Method and system for optimizing and controlling slip rate of crawler-type dredging robot

Abstract

The invention discloses a crawler-type dredging robot slip rate optimization control method and system, and relates to the technical field of crawler-type mobile robot control, and the method comprises the steps: 1, importing a dredging robot three-dimensional diagram, carrying out the modeling of a crawler chassis, obtaining a kinetic model, applying torque drive in the model, and obtaining motion data; 2, establishing a dynamic mathematical model according to the dynamic characteristics of the dredging robot, and obtaining the relation between the motion state of the dredging robot and torque driving; 3, the slipping condition of the robot is analyzed through the motion data, and the slipping rate of the robot in actual motion is obtained; 4, according to a slip rate calculation result, identifying an optimal slip rate through a self-adaptive variable step size strategy; 5, designing a driving anti-slip controller of the fuzzy sliding mode according to the dynamic mathematical model, the slip rate in actual motion and the optimal slip rate; and 6, the motion of the robot is dynamically adjusted according to the driving anti-skid controller.

Description

Technical Field

[0001] The present invention relates to the technical field of crawler mobile robot control, and more particularly to a method and system for optimizing the slip rate of a crawler dredging robot. Background Art

[0002] Tracked dredging robots, as a typical tracked walking device in the field of dredging engineering machinery, are widely used in natural water bodies for their efficiency, safety, and adaptability to complex environments. To address the slipping problem in various working conditions, dredging robots utilize a crawler chassis, which provides a certain degree of anti-slip effect. However, after completing underwater operations, the robot is still prone to slipping when climbing the slope required to transition from the bottom of the water to the shore. Specifically, in complex environments affected by multiple factors such as slippery and muddy surfaces, the robot's speed and traction vary with the torque of the drive wheels. Higher torque provides greater traction, but when the torque exceeds the maximum friction between the track and the ground, slippage occurs. Excessively high slip rates result in power loss and insufficient traction. However, excessively low slip rates lead to increased energy consumption, significant mechanical wear, and reduced track life, hindering full traction performance. Therefore, it is urgent to identify and optimize the optimal slip rate for landing in slippery and sloping environments, balancing power transmission efficiency and mechanical damage.

[0003] Therefore, it is an urgent problem for technical personnel in this field to design a method for optimizing the slip rate of a crawler dredging robot and an adaptive variable step-size optimal slip rate identification strategy in which the system can dynamically adjust the performance. Summary of the Invention

[0004] In view of this, the present invention provides a method and system for optimizing the slip rate control of a tracked dredging robot. The track slip rate and other process parameters under fuzzy sliding mode control have good stable control effects, and the driving stability of the tracked dredging robot is significantly improved.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a method for optimizing the slip rate of a crawler dredging robot, comprising: Step 1: Import the 3D image of the dredging robot, model the crawler chassis, and obtain the dynamic model; Step 2: Apply torque drive to the dynamic model to obtain motion data; Step 3: Establish a dynamic mathematical model based on the dynamic characteristics of the dredging robot, and obtain the relationship between the motion state of the dredging robot and the torque drive based on the dynamic mathematical model; Step 4: Analyze the robot's slippage using the motion data output by the dynamic model and calculate the robot's slip rate during actual motion. Step 5: Based on the slip rate calculation results in actual motion, the optimal slip rate is identified through an adaptive variable step length strategy; Step 6: Design a fuzzy sliding mode drive anti-slip controller based on the dynamic mathematical model, the slip rate in actual motion, and the optimal slip rate. Step 7: Dynamically adjust the robot's motion based on the driving anti-skid controller.

[0006] Preferably, the specific process of establishing a dynamic mathematical model includes: analyzing the robot's driving mechanical characteristics, calculating the actual traction of the robot's tracks, and the relationship between the actual traction of the tracks and the output torque of the drive wheels; analyzing the robot's straight-line motion resistance based on the robot's driving mechanical characteristics, and obtaining the robot's compaction resistance and bulldozing resistance, underwater resistance and slope resistance.

[0007] Preferably, the slip rate calculation formula is as follows: ; Where, is the output speed of the driving wheel; is the driving wheel radius; is the actual linear speed of travel; is the theoretical linear speed of the tracked dredging robot.

[0008] Preferably, the optimal slip rate is determined based on the relationship curve between the adhesion coefficient and the slip rate between the track and the ground, the slip rate corresponding to the maximum value of the adhesion coefficient is determined, and the search step length of the slip rate is dynamically adjusted based on the adaptive variable step length of the gradient sign to speed up the identification speed of the optimal slip rate.

[0009] Preferably, the design method of the fuzzy sliding mode driving anti-slip controller includes: Step 51: According to the traditional sliding mode control theory, the desired torque signal of the motor under the traditional sliding mode control law is obtained; Step 52: Determine fuzzy sets, membership functions and fuzzy rules; Step 53: Based on the fuzzy rules and membership functions, the sliding surface and its derivatives are used as inputs to generate fuzzy approximation compensation terms through fuzzy reasoning; Step 54: Perform fuzzy reasoning on the input sliding surface and its derivative according to fuzzy rules to generate a fuzzy control signal; Step 55: Defuzzify the fuzzy control signal using an average defuzzifier to obtain a torque drive adjustment signal; Step 56: Add the motor desired torque signal under the traditional sliding mode control law and the torque compensation signal under the fuzzy control to obtain the final torque drive adjustment signal.

[0010] Preferably, the collected robot motion data is filtered.

[0011] Preferably, a joint simulation is performed with the dynamic model in a Simulink environment.

[0012] Preferably, a crawler-type dredging robot anti-skid control system comprises: Model building module: used to import the 3D image of the dredging robot, model the crawler chassis, and obtain the dynamic model; Data acquisition module: applies torque drive in the dynamic model to obtain motion data; Dynamic mathematical model module: used to establish a dynamic mathematical model based on the dynamic characteristics of the dredging robot, and obtain the relationship between the motion state of the dredging robot and the torque drive based on the dynamic mathematical model; Slip rate calculation module: used to analyze the robot's slippage through the dynamic model and calculate the robot's slip rate in actual motion; Optimal slip rate identification module: used to identify the optimal slip rate through adaptive variable step length strategy based on the slip rate calculation results in actual motion; Fuzzy sliding mode control module: used to design a fuzzy sliding mode drive anti-slip controller based on the dynamic mathematical model, the slip rate in actual motion, and the optimal slip rate; Servo motor drive module: used to dynamically adjust the robot's movement according to the driving anti-skid controller.

[0013] Beneficial effects

[0014] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for optimizing the slip rate of a crawler dredging robot, which has the following beneficial effects: The present invention comprehensively considers the ground environment, kinematic characteristics and drive system construction, and obtains the dynamic model of the crawler dredging robot under straight climbing conditions. The slip rate is determined by the dynamic model of the crawler dredging robot. and ground adhesion coefficient Based on the identification of the optimal slip rate with a fixed step size, an adaptive variable step size optimal slip rate identification method based on the gradient sign is proposed. This method can improve the slip rate oscillation problem in the transition stage and keep the slip rate curve stable during the entire climbing process. Based on the dynamic model of the crawler dredging robot, adaptive sliding mode control systems based on fuzzy approximation compensation are designed in the Simulink environment. The driving simulation under climbing conditions is compared and analyzed with traditional sliding mode control and fuzzy PID single control algorithms. The simulation results show that the designed fuzzy sliding mode control system can keep the machine body's operating slip rate and other operating process parameters relatively stable, significantly improve the driving stability, and has a good driving anti-skid effect.

[0015] In summary, the adaptive variable-step-size optimal slip rate identification and drive anti-slip control method proposed in this paper is particularly suitable for crawler dredging robots climbing uphill after completion of operations. The drive anti-slip control algorithm maintains the real-time slip rate at the optimal rate that fully utilizes the robot's traction performance. This significantly reduces the potential for stalling and sliding caused by climbing failures, achieving stable and reliable climbing and landing. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0017] Figure 1 Flowchart provided for the present invention; Figure 2 A block diagram of the DC servo motor system model provided by the present invention; Figure 3 The relationship curves between adhesion coefficient and slip rate of different road surfaces provided by the present invention; Figure 4 The optimal slip rate identification strategy diagram provided by the present invention; Figure 5 Input and output membership function diagrams provided by the present invention; Figure 6 This is a block diagram of the drive anti-skid control system provided by the present invention; Figure 7 This is a virtual prototype image of the Recurdyn crawler dredging robot provided by the present invention; Figure 8 The Recurdyn riverbank ground simulation environment map provided by the present invention; Figure 9The road spectrum of the riverbank ground simulation environment provided by the present invention; Figure 10 This is the linear velocity filtering result diagram provided by the present invention; Figure 11 This is the angular velocity filtering result diagram provided by the present invention; Figure 12 This is the optimal slip rate identification effect diagram of the fixed variable step size under the fuzzy sliding mode control provided by the present invention; Figure 13 This is a diagram showing the optimal slip rate control effect with a fixed variable step size under the fuzzy sliding mode control provided by the present invention; Figure 14 This is a graph showing the driving wheel slip control results under different algorithms provided by the present invention; Figure 15 This is a diagram showing the control results of the driving wheel linear speed under different algorithms provided by the present invention; Figure 16 This is a diagram showing the driving wheel angular velocity control results using different algorithms provided by the present invention; Figure 17 This is a diagram of the driving wheel output torque control results under different algorithms provided by the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0019] like Figure 1 As shown, an embodiment of the present invention discloses a method for optimizing the slip rate of a crawler dredging robot, comprising: Step 1: Import the 3D image of the dredging robot, model the crawler chassis, and obtain the dynamic model; Step 2: Apply torque drive to the dynamic model to obtain motion data; Step 3: Establish a dynamic mathematical model based on the dynamic characteristics of the dredging robot, and obtain the relationship between the motion state of the dredging robot and the torque drive based on the dynamic mathematical model; Step 4: Analyze the robot's slippage using the motion data output by the dynamic model and calculate the robot's slip rate during actual motion. Step 5: Based on the slip rate calculation results in actual motion, the optimal slip rate is identified through an adaptive variable step length strategy; Step 6: Design a fuzzy sliding mode drive anti-slip controller based on the dynamic mathematical model, the slip rate in actual motion, and the optimal slip rate. Step 7: Dynamically adjust the robot's motion based on the driving anti-skid controller.

[0020] In a specific embodiment of the present invention, recurdyn is selected to model the crawler chassis.

[0021] Specifically, the specific process of establishing the dynamic model includes: analyzing the robot's driving mechanical characteristics, calculating the actual traction of the robot's tracks, and the relationship between the actual traction of the tracks and the output torque of the drive wheels; analyzing the robot's straight-line motion resistance based on the robot's driving mechanical characteristics, and obtaining the robot's compaction resistance and bulldozing resistance, underwater resistance and slope resistance.

[0022] In a specific embodiment of the present invention, the dredging robot chassis adopts an inverted trapezoidal crawler track, which mainly includes a body, drive wheels, support wheels, guide wheels, and load-bearing wheels. To achieve coordinated movement of each mechanism, the constraint relationship between each component is set as shown in Table 1: Table 1 Connection and constraint relationships of various mechanisms of the crawler chassis

[0023] In a specific embodiment of the present invention, the process of the optimal slip rate identification method is as follows: collect the motion data of the recurdyn dynamic model and calculate the real-time slip rate of the robot->find the relationship between the slip rate i and the ground adhesion coefficient μ by establishing the dynamic equation during the rotation of the driving wheel of the crawler dredging robot->establish the optimal slip rate identification function, and perform adaptive step size adjustment to obtain the optimal slip rate.

[0024] Furthermore, the driving dynamics performance of a tracked dredging robot mainly depends on the track traction and ground adhesion. Track traction is the reaction force applied by the robot drive system to the ground through the track to drive its own movement, which is mainly affected by factors such as the output torque of the drive wheel, the ground contact area of ​​the track, and the maximum shear strength of the ground. Ground adhesion is the maximum friction between the track and the ground, which is mainly affected by factors such as the effective contact area between the track and the ground, the friction coefficient, and the ground conditions. Under ideal conditions where no slipping occurs, the track traction is equal to the ground adhesion. When the traction is affected by the output torque of the drive wheel and is greater than the ground adhesion, it will cause slipping. The expression for the maximum traction of the track when the ground conditions are certain is: (1) Where A is the track contact area, is the maximum shear strength of the ground, c and are terrain parameters, namely soil cohesion coefficient and internal friction angle, is the pressure exerted by the track on the ground, and N is the positive pressure of the body.

[0025] From the above formula, we can know that when the track parameters of the machine body are constant, the maximum traction provided by the ground is Mainly affected by the maximum shear strength of the ground The influence, that is, the maximum traction force depends on the shear stress of the track on the ground. According to Bekker's vehicle ground mechanics theory, the actual traction force of the track is expressed as: (2) Where F is the actual traction force, B is the track width, and L is the track ground length. is the actual shear stress of the soil distributed along the length of the track, i is the slip rate of the track relative to the ground, x is the shear displacement of the track, and K is the horizontal shear modulus of the soil.

[0026] When the crawler dredging robot moves under stable working conditions, the actual traction force and the output torque of the drive wheel have the following relationship: (3) Where, is the track transmission efficiency, T is the output torque of the track driving wheel, and r is the driving wheel radius.

[0027] From formula (2) and formula (3), it can be seen that there is a functional relationship between the traction force of the crawler dredging robot and the slip rate of the crawler relative to the ground. The slip rate can be controlled by controlling the output torque of the crawler drive wheel to stabilize it within the desired range.

[0028] Furthermore, when the crawler dredging robot travels on a slippery and soft underwater ground environment, the vertical pressure of the tracks on the ground causes the ground soil to be squeezed, resulting in compression deformation during the ground squeezing process, and sinking during the operation. In this process, it is necessary to overcome the friction and cohesion between soil particles and the reaction force of underwater silt and other sediments on the body, thereby generating compaction resistance and bulldozing resistance. and bulldozing resistance They are calculated by the following formulas: (4) (5) Where B is the track width; n is the sinking index; m is the mass of the dredging robot; 、 are the cohesive deformation modulus and the friction deformation modulus respectively; L is the ground contact length of the track; z is the amount of subsidence; and is the passive soil coefficient, which can be obtained by the following formula: (6) (7) Where, and is the Terzaghi bearing capacity coefficient, which can be obtained by the soil internal friction angle Obtained from the Terzaghi bearing capacity coefficient table.

[0029] Furthermore, since the dredging robot is working underwater and has a large water-facing area, it will be subject to water resistance caused by static or dynamic water flow during driving. For such unsteady flow, underwater resistance It can be obtained by the following formula: (8) Where, is the water resistance coefficient; is the water density; The water-facing area of ​​the dredging robot; and are the driving speed of the dredging robot and the water flow rate respectively.

[0030] Furthermore, when the crawler dredging robot climbs a slope, it will be affected by the combined force of the slope resistance and the slope friction resistance. , whose expression is: (9) Where, M is the mass of the dredging robot; is the slope angle; is the slope resistance coefficient.

[0031] Combined with the above analysis of the straight motion resistance of the crawler dredging robot, the dynamic model can be obtained as follows: (10) (11) Where, J is the moment of inertia of the driving wheel of the machine body with respect to the motor shaft; is the track rotation angular velocity; T is the servo motor driving torque; F is the track traction force; v is the machine body travel speed.

[0032] Furthermore, for tracked dredging robots that use permanent magnet DC servo motors as drive sources, in order to achieve precise control of the servo motor output torque, it is necessary to establish a mathematical model of the permanent magnet DC servo motor, which mainly includes electrical equations, torque output equations, and mechanical system motion balance equations. The electrical part mainly describes the motor input voltage. With current The relationship is expressed as follows: (12) Where R is the armature resistance; is the back electromotive force constant; is the motor angular velocity; is the inverse electromotive force equation, generated by the motor rotation and proportional to the speed; The torque output equation mainly describes the motor output torque With input current The relationship is expressed as follows: (13) Where, is the torque constant, and B is the viscous friction coefficient. When friction is neglected ( ) or steady state ( When constant), the above formula can be simplified to: (14) It can be seen that the output torque and input current are approximately in linear proportional relationship.

[0033] Combining Newton's second law and torque balance, the mechanical system motion equilibrium equation can be obtained as follows: (15) Where, J is the moment of inertia of the rotor and load; is the external load torque; By combining the above equations, the input voltage of the permanent magnet DC servo motor can be obtained and output speed The dynamic transfer function model is as follows: (16) The basic relationship diagram of its parameters is as follows Figure 2 shown.

[0034] Furthermore, step 2 of the optimal slip rate identification method—collecting motion data during driving and calculating the robot slip rate—includes: establishing a relationship between the linear velocity, angular velocity, and slip rate during the robot's driving process, and obtaining the robot's slip rate.

[0035] Specifically, the slip rate calculation formula is as follows: (17) Where, is the output speed of the driving wheel; r is the radius of the driving wheel; v is the actual linear speed of travel; is the theoretical linear speed of the tracked dredging robot.

[0036] Furthermore, when When the driving force applied to the track exceeds the friction provided by the ground, the wheel rotates faster than the actual driving speed, causing slippage; When the crawler dredging robot is in a state of vehicle body deviation, the ground exerts resistance on the deviation side crawler, causing the wheel to rotate slower than the actual driving speed and slipping; the two forms of vehicle body driving failure, slipping and sliding, are collectively referred to as skidding.

[0037] Specifically, the optimal slip rate is determined based on the relationship curve between the adhesion coefficient and the slip rate between the track and the ground, and the slip rate corresponding to the maximum value of the adhesion coefficient is determined. The search step length of the slip rate is dynamically adjusted based on the adaptive variable step length of the gradient sign to speed up the identification speed of the optimal slip rate.

[0038] Furthermore, step 3 of the optimal slip rate identification method—finding the relationship between the slip rate i and the ground adhesion coefficient μ by establishing a dynamic equation for the crawler dredging robot's driving wheel rotation—includes the following: The maximum static friction force that the ground can provide to the crawler tracks during the robot's driving process is the ground adhesion. When the crawler traction force exceeds the ground adhesion, slippage occurs. The expression is as follows: (18) (19) (20) Where, for ground adhesion; is the normal force applied to the track; G is the attachment weight; is the adhesion coefficient between the track and the ground.

[0039] The optimal slip ratio in this embodiment refers to the slip ratio when the track traction and ground adhesion are close. The relationship between the slip ratio i and the ground adhesion coefficient μ under different road conditions is as follows: (twenty one) Where, 、 、 Different constant values ​​are taken for different ground conditions. According to the above function expression, the relationship between adhesion coefficient and slip rate under different ground conditions is obtained as follows: Figure 3 shown.

[0040] from Figure 3 It can be concluded that under different road conditions, the ground adhesion coefficient increases first and then decreases with the increase of slip rate. The slip rate corresponding to the maximum adhesion coefficient point is the optimal slip rate. At this time, the traction performance of the crawler dredging robot is the best, and it can also effectively improve its energy consumption and track wear problems. However, its relationship parameter model is difficult to express accurately, so the optimal slip rate can be calculated based on the curve change rate. Identification: When , that is, the slip rate When , the road adhesion coefficient increases with the increase of slip rate; , that is, the slip rate When the optimal slip rate is reached; Slip rate As the road surface adhesion coefficient decreases, the optimal slip rate can be identified by deriving the relationship between the two.

[0041] The dynamic equation of the crawler dredging robot during the rotation of the driving wheel can be simplified as follows: (twenty two) Substituting formula (18) into the above formula, we can get: (twenty three) (twenty four) Taking the derivative with respect to time t, we get: (25) According to the slip rate calculation formula (17), the slip rate change rate over time can be obtained: (26) From formula (24) and formula (25), the relationship between slip rate i and ground adhesion coefficient μ can be obtained: (27) It can be seen that the curve change rate of road adhesion coefficient and slip rate can be obtained from Therefore, the optimal slip rate identification strategy framework is as follows: Figure 4 shown.

[0042] Furthermore, the optimal slip rate identification function can be expressed as: (28) Where, is a step coefficient greater than zero. >0, slip rate Increase ; When the slip rate Reduce ; The optimal slip rate can be obtained through accumulation.

[0043] To achieve the step coefficient To speed up the identification of the optimal slip rate, an adaptive variable step size optimal slip rate identification method based on gradient sign is proposed, and its expression is as follows: (29) in:

[0044] Where k is the initial value of the step size coefficient, which is consistent with the step size coefficient of the fixed step size search method; is the minimum real-time slip rate searched during the iteration process; is the maximum real-time slip rate; is the search gradient; is the maximum value of the searched gradient. The dynamic update rules of the slip rate and search gradient are as follows: (30) (31) (32) (33) Through the above expression and Figure 3 of The relationship curve shows that when When the step length increases with the slip rate Boosting and searching gradients Decrease and decay to ensure the optimal slip rate gradually converges; when When the step length increases with the slip rate Boosting and searching gradients Decrease and increase, accelerating the return to the optimal slip rate.

[0045] Specifically, the design method of the fuzzy sliding mode driving anti-slip controller includes: Step 51: According to the traditional sliding mode control theory, the desired torque signal of the motor under the traditional sliding mode control law is obtained; Step 52: Determine fuzzy sets, membership functions and fuzzy rules; Step 53: Based on the fuzzy rules and membership functions, the sliding surface and its derivatives are used as inputs to generate fuzzy approximation compensation terms through fuzzy reasoning; Step 54: Perform fuzzy reasoning on the input sliding surface and its derivative according to fuzzy rules to generate a fuzzy control signal; Step 55: Defuzzify the fuzzy control signal using an average defuzzifier to obtain a torque drive adjustment signal; Step 56: Add the motor desired torque signal under the traditional sliding mode control law and the torque compensation signal under the fuzzy control to obtain the final torque drive adjustment signal.

[0046] Furthermore, during the driving process of the crawler dredging robot, the body system is affected by factors such as the road environment, slope, and track structure, and has a high degree of uncertainty and nonlinearity. In order to achieve the robustness and flexibility of the body control system, the adaptive sliding mode control theory is now combined to find the relationship between the driving torque and the real-time slip rate, and an adaptive sliding mode controller is designed to achieve control of the track wheel slip rate. The control goal is to make the real-time slip rate of the track wheel tend to the optimal slip rate, and the slip rate error tends to zero. It should be pointed out in particular that the optimal slip rate changes in real time. Therefore, it is not easy to achieve absolute zero error in actual control. Real-time slip rate i and optimal slip rate The error e can be expressed as: (34) Define the tracking error function s as: (35) Where: c is the slope of the sliding surface.

[0047] When the system reaches a steady state, the tracking error function s=0 is always present. .

[0048] The sliding mode control law is composed of equivalent control and Switch Control According to the calculation formula (17), formula (23), formula (34), and formula (35), the equivalent control : (36) In order to ensure that the sliding mode conditions are established and the real-time slip rate quickly approaches the optimal slip rate, the switching control The design is as follows: (37) Combining formula (35) and formula (36), the control law of the traditional sliding mode control for the drive anti-slip system can be obtained as: (38) Where, is the desired torque.

[0049] Pick The function is: (39) The derivative is: (40) in: (41) Take stable conditions , (42) Therefore, the stability condition of the sliding mode control law can be obtained: (43) In order to reduce the inherent chattering defect of sliding mode control, the approach rate is rewritten using a saturation function: (44) in, is the boundary layer thickness.

[0050] Therefore, the expected torque expression of the driving anti-slip control motor under the traditional sliding mode control law is as follows: (45) In another specific embodiment of the present invention, the crawler dredging robot climbs ashore in the process of climbing, especially in the transition condition between flat ground and slope, due to the terrain environment disturbance and the accuracy defects of the dynamic model, it is easy to cause problems such as high uncertainty and instability of the control system. Therefore, adaptive fuzzy logic compensation is introduced to reduce the dependence on dynamic modeling, and at the same time further improve the vibration defects of traditional sliding mode control, and compensate for the unmodeled state of the system and the influence of external disturbance factors.

[0051] Combining fuzzy system approximation theory with switching control of sliding mode drive anti-slip control system Make the following improvements: (46) Where, is the fuzzy approximation compensation term.

[0052] In order to ensure that the control system reaches and remains on the sliding surface, it is necessary to satisfy , so the fuzzy system is based on the sliding surface s and its derivative As input, fuzzy approximation compensation is the output. The fuzzy set is (NB, NM, NS, ZO, PS, PM, PB). Its domain can be preliminarily determined by traditional sliding mode control. The membership function of input and output is a combination of triangular and Gaussian membership functions, such as Figure 5 As shown, the fuzzy rules are as follows:

[0053] In the formula , is the label of the i-th fuzzy rule; and Input rule set for fuzzy system, Outputs a set of rules for the fuzzy system.

[0054] The fuzzy system is defuzzified by using the average defuzzifier, and the fuzzy approximation compensation term is It can be expressed as: (47) Where, is the adjustable parameter vector; is the fuzzy basis function.

[0055] set up The optimal value is (48) Where, for A collection of .

[0056] but: (49) Where, is the approximation error of the driving anti-slip fuzzy control system.

[0057] (50)

[0058] Pick The function is: (51) Where, , , is the optimal parameter.

[0059] Substitute the sliding surface dynamics , and taking its derivative we can get: (52) Take the adaptive law as ,but: (53) Pick , then when hour, . Prove that the system satisfies the ultimate boundedness and tends to 0.

[0060] Specifically, the collected robot motion data is filtered.

[0061] Specifically, a joint simulation is performed with the dynamic model in a Simulink environment.

[0062] Furthermore, the simulation test results analysis includes:

[0063] The anti-skid control system for tracked dredging robots mainly includes a multi-body dynamics module, a parameter acquisition preprocessing module, a real-time slip rate calculation module, an optimal slip rate identification module, a servo system drive module, and a fuzzy sliding mode control module. The input parameters of the dynamics model are the real-time torque of the left and right tracks, and the output is the real-time angular velocity of the left and right drive wheels and the real-time linear velocity of the left and right tracks; the input parameters of the control system mainly include the real-time angular velocity of the track wheels, torque, machine body travel speed, and the identified optimal slip rate, and the output is the real-time slip rate. The relationship between the modules is as follows: Figure 6 shown.

[0064] This embodiment combines the main working scene of the dredging robot with the wet and slippery ground. And establishes the riverbank terrain environment as follows: The virtual prototype of the Recurdyn crawler dredging robot is shown in the figure below. Figure 7 As shown in the figure, the Recurdyn wet riverbank simulation environment and riverbank slope road spectrum are as follows Figure 8-9 As shown, it is mainly composed of a flat riverbed environment and a river bank with a slope of 20°.

[0065] In order to effectively suppress the abnormal noise fluctuations of the original data such as angular velocity and linear velocity of the track drive anti-skid control system in complex ground environments and restore the real motion data, Kalman filtering is used to pre-process the collected data. Taking the motion parameter filtering under uncontrolled conditions as an example, the effect is as follows: Figure 10-11 shown.

[0066] Further, such as Figure 12-13 The figure shows the optimal slip rate and actual control results obtained using the fixed-step and adaptive variable-step optimal slip rate identification methods for fuzzy sliding mode control. It can be seen that when the step size is adaptively adjusted, the optimal slip rate changes relatively steadily, with less fluctuation in the overall curve, and the actual slip rate control effect is closer to the identified optimal slip rate. This is especially true for the tracked dredging robot, which is in the extremely unstable transition between straight-ahead driving on flat ground and climbing a slope. The slip rate oscillation using the adaptive variable-step optimal slip rate is relatively small.

[0067] Furthermore, under the adaptive variable step size optimal slip rate identification method, the slip rate control effects of three control algorithms, traditional sliding mode control, fuzzy PID and adaptive fuzzy sliding mode control, are compared. The results are as follows: Figure 14As shown in the figure, due to the sudden increase in track torque and the slippery surface in the initial stage, the slip rates of both the left and right tracks in the uncontrolled state increased dramatically, even reaching a state of complete slip. All three control algorithms demonstrated significant slip suppression. In particular, the traditional sliding mode control and adaptive fuzzy sliding mode control kept the initial peak slip rates of both the left and right tracks between 43% and 49%. During the approximately 0.5-1.2 second period, the tracked dredging robot was in the straight-ahead phase on the riverbed, with similar performance across all control algorithms, keeping the slip rate below 4%. During the approximately 1.2-2.8 second period, the tracked dredging robot was in the transition phase between straight-ahead and climbing. During this period, the front guide wheels of the tracks were in contact with the slope, while the rear driven wheels were in contact with the ground, causing the tracks and the robot to partially become suspended. Due to the rear-wheel drive nature of the track drive wheels and the sudden changes in the ground conditions, the slip rates of the left and right tracks exhibited a high degree of uncertainty and instability. Under no control, the left track's slip rate peaked at 24.61%, and the right track's slip rate peaked at 35.39%. The overall trend was oscillating. All three control algorithms showed significant improvement in slip rates during this phase, keeping both left and right track's peak slip rates between 8% and 12%. However, fuzzy sliding mode control offered a clear advantage at this stage: While single sliding mode control and fuzzy PID control still inevitably experience a sharp rise in slip rates, fuzzy sliding mode control reduced both the peak and oscillation amplitudes of the slip rates, resulting in a relatively stable overall state. Peak slip rates for the left and right tracks reached only 8.63% and 8.19%, respectively, demonstrating effective anti-slip control during the transition between flat ground and climbing. For approximately 4.2 to 10 seconds, the tracked dredging robot maintained a straight-ahead approach on the slope. At this point, there's a significant difference in slip rates between the uncontrolled and three control methods: Under uncontrolled conditions, the left and right track slip rates remain between 10% and 20%, with noticeable slippage. The slip rates under the three control algorithms, on the other hand, remain between 5% and 9%. Fuzzy sliding mode control also exhibits more stable slip curve fluctuations than the other two single control algorithms. After 8 seconds, as the terrain no longer undergoes sudden changes, the optimal slip rates calculated under each algorithm are closer. As a result, the actual slip rates of the left and right tracks exhibit an alternating and stable trend, ultimately settling between 3% and 7%. Therefore, the slip rate changes at each stage under fuzzy sliding mode control are relatively stable, with minimal curve fluctuations.

[0068] Further, such as Figure 15Figure 2 shows the simulated linear velocity curves for the three control algorithms and the uncontrolled state. During the straight-ahead phase on flat ground, starting at approximately 1.2 seconds, the linear velocity of the machine increases first and then stabilizes. However, during the flat-to-climbing transition period, from approximately 1.2 to 2.5 seconds, the linear velocity drops sharply. Compared to uncontrolled and other control algorithms, the linear velocity of the left and right tracks under the fuzzy sliding mode control system exhibits a significantly slower decline, low fluctuation, and even a brief period of stability. At approximately 2.5 seconds, as the machine enters the climbing phase, the linear velocity experiences a rebound. Due to the chattering defects of traditional sliding mode control, the speed experiences significant fluctuations during the transition to the climbing phase, while the linear velocity rebound under sliding mode control is less pronounced. Subsequently, the linear velocity under uncontrolled and all three control algorithms stabilizes at approximately 0.38 m / s. This demonstrates that fuzzy sliding mode control can effectively mitigate speed fluctuations during the flat-to-climbing transition.

[0069] Further, such as Figure 16 Figure 2 shows the simulated angular velocity curves for the three control algorithms and the uncontrolled state. Due to a brief period of complete slippage during the initial acceleration phase, the angular velocities of the left and right track drive wheels under uncontrolled conditions exhibit significant oscillation. However, all three control algorithms achieved significant suppression, particularly fuzzy sliding mode control, which kept the initial angular velocities of both tracks at approximately 4 rad / s. During the 1.2-2.5 second flat-to-climb transition period, the fuzzy sliding mode control exhibited less oscillation in the right track angular velocity, while the left track angular velocity dipped more gently, exhibiting a brief period of stabilization compared to uncontrolled and other control algorithms. Therefore, fuzzy sliding mode control achieved significant stabilization of the angular velocities during both the initial and flat-to-climb transition periods.

[0070] Furthermore, the torque output of the tracks on both sides is as follows Figure 17 As shown in the figure. Due to the inherent defects of chattering, the overall amplitude of the output torque under traditional sliding mode control is large, which is particularly noticeable during the flat-to-climbing transition period of approximately 1.2-2.5 seconds. Both fuzzy PID and fuzzy sliding mode control have relatively stable output effects, with relatively small amplitudes during the transition period. However, the initial instantaneous torque amplitude under fuzzy PID is more significant. The results show that after adopting the fuzzy sliding mode anti-slip control strategy, the torque output of the crawler dredging robot is relatively stable, and the driving stability is significantly improved.

[0071] In summary, the fuzzy sliding model anti-slip drive control strategy for a tracked dredging robot climbing a slope and landing showed, compared to a single control algorithm, a lower slip rate on both tracks during the transition phase, and a relatively stable slip rate curve throughout the entire motion process. The robot's operating speed and the drive wheel's angular velocity remained relatively stable, demonstrating significant drive stability.

[0072] Specifically, a crawler-type dredging robot anti-skid control system includes: Model building module: used to import the 3D image of the dredging robot, model the crawler chassis, and obtain the dynamic model; Data acquisition module: applies torque drive in the dynamic model to obtain motion data; Dynamic mathematical model module: used to establish a dynamic mathematical model to obtain the relationship between the motion state of the dredging robot and the torque drive; Slip rate calculation module: used to analyze the robot's slippage through the dynamic model and calculate the robot's slip rate in actual motion; Optimal slip rate identification module: used to identify the optimal slip rate through adaptive variable step length strategy based on the slip rate calculation results in actual motion; Fuzzy sliding mode control module: used to design a fuzzy sliding mode drive anti-slip controller based on the dynamic mathematical model, the slip rate in actual motion, and the optimal slip rate; Servo motor drive module: used to dynamically adjust the robot's motion based on the fuzzy sliding mode controller.

[0073] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0074] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing the slip rate of a crawler dredging robot, characterized in that: include: Step 1: Import the 3D image of the dredging robot, model the crawler chassis, and obtain the dynamic model; Step 2: Apply torque drive to the dynamic model to obtain motion data; Step 3: Establish a dynamic mathematical model based on the dynamic characteristics of the dredging robot to obtain the relationship between the motion state of the dredging robot and the torque drive; Step 4: Analyze the robot's slippage using the motion data output by the dynamic model and calculate the robot's slip rate during actual motion. Step 5: Based on the slip rate calculation results in actual motion, the optimal slip rate is identified through an adaptive variable step length strategy; Step 6: Design a fuzzy sliding mode drive anti-slip controller based on the dynamic mathematical model, the slip rate in actual motion, and the optimal slip rate. Step 7: Dynamically adjust the robot's motion based on the driving anti-skid controller.

2. The method for optimizing the slip rate of a crawler dredging robot according to claim 1, characterized in that: The specific process of establishing a dynamic mathematical model includes: analyzing the robot's driving mechanical characteristics, calculating the actual traction of the robot's tracks, and the relationship between the actual traction of the tracks and the output torque of the drive wheels; analyzing the robot's straight-line motion resistance based on the robot's driving mechanical characteristics, and obtaining the robot's compaction resistance and bulldozing resistance, underwater resistance and slope resistance.

3. The method for optimizing the slip rate of a crawler dredging robot according to claim 1, characterized in that: The slip rate calculation formula is as follows: ； Where, is the output speed of the driving wheel; is the driving wheel radius; is the actual linear speed of travel; is the theoretical linear speed of the tracked dredging robot.

4. The method for optimizing the slip rate of a crawler dredging robot according to claim 1, wherein: The optimal slip rate is determined based on the relationship curve between the adhesion coefficient and the slip rate between the track and the ground, and the slip rate corresponding to the maximum adhesion coefficient is determined. The search step size of the slip rate is dynamically adjusted based on the adaptive variable step size of the gradient sign to speed up the identification speed of the optimal slip rate.

5. The method for optimizing the slip rate of a crawler dredging robot according to claim 1, characterized in that: The design method of the fuzzy sliding mode driving anti-slip controller includes: Step 51: According to the traditional sliding mode control theory, the desired torque signal of the motor under the traditional sliding mode control law is obtained; Step 52: Determine fuzzy sets, membership functions and fuzzy rules; Step 53: Based on the fuzzy rules and membership functions, the sliding surface and its derivatives are used as inputs to generate fuzzy approximation compensation terms through fuzzy reasoning; Step 54: Perform fuzzy reasoning on the input sliding surface and its derivative according to fuzzy rules to generate a fuzzy control signal; Step 55: Defuzzify the fuzzy control signal using an average defuzzifier to obtain a torque drive adjustment signal; Step 56: Add the motor desired torque signal under the traditional sliding mode control law and the torque compensation signal under the fuzzy control to obtain the final torque drive adjustment signal.

6. The method for optimizing the slip rate of a crawler dredging robot according to claim 1, characterized in that: The collected robot motion data is filtered.

7. The method for optimizing the slip rate of a crawler dredging robot according to claim 1, characterized in that: A co-simulation is performed with the dynamic model in the Simulink environment.

8. A crawler dredging robot slip rate optimization control system, characterized in that: include: Model building module: used to import the 3D image of the dredging robot, model the crawler chassis, and obtain the dynamic model; Data acquisition module: applies torque drive in the dynamic model to obtain motion data; Dynamic mathematical model module: used to establish a dynamic mathematical model based on the dynamic characteristics of the dredging robot, and obtain the relationship between the motion state of the dredging robot and the torque drive based on the dynamic mathematical model; Slip rate calculation module: used to analyze the robot's slippage through the dynamic model and calculate the robot's slip rate in actual motion; Optimal slip rate identification module: used to identify the optimal slip rate through adaptive variable step length strategy based on the slip rate calculation results in actual motion; Fuzzy sliding mode control module: used to design a fuzzy sliding mode drive anti-slip controller based on the dynamic mathematical model, the slip rate in actual motion, and the optimal slip rate; Servo motor drive module: used to dynamically adjust the robot's movement according to the driving anti-skid controller.

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