalized touch coordinates of the virtual joystick interaction. The normalized touch coordinates are extracted by origin reference matching to obtain the calibration origin offset of the normalized touch coordinates; Based on the calibration origin offset, the normalized touch coordinates are compensated for origin drift to obtain the compensated touch coordinates. The compensated touch coordinates are subjected to coordinate boundary clamping constraints to obtain the positioning virtual control area of ​​the fork-type mobile robot.

3. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 1, characterized in that, Based on the center reference coordinates of the virtual control area, the coordinates of the user's real-time touch points on the fork-type mobile robot are differentially compared to obtain the vector parameters of the fork-type mobile robot. These vector parameters include the control amplitude and control direction, and include: The center of gravity of the virtual control area is extracted to obtain the center reference coordinates of the fork-type mobile robot. Based on the central reference coordinates, the user's real-time touch point coordinates of the fork-type mobile robot are decomposed by difference to obtain the lateral offset component and longitudinal offset component of the fork-type mobile robot. The directional polarity of the lateral offset component is determined to obtain the control direction of the fork-type mobile robot; The longitudinal offset component is normalized and calibrated to obtain the control range of the fork-type mobile robot. The control direction and the control amplitude are integrated into the vector parameters of the fork-type mobile robot.

4. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 1, characterized in that, The process of identifying minute touch offsets in the vector parameters and performing adaptive nonlinear compensation correction on the identified minute touch offsets to obtain optimized vector parameters for the fork-type mobile robot includes: The vector parameters are subjected to low-pass filtering to obtain a filtered data stream of the vector parameters, and the minute touch offset characteristics of the vector parameters are obtained based on the filtered data stream. Based on a preset noise filtering threshold range, the minute touch offset features are filtered and divided to obtain the invalid jitter component or the effective control component of the minute touch offset features. The invalid jitter component is the amount of minute accidental touch interference, and the effective control component is the amount of active touch control offset. The effective control components are subjected to piecewise gain mapping to obtain the intermediate parameters of the fork-type mobile robot. Based on the current operating state of the forklift mobile robot, the intermediate parameters are dynamically phase-adjusted to obtain the preliminary compensation vector of the forklift mobile robot; The preliminary compensation vector is superimposed with the vector parameters, and the superimposed vector is subjected to smoothness constraints to obtain the optimized vector parameters of the fork-type mobile robot.

5. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 4, characterized in that, The step of performing piecewise gain mapping on the effective control components to obtain the intermediate parameters of the fork-type mobile robot includes: Obtain the control amplitude in the vector parameters, and determine the nonlinear mapping range of the effective control component based on the control amplitude; Based on the nonlinear mapping interval, adaptive weight allocation is performed on the effective control components to obtain dynamic compensation coefficients; Based on the dynamic compensation coefficient, the amplitude of the effective control component is corrected to obtain the intermediate parameter.

6. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 5, characterized in that, The formula for calculating the dynamic compensation coefficient is as follows: ； in, The dynamic compensation coefficient is... The preset maximum gain adjustment factor. The preset nonlinear intensity adjustment factor, The control range, This is the median reference value of the nonlinear mapping interval.

7. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 1, characterized in that, The method of responding to the virtual emergency stop signal of the forklift mobile robot and clearing and locking the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot includes: In response to the virtual emergency stop signal of the fork-type mobile robot, the output data channel of the optimized vector parameters is forcibly blocked to obtain the no-output truncation parameters of the fork-type mobile robot. Logical verification is performed on the associated safety loop state without output truncation parameters to obtain the loop safety confirmation signal of the fork-type mobile robot; Based on the loop safety confirmation signal, the zero-output cutoff parameter is controlled by zeroing to obtain the locking state maintenance parameter of the fork mobile robot. By marking the locking state holding parameters with anti-rebound markers, the interlocking vector parameters of the fork-type mobile robot are obtained.

8. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 1, characterized in that, The step of assigning velocity-azimuth stepless coupling values ​​to the interlocking vector parameters to obtain the integrated vector parameters of the forklift mobile robot includes: The interlocking vector parameters are analyzed by component analysis to obtain the velocity scalar parameters and azimuth scalar parameters of the interlocking vector parameters; The speed scalar parameter is continuously mapped to the speed range to obtain the speed assignment data of the fork-type mobile robot. Based on the azimuth scalar parameter, the velocity assignment data is subjected to azimuth projection mapping to obtain the azimuth assignment data of the fork-type mobile robot. The velocity assignment data and the orientation assignment data are dynamically fused to obtain the integrated vector parameters of the fork-type mobile robot.

9. The method for controlling a fork-type mobile robot based on a virtual joystick as described in claim 1, characterized in that, The step of generating composite control commands for the forklift mobile robot based on the integrated vector parameters includes: The integrated vector parameters are analyzed in the parameter domain to obtain the velocity component parameters and azimuth component parameters of the integrated vector parameters; By binding the velocity component parameters and the orientation component parameters with control primitives, an integrated control primitive carrier for the fork-type mobile robot is obtained. The integrated control element carrier is filled with control words to obtain the original control word sequence of the fork-type mobile robot; A checksum is attached to the original control word sequence to obtain a checksum-enabled control word sequence. Based on the control word sequence with check, the control word sequence with check is packaged into instruction frames to obtain the composite control instructions for the fork-type mobile robot.

10. A control system for a fork-type mobile robot based on a virtual joystick, characterized in that, The system is used to implement the virtual joystick-based control method for a fork-type mobile robot as described in claim 1, the system comprising: The touch area calibration module is used to configure the touch coordinates of the virtual joystick interaction of the fork-type mobile robot to obtain the positioning virtual control area of ​​the fork-type mobile robot. The touch vector calculation module is used to perform differential comparison of the real-time touch point coordinates of the user of the fork-type mobile robot based on the center reference coordinates of the positioning virtual control area, so as to obtain the vector parameters of the fork-type mobile robot, the vector parameters including the control amplitude and control direction; The offset adaptive correction module is used to identify minute touch offsets in the vector parameters and perform adaptive nonlinear compensation correction on the identified minute touch offsets to obtain the optimized vector parameters of the fork-type mobile robot. The emergency stop interlocking module is used to respond to the virtual emergency stop signal of the forklift mobile robot and to clear and lock the optimized vector parameters to obtain the interlocking vector parameters of the forklift mobile robot. The velocity-orientation coupling assignment module is used to perform velocity-orientation stepless coupling assignment on the interlocking vector parameters to obtain the integrated vector parameters of the fork-type mobile robot. The composite instruction generation module is used to generate composite control instructions for the fork-type mobile robot based on the integrated vector parameters.

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