Intelligent robot motion control method and system

Abstract

The application discloses a kind of intelligent robot motion control method and system, it is related to intelligent robot control technical field, method includes: obtaining robot running data, the robot running data includes drive wheel rotation speed data, robot motion speed data and robot attitude angular velocity data;According to the robot running data, generate passage abnormal signal, the passage abnormal signal is used to reflect robot travel on low adhesion road;In response to the passage abnormal signal, according to the drive wheel rotation speed data and robot motion speed data, identify motion speed drop reason, the motion speed drop reason includes terrain resistance increase and drive wheel slip;According to the motion speed drop reason, adjust robot control parameter.The application can be combined with robot running data and motion speed drop reason to adjust robot control parameter, to realize robot motion control, improve accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of intelligent robot control technology, and in particular to an intelligent robot motion control method and system. Background Technology

[0002] In complex and dynamic scenarios such as disaster relief, mobile robots typically rely on perception systems to assess the terrain ahead and plan their movement speed accordingly. In existing speed control systems, when the robot travels on loose gravel, mud, or other non-rigid surfaces, the drive wheels experience interaction mismatch with the ground at high speeds, easily leading to wheel slippage or sinking. In this situation, the actual vehicle speed detected by the wheel speed sensor will be far lower than the target speed. Existing speed control systems often misinterpret this speed deviation caused by low road adhesion as simply increased terrain resistance. Based on this misinterpretation, the control system immediately increases the output torque according to a predetermined control law, attempting to forcibly increase the wheel speed. However, this torque compensation not only fails to increase speed but also further damages the structure of the loose ground, causing the wheels to sink deeper into the ground or dig deeper, resulting in a sudden tilt or sway of the vehicle. When the attitude sensor detects that the attitude angle is rapidly approaching the safety boundary, the system often triggers a high-priority stability protection mechanism, causing the robot to brake suddenly, ultimately forcing the rapid movement mission to be interrupted or failed, resulting in low control accuracy and low reliability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose an intelligent robot motion control method and system that can adjust robot control parameters by combining robot operation data and the reasons for the decrease in motion speed, thereby achieving robot motion control and improving accuracy and reliability.

[0005] On one hand, embodiments of the present invention provide a method for controlling the motion of an intelligent robot, comprising the following steps: Acquire robot operation data, which includes drive wheel speed data, robot motion speed data, and robot attitude angular velocity data; Based on the robot's operating data, a passage anomaly signal is generated, which is used to reflect that the robot is traveling on a road surface with low traction; In response to the passage abnormality signal, the cause of the decrease in movement speed is identified based on the drive wheel rotation speed data and robot movement speed data. The cause of the decrease in movement speed includes increased terrain resistance and drive wheel slippage. Based on the cause of the decrease in movement speed, the robot control parameters are adjusted, including the current output torque.

[0006] On the other hand, embodiments of the present invention provide an intelligent robot motion control system, including: The data acquisition module is used to acquire robot operation data, which includes drive wheel speed data, robot motion speed data, and robot attitude angular velocity data. The abnormal passage signal generation module is used to generate an abnormal passage signal based on the robot's operating data. The abnormal passage signal is used to reflect that the robot is traveling on a road surface with low adhesion. The speed reduction cause identification module is used to respond to the passage abnormality signal and identify the cause of the speed reduction based on the drive wheel rotation speed data and robot movement speed data. The cause of the speed reduction includes increased terrain resistance and drive wheel slippage. The control parameter adjustment module is used to adjust the robot control parameters according to the cause of the decrease in movement speed, and the robot control parameters include the current output torque.

[0007] The embodiments of this application include at least the following beneficial effects: First, the robot operation data is acquired. Then, based on the robot operation data, a passage abnormality signal is generated. Next, based on the drive wheel speed data and the robot movement speed data, the cause of the decrease in movement speed is identified. Finally, based on the cause of the decrease in movement speed, the robot control parameters are adjusted. Thus, the robot control parameters can be adjusted by combining the robot operation data and the cause of the decrease in movement speed to achieve robot motion control, thereby improving accuracy and reliability.

[0008] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0010] Figure 1 This is a flowchart of a motion control method for an intelligent robot according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the steps of generating a passage abnormality signal based on robot operation data in an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall architecture of an intelligent robot motion control method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an intelligent robot motion control system according to an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0012] In related technologies, mobile robots in complex and dynamic scenarios such as disaster relief typically rely on their own perception systems to assess the terrain ahead, classifying it into passable or impassable areas, and planning their movement speed and path accordingly. On flat and solid surfaces, the speed commands issued by the control system are highly consistent with the rotational speed of the drive wheels and the actual movement speed of the vehicle. Slight speed deviations can be compensated for in real-time torque through closed-loop speed control to maintain efficient and stable operation.

[0013] However, in actual disaster relief operations, robots often need to traverse non-rigid, loose slopes composed of gravel, mud, or broken building components. At this point, a boundary conflict arises between terrain assessment and accessibility classification logic and the physical interaction between the drive wheels and the loose ground. When the robot's vision system identifies such loose gravel areas, which are at the critical point between stability and slippage, as rough but passable terrain, the motion planner, based on the rigid assumption that the ground can provide sufficient driving force, outputs a high target speed to the upper controller to meet the response requirement of quickly reaching the target location. The drive wheels interact with the loose gravel at high speeds, causing the wheels to slip or sink instantly, and the wheel speed sensor detects that the actual vehicle speed is far lower than the target speed. Existing speed control systems typically misinterpret this speed deviation as a normal increase in terrain resistance, and then immediately increase the output torque according to the control law, attempting to forcibly increase the wheel speed. This torque compensation not only fails to increase the vehicle speed but also further damages the structure of the loose ground, causing the wheels to sink deeper into the loose ground or dig deeper, resulting in a sudden tilt and sway of the vehicle body. When the attitude sensor detects that the attitude angle is rapidly approaching the safety boundary, a high-priority stability protection mechanism is triggered to perform full-power emergency braking, causing the robot to come to a complete stop. In the scenario of loose gravel slopes in disaster relief, when the mobile robot's terrain assessment logic mistakenly classifies the critically passable loose ground into the ordinary passable category and issues a high-response drive command accordingly, slippage occurs due to the mismatch between the physical interaction between the drive wheels and the ground. This causes the speed control based on the rigid ground assumption to produce incorrect torque compensation, ultimately triggering the attitude instability limit and forcing the stability protection mechanism to stop abruptly. This results in the failure of the rapid movement task, low control accuracy, and low reliability.

[0014] The embodiments of this application will be explained in detail below with reference to the accompanying drawings: Figure 1 This is an optional flowchart of an intelligent robot motion control method provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0015] Step S101: Obtain robot operation data, which includes drive wheel speed data, robot motion speed data, and robot attitude angular velocity data; Step S102: Generate a passage anomaly signal based on the robot's operating data. The passage anomaly signal is used to reflect that the robot is traveling on a road surface with low adhesion. Step S103: In response to the passage abnormality signal, identify the cause of the decrease in movement speed based on the drive wheel speed data and robot movement speed data. The causes of the decrease in movement speed include increased terrain resistance and drive wheel slippage. Step S104: Adjust the robot control parameters according to the reason for the decrease in movement speed. The robot control parameters include the current output torque.

[0016] Steps S101 to S104 shown in the embodiments of this application can adjust the robot control parameters by combining robot operation data and the reasons for the decrease in movement speed, so as to realize robot motion control and improve accuracy and reliability.

[0017] In some embodiments, steps S101-S104 can first acquire robot operation data, including drive wheel speed data, robot motion speed data, and robot attitude angular velocity data. Various motion state information of the robot chassis and body can be collected in real time. Robot operation data can be acquired by installing high-precision photoelectric encoders on each drive motor shaft to collect drive wheel speed data in real time; simultaneously, the robot's absolute motion speed data is acquired through an RTK-GPS module mounted on the top of the robot; furthermore, the robot's attitude angular velocity data is collected using a six-axis inertial measurement unit installed at the robot's center of mass. Furthermore, the above three types of data can be managed with unified timestamps by the same main controller. In practice, the pulse signals output by the photoelectric encoder can be counted by the motor driver or chassis control board and then converted into the current wheel speed of the corresponding drive wheel; the RTK-GPS module can output the robot's speed information in the geographic coordinate system, which the main controller then converts into motion speed data in the robot's forward direction; the six-axis inertial measurement unit continuously outputs angular velocity information around each attitude axis, from which the main controller can extract attitude angular velocity data related to the vehicle's pitch, roll, and yaw. If satellite signals are blocked at the disaster site, the robot's motion speed data can also be provided by wheeled odometers, visual odometers, laser odometers, or inertial navigation fusion results, as long as the speed data can reflect the vehicle's true motion trend relative to the external environment.

[0018] Then, based on the robot's operational data, a passage anomaly signal is generated to reflect that the robot is traveling on a low-traction surface. After acquiring this basic operational data, the system needs to determine whether it is currently in an abnormal driving environment. For example, the specific implementation of generating the passage anomaly signal is as follows: a fixed speed deviation threshold is pre-set in the controller, and the theoretical speed calculated from the drive wheel speed data is compared in real time with the robot's motion speed data. When the difference between the two continuously exceeds the fixed threshold for a certain period of time, a passage anomaly signal is directly generated. Furthermore, the theoretical speed does not refer to the actual speed that the robot can necessarily reach, but rather to the wheel flange linear velocity calculated from the drive wheel speed data and the drive wheel geometry under ideal conditions of "no significant slippage between the wheels and the ground." The controller can first read the chassis structural parameter of the drive wheel diameter, and then combine it with the current wheel speed to obtain the corresponding theoretical speed. Subsequently, this theoretical speed is compared in the same direction with the robot's motion speed data to obtain the real-time speed deviation. To avoid false triggering when the robot starts, corrects its direction, or briefly crosses small obstacles, the controller does not immediately output a passage abnormality signal when a single deviation exceeds the limit. Instead, it first initiates a continuous judgment process.

[0019] Specifically, the deviation is confirmed to be persistent only when it exceeds a fixed speed deviation threshold for multiple consecutive control cycles, at which point a traffic anomaly signal is generated. If the speed deviation recovers to within the threshold during the persistent judgment process, the accumulated status is cleared and monitoring continues. The fixed speed deviation threshold and duration can be obtained through pre-calibration. One feasible calibration method is as follows: first, collect multiple sets of wheel speed and vehicle speed data on a solid, flat road surface, a rigid slope, and a typical low-traction road surface, respectively; then, statistically analyze the speed deviation fluctuation range under normal passage conditions and the speed deviation duration range under slippage conditions; finally, select a threshold combination that can distinguish between the two types of conditions as the preset parameters in the controller. The reason for adopting this method is that different robot chassis masses, wheel diameters, tire materials, and reduction ratios vary, resulting in different natural fluctuation ranges of speed deviation. If a uniform, uncalibrated value is directly used, false alarms or missed alarms are likely to occur.

[0020] In response to the traffic anomaly signal, the system identifies the cause of the speed decrease based on the drive wheel speed data and the robot's speed data. The causes include increased terrain resistance and drive wheel slippage. After generating the traffic anomaly signal, the system recognizes an anomaly in the current driving state and then needs to further analyze the root cause of the speed decrease. For example, identifying the cause of the speed decrease can be achieved by establishing a proportional judgment logic. This involves calculating the ratio between the drive wheel speed data and the robot's speed data. If the ratio falls within a first preset range, it is determined that the terrain resistance has increased; if the ratio falls within a second preset range, it is determined that the drive wheel has slipped. Furthermore, the core of the proportional judgment logic is not simply comparing the magnitudes of two values, but rather using the physical characteristic of whether the wheel speed significantly exceeds the vehicle speed to distinguish between the two conditions. When terrain resistance increases, although the robot has difficulty moving forward, the drive wheels still maintain relatively stable adhesion to the ground. Therefore, the drive wheel speed data and the robot's speed data usually maintain a relatively close correspondence. However, when the drive wheel slips, the drive wheel speed increases significantly, but the increase in robot speed is limited, and the proportional relationship between the two becomes significantly larger.

[0021] In practical implementation, the controller first performs directional consistency processing and short-time filtering on the drive wheel speed data and robot motion speed data, then calculates the proportional characteristic value of the two, and then compares this characteristic value with the pre-stored first and second preset intervals. If the characteristic value falls into a nearly consistent interval, it indicates that the wheel speed and vehicle speed are basically matched, and it can be determined that the terrain resistance has increased; if the characteristic value falls into a significantly mismatched interval, it indicates that the wheel speed is much higher than the vehicle speed, and it can be determined that the drive wheel is slipping. To more clearly and completely describe how this proportional judgment logic is obtained, the following preset process can be adopted: First, construct rigid obstacle crossing scenarios and loose gravel slipping scenarios in the test site, and control the robot to repeatedly pass through at different target driving speeds and different load states, recording the drive wheel speed data, robot motion speed data, and manually labeled real working condition categories during each passage; then, statistically analyze the distribution range of the proportional characteristic values ​​under the two working conditions from these samples; finally, set the range mainly corresponding to increased terrain resistance as the first preset interval, set the range mainly corresponding to drive wheel slipping as the second preset interval, and write it into the controller parameter area. If there is a transition zone between the two intervals, the transition zone can be defined as a state to be confirmed. In the state to be confirmed, the attitude angular velocity data, historical continuous judgment results or current output torque change trend are combined for secondary confirmation to avoid misclassification under boundary conditions.

[0022] Finally, based on the cause of the decrease in movement speed, the robot control parameters are adjusted. These parameters include the current output torque. After identifying the specific cause of the decrease, the host controller will intervene in the chassis's power output accordingly. For example, adjusting the robot control parameters can be achieved by calling a preset torque mapping table. For instance, when increased terrain resistance is detected, the corresponding high-torque output command in the mapping table is directly called to increase the current output torque; when drive wheel slippage is detected, the low-torque output command in the mapping table is called to decrease the current output torque. Furthermore, the preset torque mapping table can be pre-established according to the "working condition category - control action" method, rather than being limited to a single fixed torque value. In specific implementation, the mapping table can at least include the following: when terrain resistance increases but the robot maintains a stable posture, a corresponding output torque increase strategy is implemented; when drive wheel slippage occurs and posture disturbance is significant, a corresponding output torque decrease strategy is implemented; and when the abnormal state is resolved, a corresponding output torque recovery strategy is implemented.

[0023] To avoid sudden changes in torque commands causing new attitude shocks, the controller can gradually adjust the current output torque in stages after calling the torque mapping table, rather than jumping to the target value all at once. For example, when increased terrain resistance is detected, the controller can first increase the current output torque to a higher level. If the robot's speed does not recover and no slippage is detected after several control cycles, it can be increased further. When drive wheel slippage is detected, the controller can first reduce the current output torque to a lower level while continuously monitoring wheel speed and vehicle speed. If the slippage decreases, the torque can be maintained or slowly restored. If the slippage continues, it can be further reduced. The torque mapping table can be established through pre-experimentation. One feasible method is to test the impact of multiple torque output strategies on passability, attitude stability, and motor load on gravel roads, mud roads, and rigid obstacle roads with different slopes and particle sizes, and record the control actions that can balance passability and stability as mapping relationships. For example, "increased terrain resistance, stable attitude, and persistent speed deviation" can be mapped to "increase torque by one level"; "drive wheel slippage, attitude disturbance, and persistently high slip ratio" can be mapped to "decrease torque by one level and continue observation"; and "abnormality resolved and vehicle speed restored" can be mapped to "gradually returning to the normal torque range according to the recovery slope". The mapping table established in this way is more in line with the actual chassis characteristics and can be directly used for online calls by the controller.

[0024] Understandably, low-traction surfaces refer to non-rigid surfaces composed of loose gravel, mud, sand, etc. These surfaces cannot provide sufficient friction for the wheels, making them prone to relative slippage. Furthermore, low-traction surfaces are not simply "uneven surfaces." Their core characteristic is that when the wheels apply driving force, surface particles shift, collapse, or rearrange, making it difficult to establish a stable force transmission relationship between the tires or tracks and the ground. For example, a gravel slope may appear stable when stationary, but when the drive wheel applies a large torque, the gravel in the contact area will roll or slide downstream, causing the wheel to spin rapidly while the vehicle moves very little. Muddy surfaces may form a localized shear layer after the tire treads cut into the mud, causing the contact surface to quickly become unstable.

[0025] Drive wheel slippage refers to the physical phenomenon where, under the influence of driving torque, the rotational linear velocity of a wheel is significantly greater than the actual forward speed of the vehicle. Visually, it manifests as the wheel spinning at high speed while stationary or moving slowly. Furthermore, drive wheel slippage can occur on a single drive wheel, or simultaneously on multiple drive wheels on the same side, or all drive wheels. On slopes, at the edge of gravel piles, or in areas of unilateral collapse, it may also manifest as inconsistent slippage between the left and right wheels, accompanied by yaw, roll, or periodic vibrations. This phenomenon differs from the brief, slight wheel speed advance that occurs during normal acceleration; the former is continuous and is simultaneously manifested as difficulty in increasing actual speed, increased attitude disturbance, and noticeable scouring of the ground surface.

[0026] Increased terrain drag refers to situations where a robot encounters rigid obstacles such as steps, protruding hard rocks, or steep slopes during its movement, resulting in a significant increase in forward resistance. In these cases, the wheels do not slip, but greater power is required to overcome the obstacle. Furthermore, a key characteristic of this type of situation is that the ground or obstacle itself has strong support capabilities, and the drive wheels can maintain a relatively stable adhesion to the contact surface. Therefore, although wheel speed and vehicle speed may decrease simultaneously, there will not be a continuously expanding severe mismatch between them. For example, when the robot's front wheels hit a fixed rock, the vehicle's forward speed will decrease, but if the torque is increased moderately, the wheels may still generate enough thrust to complete the climb; however, in loose gravel, increasing torque often only causes the gravel to continue to collapse.

[0027] Through the above technical solution, this embodiment changes the traditional control logic's strategy of blindly compensating for torque based solely on speed deviation when facing complex disaster relief terrain. Its working principle lies in first sensitively capturing abnormal states where the vehicle may be on low-traction surfaces through multi-dimensional operational data. Then, under these abnormal states, it further analyzes the physical relationship between the drive wheel speed and the actual movement speed, accurately separating two distinct but superficially similar physical conditions: increased terrain resistance and drive wheel slippage. Based on this precise condition identification, the system can adjust the current output torque accordingly. This logical deduction ensures that when the robot encounters loose gravel causing slippage, it will not mistakenly increase torque to exacerbate the digging phenomenon, thus effectively avoiding severe instability of the vehicle's posture and the resulting system abrupt stop, significantly improving the robot's sustained maneuverability in extreme terrains such as non-rigid loose slopes.

[0028] In some embodiments, such as Figure 2 As shown, in step S102, a passage abnormality signal is generated based on the robot's operating data, which may include, but is not limited to, the following steps: Step S201: Obtain the diameter of the drive wheel and the target driving speed; Step S202: Extract the current motion speed from the robot's motion speed data; Step S203: Calculate the slip ratio based on the drive wheel diameter, drive wheel speed data, and current movement speed; Step S204: Identify the robot's attitude disturbance state based on the robot's attitude angular velocity data; Step S205: Calculate the first speed difference based on the target driving speed and the current moving speed; Step S206: Generate a passage anomaly signal based on the slip ratio, robot posture disturbance state, and first velocity difference.

[0029] In some embodiments, the diameter of the drive wheels and the target travel speed can be obtained first. The current travel speed is then extracted from the robot's motion speed data. The drive wheel diameter can be obtained from a pre-stored system parameter library, and the target travel speed can be received from the navigation planning module. The actual current travel speed of the vehicle body is then extracted from the robot's motion speed data provided by the positioning module.

[0030] Then, based on the drive wheel diameter, drive wheel speed data, and current movement speed, the slip ratio is calculated. Combining these data, a slip ratio that intuitively reflects the degree of wheel spin can be calculated. Furthermore, based on the robot's angular velocity data, the robot's attitude disturbance state is identified. For example, identifying the robot's attitude disturbance state can be achieved by monitoring the instantaneous rate of change of angular velocity; when the rate of change of angular velocity exceeds the baseline value for smooth movement, an attitude disturbance is determined to exist.

[0031] Then, based on the target speed and the current speed, a first speed difference is calculated to quantify the macroscopic task execution deviation. A passage anomaly signal is generated based on the slip ratio, robot posture perturbation state, and the first speed difference. The passage anomaly signal can be generated by combining these three dimensions of indicators.

[0032] In practical implementation, a multi-condition triggering logic gate can be constructed. A high-level passage abnormality signal is only output synchronously when the slip ratio exceeds a preset slip threshold, a robot posture disturbance is detected, and the first speed difference exceeds a preset speed difference threshold. Furthermore, the acquisition and processing of the above input information can be implemented as follows: the drive wheel diameter can be measured and written into the parameter library during robot factory calibration. If tires or rims are subsequently replaced, it can be re-entered through the maintenance interface; the target travel speed can be issued in real-time by the navigation planning module based on the current path segment's traffic level, task timeliness requirements, and safety policies; the current motion speed can be obtained by projecting the velocity vector output by the positioning module onto the robot's forward direction to avoid interference from the lateral component during turning. For the identification of posture disturbance states, the controller can first denoise and smooth the robot's posture angular velocity data, and then observe whether the pitch angular velocity, roll angular velocity, and yaw angular velocity show abnormal amplification, sudden changes, or periodic oscillations within a continuous control cycle. If the robot experiences a change in yaw rate only due to normal turning, but pitch and roll remain stable, and the difference between the slip ratio and the first velocity does not exceed the limit, no passage anomaly signal will be generated. If the robot exhibits a concurrent state of increased wheel speed, decreased vehicle speed, and enhanced roll rate fluctuations on a gravel slope, the anomaly can be more credibly identified. Preset slip thresholds, velocity difference thresholds, and attitude disturbance baseline values ​​can also be obtained through pre-experimentation. One feasible approach is to collect multiple sets of data in scenarios such as normal flat roads, rigid obstacle crossing, loose gravel slippage, and muddy slippage, statistically analyze the slip ratio, attitude angular velocity change characteristics, and typical ranges of the first velocity difference in each scenario, and then select a threshold combination that can effectively distinguish low-adhesion slippage conditions as the basis for the multi-condition triggering logic gate's judgment.

[0033] Through the above technical solution, this embodiment constructs a rigorous state monitoring logic. Its core principle lies in the deep integration of the microscopic interaction between the wheel and the ground, the macroscopic vehicle stability performance, and the execution deviation at the task level. On loose gravel slopes, wheel slippage is inevitably accompanied by a high slip ratio, digging and sinking inevitably cause abnormal swaying of the vehicle body, and slippage directly results in the actual vehicle speed failing to reach the target speed. The concurrence of these three characteristics is a typical physical representation of slippage conditions on low-adhesion surfaces. Therefore, this embodiment effectively filters out interference caused by transient signal fluctuations through the joint determination of multi-dimensional features, greatly improving the accuracy and robustness of traffic anomaly signal generation. Furthermore, this joint determination is superior to a single threshold determination because different anomaly sources exhibit different behaviors across the three dimensions. For example, when a robot briefly crosses a small rigid protrusion, the initial velocity difference may increase instantaneously, but the slip ratio may not continue to rise, and the attitude disturbance is usually short. When the robot turns in place, the yaw rate may be large, but if the relationship between the current movement speed and the target travel speed is still normal, and the wheel speed matches the vehicle speed, it should not be considered a low adhesion anomaly. Only when the slip ratio, attitude disturbance state, and initial velocity difference simultaneously meet the abnormal conditions can it be said that the robot has experienced wheel-to-ground slippage, impaired vehicle stability, and task execution deviation. These three factors collectively point to a traffic anomaly caused by a low-adhesion road surface. Therefore, this embodiment not only improves the accuracy of anomaly identification but also provides a more reliable triggering premise for subsequent cause identification and torque adjustment.

[0034] In some embodiments, in step S203, calculating the slip ratio based on the drive wheel diameter, drive wheel speed data, and current movement speed may include, but is not limited to, the following steps: Extract the current wheel speed from the drive wheel rotation speed data; Calculate the linear velocity of the drive wheel based on the diameter of the drive wheel and the current wheel speed; Calculate the second speed difference based on the linear velocity of the drive wheel and the current speed. The maximum value between the linear velocity of the drive wheel and the current speed is selected as the speed to be compared. Calculate the slip ratio based on the second speed difference and the speed to be compared.

[0035] In some embodiments, the current wheel speed can be extracted from the drive wheel rotation speed data first. The real-time current wheel speed can be extracted from the drive wheel rotation speed data reported by the chassis drive. The linear velocity of the drive wheel is then calculated based on the drive wheel diameter and the current wheel speed. Alternatively, the drive wheel diameter, pi, and the current wheel speed can be multiplied to calculate the linear velocity of the drive wheel at the point of contact between the drive wheel edge and the ground.

[0036] Then, based on the linear velocity of the drive wheels and the current speed, the second speed difference is calculated. The current speed of the vehicle body can be subtracted from the linear velocity of the drive wheels to obtain the second speed difference, which reflects the absolute amount of relative slippage between the two. The maximum value between the linear velocity of the drive wheels and the current speed is selected as the speed to be compared. The numerical values ​​of the linear velocity of the drive wheels and the current speed can be compared in real time, and the maximum value is selected as the speed to be compared.

[0037] Then, based on the second speed difference and the speed to be compared, calculate the slip ratio. The final slip ratio can be calculated by dividing the second speed difference by the speed to be compared.

[0038] Furthermore, to ensure the calculation process is stably implemented in the controller, the actual processing can be performed in the following order: First, obtain the current wheel speed from the encoder count results, and then calculate the linear speed of the drive wheel by combining it with the drive wheel diameter; then, unify the drive wheel linear speed and the current motion speed to the same speed unit and the same time reference; next, take the absolute value of the difference between the two as the second speed difference to avoid sign interference during forward, backward, or short-term reverse rolling; then, select the larger of the drive wheel linear speed and the current motion speed as the speed to be compared; finally, obtain the slip ratio result and output it within a reasonable range. If both the drive wheel linear speed and the current motion speed are extremely low, it indicates that the robot may be in a stationary, pre-start, or actively stopped state. In this case, the controller can first determine whether the target travel speed is valid. Only when the target travel speed is greater than zero and the robot is performing a passage task will the slip ratio calculation result be used to participate in the anomaly judgment to avoid meaningless triggering in a stationary state. For multi-drive wheel chassis, the slip ratio of each drive wheel can also be calculated separately, and then the maximum slip ratio, average slip ratio, or a combination of drive-side slip ratios can be selected as the overall vehicle slip characterization quantity. If the slip ratio of one drive wheel is consistently higher than that of the other, it usually means that the contact between that side and the ground is looser or that a local collapse has occurred. In this case, this information can also help explain the source of the robot's yaw or tilt.

[0039] To illustrate this technical solution more clearly, a specific example is used below. Suppose a robot is struggling in a pile of rubble, with the drive wheels reaching a linear speed of 2.0 m / s, while due to severe slippage, the robot's current speed is only 0.5 m / s. At this point, the difference in speed is 1.5 m / s. The system selects the maximum of these two speeds, 2.0 m / s, as the speed to be compared, and calculates a slip ratio of 0.75.

[0040] Through the above technical solution, this embodiment establishes an adaptive slip ratio quantification mechanism. Its core working principle lies in dynamically selecting the maximum value as the speed to be compared. This design cleverly solves the calculation benchmark problem under complex motion states. For example, when the robot is completely stuck in front of an obstacle, the current motion speed drops to 0. Using the current motion speed as the denominator would lead to a division-by-zero error. However, this embodiment automatically switches to using the linear velocity of the drive wheels as the denominator, accurately calculating the slip ratio as 1. Conversely, when emergency braking on a downhill slope causes the wheels to lock up and slide, the linear velocity of the drive wheels approaches 0. This embodiment automatically uses the current motion speed as the denominator, also stably outputting the braking slip ratio. Therefore, this embodiment ensures that regardless of the robot's extreme working conditions, the calculated slip ratio is always within a reasonable range of 0 to 1, providing the control system with a continuous, stable, and dimensionless standardized evaluation index. Furthermore, the effectiveness of this technology can be clearly explained from a computational perspective: the second speed difference reflects the degree of inconsistency between wheel speed and vehicle speed, while the speed to be compared reflects the dominant speed scale that can be used as a reference under the current motion state. When the maximum value of the two is used as the speed to be compared, whether the wheel speed is much greater than the vehicle speed due to wheel spin or the vehicle speed is much greater than the wheel speed due to braking slip, the slip ratio will not be distorted due to improper selection of the reference value. In this way, the slip ratio obtained by the controller is always comparable, which facilitates the uniform setting of thresholds across different operating conditions and also facilitates the formation of a stable anomaly identification basis together with the attitude disturbance state and the first speed difference.

[0041] In some embodiments, step S204, identifying the robot's attitude disturbance state based on the robot's attitude angular velocity data, may include, but is not limited to, the following steps: The robot's attitude angular velocity data is decomposed into three axes to obtain X-axis angular velocity data, Y-axis angular velocity data, and Z-axis angular velocity data; Calculate the amplitude of the first angular velocity based on the X-axis angular velocity data; Calculate the amplitude of the second angular velocity based on the Y-axis angular velocity data; Calculate the amplitude of the third angular velocity based on the Z-axis angular velocity data; The robot's posture perturbation state is identified based on the first angular velocity amplitude, the second angular velocity amplitude, and the third angular velocity amplitude.

[0042] In some embodiments, the robot's attitude angular velocity data can be decomposed into three axes to obtain X-axis angular velocity data, Y-axis angular velocity data, and Z-axis angular velocity data. Specifically, after receiving data from the six-axis inertial measurement unit, the controller first separates the three-axis angular velocity data. Three-axis decomposition refers to breaking down complex rotational motion in space into independent rotational components about three orthogonal axes of the robot's own coordinate system: the X-axis (roll) angular velocity reflecting body roll, the Y-axis (pitch) angular velocity reflecting forward and backward swaying, and the Z-axis (yaw) angular velocity reflecting left and right swaying. To ensure that the three-axis decomposition results are consistent with the actual movement direction of the vehicle, the installation correspondence between the sensor coordinate system and the vehicle coordinate system can be pre-established during robot factory calibration. For example, the forward direction of the vehicle's front can be designated as the forward axis, the left and right width direction as the lateral axis, and the vertical upward direction as the normal axis. The six-axis inertial measurement unit is then fixed to the chassis using the reference surface of the mounting fixture, ensuring a one-to-one correspondence between the sensor output axes and the vehicle coordinate axes. If there is a small angular deviation in the installation position, a static data segment can be collected under flat, stationary conditions, and the installation error can be corrected by combining the direction of gravity.

[0043] Then, based on the X-axis angular velocity data, the first angular velocity amplitude is calculated. Based on the Y-axis angular velocity data, the second angular velocity amplitude is calculated. And based on the Z-axis angular velocity data, the third angular velocity amplitude is calculated. Within a set sliding time window (e.g., 0.5 seconds), the maximum and minimum values ​​of the three axial angular velocity sequences can be extracted respectively, and the two are subtracted to calculate the first, second, and third angular velocity amplitudes. The sliding time window can be continuously updated according to the control cycle. For example, the latest data in the window is updated once per control cycle, and the oldest data is removed to ensure the real-time nature of the recognition results. In the selection of the window length, it is preferable to consider two requirements: on the one hand, the window cannot be too short, otherwise it is easy to mistake a single collision vibration for continuous instability; on the other hand, the window cannot be too long, otherwise it will mask the rapid deterioration process in the early stage of the slip. The amplitude calculation aims to quantify the severity of attitude fluctuations in each direction, thereby filtering out gentle attitude changes. This amplitude can be understood as the overall amplitude of the angular velocity swing of that axis within a set time range, reflecting whether the attitude change in that direction has sudden, impactful, and reciprocating jitter characteristics. For example, when a robot is climbing a slope smoothly, its Y-axis (pitch) angular velocity may be biased in the same direction for a long time and change slowly. At this time, although the attitude is changing, the fluctuation amplitude within the window is usually small. However, when the wheels repeatedly spin on a loose slope, gripping and losing grip, the vehicle body will experience repeated shaking in a short period of time. At this time, the amplitude of multiple axes will increase simultaneously, making it more suitable as a basis for attitude disturbance recognition.

[0044] Then, based on the amplitudes of the first, second, and third angular velocities, the robot's attitude disturbance state is identified. These three amplitudes can be compared with preset roll disturbance thresholds, pitch disturbance thresholds, and yaw disturbance thresholds, respectively. The preset roll disturbance thresholds, pitch disturbance thresholds, and yaw disturbance thresholds can be obtained through real-vehicle testing: three-axis angular velocity data can be collected under various typical working conditions, such as flat hard ground, normal climbing, turning in place, crossing low rigid obstacles, and slipping on loose gravel, to statistically analyze the stable distribution range of the amplitudes under each working condition, and then select the threshold intervals that can distinguish between normal maneuvering and abnormal disturbances and write them into the controller parameter table. To enhance adaptability, different threshold groups can also be set according to the robot's load state, slope range, or differences in tracked / wheeled structure, and the corresponding parameters can be loaded according to the current configuration before the task begins. When the wheels slip and create ruts in loose gravel, one wheel often sinks into the ground, causing a significant increase in the X-axis amplitude. Simultaneously, intermittent grip causes the vehicle to lurch forward and backward, increasing the Y-axis amplitude. Furthermore, uneven adhesion between the left and right drive wheels causes the front of the vehicle to sway left and right, leading to an increase in the Z-axis amplitude. To avoid misjudgments caused by occasional single-axis overshoots, a joint judgment method can be used to identify robot posture disturbances. For example, a posture disturbance state is only identified when at least two axial amplitudes reach their corresponding thresholds, or when one axial amplitude significantly exceeds the limit and is accompanied by abnormal wheel speed and increased slip ratio.

[0045] Through the above technical solution, this embodiment decomposes the overall angular velocity data into amplitude features in three orthogonal directions, which can comprehensively characterize the instability of the vehicle body in three-dimensional space. This multi-dimensional joint judgment effectively distinguishes between normal maneuvering (such as turning in place with a simple increase in Z-axis amplitude or smooth climbing with a simple increase in Y-axis amplitude) and abnormal comprehensive disturbances caused by slippage, greatly improving the accuracy of attitude disturbance state identification and avoiding false triggering in complex terrain. The reason is that normal maneuvering usually manifests as continuous and predictable attitude changes in a certain main direction, while slippage and instability are more often manifested as short-term coupled fluctuations in multiple directions, and the three-axis amplitude joint analysis can precisely capture this difference.

[0046] In some embodiments, step S103, identifying the cause of the decrease in motion speed based on the drive wheel rotation speed data and the robot motion speed data, may include, but is not limited to, the following steps: Obtain historical slip ratio variation curves; Calculate the wheel speed change trend based on the drive wheel speed data; Calculate the trend of motion speed change based on the robot's motion speed data; Calculate the slip ratio trend based on the historical slip ratio change curve; Identify the reasons for the decrease in motion speed based on the trends of wheel speed change, motion speed change, and slip ratio change.

[0047] In some embodiments, historical slip ratio change curves can be obtained first. Specifically, the controller allocates a circular buffer in memory to store slip ratio data from several past control cycles in real time, and uses a curve fitting algorithm to generate historical slip ratio change curves. The circular buffer can be understood as a fixed-length historical data queue; when new slip ratio data is written, the oldest data is automatically overwritten, thus continuously retaining the evolution information of the most recent period without increasing storage burden. The process of obtaining slip ratio data can be as follows: the controller synchronously reads the drive wheel speed data and the robot's motion speed data, converts the drive wheel speed into the theoretical linear velocity of the drive wheel, and then compares it with the robot's motion speed data to obtain the slip ratio corresponding to each control cycle. To avoid the amplified effect of jitter errors near zero speed on the slip ratio, a minimum effective speed threshold can be set when the robot starts at low speed or approaches a standstill. Below this threshold, a slow-start identification strategy or temporary suspension of slip ratio trend analysis is adopted. The curve fitting algorithm can adopt a method that is easy to execute in real time, such as performing local smooth fitting on the historical slip ratio sequence of the most recent period, thereby eliminating the influence of abnormal fluctuations at a single sampling point on the overall trend judgment. The pre-built trend analysis rules can also be developed through experiments: Wheel speed, vehicle speed, and slip ratio time series are collected under various scenarios, including loose sand slopes, gravel slopes, dry hard slopes, and rigid obstacle crossings. Corresponding working conditions are manually labeled, and a rule base for identification is established based on typical trend combinations under each working condition, and stored in the controller. The historical slip ratio change curve provides a continuous observation window for assessing the deterioration or mitigation of the relative slip state between the wheel and the ground.

[0048] Then, based on the drive wheel speed data, the wheel speed change trend is calculated. Similarly, based on the robot's motion speed data, the motion speed change trend is calculated. The first derivative can be calculated on the drive wheel speed data and robot motion speed data within the current time window to obtain the wheel speed change trend and motion speed change trend reflecting the acceleration state, respectively. In engineering implementation, this first derivative calculation can employ a more easily executed discrete difference method, comparing the direction and rate of change between adjacent control cycles or several adjacent sampling points, and using smoothing filtering to avoid misinterpreting encoder jitter or small fluctuations in speed estimation as the true trend. Simultaneously, the slip ratio change trend is calculated based on the historical slip ratio change curve. The slip ratio change trend can be obtained by differentiating the historical slip ratio change curve. The change trend can be understood as whether a physical quantity rises overall, falls overall, remains basically stable, or rises first and then falls, or falls first and then rises, over multiple consecutive control cycles. Unlike single-point values, change trends are more suitable for describing the evolution of working conditions. For example, in the early stages of slippage, even if the current slip ratio has not yet reached a high level, if it continues to increase over several consecutive cycles, it can still indicate that the ground adhesion conditions are rapidly deteriorating. Another example is in situations where terrain resistance increases, the robot's forward movement is hindered, and the motors drive the wheels and the vehicle body to decelerate together. At this time, the wheel speed and the overall movement speed will show a high degree of consistency, while the slip ratio change tends to be limited. This is clearly different from the "increased wheel speed, decreased vehicle speed, and deteriorating slip ratio" caused by the slipping of the drive wheels.

[0049] The system then identifies the cause of the decrease in speed based on the trends in wheel speed, vehicle speed, and slip ratio. During cause identification, the system considers all three trends: if the wheel speed trend is negative (i.e., wheel speed is decreasing), the vehicle speed trend is negative, and the slip ratio trend is relatively stable, it indicates that the wheels and vehicle body are decelerating synchronously, and the system determines this as increased terrain resistance. If the wheel speed trend is positive (i.e., wheel speed is increasing) or stable, the vehicle speed trend is negative, and the slip ratio trend shows a significant positive surge, it indicates that the wheels are spinning faster but the vehicle body is moving slower, and the system determines this as drive wheel slippage. To enhance identification reliability, the controller can also set a duration condition, meaning the final result is output only after the above trend combination has been continuously satisfied for several control cycles, thereby reducing misjudgments caused by short-term impacts, boulder jumps, or sensor glitch.

[0050] Through the above technical solution, this embodiment upgrades static numerical comparison to dynamic trend evolution analysis. By capturing the relative changes in wheel speed, vehicle speed, and slip ratio over time, the system can keenly detect subtle signs of deterioration in the interaction between the wheel and the ground. This cross-validation based on multivariate dynamic trends effectively overcomes the limitations of single-point static judgment, enabling the system to predict the situation before the slippage completely worsens and leads to serious vehicle entrapment, significantly improving the robustness and foresight of cause identification in complex dynamic scenarios. The underlying reason is that although both increased terrain resistance and drive wheel slippage may manifest as a decrease in speed, they differ fundamentally in their wheel-to-ground energy transfer paths: the former involves power being consumed by external resistance, while the latter involves power being wasted in the relative slippage between the wheel and the ground. Therefore, trend coupling analysis over the time dimension can effectively distinguish between the two.

[0051] In some embodiments, step S104, adjusting the robot control parameters according to the cause of the decrease in motion speed, may include, but is not limited to, the following steps: Step S301: If the decrease in speed is due to increased terrain resistance, increase the current output torque to improve the current speed. Step S302: If the decrease in motion speed is due to drive wheel slippage, then adjust the output torque based on the drive wheel speed data, robot motion speed data, and slip ratio.

[0052] In some embodiments, the demands on chassis power from increased terrain drag and drive wheel slippage are diametrically opposed in actual operation. Faced with increased terrain drag due to rigid obstacles, the robot does indeed require stronger power to overcome gravity or resistance and complete obstacle crossing; however, faced with drive wheel slippage due to loose ground, blindly increasing torque will only exacerbate the damage caused by surface particles, causing the wheels to sink deeper. Therefore, to ensure the correctness of control commands, it is necessary to formulate clear and differentiated basic principles for torque adjustment for these two distinct physical conditions.

[0053] Therefore, the cause of the decrease in movement speed can be determined first. If the cause is increased terrain resistance, the current output torque can be increased to improve the current movement speed. In practice, when the system determines that the current working condition is increased terrain resistance, the controller will gradually increase the current output torque according to a preset step size until the robot's movement speed recovers to the target travel speed or reaches the maximum safe torque limit of the motor. The preset step size can be calibrated based on the response characteristics of the robot's power system and should generally meet the requirement of "generating a perceptible propulsion effect after increasing torque without causing abrupt impacts." To avoid repeated torque increases and decreases near rigid obstacles, which can cause torque oscillations, a torque holding time can be set. After each torque increase, a short observation period can be maintained, and then the decision on whether to continue increasing torque can be made based on the recovery of the movement speed.

[0054] If the decrease in motion speed is due to drive wheel slippage, the output torque is adjusted based on drive wheel speed data, robot motion speed data, and slip ratio. When drive wheel slippage is detected, the controller immediately stops any torque increase attempts and enters the anti-slip closed-loop adjustment mode. In this mode, the controller reads drive wheel speed data, robot motion speed data, and slip ratio in real time, using these real-time states as feedback variables to dynamically calculate an appropriate torque reduction range to suppress the aggravation of slippage. To avoid the robot losing attachment and stalling or sliding due to a sudden drop in torque, the torque reduction can also adopt a graded retreat approach. That is, it first quickly retreats to a safe range, and then makes small, fine adjustments based on the subsequent recovery of the slip ratio. If a significant drop in slip ratio, a reduction in attitude disturbance, and a stabilization of the robot's motion speed are detected, the torque can be slowly restored within the safe boundary.

[0055] This embodiment addresses the torque increase operation for increased terrain resistance to provide sufficient traction to overcome rigid obstacles. The torque adjustment for drive wheel slippage is no longer a simple unidirectional increase or decrease, but requires fine-grained closed-loop adjustment combining multiple real-time operational data to find the optimal power output point that the loose ground can withstand. It's understood that increasing the current movement speed does not necessarily mean restoring the robot's idle speed on flat ground, but rather restoring the robot's movement speed to a target range for sustainable propulsion within the current task environment and safety boundaries. This target speed can be issued by the upper-level navigation module or automatically reduced by the controller based on slope, ground type, and load status. The core of the output torque adjustment is not simply reducing power, but preserving necessary traction while suppressing severe slippage, keeping the robot in a controllable micro-slip state. Micro-slip refers to a state where limited relative sliding between the wheels and the ground is allowed to achieve higher traction efficiency without developing into severe spinning that damages the ground.

[0056] Through the above technical solution, this embodiment establishes the core control logic of "increasing torque to increase resistance and adjusting torque during slippage." By differentiating strategies, it completely avoids the fatal flaw of traditional control logic that indiscriminately attributes speed decreases to insufficient power and blindly increases torque. This not only ensures the robot has sufficient obstacle-crossing capability when facing rigid obstacles, but also effectively protects fragile ground adhesion conditions when facing low-traction surfaces such as loose gravel, preventing accidents caused by incorrect power compensation leading to digging and getting stuck. The technical reason is that increased terrain resistance requires raising the upper limit of the wheel-to-ground tangential force, while wheel slippage requires constraining the wheel-to-ground tangential force within the ground's tolerance range. Since the control directions of these two are opposite, a differentiated torque strategy must be adopted.

[0057] In some embodiments, step S302, the output torque adjustment process based on the drive wheel speed data, robot motion speed data, and slip ratio may include, but is not limited to, the following steps: Based on the drive wheel rotation speed data, robot motion speed data, and slip ratio, estimate the maximum effective adhesion coefficient on the ground; Based on the maximum effective adhesion coefficient on the ground, the slip critical torque is determined. The slip critical torque is used to represent the critical output torque that causes the robot to change from stable driving to slipping of the drive wheels. The current output torque is adjusted based on the slip critical torque so that the current output torque is less than the slip critical torque.

[0058] In some embodiments, if the torque is reduced too much, the robot will completely lose forward momentum, stall on the slope, or even roll away; if it is reduced too little, the wheels will still be in a state of severe slippage, continuing to damage the ground structure. To find the optimal balance between "maintaining maximum traction" and "preventing severe slippage," it is necessary to delve into the mechanical interaction characteristics between the wheels and the loose ground, and to use real-time operational data to deduce the maximum grip that the current ground can provide, thereby giving a precise upper limit for torque adjustment.

[0059] To this end, the maximum effective coefficient of adhesion can be estimated based on the drive wheel speed data, robot speed data, and slip ratio. In practice, the controller has a pre-built model of the slip ratio-coefficient of adhesion curve (i.e., the μ-s curve) characterizing the interaction between the tires and different ground surfaces. When slippage occurs, the controller inputs the real-time drive wheel speed data, robot speed data, and slip ratio into a pre-built nonlinear state observer. Combining this with the current motor output torque and the known vehicle mass, it then calculates the maximum effective coefficient of adhesion that the current contact surface can provide. The μ-s curve model can be obtained through offline experiments, specifically by selecting several common ground types encountered during robot use, such as dry gravel, loose sand, wet soft mud, compacted slopes, and mixed landslide deposits. Traction tests with varying degrees of slippage are conducted in a controlled test area. During the tests, the drive wheel speed, robot speed, slip ratio, output torque, attitude changes, and any significant pitting or sinking phenomena are recorded simultaneously. The correlation between slip ratios and available adhesion for different ground types is then established, forming multiple sets of ground model parameters, which are saved in the controller. If the ground type is unknown, the controller can initially use a conservative model, which is then gradually corrected based on real-time observations during operation.

[0060] Understandably, the maximum effective adhesion coefficient refers to the maximum effective grip that the ground can provide to the wheels before significant shear failure occurs, given the current ground material, moisture content, compaction degree, slope, and load conditions. This quantity is not a fixed constant but changes in real time with factors such as repeated wheel rolling, loosening of ground particles, deepening of subsidence, or dampness. The nonlinear state observer can be understood as an estimation module that continuously approximates intangible physical quantities using measurable signals. Its inputs include drive wheel speed data, robot motion speed data, slip ratio, current output torque, vehicle mass, drive wheel radius, and, if necessary, slope and attitude information; its output is the current maximum effective adhesion coefficient and its changing trend. To ensure the stability of the estimation results, the observer is preferably executed in the controller software at a fixed control cycle, and amplification and reliability checks are performed on abrupt estimation results. For example, when the encoder malfunctions, the speed source loses lock, or the robot leaps over obstacles, the adhesion coefficient estimation can be paused, and the previous reliable value can be used or a conservative torque-limiting mode can be switched.

[0061] Then, based on the maximum effective adhesion coefficient on the ground, the slip critical torque is determined. The slip critical torque represents the critical output torque required for the robot to transition from stable movement to slippage of the drive wheels. This slip critical torque can be calculated by multiplying the adhesion coefficient by the vertical load at the wheel's location and the drive wheel radius. This vertical load is not simply an average value distributed across the entire vehicle, but is adjusted based on the robot's current posture and load distribution. For example, in uphill conditions, the load on the front and rear wheels will be redistributed; when one wheel is stuck in a gravel pit or the vehicle tilts, the forces on the left and right wheels will also differ significantly. The controller can estimate the current vertical load on each drive wheel based on the posture angle, suspension status signals, wheel pressure sensor signals, or a calibrated load distribution model, and then obtain the slip critical torque for each drive wheel. For multi-drive-wheel robots, torque limiting strategies can be applied to a single wheel, a single side, or the entire vehicle to adapt to differential drive, four-wheel independent drive, or tracked drive structures.

[0062] The current output torque is then adjusted based on the slip threshold torque to ensure it is below the slip threshold torque. The current output torque can be forcibly limited to a specific percentage (e.g., 90% to 95%) of the slip threshold torque. If the current output torque exceeds this threshold, it is immediately reduced below it; if it falls below, fine-tuning is allowed below the threshold. This specific percentage setting aims to provide a safety margin for fluctuations in ground adhesion. This percentage can be calibrated through repeated experiments: on several typical loose surfaces, gradually approaching the slip boundary, recording the torque range that maintains forward movement without significant cratering, and then writing the corresponding threshold percentage into the controller parameter library. If the mission environment is predominantly extremely loose ground, a more conservative percentage can be selected; if the ground is relatively stable, the percentage can be appropriately increased to enhance traction output.

[0063] In this embodiment, the maximum effective ground adhesion coefficient is a dynamically changing physical quantity, representing the maximum friction coefficient that the current contact surface can provide without macroscopic shear failure. The slip critical torque is a dynamic boundary value calculated based on this adhesion coefficient. Once the motor's output torque exceeds this boundary value, the shear force exerted by the wheel on the ground will exceed the ground's shear strength, thus triggering macroscopic slippage. The "critical output torque at which stable driving changes into drive wheel slippage" can be understood as the dividing point where the wheel-ground system changes from "being able to effectively convert torque into forward traction" to "torque continuing to increase but mainly manifested as wheel-ground relative spinning." This dividing point does not necessarily correspond to a single instantaneous value, but can be a safety boundary that slowly drifts with environmental changes. Therefore, during control, the output torque is usually not pushed to this boundary itself, but rather an appropriate margin is reserved to cope with rapid fluctuations in ground conditions.

[0064] Through the above technical solution, this embodiment extends macroscopic kinematic control to the microscopic wheel-ground dynamics level. By estimating ground adhesion in real time, the system can dynamically calculate the maximum allowable torque without severe slippage and precisely control the actual output torque below this critical value. This dynamic torque-limiting strategy based on physical limits achieves the ultimate squeezing of traction force on loose ground. It prevents excessive wheel slippage leading to digging and sinking, while ensuring that the robot obtains the maximum forward momentum available under the current ground conditions. This keeps the wheels operating within the optimal traction range of micro-slippage, greatly improving the robot's autonomous extrication and continuous climbing capabilities on low-adhesion surfaces. The technical mechanism is as follows: when the output torque is limited below the slippage critical torque, the shearing force of the wheel on the ground is still within the ground's tolerance range, and the ground structure has not yet undergone rapid damage, so traction force can be continuously built up; however, once this boundary is exceeded, ground particles become significantly loose and slip, and the subsequent increase in torque will be more converted into idle spinning and digging, which is actually detrimental to the robot's extrication. By tracking this physical boundary in real time and limiting torque in a closed loop, the robot can continue to operate in a better traction state on dynamically changing loose slopes.

[0065] In some embodiments, after adjusting the robot control parameters according to the cause of the decrease in motion speed, the method may also include, but is not limited to, the following steps: Step S401: Obtain the robot pitch angle, robot roll angle, and robot mechanical parameters, including wheel track, wheelbase, center of gravity height, and vehicle mass. Step S402: Calculate the current vertical normal force on the wheel on the non-slipping side using the static equilibrium equation based on the robot's pitch angle, roll angle, and mechanical parameters. Step S403: Calculate the upper limit of the adhesion torque of the wheel on the non-slip side based on the current vertical normal pressure and the maximum effective adhesion coefficient of the ground. Step S404: Based on the current output torque and the upper limit of the adhesion torque of the wheel on the non-slip side, adjust the output torque of the slipping side and the output torque of the non-slip side, so that the sum of the output torque of the slipping side and the output torque of the non-slip side is equal to the current output torque.

[0066] In some embodiments, low-traction surfaces are often uneven in actual disaster relief scenarios. For example, the robot's left wheel may become stuck in loose gravel and slip severely, while the right wheel remains on relatively solid ground. In this case, simply applying uniform torque limiting control to the entire vehicle can suppress left-side slippage, but it also wastes the good grip of the right side; conversely, if high torque is forcibly maintained to extricate the robot from trouble, the left wheel will sink deeper and deeper. To solve the power distribution problem caused by unilateral slippage or inconsistent adhesion conditions on both sides, it is necessary to further achieve intelligent power transfer between the left and right sides on top of overall vehicle torque limiting.

[0067] To achieve this, the robot's pitch angle, roll angle, and mechanical parameters can be obtained first. These mechanical parameters include wheelbase, track width, center of gravity height, and vehicle mass. In practical implementation, it is first necessary to accurately perceive the impact of the vehicle's posture on the forces acting on the wheels. The robot's pitch angle and roll angle can be calculated in real time by a six-axis inertial measurement unit. The robot's mechanical parameters, such as wheelbase, track width, center of gravity height, and vehicle mass, are inherent physical properties of the chassis and are pre-stored in the controller's parameter library.

[0068] Then, based on the robot's pitch angle, roll angle, and mechanical parameters, the current vertical normal force on the wheel on the non-slipping side is calculated using static equilibrium equations. When the robot travels on a slope or tilts, the line of action of gravity on the entire vehicle shifts, causing the vertical load distributed among the wheels to become uneven. In practice, the controller uses the acquired robot pitch angle, roll angle, and mechanical parameters to construct the spatial static equilibrium equations for the entire vehicle. For example, in an uphill situation with a leftward tilt, the center of gravity shifts backward and to the left. At this time, through torque balance calculations, the actual vertical normal force currently borne by the right-side wheel (assuming it is the non-slipping side) can be accurately determined.

[0069] Next, based on the current vertical normal force and the maximum effective ground adhesion coefficient, calculate the upper limit of the adhesion torque for the non-slipping wheel. This can be achieved by multiplying the current vertical normal force by the estimated maximum effective ground adhesion coefficient, and then multiplying by the drive wheel radius. This yields the upper limit of the adhesion torque that the non-slipping wheel can withstand in the current posture. This upper limit represents the maximum driving torque that the non-slipping wheel can apply to the ground without slipping.

[0070] Finally, based on the current output torque and the maximum adhesion torque of the non-slipping wheel, the output torque of the slipping side and the non-slipping side are adjusted, and the sum of the output torque of the slipping side and the non-slipping side equals the current output torque. While keeping the overall vehicle's current output torque (i.e., total required torque) unchanged, the torque on the left and right sides is redistributed. Specifically, the excess torque originally evenly distributed to the slipping side is transferred to the non-slipping side, which has greater grip potential, while ensuring that the torque allocated to the non-slipping side does not exceed its maximum adhesion torque.

[0071] Through the above technical solution, this embodiment realizes a dynamic torque distribution mechanism based on real-time load transfer. Its technical effect is that when the robot encounters a unilateral low-adhesion stuck situation, it can fully utilize the remaining grip force of the non-slipping side for effective extrication, while preventing the slipping side from continuing to dig due to excessive torque. Simultaneously, by introducing the influence of attitude on vertical normal force through static equilibrium equations, the calculation of the upper limit of adhesion torque more closely reflects the actual physical state, preventing slippage on the non-slipping side due to blind torque transfer under extreme tilting postures. This ensures maximum overall vehicle traction while maintaining stability in the driving direction.

[0072] In some embodiments, in step S404, adjusting the output torque on the slipping side and the output torque on the non-slipping side based on the current output torque and the upper limit of the adhesion torque of the non-slipping wheel may include, but is not limited to, the following steps: Divide the current output torque by 2 to obtain the output torque on one side; Calculate the first torque difference based on the single-sided output torque and the upper limit of the adhesion torque; The output torque on the non-slip side is adjusted based on the first torque difference. Calculate the second torque difference based on the current output torque and the adjusted output torque on the non-slip side; The output torque on the slip side is adjusted based on the second torque difference.

[0073] In some embodiments, the current output torque can be divided by 2 to obtain the single-sided output torque. The current output torque of the entire vehicle can be divided into two equal halves to obtain the theoretical single-sided output torque, which represents the normal distribution state under ideal smooth road conditions. Then, a first torque difference is calculated based on the single-sided output torque and the upper limit of the adhesion torque. The first torque difference can be obtained by subtracting the calculated upper limit of the adhesion torque of the non-slipping wheel from the single-sided output torque.

[0074] Then, the output torque on the non-slip side is adjusted based on the first torque difference. The output torque on the non-slip side can be adjusted specifically according to the sign and magnitude of the first torque difference. For example, if the output torque on one side, plus the compensation torque that needs to be transferred from the slipping side, is still less than the upper limit of the adhesion torque, then the non-slip side can fully absorb this transferred torque; if the absorbed torque exceeds the upper limit of the adhesion torque, then the output torque on the non-slip side is forcibly limited to that upper limit to protect it from new slippage.

[0075] Next, based on the current output torque and the adjusted output torque on the non-slip side, a second torque difference is calculated. After determining the adjusted output torque on the non-slip side, to ensure that the total thrust of the vehicle does not change abruptly, the controller subtracts the adjusted output torque on the non-slip side from the total current output torque to calculate the second torque difference. The output torque on the slip side is then adjusted based on this second torque difference. This second torque difference can be used as the final torque that the slip side wheel should output.

[0076] Through the above technical solution, this embodiment establishes a torque redistribution algorithm that prioritizes "protecting the upper limit first, then calculating the margin." This calculation method has clear logic and low computational load, making it very suitable for high-frequency real-time calculations in resource-constrained embedded low-level controllers. Its unique technical effect lies in strictly maintaining the physical adhesion limit of the non-slipping side, ensuring absolute safety during the power transfer process; at the same time, by subtracting the torque of the non-slipping side from the total torque to determine the torque of the slipping side, it ensures that the sum of the output torques on both sides always equals the torque required by the entire vehicle, avoiding unexpected acceleration or deceleration of the entire vehicle due to uneven torque distribution, and enabling the robot to maintain smooth longitudinal motion control even during unilateral slippage and escape.

[0077] Figure 3 This is a schematic diagram of the overall architecture of an intelligent robot motion control method provided in an embodiment of this application. First, the robot's drive wheel rotation speed data, motion speed data, and attitude angular velocity data are acquired. Then, it is analyzed whether the robot is traveling on a low-traction surface, generating a passage anomaly signal. Next, it is identified whether the decrease in motion speed is due to increased terrain resistance or drive wheel slippage. Finally, based on the cause of the decrease in motion speed, the robot's control parameters, such as the current output torque, are adjusted, thereby achieving robot motion control and improving accuracy and reliability.

[0078] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of this application first acquire robot operation data, then generate a passage abnormality signal based on the robot operation data, then identify the cause of the decrease in movement speed based on the drive wheel speed data and the robot movement speed data, and finally adjust the robot control parameters based on the cause of the decrease in movement speed. Thus, the robot control parameters can be adjusted by combining the robot operation data and the cause of the decrease in movement speed to achieve robot motion control, thereby improving accuracy and reliability.

[0079] like Figure 4 As shown, this embodiment of the invention also provides an intelligent robot motion control system, including: The data acquisition module 501 is used to acquire robot operation data, which includes drive wheel speed data, robot motion speed data, and robot attitude angular velocity data. The passage abnormality signal generation module 502 is used to generate a passage abnormality signal based on the robot's operating data. The passage abnormality signal is used to reflect that the robot is traveling on a road surface with low adhesion. The speed reduction cause identification module 503 is used to respond to the traffic abnormality signal and identify the cause of the speed reduction based on the drive wheel speed data and robot movement speed data. The causes of the speed reduction include increased terrain resistance and drive wheel slippage. The control parameter adjustment module 504 is used to adjust the robot control parameters according to the cause of the decrease in motion speed. The robot control parameters include the current output torque.

[0080] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0081] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A method for controlling the motion of an intelligent robot, characterized in that, Includes the following steps: Acquire robot operation data, which includes drive wheel speed data, robot motion speed data, and robot attitude angular velocity data; Based on the robot's operating data, a passage anomaly signal is generated, which is used to reflect that the robot is traveling on a road surface with low traction; In response to the passage abnormality signal, the cause of the decrease in movement speed is identified based on the drive wheel rotation speed data and robot movement speed data. The cause of the decrease in movement speed includes increased terrain resistance and drive wheel slippage. Based on the cause of the decrease in movement speed, the robot control parameters are adjusted, including the current output torque.

2. The method according to claim 1, characterized in that, The step of generating a passage anomaly signal based on the robot's operating data includes: Obtain the drive wheel diameter and target speed; Extract the current motion speed from the robot's motion speed data; Calculate the slip ratio based on the drive wheel diameter, the drive wheel rotation speed data, and the current speed. Based on the robot's angular velocity data, identify the robot's attitude disturbance state; Calculate the first speed difference based on the target driving speed and the current moving speed; The passage anomaly signal is generated based on the slip ratio, the robot posture disturbance state, and the first velocity difference.

3. The method according to claim 2, characterized in that, The step of calculating the slip ratio based on the drive wheel diameter, the drive wheel rotation speed data, and the current speed includes: Extract the current wheel speed from the drive wheel rotation speed data; Calculate the linear velocity of the drive wheel based on the diameter of the drive wheel and the current wheel speed; Calculate the second speed difference based on the linear velocity of the drive wheel and the current speed. The maximum value between the linear velocity of the drive wheel and the current speed is selected as the speed to be compared. The slip ratio is calculated based on the second speed difference and the speed to be compared.

4. The method according to claim 2, characterized in that, The step of identifying the robot's attitude disturbance state based on the robot's attitude angular velocity data includes: The robot's attitude angular velocity data is decomposed into three axes to obtain X-axis angular velocity data, Y-axis angular velocity data, and Z-axis angular velocity data; Calculate the first angular velocity amplitude based on the X-axis angular velocity data; Calculate the second angular velocity amplitude based on the Y-axis angular velocity data; Calculate the amplitude of the third angular velocity based on the Z-axis angular velocity data; The robot's posture disturbance state is identified based on the first angular velocity amplitude, the second angular velocity amplitude, and the third angular velocity amplitude.

5. The method according to claim 1, characterized in that, The step of identifying the cause of the decrease in movement speed based on the drive wheel rotation speed data and the robot movement speed data includes: Obtain historical slip ratio variation curves; Calculate the wheel speed change trend based on the drive wheel speed data; Calculate the trend of motion speed change based on the robot's motion speed data; Calculate the slip ratio change trend based on the historical slip ratio change curve; Based on the trends of wheel speed change, motion speed change, and slip ratio change, identify the cause of the decrease in motion speed.

6. The method according to claim 2, characterized in that, The step of adjusting the robot control parameters based on the cause of the decrease in motion speed includes: If the decrease in speed is due to increased terrain resistance, then increase the current output torque to increase the current speed. If the decrease in movement speed is due to drive wheel slippage, then the output torque is adjusted based on the drive wheel rotation speed data, the robot movement speed data, and the slip ratio.

7. The method according to claim 6, characterized in that, The step of adjusting the output torque based on the drive wheel rotation speed data, the robot motion speed data, and the slip ratio includes: Based on the drive wheel rotation speed data, the robot movement speed data, and the slip ratio, estimate the maximum effective adhesion coefficient on the ground; Based on the maximum effective adhesion coefficient of the ground, the slip critical torque is determined. The slip critical torque is used to represent the critical output torque that causes the robot to change from stable driving to slipping of the drive wheels. The current output torque is adjusted based on the slip critical torque so that the current output torque is less than the slip critical torque.

8. The method according to claim 7, characterized in that, After adjusting the robot control parameters based on the cause of the decrease in motion speed, the method further includes: The robot's pitch angle, roll angle, and mechanical parameters are obtained, including wheel track, wheelbase, center of gravity height, and vehicle mass. Based on the robot's pitch angle, roll angle, and mechanical parameters, the current vertical normal force on the wheel on the non-slipping side is calculated using the static equilibrium equation. Calculate the upper limit of the adhesion torque of the wheel on the non-slip side based on the current vertical normal force and the maximum effective adhesion coefficient of the ground. Based on the current output torque and the upper limit of the adhesion torque of the wheel on the non-slip side, adjust the output torque on the slipping side and the output torque on the non-slip side, such that the sum of the output torque on the slipping side and the output torque on the non-slip side is equal to the current output torque.

9. The method according to claim 8, characterized in that, The step of adjusting the output torque on the slipping side and the output torque on the non-slipping side based on the current output torque and the upper limit of the adhesion torque of the non-slipping wheel includes: Divide the current output torque by 2 to obtain the single-sided output torque; Calculate the first torque difference based on the single-sided output torque and the upper limit of the attachment torque; The output torque on the non-slip side is adjusted based on the first torque difference. Calculate the second torque difference based on the current output torque and the adjusted output torque on the non-slip side; The output torque on the slip side is adjusted based on the second torque difference.

10. A smart robot motion control system, characterized in that, include: The data acquisition module is used to acquire robot operation data, which includes drive wheel speed data, robot motion speed data, and robot attitude angular velocity data. The abnormal passage signal generation module is used to generate an abnormal passage signal based on the robot's operating data. The abnormal passage signal is used to reflect that the robot is traveling on a road surface with low adhesion. The speed reduction cause identification module is used to respond to the passage abnormality signal and identify the cause of the speed reduction based on the drive wheel rotation speed data and robot movement speed data. The cause of the speed reduction includes increased terrain resistance and drive wheel slippage. The control parameter adjustment module is used to adjust the robot control parameters according to the cause of the decrease in movement speed, and the robot control parameters include the current output torque.

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