Crawler chassis motion control method

Abstract

The invention discloses a track chassis motion control method, which relates to the technical field of motion control, and comprises the following steps: establishing a kinematic model of a track chassis, analyzing motion characteristics of the track chassis, and determining influence factors; based on the kinematics model, a linear quadratic optimal control algorithm is adopted to perform motion control on the crawler chassis; solving an algebraic Riccati equation, constructing an optimal control law, and calculating to obtain a control quantity; and applying the calculated control quantity to a driving system of the crawler chassis to enable the chassis to move according to an expected path. According to the method, kinematic modeling is carried out on the crawler-type chassis, the kinematic characteristics of the crawler-type chassis are analyzed, and the accuracy of a kinematic model and a control strategy is achieved. By establishing a linearized kinetic model of the crawler chassis and designing an optimal controller by adopting an LQR method, accurate control on the movement of the crawler chassis is realized.

Description

Technical Field

[0001] The present invention relates to the field of motion control technology, and more particularly to a crawler chassis motion control method. Background Art

[0002] With the development of the construction industry, the use of intelligent construction robots is becoming more and more widespread. Different robot chassis have different operating performances on different terrains. When intelligent construction robots operate on different terrains, they need to have stable chassis motion control capabilities. Because tracked chassis control is affected by many factors, such as the irregularity of the terrain, the nonlinear characteristics of the chassis itself, and the uncertainty of the external environment, these factors make the chassis movement difficult to control, the operation accuracy is low, and the anti-interference ability is weak.

[0003] Therefore, how to provide a method for controlling the motion of a crawler chassis to accurately control the motion of the crawler chassis is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0004] In view of this, the present invention provides a crawler chassis motion control method, which realizes the accuracy of the kinematic model and control strategy by performing kinematic modeling on the crawler chassis and analyzing the motion characteristics of the crawler chassis. By establishing a linearized dynamic model of the crawler chassis and using the LQR method to design an optimal controller, the precise control of the crawler chassis motion is achieved.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: a crawler chassis motion control method, comprising:

[0006] Establish the kinematic model of the crawler chassis, analyze the motion characteristics of the crawler chassis, and determine the influencing factors;

[0007] Based on the kinematic model, a linear quadratic optimal control algorithm is used to control the motion of the crawler chassis;

[0008] Solve the algebraic Riccati equation, construct the optimal control law, and calculate the control quantity;

[0009] The calculated control quantity is applied to the drive system of the tracked chassis to make the chassis move along the desired path.

[0010] Preferably, a kinematic model of the crawler chassis is established, including:

[0011] The position of the tracked chassis is represented by a coordinate system; usually a rectangular coordinate system or a polar coordinate system is used to represent the position of the tracked chassis.

[0012] Determining state variables of the kinematic model, the state variables including position, velocity and acceleration;

[0013] The kinematic equation of the crawler chassis is determined according to the state variables, and the kinematic equation is expressed as:

[0014]

[0015] in, and are the speeds of the crawler chassis rotating along the x-axis, y-axis and around the z-axis in the global coordinate system, v(t) is the linear velocity of the chassis, ω(t) is the angular velocity of the chassis, and θ(t) is the heading angle of the crawler chassis. The kinematic equation of the crawler chassis can be obtained based on the determined state variable parameters to describe the position change and movement of the crawler chassis.

[0016] Preferably, the position change of the crawler chassis is described by a displacement equation, and for the movement on a two-dimensional plane, it can be expressed by the following equation:

[0017] x(t)=x0+∫0 t υ x (tau)dτ

[0018] y(t)=y0+∫0 t υ y (τ)dtau;

[0019] Where x(t) and y(t) are the positions at time t, x0 and y0 are the initial positions, and v x (t) and v y (t) are the velocities along the x-axis and y-axis respectively;

[0020] The speed equation is used to describe the speed change of the track chassis. For a differentially driven track chassis, the speeds of the left and right tracks are v and L (t) and v R (t), then the angular velocity ω(t) of the chassis is expressed as:

[0021]

[0022] Where W is the distance between the left and right tracks (track width);

[0023] The acceleration equation is used to describe the acceleration change of the robot; the linear acceleration and angular acceleration are expressed as:

[0024]

[0025]

[0026] Where ax(t) and ay(t) are the linear accelerations along the x-axis and y-axis respectively, and α(t) is the angular acceleration.

[0027] Preferably, the influencing factors include: speed of the left track, speed of the right track, turning radius, track width, mass distribution, drive system performance and ground conditions.

[0028] Preferably, a linear quadratic optimal control algorithm is used to control the motion of the crawler chassis, including:

[0029] Define the performance index J and construct a quadratic performance index function, expressed as:

[0030]

[0031] Among them, Q and R are weight matrices, corresponding to the weights of the system state variables and control inputs, respectively; u(t) is the control input vector, x(t) is the system state vector, and F is the terminal cost matrix, which is usually a semi-positive definite matrix and reflects the importance of the final state. Q(t) is the state weight matrix, which is also semi-positive definite and is used to weight the state vector, reflecting the importance of the state variables in the control process. R(t) is the control input weight matrix, which is usually positive definite and is used to weight the control input vector, reflecting the cost of control energy consumption. t0 and t f are the initial time and the end time of the system respectively; the Q matrix is ​​selected to be semi-positive definite, and the R matrix is ​​selected to be positive definite. The specific values ​​of these two matrices are usually selected based on the designer's requirements for system performance and actual experience.

[0032] By adjusting the relative size of Q and R, we can achieve the control requirements for different aspects of the system. If we want to improve the stability of the system, we can reduce the Q value; if we want to reduce the amplitude of the control input, we can increase the R value;

[0033] Preferably, solving the algebraic Riccati equation comprises:

[0034] The gain matrix K of the optimal control law is obtained by solving the Riccati equation through analytical derivation or numerical method.

[0035] Preferably, the constructing of the optimal control law and calculating the control quantity include:

[0036] The Riccati algebraic equation is expressed as:

[0037] PA+A T P-PBR -1 B T P+C T QC=0;

[0038] Solve the Riccati algebraic equation to obtain the matrix P;

[0039] The optimal control input is calculated according to the matrix P, which is expressed as follows:

[0040] u * =-R -1 B T Px;

[0041] Where P is a positive definite constant matrix that satisfies the Riccati algebraic equation (ARE);

[0042] Constructing the optimal control law: Once the matrix P is obtained, it can be substituted into the formula of the optimal control input to obtain u*=-Kx;

[0043] Where K represents the gain matrix of the optimal control law, K = R -1 B T P; x is the system state vector;

[0044] By adding control variables to adjust the state equation of the system, the state equation of the closed-loop system is formed:

[0045]

[0046] By using state feedback for control, the current state quantity is linearly combined through the gain matrix K to obtain the control quantity, so that the system is stable and can meet the control requirements. The current state of the crawler chassis, such as position, speed, etc., is obtained through sensors, and it is calculated through the gain matrix K to obtain the control quantity. This control quantity will be used to adjust the movement of the crawler chassis, such as changing its speed or direction.

[0047] Preferably, applying the calculated control amount to a drive system of the crawler chassis to make the chassis move along a desired path includes:

[0048] The state of the crawler chassis is measured in real time by sensors to determine the state variables of the crawler chassis;

[0049] Construct a state vector using the selected state variables as components;

[0050] Substitute the measured system state vector x into the control law u*=-Kx, and use the gain matrix K to calculate the optimal control input u*; this control input is derived based on the current state of the system and the expected performance indicators, with the aim of making the system achieve the desired dynamic response and minimize the control cost.

[0051] Apply the calculated control input u* to the drive system of the crawler chassis to adjust the speed or direction of the motor and change the motion state of the crawler chassis;

[0052] At the next moment, the system state is remeasured, and the new control input is calculated based on the new system state and control law to form a closed-loop control system.

[0053] Preferably, the method further includes: during the operation of the crawler chassis, continuously monitoring the motion state of the crawler chassis, adjusting the gain matrix K according to feedback information, and adjusting the control law according to the feedback information, so as to ensure that the chassis can stably move along the predetermined path and meet the performance requirements.

[0054] In the implementation control process, the calculated control input is applied to the power system of the crawler chassis to adjust its speed and direction. This may involve controlling the motor, such as adjusting the speed or direction of the motor.

[0055] It can be known from the above technical solutions that, compared with the prior art, the present invention discloses a method for controlling the motion of a crawler chassis, including: establishing a kinematic model of the crawler chassis, analyzing the motion characteristics of the crawler chassis, and determining the influencing factors; based on the kinematic model, using a linear quadratic optimal control algorithm to control the motion of the crawler chassis; solving the algebraic Riccati equation, constructing the optimal control law, and calculating the control quantity; applying the calculated control quantity to the drive system of the crawler chassis to make the chassis move along the desired path. The present invention achieves the accuracy of the kinematic model and control strategy by performing kinematic modeling on the crawler chassis and analyzing the motion characteristics of the crawler chassis. By establishing a linearized dynamic model of the crawler chassis and using the LQR method to design the optimal controller, precise control of the crawler chassis motion is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0057] Figure 1 A schematic flow chart of a crawler chassis motion control method provided by the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0059] The embodiment of the present invention discloses a crawler chassis motion control method, which performs kinematic analysis modeling on a crawler robot and jointly controls the rotation of the crawlers on both sides to ensure that the robot can perform linear or circular motion according to predetermined parameters. Figure 1 As shown, including:

[0060] Establish the kinematic model of the crawler chassis, analyze the motion characteristics of the crawler chassis, and determine the influencing factors;

[0061] Based on the kinematic model, a linear quadratic optimal control algorithm is used to control the motion of the crawler chassis;

[0062] Solve the algebraic Riccati equation, construct the optimal control law, and calculate the control quantity;

[0063] The calculated control quantity is applied to the drive system of the tracked chassis to make the chassis move along the desired path.

[0064] The kinematic model describes the motion characteristics of the crawler chassis, including state variables such as displacement, velocity, acceleration, etc. For the crawler chassis, special attention should be paid to its special motion mechanism, such as differential drive or synchronous drive.

[0065] Specifically, for differential drive, differential control is achieved based on the speed difference between the left and right tracks. When the speeds of the tracks on both sides are the same, the robot will move forward in a straight line; when the speed of one track is greater than the other, the robot will turn to the slower side; if one track is moving forward and the other is moving backward, the robot will rotate on the spot.

[0066] In order to realize differential speed control, it is necessary to accurately control the speed of each track. The embodiment of the present invention realizes this by adjusting the speed of the driving motor, and uses a PWM (pulse width modulation) signal to control the speed of the motor.

[0067] In order to improve the accuracy and stability of control, the embodiment of the present invention integrates an encoder or other sensors to monitor the actual speed of the track, and adjusts the PWM signal through a closed-loop control system to ensure that the track speed is consistent with expectations.

[0068] The embodiment of the present invention uses Arduino or other microcontrollers as the lower computer to execute specific hardware control instructions. At the same time, ROS can be used as the upper computer to perform high-level decision-making and data processing to achieve precise control of the crawler vehicle.

[0069] The embodiment of the present invention realizes differential steering by adjusting the speed difference of the crawler tracks on both sides; by increasing the speed of the crawler track on one side, the chassis is turned to that side; by reducing the speed of the crawler track on one side, the chassis is turned to the other side.

[0070] Specifically, straight-line driving control: when a straight-line driving command is received, the single-chip microcomputer control unit sends a control signal to open the hydraulic servo valves of the left and right crawler drive units, and control the hydraulic motor to drive the crawler work vehicle to drive in a straight line according to the set initial angular velocity value ω.

[0071] Steering control: When receiving a steering command, the single-chip control unit sets the angular velocity change value ω of the left and right crawler drive hydraulic motors according to the turning radius r. The steering of the crawler vehicle is achieved by changing the angular velocities ω1 and ω2 of the left and right crawler drive hydraulic motors.

[0072] Specifically, the kinematic model of the tracked chassis is established, including:

[0073] The position of the tracked chassis is represented by a coordinate system; usually a rectangular coordinate system or a polar coordinate system is used to represent the position of the tracked chassis.

[0074] Determining state variables of the kinematic model, the state variables including position, velocity and acceleration;

[0075] The kinematic equation of the crawler chassis is determined according to the state variables, and the kinematic equation is expressed as:

[0076]

[0077] in, and are the speeds of the crawler chassis rotating along the x-axis, y-axis and around the z-axis in the global coordinate system, v(t) is the linear velocity of the chassis, ω(t) is the angular velocity of the chassis, and θ(t) is the heading angle of the crawler chassis. The kinematic equation of the crawler chassis can be obtained based on the determined state variable parameters to describe the position change and movement of the crawler chassis.

[0078] Specifically, the position change of the crawler chassis is described by the displacement equation. For the movement on the two-dimensional plane, it can be expressed by the following equation:

[0079] x(t)=x0+∫0 t υ x (tau)dτ

[0080] y(t)=y0+∫0 t υ y (τ)dtau;

[0081] Where x(t) and y(t) are the positions at time t, x0 and y0 are the initial positions, and v x (t) and v y (t) are the velocities along the x-axis and y-axis respectively;

[0082] The speed equation is used to describe the speed change of the track chassis. For a differentially driven track chassis, the speeds of the left and right tracks are v and L (t) and v R (t), then the angular velocity ω(t) of the chassis is expressed as:

[0083]

[0084] Where W is the distance between the left and right tracks (track width);

[0085] The acceleration equation is used to describe the acceleration change of the robot; the linear acceleration and angular acceleration are expressed as:

[0086]

[0087] Where ax(t) and ay(t) are the linear accelerations along the x-axis and y-axis respectively, and α(t) is the angular acceleration.

[0088] Specifically, the influencing factors include: the speed of the left track, the speed of the right track, the turning radius, the track width, the mass distribution, the drive system performance and the ground conditions.

[0089] The present invention analyzes the kinematic characteristics of the chassis to determine factors that affect its linear and circular motion, such as the speed of the tracks, the turning radius, etc. The model is analyzed to determine the kinematic characteristics of the chassis under various driving conditions. For example, when the tracks on both sides rotate at the same speed, the chassis will move in a straight line; when the speed of the tracks on one side is different from that on the other side, the chassis will move in an arc. These characteristics will determine how to control the tracks to achieve the desired motion.

[0090] Specifically, a) Track speed (the speed of the left track and the speed of the right track): The speed of the track directly affects the moving speed of the chassis. In straight-line motion, the speed of the tracks on both sides is the same, and the chassis will move in a straight line. In turning motion, the speed of one track usually changes, causing the moving plate to rotate around a center point.

[0091] b) Turning radius: The turning radius refers to the radius of the arc when the chassis performs circular motion. This radius depends on the speed difference between the two tracks. The greater the speed difference, the smaller the turning radius, that is, the tighter the robot turns.

[0092] c) Track Width: Track width is the distance between the two tracks. This distance affects the stability and steering performance of the chassis. A wider track can provide better stability, but may reduce the maneuverability of the chassis.

[0093] d) Mass distribution: The mass distribution of the chassis also affects its motion characteristics. If the mass distribution is uneven, the chassis may generate additional torque when moving, causing instability or deviation from the expected path.

[0094] e) Drive system: The performance of the drive system, including the motor torque, response speed and control system accuracy, will affect the kinematic characteristics of the chassis.

[0095] f) Ground conditions: Factors such as the friction coefficient, flatness and hardness of the ground will also affect the movement of the chassis. For example, the more slippery the ground, the smaller the friction between the track and the ground, which may cause the chassis to be unable to accelerate or decelerate effectively.

[0096] Specifically, according to the kinematic model and motion characteristics, a control strategy is designed, and the LQR control algorithm is used to adjust the speed and steering of the tracks on both sides.

[0097] After the crawler chassis is subjected to motion analysis, the control strategy of the crawler chassis needs to be designed. In the embodiment of the present invention, a linear quadratic optimal control algorithm is used to control the crawler chassis motion. This algorithm is based on establishing a kinematic model of the crawler chassis and realizing precise control of the crawler chassis motion by designing an optimal controller. The use of the linear quadratic optimal control algorithm can effectively reduce the acceleration, speed and deformation of the track wheels of the crawler chassis, thereby improving the smoothness of the crawler chassis.

[0098] Specifically, 1. Establish a dynamic model of a crawler chassis. The dynamic model usually includes state equations, which describe the dynamic behavior of the chassis, including its state variables such as position, velocity, acceleration, and how the control input affects these state variables. According to the physical characteristics and kinematic equations of the crawler chassis, a dynamic model is established. The dynamic model includes factors such as the mass and moment of inertia of the chassis, the dynamic characteristics of the drive system, and the interaction with the ground.

[0099] (1) Design structural modules: The structure of the crawler chassis is designed according to the modular concept, including the body, travel system, power system and control system.

[0100] As the skeleton of the crawler chassis, the main frame needs to have sufficient strength and rigidity to support the weight of the entire machine and provide an interface for installing other modules.

[0101] The track system includes driving wheels, driven wheels, track shoes and tensioning mechanisms, etc. These components work together to ensure that the tracks can travel effectively on various terrains.

[0102] The power transmission system includes the engine, drive shaft and reducer, which is responsible for transmitting power to the track system to drive the chassis forward or backward.

[0103] The control system includes sensors, controllers and actuators, etc., which are used to achieve precise control of the tracked chassis, such as speed regulation, direction control, etc.

[0104] Modular interface: In order to improve the utilization and adaptability of the entire machine, the modular design also considers the common interface between modules to facilitate the rapid replacement or upgrade of specific modules.

[0105] During the structural design process, topology optimization analysis is used to optimize the structure of the crawler motion device frame and the driving wheel support to reduce weight and improve load-bearing capacity and durability.

[0106] (2) Determine the system state: define the state variables of the system. For a tracked chassis, common state variables include position, velocity, acceleration, etc. These state variables are represented by vector x(t).

[0107] (3) Analyze the force conditions: Analyze the force on the crawler chassis to determine the forces and moments that affect its movement, including gravity, ground reaction force, friction, and output force of the drive system.

[0108] (4) Establish kinematic equations: Based on Newton’s laws of motion and the principles of kinematics, establish differential equations describing the motion of the tracked chassis, including the translational motion equation and the rotational motion equation.

[0109] Translational motion equation: For a tracked chassis, consider all the forces acting on the chassis, such as driving force, friction, gravity, etc., and decompose them along each coordinate axis to obtain a set of equations. That is, F = ma, where F is the resultant force, m is the mass, and a is the acceleration.

[0110] Rotational motion equation: For a tracked chassis, consider all the torques acting on the chassis, such as the torque of the drive wheel, the resistance torque of the ground, etc., and combine them in a suitable way to obtain the equation of rotational motion. That is, τ = I*α, where τ is the torque, I is the moment of inertia, and α is the angular acceleration.

[0111] (5) Multi-body dynamics simulation: For complex tracked chassis and drive systems, multi-body dynamics simulation technology is used, which involves comprehensive analysis of structure, control and motion simulation optimization.

[0112] (6) Linearization: In order to apply the LQR algorithm, the dynamic model is linearized. Taylor expansion is performed on the nonlinear equation near the working point of the crawler chassis and high-order terms are ignored.

[0113] (7) Arrange into standard form: Arrange the above equation into a standard state space form suitable for LQR algorithm processing, that is, Where x(t) represents the state vector of the system, u(t) represents the control input, A represents the state matrix, and B represents the input matrix.

[0114] 2. Convert to state space representation: Convert the dynamic model to state space representation, including state equations and output equations. The state equations describe the evolution of the system state over time, and the output equations relate the system state to the measured output.

[0115] 3. Define performance indicators: In LQR, define a performance indicator; the choice of performance indicators reflects the control system designer's expectations for system performance, such as the trade-off between stability, fast response, and minimum energy consumption. Define a quadratic cost function that includes the square terms of system state variables and control inputs and their weighted sums. This cost function is used to quantify the performance of the control system, such as response speed, stability, and accuracy.

[0116] Specifically, a linear quadratic optimal control algorithm is used to control the motion of the crawler chassis, including: defining a performance index J, which is a quadratic function of the system state x(t) and the control input u(t). The performance index usually consists of two parts: one is the integral term, which represents the time from the initial time t0 to the end time t f The other part is the terminal penalty term, which means that at the end time t f The weighting of the system state at time t;

[0117] Construct a quadratic performance index function, expressed as:

[0118]

[0119] Among them, Q and R are weight matrices, corresponding to the weights of the system state variables and control inputs, respectively; u(t) is the control input vector, x(t) is the system state vector, and F is the terminal cost matrix, which is usually a semi-positive definite matrix and reflects the importance of the final state. Q(t) is the state weight matrix, which is also semi-positive definite and is used to weight the state vector, reflecting the importance of the state variables in the control process. R(t) is the control input weight matrix, which is usually positive definite and is used to weight the control input vector, reflecting the cost of control energy consumption. t0 and t f They are the initial time and the end time of the system respectively;

[0120] The weighted matrices Q and R in the performance index correspond to the weights of the system state variables and control inputs, respectively. The Q matrix is ​​usually semi-positive definite, while the R matrix is ​​positive definite. Selection rules: When selecting the values ​​of Q and R, it is necessary to consider the trade-off between the dynamic response of the system and the control cost. A larger Q value will make the system pay more attention to the rapid convergence of the state variables, while a larger R value will reduce the amplitude of the control input, but may sacrifice the dynamic performance of the system. Typically, the Q matrix is ​​selected as semi-positive definite, while the R matrix is ​​selected as positive definite. The specific values ​​of these two matrices are usually selected based on the designer's requirements for system performance and actual experience.

[0121] Adjustment method: By adjusting the relative size of Q and R, you can achieve the control requirements of different aspects of the system. If you want to improve the stability of the system, you can reduce the Q value; if you want to reduce the amplitude of the control input, you can increase the R value;

[0122] Specifically, solving the algebraic Riccati equation includes:

[0123] In order to find the optimal control law, the Riccati equation is solved by analytical derivation or numerical method to obtain the gain matrix K of the optimal control law. The gain matrix K is related to the form of the control law and the minimization of the performance index.

[0124] Specifically, the construction of the optimal control law and calculation of the control quantity include:

[0125] Calculate the optimal controller: Based on the obtained gain matrix K, calculate the optimal controller; the controller links the system state with the control input to achieve optimal control of the system, and ultimately guides the movement of the crawler chassis to achieve the desired speed, direction and path planning.

[0126] The Riccati algebraic equation is expressed as:

[0127] PA+A T P-PBR -1 B T P+C T QC=0;

[0128] Among them, A represents the state matrix, B represents the input matrix, and C represents the output matrix;

[0129] Solve the Riccati algebraic equation to obtain the matrix P;

[0130] The optimal control input is calculated according to the matrix P, which is expressed as follows:

[0131] u * =-R -1 B T Px;

[0132] Where P is a positive definite constant matrix that satisfies the Riccati algebraic equation (ARE);

[0133] Constructing the optimal control law: Once the matrix P is obtained, it can be substituted into the formula of the optimal control input to obtain u*=-Kx;

[0134] Where K represents the gain matrix of the optimal control law, K = R -1 B T P; x is the system state vector;

[0135] By adding control variables to adjust the state equation of the system, the state equation of the closed-loop system is formed:

[0136]

[0137] By using state feedback for control, the current state quantity is linearly combined through the gain matrix K to obtain the control quantity, so that the system is stable and can meet the control requirements. The current state of the crawler chassis, such as position, speed, etc., is obtained through sensors, and it is calculated through the gain matrix K to obtain the control quantity. This control quantity will be used to adjust the movement of the crawler chassis, such as changing its speed or direction.

[0138] Finally, according to the obtained gain matrix K, the control law u*=-Kx can be realized, where x is the system state vector. This control law will guide the movement of the tracked chassis to achieve the desired dynamic response and minimum control cost.

[0139] Specifically, the calculated control amount is applied to the drive system of the crawler chassis to make the chassis move along the desired path, including:

[0140] The state of the crawler chassis is measured in real time by sensors to determine the state variables of the crawler chassis, including position, speed, acceleration, etc., and these information are combined into a system state vector x;

[0141] The selected state variables are used as components to construct the state vector; if the tracked chassis has n state variables, it can be expressed as:

[0142] x1(t), x2(t), …, x n (t);

[0143] Then the state vector x of the system is an n-dimensional vector composed of these state variables:

[0144] [x1(t), x2(t), …, x n (t)] T ;

[0145] Substitute the measured system state vector x into the control law u*=-Kx, and use the gain matrix K to calculate the optimal control input u*; this control input is derived based on the current state of the system and the expected performance indicators, with the aim of making the system achieve the desired dynamic response and minimize the control cost.

[0146] Apply the calculated control input u* to the drive system of the crawler chassis to adjust the speed or direction of the motor and change the motion state of the crawler chassis;

[0147] At the next moment, the system state is remeasured, and the new control input is calculated based on the new system state and control law to form a closed-loop control system.

[0148] Specifically, it also includes: in the actual operation of the crawler chassis, it is necessary to continuously monitor the movement state of the crawler chassis, and adjust the gain matrix K according to the feedback information, and adjust the control law according to the feedback information to ensure that the chassis can stably move along the predetermined path and meet the performance requirements.

[0149] In the implementation control process, the calculated control input is applied to the power system of the crawler chassis to adjust its speed and direction. This may involve controlling the motor, such as adjusting the speed or direction of the motor.

[0150] Specifically, it also includes: path planning for the operation of the crawler chassis; for path planning, the desired path can be decomposed into a series of target states or set points; then, the LQR algorithm is used to control the chassis to move along these set points.

[0151] Specifically, environmental modeling: First, the environment in which the robot is located needs to be modeled, including the creation of maps, marking of obstacles, and identification of other relevant features.

[0152] Path search: Use the graph search algorithm RRT to find one or more candidate paths from the starting point to the end point in the environment model, and find an optimized path from them.

[0153] Path smoothing: Since the path generated by the graph search algorithm may contain many sharp corners, Bezier curve path smoothing is used to optimize the path. Specifically, the path optimization method of the embodiment of the present invention includes: (1) analyzing the path, analyzing the path generated by the graph search algorithm, determining each node or waypoint on the path, and using these nodes or waypoints as the basis of Bezier curve control points.

[0154] (2) Determine the control points, that is, determine the control points of the Bezier curve. The control points can be points on the original path or user-defined points. The number and position of the control points determine the shape of the Bezier curve.

[0155] (3) Adjust the control points and use the optimization algorithm to adjust the positions of the control points and change the shape of the Bezier curve to make it smoother while avoiding sharp corners.

[0156] (4) Calculate the Bezier curve and use the definition of the Bezier curve to calculate the points on the curve.

[0157] (5) Generate a smooth path by connecting the points on the calculated Bezier curve to generate a smooth path.

[0158] Selection of set points: A series of set points are selected on the path at certain intervals or conditions. These points should be dense enough so that the LQR controller can accurately track the path and ensure that the robot can move accurately along the desired path. When selecting set points, the dynamic constraints of the system need to be considered to ensure that the changes between set points do not exceed the range that the system can safely track.

[0159] Specifically, according to the maximum feasible distance between the set points, the system is subjected to the maximum acceleration a max limit, then from the static state to v max The minimum distance d required acc Calculated as:

[0160]

[0161] Likewise, the minimum distance d required for the system to decelerate to a stop dec for:

[0162]

[0163] For the setting point of the curved section, according to the minimum turning radius R min To ensure that the curvature variation between set points is something the system can handle. The curvature κ is related to the turning radius R and is defined as:

[0164]

[0165] Among them, v max is the maximum speed that the system can achieve; a max is the maximum acceleration that the system can safely withstand; d max is the maximum deceleration that the system can safely withstand; R min It is the minimum turning radius that can be achieved without the chassis slipping or losing stability.

[0166] To ensure that the changes between set points do not exceed what the system can safely track, the following conditions should be met on the path:

[0167] 1. The distance between adjacent set points shall not be less than d acc and d dec ;

[0168] 2. The curvature of the path must not change more than the system can achieve at R min within the scope of safe handling.

[0169] Target state definition: For each set point, define a target state, including position, velocity, direction, etc. These target states will be used as inputs to the LQR controller to calculate the control inputs to move the system along the predetermined path.

[0170] Time interval assignment: Assign a time tag to each setpoint, indicating when it is expected to arrive. This helps synchronize trajectory tracking and calculation of control inputs.

[0171] Verify and adjust: Verify that these set points are able to guide the track pans to move correctly, through simulation and / or actual testing, before actual deployment. Adjust the set points based on test results to compensate for inaccuracies in the environmental model or differences in chassis dynamics.

[0172] Real-time Update: As the chassis performs its mission, the set points are updated in real time to respond to environmental changes, such as the appearance of dynamic obstacles or other unpredictable factors.

[0173] Monitor system status: In actual operation, it is necessary to monitor the system status in real time and adjust the control parameters based on feedback information to deal with external interference and model errors.

[0174] Adjust performance indicators: If the actual performance of the system does not meet expectations, reconsider the choice of performance indicators, adjust the weighting matrices Q and R, and then recalculate the gain matrix K.

[0175] Achieve closed-loop control: Apply the optimal control law to the dynamics model to achieve closed-loop control. At this time, the control system will adjust the control input in real time according to the current system state to maintain the best performance of the system.

[0176] Simulate and test: Before actually applying it to the tracked chassis, test the performance of the LQR controller through simulation. This helps identify potential problems and adjust and optimize the control strategy.

[0177] Field Application: Apply the tested and proven LQR controller to an actual track undercarriage system for precise motion control.

[0178] In one embodiment of the present invention, it also includes: chassis sensor integration. The tracked chassis usually integrates multiple sensors, such as IMU (inertial measurement unit), laser radar, depth camera, etc., to provide environmental information and motion state feedback. The data of these sensors need to be fused to provide accurate input for the control algorithm.

[0179] Choose the right sensor: Choose the right sensor type based on the application scenario and task requirements of the tracked chassis. For example, IMU is used to capture the robot's dynamic information, such as acceleration and angular velocity; LiDAR is used for map construction and obstacle detection; and depth camera is used to obtain three-dimensional information of the environment.

[0180] Sensor installation: Install sensors on the crawler chassis and ensure that they are firmly fixed in the intended position to avoid displacement or vibration during operation. The sensors should be installed in a position that provides the best view while avoiding being blocked by other chassis parts or structures.

[0181] Electrical connections: Complete the electrical connections of the sensor, ensuring that the power supply and data transmission lines are properly connected. This may require designing a circuit board or using terminal blocks.

[0182] System Integration: Integrating sensors with control systems, which typically involves writing or configuring software to read sensor data and convert it into usable information.

[0183] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0184] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be 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 will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A crawler chassis motion control method, It is characterized in that include: Establish the kinematic model of the crawler chassis, analyze the motion characteristics of the crawler chassis, and determine the influencing factors; Based on the kinematic model, a linear quadratic optimal control algorithm is used to control the motion of the crawler chassis; Solve the algebraic Riccati equation, construct the optimal control law, and calculate the control quantity; The calculated control quantity is applied to the drive system of the tracked chassis to make the chassis move along the desired path.

2. A crawler chassis motion control method according to claim 1, It is characterized in that Establish the kinematic model of the tracked chassis, including: The position of the track chassis is represented by a coordinate system; Determining state variables of the kinematic model, the state variables including position, velocity and acceleration; The kinematic equation of the crawler chassis is determined according to the state variables, and the kinematic equation is expressed as: in, and are the speeds of the tracked chassis along the x-axis, y-axis and around the z-axis in the global coordinate system, v(t) is the linear velocity of the chassis, ω(t) is the angular velocity of the chassis, and θ(t) is the heading angle of the tracked chassis.

3. A crawler chassis motion control method according to claim 2, It is characterized in that The position change of the track chassis is described by the displacement equation: Where x(t) and y(t) are the positions at time t, x 0 and 0 is the initial position, v x (t) and v y (t) are the velocities along the x-axis and y-axis respectively; The speed equation is used to describe the speed change of the track chassis. For a differentially driven track chassis, the speeds of the left and right tracks are v and L (t) and v R (t), then the angular velocity ω(t) of the chassis is expressed as: Where W is the distance between the left track and the right track; The acceleration equation is used to describe the acceleration change of the robot; the linear acceleration and angular acceleration are expressed as: Among them, a x (t) and a y (t) are the linear accelerations along the x-axis and y-axis respectively, and α(t) is the angular acceleration.

4. A crawler chassis motion control method according to claim 1, It is characterized in that The influencing factors include: left track speed, right track speed, turning radius, track width, mass distribution, drive system performance and ground conditions.

5. A crawler chassis motion control method according to claim 1, It is characterized in that The linear quadratic optimal control algorithm is used to control the motion of the crawler chassis, including: Define the performance index J and construct a quadratic performance index function, expressed as: Where Q and R are weight matrices, u(t) is the control input vector, x(t) is the state vector of the system, F is the terminal cost matrix, Q(t) is the state weight matrix, R(t) is the control input weight matrix, t 0 and t f are the initial time and the end time of the system respectively; By adjusting the relative sizes of Q and R, the control requirements for different aspects of the system can be achieved.

6. A crawler chassis motion control method according to claim 1, It is characterized in that The solution of the algebraic Riccati equation comprises: The gain matrix K of the optimal control law is obtained by solving the Riccati equation through analytical derivation or numerical method.

7. A crawler chassis motion control method according to claim 6, It is characterized in that The constructing of the optimal control law and calculating the control quantity include: The Riccati algebraic equation is expressed as: PA+A T P-PBR -1 B T P+C T QC=0; Solve the Riccati algebraic equation to obtain the matrix P; The optimal control input is calculated according to the matrix P, which is expressed as follows: u * =-R -1 B T Px; Where P is a positive definite constant matrix that satisfies the Riccati algebraic equation; We get u*=-Kx; Where K represents the gain matrix of the optimal control law, K = R -1 B T P; x is the system state vector; By adding control variables to adjust the state equation of the system, the state equation of the closed-loop system is formed: The current state of the crawler chassis is obtained through the sensor, and is calculated through the gain matrix K to obtain the control quantity.

8. A crawler chassis motion control method according to claim 7, It is characterized in that Apply the calculated control quantity to the drive system of the crawler chassis to make the chassis move along the desired path, including: The state of the crawler chassis is measured in real time by sensors to determine the state variables of the crawler chassis; Construct a state vector using the selected state variables as components; Substitute the measured system state vector x into the control law u*=-Kx, and use the gain matrix K to calculate the optimal control input u*; Apply the calculated control input u* to the drive system of the crawler chassis to adjust the speed or direction of the motor and change the motion state of the crawler chassis; At the next moment, the system state is remeasured, and the new control input is calculated based on the new system state and control law to form a closed-loop control system.

9. A crawler chassis motion control method according to claim 1, It is characterized in that Also includes: During the operation of the crawler chassis, the motion state of the crawler chassis is constantly monitored, and the gain matrix K is adjusted according to the feedback information, and the control law is adjusted according to the feedback information.

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