Mobile robot attitude and orbit adaptive control method and system based on data timeliness

Abstract

The invention relates to the technical field of mobile robot motion control, in particular to a mobile robot attitude and orbit self-adaptive control method and system based on data timeliness. The method comprises the following steps: establishing a clock alignment reference between a controller and a server; receiving the control data packet, analyzing the control data packet based on a clock alignment reference, and performing differential operation in combination with a local receiving moment to generate instruction lagging time delay data; and inputting the lagging time delay data of the instruction into a preset aging attenuation function, and executing significance evaluation in combination with a historical evolution trend to generate a dynamic security attenuation coefficient for quantifying the credible weight of the instruction. According to the method, the communication time delay is continuously mapped into the dynamic motion envelope boundary with the attitude and orbit coupling suppression characteristic, so that the control performance of the mobile robot from jerking type frequent sudden stop to smooth self-adaptive safe sliding in an unstable communication environment is realized; and the safety, the control continuity and the response stability of the motion control of the mobile robot are improved.

Description

Technical Field

[0001] This invention relates to the field of mobile robot motion control technology, and in particular to a mobile robot attitude and trajectory adaptive control method and system based on data timeliness. Background Technology

[0002] Current mobile robot control technologies generally employ a binary judgment mechanism based on signal strength thresholds or heartbeat packet detection. This leads to either frequent emergency stop protection triggers and significant motion jerking when communication quality fluctuates, severely impacting work efficiency and equipment lifespan; or the robot continues to execute expired instructions until the connection is completely broken, lacking dynamic assessment of instruction timeliness during this period, significantly increasing collision risk and trajectory deviation. Simultaneously, traditional control methods impose proportional restrictions on all robot motion parameters when communication quality deteriorates, ignoring the greater impact of turning operations on positioning accuracy. This simplistic approach fails to consider that in situations of insufficient information, angular velocity command execution can cause position estimation errors to increase exponentially, while the impact of linear velocity is relatively small. More critically, current technologies generally only focus on the instantaneous judgment of a single frame of communication status, lacking the ability to perceive trends in communication quality changes. This lack of forward-looking design prevents the control system from proactively addressing deterioration in the communication environment, forcing a reactive response only when problems become severe, delaying the implementation of protective measures and increasing safety hazards.

[0003] In summary, existing technologies lack an adaptive mechanism that directly maps communication quality as a continuous variable to robot kinematic constraints, thus failing to maximize robot operational efficiency while ensuring safety. Summary of the Invention

[0004] Therefore, it is necessary to provide a mobile robot attitude and trajectory adaptive control method and system based on data timeliness to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a mobile robot attitude and trajectory adaptive control method based on data timeliness includes the following steps:

[0006] Step S1: Establish a clock alignment reference between the controller and the server; receive control data packets, parse the control data packets based on the clock alignment reference, extract the sending timestamp, and perform differential calculations in conjunction with the local receiving time to generate instruction lag delay data;

[0007] Step S2: Input the instruction lag delay data into the preset time decay function, and perform a significance evaluation in combination with the historical evolution trend to generate a dynamic security decay coefficient for quantifying the instruction trust weight.

[0008] Step S3: Obtain the physical limit parameters of the mobile robot, wherein the physical limit parameters include at least the preset maximum linear velocity and maximum angular velocity. Use the dynamic safety attenuation coefficient to nonlinearly couple and scale the physical limit parameters to generate a real-time motion envelope threshold for defining the current motion boundary.

[0009] Step S4: Obtain the original target instruction, perform priority progressive limiting on the original target instruction using the real-time motion envelope threshold to obtain the clamping modulation vector; perform signal conversion on the clamping modulation vector based on the kinematic model to form the execution-level drive signal.

[0010] This invention eliminates reference deviations across device hardware environments through high-precision clock synchronization and time delay differential calculation, achieving microsecond-level precise quantification of communication quality loss. Utilizing trend significance assessment and risk scenario correction, it transforms discrete and fluctuating network latency into continuous, smooth, and predictive trust weights, effectively filtering random communication noise and overcoming control response lag. Based on a nonlinear coupling strategy, it dynamically reconstructs the motion envelope boundary, prioritizing tightening of steering capabilities to address the risk of position divergence caused by track drift during signal degradation. Combining priority-based progressive amplitude limiting and slope constraint logic, it maximizes the geometric consistency of the original operating trajectory under stringent constraints, eliminating mechanical shocks and drive oscillations caused by command steps. Coupled with real-time closed-loop deviation monitoring, it comprehensively ensures the operational stability and active safety of the mobile robot in complex communication environments.

[0011] Preferably, the present invention also provides a mobile robot attitude and trajectory adaptive control system based on data timeliness, for executing the mobile robot attitude and trajectory adaptive control method based on data timeliness as described above. The mobile robot attitude and trajectory adaptive control system based on data timeliness includes:

[0012] The delay status detection module is used to establish a clock alignment reference between the controller and the server; it receives control data packets, parses the control data packets based on the clock alignment reference, extracts the sending timestamp, and performs differential calculations in combination with the local receiving time to generate instruction lag delay data.

[0013] The confidence assessment module is used to input instruction lag delay data into a preset time decay function, and perform a significance assessment in combination with historical evolution trends to generate a dynamic security decay coefficient for quantifying instruction confidence weight.

[0014] The dynamic motion boundary calculation module is used to obtain the physical limit parameters of the mobile robot, and to perform nonlinear coupling scaling on the physical limit parameters using a dynamic safety attenuation coefficient to generate a real-time motion envelope threshold for defining the current motion boundary.

[0015] The instruction modulation execution module is used to acquire the original target instruction, perform progressive amplitude limiting on the original target instruction using a real-time motion envelope threshold, and obtain a clamping modulation vector; based on the kinematic model, the clamping modulation vector is converted into an execution-level drive signal.

[0016] This system achieves deep decalibration and linkage between communication perception layer indicators and motion control execution layer through a modular architecture. The time base drift between distributed hardware is eliminated using a time delay state detection module, ensuring real-time performance and high accuracy of time delay quantization. The control confidence assessment module enhances the system's robustness to complex network environments through trend analysis algorithms and risk weighting mechanisms, providing forward-looking trust weights for robot decision-making. The dynamic motion boundary calculation module realizes the nonlinear mapping reconstruction of physical limit parameters and communication confidence, effectively suppressing track divergence and lateral instability risks in signal fluctuation environments by dynamically tightening the motion envelope. The command modulation execution module ensures smooth transition of control signals and trajectory geometric consistency under extreme amplitude limiting conditions through a priority progressive amplitude limiting mechanism. The collaborative operation of these modules significantly improves the continuity of mobile robot operations, the smoothness of its movement, and the inherent safety of the system in highly interference electromagnetic environments. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the steps of a mobile robot attitude and trajectory adaptive control method based on data timeliness;

[0018] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1 in this invention. Detailed Implementation

[0019] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0021] It should be understood that the term “and / or” as used herein includes any and all combinations of one or more of the associated items listed.

[0022] In this embodiment of the invention, reference Figures 1 to 2 The diagram shown illustrates the steps of the mobile robot attitude and trajectory adaptive control method based on data timeliness according to the present invention. In this example, the mobile robot attitude and trajectory adaptive control method based on data timeliness includes the following steps:

[0023] Step S1: Establish a clock alignment reference between the controller and the server; receive control data packets, parse the control data packets based on the clock alignment reference, extract the sending timestamp, and perform differential calculations in conjunction with the local receiving time to generate instruction lag delay data;

[0024] In this embodiment of the invention, the control unit performs a clock alignment handshake with the server, records the local time of the synchronization request and response round trip and the server's current time, calculates and applies the unit clock synchronization compensation value, and maps the original transmission timestamp in the control data packet to the control unit's local time coordinate system in real time, establishing a globally unified defined transmission time. Subsequently, the control unit captures the hardware trigger instant of the control data packet, reads the local reception time and performs differential operation with the globally unified defined transmission time to accurately quantify the physical time loss generated by the control command in the wireless link transmission, protocol unpacking and network congestion process, and generate command lag delay data.

[0025] Step S2: Input the instruction lag delay data into the preset time decay function, and perform a significance evaluation in combination with the historical evolution trend to generate a dynamic security decay coefficient for quantifying the instruction trust weight.

[0026] In this embodiment of the invention, the control unit injects instruction lag delay data into a fixed-length circular buffer to generate a historical sequence. A time-weighted algorithm is used to calculate the delay change trend value, and the statistical variance of the sequence is combined to determine the significance of the trend, distinguishing between deterministic trends and random communication disturbances, thus generating a trend significance index. Subsequently, the control unit performs forward-looking correction on the original timeliness decay value based on the trend significance index, and performs contextual correction in conjunction with the risk level matrix extracted from the original target instruction. Finally, a first-order lag filter is used to eliminate numerical jumps, generating a dynamic security decay coefficient that quantifies the instruction trust weight.

[0027] Step S3: Obtain the physical limit parameters of the mobile robot, and use the dynamic safety attenuation coefficient to nonlinearly couple and scale the physical limit parameters to generate a real-time motion envelope threshold for defining the current motion boundary;

[0028] In this embodiment of the invention, the control unit constructs quadratic suppression logic based on a dynamic safety attenuation coefficient to generate nonlinear coupling mapping coefficients. This linearly reduces the maximum linear velocity of the mobile robot and applies a stronger quadratic nonlinear constraint to the maximum angular velocity, ensuring that the attenuation rate of steering capability is always higher than the linear velocity. Simultaneously, the control unit calculates a sideslip risk index by combining real-time driving speed, moment of inertia, and physical wheelbase parameters. It then performs safety compensation on the upper limit of steering curvature under speed dependence. Finally, it encapsulates the multidimensional physical limit parameters with execution logic to generate a real-time motion envelope threshold used to constrain the current degree of freedom of motion.

[0029] Step S4: Obtain the original target command, perform priority progressive limiting on the original target command using the real-time motion envelope threshold to obtain the clamping modulation vector; perform signal conversion on the clamping modulation vector based on the kinematic model to form the execution-level drive signal;

[0030] In this embodiment of the invention, the control unit performs a priority-based progressive clamping on the standardized original target command using a real-time motion envelope threshold, prioritizing angular velocity over linear velocity. It calculates the angular velocity clamping ratio to achieve proportional linkage adjustment of the linear velocity, generating a clamping modulation vector that maintains the geometric consistency of the trajectory. Subsequently, the control unit uses slope limiting logic to truncate the vector in the time domain to eliminate command step, and converts it into an execution-level drive signal for the actuator through inverse kinematics. It synchronously compares the motor feedback state with the command deviation in real time, and performs a reset operation to achieve safe braking when the deviation continues to exceed the limit.

[0031] refer to Figure 2 As shown, step S1 includes:

[0032] S11: Based on the local time of the synchronization request and the local time of the server response, and combined with the current server time carried in the response data packet, clock deviation is calibrated to generate unit clock synchronization compensation value.

[0033] S12: Use the unit clock synchronization compensation value to perform time coordinate mapping on the original transmission timestamp in the subsequent control data packet to generate a globally unified defined transmission time.

[0034] S13: Calculate the absolute physical difference between the current local reception time and the globally unified defined transmission time, and generate instruction lag delay data.

[0035] In one embodiment, the control unit sends a synchronization request to the server via a wireless communication link and records the first local moment of transmission. And the second local time at the instant of receiving the server synchronization response packet. The server synchronization response packet is parsed to extract the current server time recorded within it. Use the following formula:

[0036] ;

[0037] Calculate the clock synchronization compensation value of the generation unit. ;in, T_1 is the time when the synchronization request is sent, T_2 is the time when the synchronization response is received, and T_s is the current time of the server. The unit clock synchronization compensation value C_bias is used to compensate for the difference in crystal oscillator frequency and the start time offset between the server and the control unit.

[0038] In another embodiment, the control unit obtains the original transmission timestamp generated by the server at the instant of encapsulation instruction from the subsequently received control data packets. By performing addition operations Generate a globally unified definition of the transmission time. ;in, To control the original timestamp of data packet transmission, This is the unit clock synchronization compensation value. This step aligns the server time base in the heterogeneous hardware environment to the local time coordinate system of the control unit in real time, establishing a unified time measurement scale.

[0039] In one embodiment, the control unit reads the controller's high-precision local reception time at the moment it receives the hardware trigger of the control data packet. By performing subtraction. Generate instruction lag delay data ;in, For the controller's local reception time, The transmission time is defined globally and uniformly. The instruction lag delay data... The time loss of control commands during wireless communication link transmission, protocol stack unpacking, and network congestion processes was precisely quantified using absolute physical time values, providing real-time data support for subsequent motion constraint adjustments.

[0040] Preferably, the significance assessment performed in step S2, in conjunction with historical evolution trends, includes:

[0041] The delay data of multiple consecutive frames of instructions are stored in a circular buffer to generate a delay history buffer sequence, and a time-weighted differential operation is performed on the delay history buffer sequence to generate a delay change trend value.

[0042] The trend value of time delay change is compared with the variance of the fluctuation of the historical time delay buffer sequence to generate a trend significance index.

[0043] The prediction gain is determined based on the trend significance index, and the original value of the time-effect decay function output is trend-compensated to generate the trend-adjusted confidence coefficient.

[0044] In one embodiment, the control unit constructs a first-in-first-out (FIFO) circular buffer of length 8 frames, storing the instruction lag delay data generated in each frame sequentially to generate a delay history buffer sequence. The control unit performs a subtraction operation on adjacent frames in the sequence to obtain the single-frame increment, and assigns a time weight factor between 0.1 and 0.9, with the weight factor being larger the closer the data reception time is to the current time. By performing an accumulation operation on the product of each group of single-frame increments and their weight factors, a delay change trend value is generated. ;in, This represents the latency trend value. The positive or negative attribute of this value defines the deterioration or improvement trend of the communication environment, respectively.

[0045] In another embodiment, the control unit calculates the second-order central moments of eight sample data points in the time-delay history buffer sequence to generate the fluctuation variance. The trend value of time delay change The absolute value divided by the fluctuation variance arithmetic square root Using the formula Generate trend significance index ;in, As a trend significance index, This represents the trend value of time delay change. For volatility variance The arithmetic square root of the time delay. This value quantifies the degree to which current time delay fluctuations deviate from historical benchmarks, and is used to distinguish between deterministic trends and random disturbances.

[0046] In one embodiment, the control unit is based on the trend significance index. Determine the prediction gain .like Greater than 1.2 and If the value is greater than 0, the predicted gain will be... Locked at 0.92; if Greater than 1.2 and If it is less than 0, the prediction gain will be... Locked to 1.08; if If the prediction gain is less than or equal to 1.2, then the prediction gain is... Locked to 1.0. Obtain the time-decrease function based on the original value generated at the current time. By performing multiplication operations Generate trend-adjusted confidence coefficient ;in, Adjust the confidence coefficient for the trend. This is the original value output by the aging decay function. This is for predicting gain. Therefore, the confidence level of the current instruction is proactively adjusted using historical data.

[0047] Preferably, the dynamic security decay coefficient for quantizing instruction trust weights in step S2 includes:

[0048] Match the corresponding risk level matrix based on the job attributes of the original target instruction, and use the corresponding scaling factor to perform contextual correction on the trend-adjusted confidence coefficient to generate the task-weighted confidence coefficient.

[0049] A low-pass filtering algorithm is used to perform time-domain smoothing on the task-weighted confidence coefficients to generate dynamic safety attenuation coefficients.

[0050] In one embodiment, the control unit parses the target linear velocity and target angular velocity contained in the original target instruction and defines them as operational attributes. These operational attributes are then mapped to a preset risk level matrix to extract the corresponding risk scaling factor K_s; wherein the risk level matrix is ​​pre-stored in the controller's read-only memory, and a higher operational attribute value corresponds to a higher risk scaling factor. The smaller the value, the better. This is achieved by performing a multiplication operation. Generate task weighted confidence coefficient ;in, Weighted confidence coefficients for the task. Adjust the confidence coefficient for the trend. This is a risk scaling factor.

[0051] In another embodiment, the control unit uses a first-order hysteresis filtering algorithm as a low-pass filtering algorithm to weight the confidence coefficients of the task. Perform time-domain smoothing. Read the task-weighted confidence coefficients for the current cycle. And call the dynamic safety attenuation coefficient of the previous control cycle. Using formulas Generate dynamic safety attenuation coefficient ;in, The dynamic safety attenuation coefficient, Weighted confidence coefficients for the task. This represents the dynamic safety attenuation coefficient of the previous control cycle. A smoothing factor of 0.35 is used. This step eliminates jumps in confidence scores caused by fluctuations in the wireless communication environment by performing an iterative weighted average in the time domain.

[0052] Preferably, the step S3 of generating the real-time motion envelope threshold for defining the current motion boundary includes:

[0053] A quadratic suppression mapping logic is constructed based on the dynamic safety attenuation coefficient to generate nonlinear coupling mapping coefficients for differentiated scaling;

[0054] The maximum linear velocity is linearly reduced using a dynamic safety attenuation coefficient to generate the adjusted upper limit of linear velocity.

[0055] The maximum angular velocity is scaled using nonlinear coupling mapping coefficients to generate an adjusted upper limit for angular velocity; wherein the attenuation ratio of the adjusted upper limit for angular velocity is greater than the attenuation ratio of the adjusted upper limit for linear velocity.

[0056] In one embodiment, the control unit utilizes a dynamic safety attenuation coefficient. Construct the quadratic suppression mapping logic. Perform the operation... Generate nonlinear coupling mapping coefficients ;in, These are the nonlinear coupling mapping coefficients. This is a dynamic safety attenuation coefficient. This logic ensures the nonlinear coupling mapping coefficient... As communication confidence decreases, it exhibits a rapid, parabolic decline.

[0057] In another embodiment, the control unit extracts the preset maximum linear velocity from the physical limit parameters. The physical limit parameters here are explicitly defined as the safe operating limits set by the mobile robot's hardware or firmware, including maximum linear velocity and maximum angular velocity. Perform multiplication. Generate the adjusted upper limit of linear velocity ;in, To adjust the upper limit of linear velocity, For the maximum linear velocity, This is the dynamic safety attenuation coefficient. This process achieves the limiting effect of dynamically and linearly adjusting the linear velocity boundary as the command confidence level decreases.

[0058] In one embodiment, the control unit extracts the preset maximum angular velocity from the physical limit parameters. Perform multiplication. Generate the adjusted upper limit of angular velocity ;in, To adjust the upper limit of the rear angular velocity, For the maximum angular velocity, These are the nonlinear coupling mapping coefficients. Due to the suppression effect of the quadratic mapping term, the upper limit of the angular velocity is adjusted. The numerical decay rate is significantly faster than the dynamic safety decay coefficient. The rate of descent makes adjusting the upper limit of the angular velocity... The attenuation ratio is always higher than the upper limit of the adjusted linear velocity. This allows the robot's steering ability to be prioritized in environments with signal interference in order to suppress track drift.

[0059] Preferably, step S3, which generates the real-time motion envelope threshold for defining the current motion boundary, further includes:

[0060] Based on the adjusted upper limit of linear velocity and the adjusted upper limit of angular velocity, a curvature constraint relationship that dynamically tightens with driving speed is established to generate speed-dependent curvature constraints.

[0061] The system acquires the rotational inertia and physical axis distance parameters of the mobile robot, calculates the lateral risk index at the current linear velocity, performs safety compensation on the velocity-dependent curvature constraint using the lateral risk index, generates inertial compensation curvature constraint, and then integrates these parameters to generate a real-time motion envelope threshold.

[0062] In one embodiment, the control unit is based on the adjusted upper limit of linear velocity. With the adjusted upper limit of angular velocity Determine the rated maximum curvature The control unit collects real-time driving speed. Using the following formula:

[0063] ;

[0064] Generation speed depends on curvature constraints ;in, For velocity-dependent curvature constraints, For the rated maximum curvature, For real-time driving speed, To adjust the upper limit of the linear speed. This logic implements a dynamic limit where the upper limit of the steering curvature tightens synchronously with the increase of driving speed.

[0065] In another embodiment, the control unit retrieves the rotational inertia of the mobile robot. Compared with physical wheelbase parameters Using the following formula:

[0066] ;

[0067] Calculate and generate lateral risk indicators ;in, As a lateral risk indicator, For real-time driving speed, For rotational inertia, This refers to the physical wheelbase parameter. This is a preset stability constant. It is calculated using the following formula:

[0068] ;

[0069] Perform safety compensation and generate inertial compensation curvature constraints. ;in, For inertial compensation curvature constraints, For velocity-dependent curvature constraints, This is a lateral slip risk indicator. This step quantifies the risk of lateral instability under high-speed steering and achieves secondary correction of the motion boundary.

[0070] In one embodiment, the control unit will adjust the upper limit of the linear velocity. Adjusting the upper limit of angular velocity and inertial compensation curvature constraint Encapsulation processing is performed to generate a real-time motion envelope threshold. This real-time motion envelope threshold constructs a multi-dimensional dynamic motion safety zone for the mobile robot under the current data timeliness, serving as the basis for determining the clamping modulation of subsequent original target commands, ensuring that the control signal is always output within the boundaries allowed by physical performance and communication confidence.

[0071] Preferably, step S4, which uses a real-time motion envelope threshold to perform priority-based progressive limiting on the original target command, includes:

[0072] The original target command is parsed into a normalized motion vector containing linear velocity vector and angular velocity vector, and a control priority weight matrix is ​​configured, setting the angular velocity vector as the first priority and the linear velocity vector as the second priority;

[0073] When the angular velocity vector exceeds the real-time motion envelope threshold, a boundary forced clamp is executed, and an angular velocity clamping ratio is generated based on the ratio of the values ​​before and after clamping.

[0074] In one embodiment, the control unit parses the original target command to extract the target linear velocity component. With the target angular velocity component This generates a standardized motion vector. By calling a preset control priority weight matrix, the target angular velocity components are... Configured as the first priority component, the target linear velocity component Configured as the second priority component. This step establishes the logical order for prioritizing steering control variables under resource-constrained conditions, ensuring that actuators respond first to core parameters affecting attitude safety.

[0075] In another embodiment, the control unit obtains the adjusted upper limit of angular velocity from the real-time motion envelope threshold. When the target angular velocity component The absolute value is greater than the adjusted upper limit of angular velocity. At that time, a boundary forced clamp is executed. This is achieved using the following formula:

[0076] ;

[0077] Calculate and generate clamping angular velocity ;in, The clamping angular velocity, For the target angular velocity component, To adjust the upper limit of the rear angular velocity, `sign` is a sign function that returns the sign of the numerical value. It is used using the following formula:

[0078] ;

[0079] Generate angular velocity clamping ratio ;in, Angular velocity clamping ratio, The clamping angular velocity, This represents the target angular velocity component. This ratio precisely quantifies the degree to which the original steering requirements are constrained by the current data timeliness, serving as an adjustment benchmark for subsequent secondary priority parameter scaling.

[0080] Preferably, step S4, which uses a real-time motion envelope threshold to perform priority-based progressive limiting on the original target command, further includes:

[0081] Calculate the velocity linkage coefficient used to adjust the linear velocity vector using the angular velocity clamping ratio;

[0082] Identify the trajectory geometric features corresponding to the standardized motion vector, and use the velocity linkage coefficient to proportionally reduce the linear velocity vector to generate a linkage adjustment vector;

[0083] The boundary consistency of the linkage adjustment vector is checked by using a real-time motion envelope threshold, and a clamping modulation vector for output mapping is generated.

[0084] In one embodiment, the control unit utilizes an angular velocity clamping ratio. Through formula Calculate the generation speed linkage coefficient ;in, For speed linkage coefficient, This is the angular velocity clamping ratio. This coefficient establishes the mapping ratio in which the low-priority linear velocity vector decays synchronously with the high-priority angular velocity vector, thus providing an adjustment benchmark for maintaining the geometric consistency of the motion trajectory.

[0085] In another embodiment, the control unit calculates the target angular velocity component. With the target linear velocity component absolute value ratio Identify the trajectory geometric features of the standardized motion vector. If If the value is between 0.1 and 1.5, it is determined to be an arc-shaped trajectory. The control unit performs a multiplication operation. The linear velocity component in the generated linkage adjustment vector ;in, To adjust the linear velocity in the linkage vector, For the target linear velocity component, This is the speed linkage coefficient. This operation prevents distortion of the robot's trajectory curvature by causing a corresponding decrease in linear velocity when a communication quality degradation triggers a limiting mechanism.

[0086] In one embodiment, the control unit utilizes the adjusted upper limit of linear velocity in the real-time motion envelope threshold. With inertial compensation curvature constraint Perform boundary consistency checks on the linkage adjustment vector. If Greater than Then let equal The control unit verifies the current trajectory curvature. Does it exceed If the limit is exceeded, then the operation will be performed. The linear velocity is corrected again, and the final value is generated by the corrected linear velocity and the clamping angular velocity. The clamping modulation vector is formed; where, For the final output linear velocity, The clamping angular velocity, The inertial compensation curvature constraint is used. This verification process ensures the dynamic compatibility of each control component under the asymmetric suppression strategy.

[0087] Preferably, step S4, which involves converting the clamping modulation vector signal based on a kinematic model to form the execution-level drive signal, includes:

[0088] The slope limiting logic is used to truncate the increment between the clamping modulation vector and the output command of the previous frame to generate a smooth control vector. The kinematic model is then called to perform inverse calculation on the smooth control vector to generate the motor control parameters of the corresponding actuator. The motor control parameters include the target speed of the left drive wheel and the target speed of the right drive wheel.

[0089] The system monitors the feedback status data of the actuator in real time. If the time-domain deviation between the motor control parameters and the feedback status data continues to exceed the safety tolerance limit, the emergency protection logic is triggered and the dynamic safety attenuation coefficient is reset to zero.

[0090] In one embodiment, the control unit extracts the linear velocity component output from the previous control cycle. With angular velocity components The control unit calculates the linear velocity in the clamping modulation vector. and The algebraic difference is used as the current linear velocity increment. This is achieved using the following formula:

[0091] ;

[0092] Calculate the linear velocity component in the generated smooth control vector ;in, To smooth the linear velocity in the control vector, The output linear velocity is the value from the previous control cycle. The linear velocity in the clamping modulation vector, To preset the physical acceleration limit, To control the cycle duration, This is a sign function. The control unit performs the same logic on the slope truncation of the angular velocity component, generating a smooth control vector. This operation eliminates command steps caused by severe fluctuations in communication delay, ensuring the motion stability of the actuator.

[0093] In another embodiment, the control unit invokes a two-wheel differential kinematic model to perform inverse kinematics calculation on the smoothed control vector. Using the formula... as well as Generate the motor control parameters for the corresponding actuator; among which, The target linear velocity of the revolver. The target linear velocity of the right wheel. To smooth the linear velocity in the control vector, To smooth the angular velocity in the control vector, The wheelbase of the robot is defined as the target linear velocity (or the corresponding converted target rotational speed) of the left and right drive wheels of the mobile robot. These motor control parameters are further converted into voltage pulse duty cycle signals and defined as execution-level drive signals, which are sent to the driver via a pulse width modulation interface to drive the motors to produce physical rotation.

[0094] In one embodiment, the control unit receives feedback status data collected by the motor encoder in real time via a controller area network (CLAN) bus. The control unit calculates the time-domain deviation between the desired frequency corresponding to the motor control parameters and the actual frequency corresponding to the feedback status data. If the time-domain deviation value Continuously exceeding the safety tolerance limit Upon reaching a duration of 0.2 seconds, the control unit triggers the emergency protection logic. This is achieved by executing a reset command to adjust the dynamic safety attenuation coefficient. Reset to zero, force the output amplitude of all motor control parameters to zero, and realize active safety braking of the robot when the physical execution deviation exceeds the limit.

[0095] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0096] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A mobile robot attitude and trajectory adaptive control method based on data timeliness, characterized in that, A control unit for a mobile robot, wherein the control unit includes a controller that communicates with a server via a wireless communication link, and the controller integrates a kinematic model, the method comprising the following steps: Step S1: Establish a clock alignment reference between the controller and the server; receive control data packets, parse the control data packets based on the clock alignment reference, extract the sending timestamp, and perform differential calculations in conjunction with the local receiving time to generate instruction lag delay data; Step S2: Input the instruction lag delay data into the preset time decay function, and perform a significance evaluation in combination with the historical evolution trend to generate a dynamic security decay coefficient for quantifying the instruction trust weight. Step S3: Obtain the physical limit parameters of the mobile robot, and use the dynamic safety attenuation coefficient to nonlinearly couple and scale the physical limit parameters to generate a real-time motion envelope threshold for defining the current motion boundary; Step S4: Obtain the original target instruction, perform priority progressive limiting on the original target instruction using the real-time motion envelope threshold to obtain the clamping modulation vector; perform signal conversion on the clamping modulation vector based on the kinematic model to form the execution-level drive signal.

2. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 1, characterized in that, Step S1 includes: Based on the local time of the synchronization request and the local time of the server response, combined with the current server time carried in the response data packet, clock deviation is calibrated to generate unit clock synchronization compensation value. The unit clock synchronization compensation value is used to map the original transmission timestamp in the subsequent control data packet to a time coordinate to generate a globally unified defined transmission time. Calculate the absolute physical difference between the current local reception time and the globally unified defined transmission time to generate instruction lag delay data.

3. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 1, characterized in that, Step S2, which incorporates historical evolution trends to perform a significance assessment, includes: The delay data of multiple consecutive frames of instructions are stored in a circular buffer to generate a delay history buffer sequence, and a time-weighted differential operation is performed on the delay history buffer sequence to generate a delay change trend value. The trend value of time delay change is compared with the variance of the fluctuation of the historical time delay buffer sequence to generate a trend significance index. The prediction gain is determined based on the trend significance index, and the original value of the time-effect decay function output is trend-compensated to generate the trend-adjusted confidence coefficient.

4. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 3, characterized in that, Step S2, which generates the dynamic security decay coefficient for quantizing instruction trust weights, includes: Match the corresponding risk level matrix based on the job attributes of the original target instruction, and use the corresponding scaling factor to perform contextual correction on the trend-adjusted confidence coefficient to generate the task-weighted confidence coefficient. A low-pass filtering algorithm is used to perform time-domain smoothing on the task-weighted confidence coefficients to generate dynamic safety attenuation coefficients.

5. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 1, characterized in that, Step S3 generates a real-time motion envelope threshold for defining the current motion boundary, including the physical limit parameters, which include the maximum linear velocity and maximum angular velocity of the mobile robot. A quadratic suppression mapping logic is constructed based on the dynamic safety attenuation coefficient to generate nonlinear coupling mapping coefficients for differentiated scaling; The maximum linear velocity is linearly reduced using a dynamic safety attenuation coefficient to generate the adjusted upper limit of linear velocity. The maximum angular velocity is scaled using nonlinear coupling mapping coefficients to generate an adjusted upper limit for angular velocity; wherein the attenuation ratio of the adjusted upper limit for angular velocity is greater than the attenuation ratio of the adjusted upper limit for linear velocity.

6. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 5, characterized in that, Step S3, which generates the real-time motion envelope threshold used to define the current motion boundary, also includes: Based on the adjusted upper limit of linear velocity and the adjusted upper limit of angular velocity, a curvature constraint relationship that dynamically tightens with driving speed is established to generate speed-dependent curvature constraints. The system acquires the rotational inertia and physical axis distance parameters of the mobile robot, calculates the lateral risk index at the current linear velocity, performs safety compensation on the velocity-dependent curvature constraint using the lateral risk index, generates inertial compensation curvature constraint, and then integrates these parameters to generate a real-time motion envelope threshold.

7. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 1, characterized in that, Step S4, which utilizes a real-time motion envelope threshold to perform priority-based progressive limiting on the original target command, includes: The original target command is parsed into a normalized motion vector containing linear velocity vector and angular velocity vector, and a control priority weight matrix is ​​configured, setting the angular velocity vector as the first priority and the linear velocity vector as the second priority; When the angular velocity vector exceeds the real-time motion envelope threshold, a boundary forced clamp is executed, and an angular velocity clamping ratio is generated based on the ratio of the values ​​before and after clamping.

8. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 7, characterized in that, Step S4, which utilizes the real-time motion envelope threshold to perform priority-based progressive limiting on the original target command, also includes: Calculate the velocity linkage coefficient used to adjust the linear velocity vector using the angular velocity clamping ratio; Identify the trajectory geometric features corresponding to the standardized motion vector, and use the velocity linkage coefficient to proportionally down-adjust the linear velocity vector to generate a linkage adjustment vector; The boundary consistency of the linkage adjustment vector is checked by using a real-time motion envelope threshold, and a clamping modulation vector for output mapping is generated.

9. The mobile robot attitude and trajectory adaptive control method based on data timeliness according to claim 8, characterized in that, Step S4 involves converting the clamping modulation vector into an execution-level drive signal based on a kinematic model, including: The slope limiting logic is used to truncate the increment between the clamping modulation vector and the output command of the previous frame to generate a smooth control vector. The kinematic model is then called to perform inverse calculation on the smooth control vector to generate the motor control parameters of the corresponding actuator. The motor control parameters include the target speed of the left drive wheel and the target speed of the right drive wheel. The system monitors the feedback status data of the actuator in real time. If the time-domain deviation between the motor control parameters and the feedback status data continues to exceed the safety tolerance limit, the emergency protection logic is triggered and the dynamic safety attenuation coefficient is reset to zero.

10. A mobile robot attitude and trajectory adaptive control system based on data timeliness, characterized in that, For executing the data-time-based mobile robot attitude and trajectory adaptive control method as described in claim 1, the data-time-based mobile robot attitude and trajectory adaptive control system includes: The delay status detection module is used to establish a clock alignment reference between the controller and the server; it receives control data packets, parses the control data packets based on the clock alignment reference, extracts the sending timestamp, and performs differential calculations in combination with the local receiving time to generate instruction lag delay data. The confidence assessment module is used to input instruction lag delay data into a preset time decay function, and perform a significance assessment in combination with historical evolution trends to generate a dynamic security decay coefficient for quantifying instruction confidence weight. The dynamic motion boundary calculation module is used to obtain the physical limit parameters of the mobile robot, and to perform nonlinear coupling scaling on the physical limit parameters using a dynamic safety attenuation coefficient to generate a real-time motion envelope threshold for defining the current motion boundary. The instruction modulation execution module is used to acquire the original target instruction, perform progressive amplitude limiting on the original target instruction using a real-time motion envelope threshold, and obtain a clamping modulation vector; based on the kinematic model, the clamping modulation vector is converted into an execution-level drive signal.

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