An unmanned double-vehicle cooperative transportation system and a trajectory tracking control method thereof

Abstract

The application provides an unmanned double-vehicle cooperative transportation system and a trajectory tracking control method thereof, and relates to the technical field of unmanned double-vehicle cooperative transportation systems. On the one hand, aiming at the difficulty of complex double-vehicle cooperative transportation system model, a whole dynamic model modeling method containing uncertainty is proposed by combining system nonholonomic constraint with Lagrange mechanics; a hierarchical adaptive robust tracking controller is designed, the control algorithm in the upper layer based on constraint following theory can effectively overcome the influence of system parameter perturbation and external disturbance uncertainty, and the lower layer takes the minimum load stress as the target to realize effective distribution of driving force. On the other hand, the application designs a matching unmanned vehicle system transportation system, which integrates a running calculation unit, a positioning unit, an information sensing unit, an execution unit, a power supply unit and a communication unit, and the system composition is more simple, thereby reducing the system cost and ensuring efficient cooperative work among units.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and in particular to an autonomous dual-vehicle cooperative transportation system and its trajectory tracking and control method. Background Technology

[0002] In the manufacturing sector, particularly in the aerospace and energy industries, transporting large components such as aircraft fuselages and wind turbine blades presents a significant challenge. These components are typically enormous in size and weight, often proving difficult to meet the demands of traditional transportation methods. Therefore, Collaborative Transportation Systems (CTS) have emerged to overcome the limitations of individual vehicles by enabling multiple vehicles to work together. However, current manually operated dual-vehicle collaborative transportation systems require multiple drivers and operators, resulting in complex systems, difficult coordination, and a high risk of damage to goods or vehicles. Furthermore, in complex road conditions, manual operation is inflexible, leading to poor maneuverability and impacting transportation efficiency.

[0003] The development of autonomous driving technology has provided new technological approaches for dual-vehicle cooperative transportation. Autonomous driving technology, through advanced sensors and intelligent algorithms, enables vehicles to navigate autonomously and control collaboratively, thereby improving transportation efficiency and safety. As one of the key technologies of autonomous driving, trajectory tracking control still faces many challenges in dual-vehicle cooperative transportation systems. Trajectory tracking control not only relies on accurate vehicle dynamics models but also needs to consider complex factors such as environmental uncertainties and real-time control inputs.

[0004] Existing control strategies for collaborative transportation systems primarily focus on interactions between small robots or between a carrier and a robotic arm. These mobile robots have simple dynamic models, and control methods largely adjust the carrier's speed and acceleration based on kinematic models, without considering the internal dynamic characteristics of the components. For collaborative transportation systems composed of heavy-duty vehicles, the effective payload is far greater than the objects moved by these small robots indoors, resulting in a more complex model. Therefore, existing collaborative transportation methods for mobile robots cannot be directly transferred and applied. Furthermore, heavy-duty vehicles require higher levels of interference resistance and robustness in complex environments, further increasing the difficulty of trajectory tracking control. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] Based on this, the present invention provides an unmanned dual-vehicle cooperative transportation system and its trajectory tracking control method to solve the problems mentioned in the background art, such as the difficulty in dynamic modeling due to the complexity of the system model, and the poor accuracy and stability of the system tracking due to the characteristics of parameter uncertainty, external unknown disturbance, nonlinearity and overdrive in trajectory tracking control.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides a trajectory tracking and control method for an unmanned dual-vehicle cooperative transportation system, comprising:

[0009] S1: Based on the system's geometric constraints, kinematic constraints, and Euler-Lagrange equations, perform dynamic modeling of the system to obtain the system's dynamic model; specific steps include:

[0010] S101: Obtain geometric constraints based on the positional relationship between the load and the carrier in the system;

[0011] The system includes a load and a carrier, with carrier 1 and carrier 2 following carrier 1; the coordinate system and key nodes are defined as follows: It is an inertial coordinate system; For a connected coordinate system, the subscript... These are used to represent the load, carrier 1, and carrier 2, respectively, in a solid coordinate system. Indicates the vertical direction of the object. This indicates the horizontal direction of the object, which includes the load, carrier 1, and carrier 2. , , These represent the object's center point, center of mass, and heading angle, respectively; the center point of the carrier is the midpoint of the line connecting the front and rear axle centers; the pose information of the load, carrier 1, and carrier 2 can be used... To express, or can be expressed using To indicate; to make , ;

[0012] Based on the load and the positional relationship between the two vehicles, the geometric constraint equations of the dual-vehicle cooperative transportation system are obtained as follows:

[0013]

[0014] in, , and These represent the distances from hinge point 1 to the load's center of mass, hinge point 1 to the center of mass of carrier 1, and hinge point 2 to the center of mass of carrier 2, respectively; hinge point 1 is the center point of carrier 1; hinge point 2 is the center point of carrier 2; the load is a uniform rectangular rigid body, and its center point and center of mass are both the midpoints of the line connecting hinge point 1 and hinge point 2.

[0015] Taking the second derivative of the geometric constraint equation, we obtain the following formula:

[0016]

[0017] in,

[0018] ;

[0019] ;

[0020] A single dot on the parameter indicates the first derivative, and two dots on the parameter indicate the second derivative.

[0021] S102: Obtain motion constraints based on the motion state of the load and carrier in the system;

[0022] Let the speed at the load center point be... Components, velocity The components and angular velocities are respectively , and ; velocity at the center point of carrier 1 Components, velocity The components and angular velocities are respectively , and ; velocity of the center point of carrier 2 Components, velocity The components and angular velocities are respectively , and State vector The system's motion constraint equations are:

[0023]

[0024] in,

[0025] ;

[0026] Based on the geometric relationship between the centroid and the center point of the object, the transformation relationship between the centroid and the center point is obtained as follows:

[0027]

[0028] Differentiating the above equation, we get

[0029]

[0030] in,

[0031] ;

[0032] It is a 3-order identity matrix;

[0033] S103: Combining the geometric constraints, the motion constraints, and the Euler-Lagrange equations, we obtain the system dynamics model;

[0034] The kinetic energy expression of the system with the center of mass as the reference point is:

[0035]

[0036] Among them, subscript These are used to represent the load, carrier 1, and carrier 2, respectively. and These represent the longitudinal and transverse components of the center-of-mass velocity in the inertial coordinate system, respectively. Angular velocity; For quality; It is the moment of inertia;

[0037] Combining the geometric constraints, the kinematic constraints, and the Euler-Lagrange equations, the dynamic model of the system is derived as follows:

[0038]

[0039] in,

[0040] ;

[0041] ;

[0042] ;

[0043] ;

[0044]

[0045] Represents a 3rd-order zero matrix; virtual control quantity middle, , and These represent the longitudinal driving force, lateral driving force, and torque of carrier 1, respectively. , and These represent the longitudinal driving force, lateral driving force, and torque of carrier 2, respectively. It is a Lagrange multiplier;

[0046] make ,get ;

[0047] The simplified system dynamics equations are as follows:

[0048]

[0049] in,

[0050] ;

[0051] ;

[0052] ;

[0053] Assuming the above parameters consist of a nominal part and a time-varying uncertain part, the parameter matrix can be decomposed as follows:

[0054]

[0055] in, Indicates time; Indicates the system status; This indicates system uncertainty; , and They are respectively , and The nominal part; , and They are respectively , and The uncertain part;

[0056] make

[0057] ;

[0058] ;

[0059] ;

[0060] Therefore, we obtain

[0061]

[0062] in, Represents the identity matrix;

[0063] S2: Based on the system dynamics model, construct an adaptive robust controller; the control terms of the adaptive robust controller include a nominal system control term, a feedback term, and an adaptive term;

[0064] S3: The control terms of the adaptive robust controller are allocated to each carrier of the system through a stress optimization algorithm to achieve driving force distribution.

[0065] On the other hand, the present invention also provides an unmanned dual-vehicle cooperative transportation system, the system being based on a master vehicle, a slave vehicle, and a load, and including: a computing unit, a positioning unit, an information sensing unit, an execution unit, a power supply unit, and a communication unit;

[0066] The operation computing unit is located in the main vehicle and is used to process data from other units, solve control quantities through controller algorithms, and issue instructions to the execution unit. The operation computing unit receives data from other units including: the main vehicle-load angle information, main vehicle obstacle and image information obtained through the main vehicle information sensing unit, the slave vehicle-load angle information, slave vehicle obstacle and image information obtained through the slave vehicle information sensing unit through the communication unit, and the load coordinate position, negative acceleration and angular velocity information obtained through the positioning unit through the communication unit.

[0067] The positioning unit is located on the load and is used to determine the load's position and heading information, including a GPS global positioning system and an inertial measurement unit;

[0068] The information perception unit includes lidar, cameras, millimeter-wave radar, and angle sensors located on the master vehicle and slave vehicle, respectively. The lidar, cameras, and millimeter-wave radar are used to identify and track static and dynamic obstacles around the cooperative transportation system. The angle sensors are located at the hinges between the master vehicle and the load, and between the slave vehicle and the load, respectively, to sense the rotation angle of the master vehicle and slave vehicle relative to the load. Lidar is located at the front of the master vehicle and the rear of the slave vehicle, cameras are located on all four sides of the master vehicle and slave vehicle, and millimeter-wave radar is located at the front left and right ends of the master vehicle and the rear left and right ends of the slave vehicle. The cameras provide real-time images for background monitoring. The lidar located on top of the vehicle provides a longer detection range and a wider field of view. The millimeter-wave radar located at the front of the master vehicle and the rear of the slave vehicle detects the presence of surrounding vehicles or obstacles, providing support for front and rear collision warnings and close-range protection.

[0069] The execution unit executes the instructions of the computing unit and controls the movement and steering of the master vehicle and slave vehicle through the drive system and steering system. The drive system includes a motor control module and a braking control module, wherein the motor control module has two modes: braking and driving. In driving mode, the motor control module adjusts the power supply voltage and frequency of the motor to achieve precise control of the motor speed and steering. In braking mode, the execution unit coordinates the motor control module and the braking control module, and uses the motor braking module to efficiently convert the kinetic energy of the carrier into electrical energy and feed it back to the battery of the power supply unit.

[0070] The power supply unit is used to provide the energy required by each unit of the dual-vehicle cooperative transportation system. The power supply unit is installed inside the carrier and the load respectively, and is equipped with a power monitoring device. While controlling and protecting the power supply of all electrical equipment, it transmits power consumption data and battery status to the operation computing unit through the communication unit.

[0071] The communication unit is used to connect the master vehicle, slave vehicle and load for real-time communication via wired or wireless means. The communication unit transmits the angle information and environmental perception data collected by the slave vehicle information sensing unit, the real-time operating status information of the slave vehicle execution unit and the position information obtained by the load positioning unit to the master vehicle's operation calculation unit, and sends the control commands generated by the master vehicle's operation calculation unit to the slave vehicle execution unit through the communication unit.

[0072] (III) Beneficial Effects

[0073] As can be seen from the above technical solution, the beneficial effects of the unmanned dual-vehicle cooperative transportation system and its trajectory tracking control method proposed in this invention are as follows:

[0074] 1. A method for modeling the overall dynamics of a two-vehicle cooperative transportation system with uncertainties, based on the combination of nonholonomic constraints and Euler-Lagrange equations, solves the problem of complex vector relationships in the system caused by the use of Newtonian mechanics in traditional methods.

[0075] 2. A hierarchical adaptive robust tracking controller was designed. The upper layer, based on the constraint following theory, can effectively overcome the influence of system parameter perturbation and external disturbance uncertainty. The lower layer aims to minimize load stress, thereby realizing the system's tracking of the desired trajectory and the effective allocation of redundant driving force.

[0076] 3. The dual-vehicle cooperative transportation system of the present invention integrates a running calculation unit, a positioning unit, an information sensing unit, an execution unit, a power supply unit, and a communication unit, providing a complete supporting platform for trajectory tracking control of the dual-vehicle cooperative transportation system, and has significant practicality. Attached Figure Description

[0077] The features and advantages of the invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the invention in any way. In the drawings:

[0078] Figure 1 This is a schematic diagram of an unmanned dual-vehicle cooperative transportation system according to an embodiment of the present invention;

[0079] Figure 2 This is a schematic diagram of motion constraint analysis for carrier 1 according to an embodiment of the present invention;

[0080] Figure 3 This is a schematic diagram illustrating the principle of trajectory tracking control of an unmanned dual-vehicle cooperative transportation system by an adaptive robust controller according to an embodiment of the present invention.

[0081] Figure 4 This is an architecture diagram of an unmanned dual-vehicle cooperative transportation system according to an embodiment of the present invention. Detailed Implementation

[0082] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] Example 1:

[0084] The dual-vehicle cooperative transportation system of this embodiment includes a load and two unmanned vehicles serving as carriers, namely carrier 1 (master vehicle) and carrier 2 (slave vehicle) following behind carrier 1. The carrier is equipped with front and rear axle steering and a four-wheel distributed drive system; the load is a rigid cuboid, hinged to the base of the carrier via undamped hinges.

[0085] In order to minimize the complexity of the vehicle model in the design of the trajectory tracking controller and facilitate theoretical analysis, the dual-vehicle cooperative transportation system is simplified as follows: (1) The pitch, roll and vertical motion of the system are ignored; (2) It is assumed that the carrier (including carrier 1 and carrier 2) is symmetrical, the steering angle of the left and right wheels is the same, and the left and right tires can be combined into one tire.

[0086] The trajectory tracking control method of the dual-vehicle cooperative transportation system in this embodiment includes:

[0087] S1: Based on the system's geometric constraints, kinematic constraints, and Euler-Lagrange equations, a dynamic model of the system is constructed to obtain the system's dynamic model; specifically including:

[0088] S101: Obtain geometric constraints based on the positional relationship between the load and the carrier in the system;

[0089] like Figure 1 As shown, the coordinate system and key nodes are defined as follows: It is an inertial coordinate system; For a solid coordinate system (in this article, the subscript...) These are used to represent the load, carrier 1, and carrier 2 respectively, in a solid coordinate system. This indicates the longitudinal direction of the object (i.e., the load, carrier 1, and carrier 2). Indicates the horizontal direction of the object; , , These represent the center point, center of mass, and heading angle of the object, respectively. The center point of the carriers (i.e., carrier 1 and carrier 2) is the midpoint of the line connecting the front axle center and the rear axle center. Therefore, the pose information of the load, carrier 1, and carrier 2 can be obtained using... To express, or can be expressed as Let represent . For ease of later derivation, let . , .

[0090] Based on the load and the positional relationship between the two vehicles, the geometric constraints of this dual-vehicle cooperative transportation system are as follows:

[0091]

[0092] in, , and These represent the distances from hinge point 1 to the load's center of mass, hinge point 1 to the center of mass of carrier 1, and hinge point 2 to the center of mass of carrier 2, respectively. In this paper, the hinge point (hinge point 1) between the load and carrier 1 is the center point of carrier 1; the hinge point (hinge point 2) between the load and carrier 2 is the center point of carrier 2; the load is a uniform rectangular rigid body, and its center point and center of mass are both the midpoint of the line connecting hinge point 1 and hinge point 2.

[0093] It is evident that the system is affected by the formula There are four independent constraints represented by generalized coordinate analytical form, therefore the generalized coordinates of the system's nine degrees of freedom are... It can be composed of 5 independent generalized coordinates express.

[0094] For the formula Taking the second derivative, we get the following formula:

[0095]

[0096] in,

[0097] ;

[0098] ;

[0099] A point on the parameter represents the first derivative, and two points on the parameter represent the second derivative.

[0100] S102: Obtain motion constraints based on the motion state of the load and carrier in the system;

[0101] like Figure 2 As shown, let the velocity at the load center point be... Components, velocity The components and angular velocities are respectively , and ; velocity at the center point of carrier 1 Components, velocity The components and angular velocities are respectively , and ; the velocity of the center point of carrier 2 Components, velocity The components and angular velocities are respectively , and Then the motion constraints of carrier 1 satisfy the following nonholonomic constraint equations:

[0102]

[0103] make Then there is

[0104] .

[0105] Let the state vector be... Similarly, the equations for the nonholonomic constraints (kinematic constraints) of the system can be obtained as follows:

[0106]

[0107] in,

[0108] .

[0109] From a kinematic perspective, using the center point as the reference point is more convenient. However, deriving the dynamic equations requires using the center of mass as the reference point for the system's trajectory tracking. The conversion relationship between the center of mass and the center point is as follows:

[0110]

[0111] For the formula Taking the derivative, we get

[0112]

[0113] S103: Combining the geometric constraints, the motion constraints, and the Euler-Lagrange equations, the system dynamics model is obtained.

[0114] The kinetic energy expression of the system with the center of mass as the reference point is:

[0115]

[0116] Among them, subscript These are used to represent the load, carrier 1, and carrier 2, respectively. and These represent the longitudinal and transverse components of the center-of-mass velocity in the inertial coordinate system, respectively. Angular velocity; For quality; This is the moment of inertia. For example: This represents the longitudinal component of the velocity of the center of mass of carrier 1.

[0117] For nonholonomically constrained systems, the dynamic model described by the Euler-Lagrange equations can be expressed as follows:

[0118]

[0119] in, To control the transformation matrix, To control the virtual control quantity of the input. For the constraint matrix, It is a Lagrange multiplier.

[0120] Formula It can be deduced that

[0121]

[0122] in,

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] Represents a 3rd-order zero matrix; virtual control quantity middle, , and These represent the longitudinal driving force, lateral driving force, and torque of carrier 1, respectively. , and These represent the longitudinal driving force, lateral driving force, and torque of carrier 2, respectively. From the formula Differentiation yields:

[0128]

[0129] make From the formula , achievable

[0130]

[0131] in, For matrix The basis matrix of the null space, i.e. .

[0132] For the formula Multiply both sides simultaneously And substitute into the formula The simplified system dynamics equations are obtained as follows:

[0133]

[0134] in,

[0135] ;

[0136] ;

[0137] .

[0138] Assumption Formula The parameters in the matrix consist of a nominal part and a time-varying uncertain part. The parameter matrix can be decomposed as follows:

[0139]

[0140] in, Indicates time; Indicates the system status; This indicates system uncertainty; , and They are respectively , and The nominal part; , and They are respectively , and The uncertain part. Furthermore, the parameter matrices of both the nominal and uncertain parts are continuous, and the uncertain part is bounded.

[0141] For ease of derivation later, let

[0142] ;

[0143] ;

[0144] ;

[0145] Therefore, we can obtain

[0146]

[0147] in, Represents the identity matrix.

[0148] S2: Based on the system dynamics model, construct an adaptive robust controller; the control terms of the adaptive robust controller include a nominal system control term, a feedback term, and an adaptive term;

[0149] The schematic diagram of trajectory tracking control of a dual-vehicle cooperative transportation system using an adaptive robust controller is shown below. Figure 3 As shown, it specifically includes:

[0150] S201: Based on the actual position and desired trajectory of the system, calculate the actual error in trajectory tracking and establish trajectory tracking constraint equations;

[0151] The trajectory tracking problem can be equivalently represented as making both the system's heading angle error and position error approach zero. Therefore, the heading angle error... Defined as the heading angle of the object (including the load, carrier 1, and carrier 2). With desired heading angle The difference; the positional error Defined as the distance from the load centroid to the desired trajectory point (a point in the desired trajectory), where, and These represent the longitudinal and lateral distances from the load's centroid to the desired trajectory point. Specifically, they are expressed as:

[0152]

[0153] in, Represents system state variables; Indicates the desired state (trajectory). This represents the coordinates of the points on the desired trajectory. (Regarding the formula...) Taking the first derivative, we get

[0154]

[0155] in, Represents the longitudinal component of the object's velocity at the desired trajectory point; This represents the curvature at the desired trajectory point.

[0156] In order for the system to follow the target trajectory, the heading angle error and position error It should satisfy: when hour, Therefore, the following equality constraint is imposed on the system dynamics model:

[0157]

[0158] in, , It is a constant. The formula... Written in matrix form, we get

[0159]

[0160] in,

[0161] ;

[0162] ;

[0163] for abbreviation, for The abbreviation of .

[0164] Formula Convert to time The second derivative form is given by...

[0165]

[0166] in,

[0167] ,

[0168] for The abbreviation (the other abbreviations in this article are similar).

[0169] The task of trajectory tracking control is to enable the load to accurately and timely track the target trajectory. In order to handle unmodeled factors such as parameter uncertainties and external disturbances during the operation of a dual-vehicle cooperative transportation system, this paper designs an adaptive robust control algorithm based on the constraint-following method. The control terms of this algorithm mainly consist of nominal system control terms, feedback terms, and adaptive terms.

[0170] S202: Based on the system dynamics model and the trajectory tracking constraint equation, the nominal system control term is obtained;

[0171] Design the nominal system control term without considering uncertainties and initial errors. Definition Then the nominal dynamic system formula It can be written in the following form:

[0172]

[0173] At the same time, the second-order equality constraint formula It can be described as:

[0174]

[0175] in, .

[0176] because and It has a one-to-one correspondence, and the formula With formula The constraints are equivalent, therefore regarding Equations They are compatible.

[0177] Formula Substitute into the formula ,get

[0178]

[0179] definition , And substitute into the formula achievable

[0180]

[0181] The above formula can be seen as about Equality constraints. Nominal dynamic system equations. about The preconditions and equations for controllable constraints They are compatible.

[0182] From the formula The required constraint control quantity can be obtained as follows:

[0183]

[0184] in, It is any vector that satisfies the order.

[0185] If the system has no uncertainty and the system satisfies the formula initially... Then we can use constraint control quantities. This ensures that the nominal system follows the equality constraint. However, in reality, there are often unknown uncertainties and potential initial errors. Therefore, further control algorithms need to be designed to address these uncertainties and initial errors.

[0186] The designed adaptive robust controller is as follows:

[0187]

[0188] in, , and These represent the nominal system control term, feedback term, and adaptive term, respectively; and .

[0189] S203: Construct feedback and adaptive terms;

[0190] In actual control processes, the presence of initial pose and velocity errors prevents the system from strictly satisfying the formula. Given the constraints, the system constraint following error is defined as:

[0191]

[0192] To suppress the effects of initial pose and velocity errors on the system, a feedback term is needed to compensate for the system's dynamic response, as shown below:

[0193]

[0194] in, ; ; It is an adjustable parameter and .

[0195] To ensure virtual control quantity The system can satisfy the properties of uniform boundedness and uniform eventual boundedness. The following assumptions are made:

[0196] Assumption 1: Let There are unknown constants. , making

[0197]

[0198] in, Representation matrix eigenvalues.

[0199] Assumption 2: There exists an unknown constant vector. and a known function , so that:

[0200]

[0201] Assumption 3: Based on Assumption 2, for each There is a function Can Compared to Linear decomposition, as shown below:

[0202]

[0203] in These are adaptive coefficients, and they satisfy... ; and , express and These two parameters have no special meaning and are used for adjustment. The equation of change.

[0204] Based on the above assumptions, for a given boundary constant ,have

[0205]

[0206] in,

[0207] ;

[0208] .

[0209] S3: The control terms of the adaptive robust controller are allocated to each carrier of the system through a stress optimization algorithm to achieve driving force distribution.

[0210] Because the unmanned dual-vehicle cooperative transportation system is an overdrive system... To form a full-rank matrix, the virtual control variables need to be... Distribute, let

[0211]

[0212] Then there is

[0213]

[0214] in, For any vector that satisfies the order.

[0215] Taking the minimum stress on the load as the objective of load distribution optimization, the objective function is as follows:

[0216]

[0217] in, The cost function represents the external force acting on the load; This is the net force acting on the load in the X direction of the inertial coordinate system; This is the net force acting on the load in the Y direction of the inertial coordinate system; and These are the horizontal and vertical decomposition matrices of the driving force for carrier 1 and carrier 2, respectively. and These represent the external resistance experienced by carrier 1 and carrier 2, respectively.

[0218] The above optimization problem can be solved using quadratic programming to achieve the allocation of driving forces.

[0219] Example 2:

[0220] like Figure 4 As shown, the dual-vehicle cooperative transportation system of this embodiment is based on the master vehicle (carrier 1), the slave vehicle (carrier 2) and the load, and includes: a running calculation unit, a positioning unit, an information sensing unit, an execution unit, a power supply unit and a communication unit.

[0221] The operation computing unit, located in the main vehicle, contains one or more computing platforms for processing data from other units and solving control variables using controller algorithms to issue instructions to the execution units. The operation computing unit receives data from other units including: main vehicle-load angle information, main vehicle obstacle and image information obtained from the main vehicle information sensing unit; slave vehicle-load angle information, slave vehicle obstacle and image information obtained from the slave vehicle information sensing unit via the communication unit; and load coordinate position, negative acceleration, and angular velocity information obtained from the positioning unit via the communication unit.

[0222] The positioning unit is located on the load and is used to determine the load's position and heading information. It includes a GPS global positioning system and an inertial measurement unit (IMU).

[0223] The information perception unit includes lidar, cameras, millimeter-wave radar, and angle sensors located on the master vehicle and slave vehicle, respectively. The lidar, cameras, and millimeter-wave radar are used to identify and track static and dynamic obstacles around the cooperative transportation system. The angle sensors are located at the hinges between the master vehicle and the load, and between the slave vehicle and the load, respectively, to sense the rotation angle of the master vehicle and slave vehicle relative to the load. Lidar is located at the front of the master vehicle and the rear of the slave vehicle, cameras are located on all four sides of the carrier (including carrier 1 and carrier 2, i.e., the master vehicle and slave vehicle), and millimeter-wave radar is located at the front left and right ends of the master vehicle and the rear left and right ends of the slave vehicle. The cameras provide real-time images for background monitoring; the lidar located on top of the carrier provides a longer detection range and a wider field of view; the millimeter-wave radar located at the front of the master vehicle and the rear of the slave vehicle detects the presence of surrounding vehicles or obstacles, providing support for front and rear collision warnings and close-range protection. This multi-layered perception system ensures that the dual-vehicle cooperative transportation system can accurately perceive its surrounding environment in different driving scenarios.

[0224] The execution unit executes the instructions from the computing unit, controlling the movement and steering of the main vehicle and slave vehicles through the drive and steering systems. The drive system includes a motor control module and a braking control module. The motor control module has both braking and driving modes. In driving mode, the motor control module adjusts the motor's supply voltage and frequency to precisely regulate the motor's speed and steering. In braking mode, the execution unit coordinates the motor control module and the braking control module, prioritizing the use of the motor braking module to efficiently convert the vehicle's kinetic energy into electrical energy and feed it back into the power supply unit's battery, thereby achieving energy recovery and utilization and improving the system's energy efficiency.

[0225] The power supply unit provides the necessary energy to each unit of the dual-vehicle cooperative transportation system. Installed inside both the vehicle and the load, the power supply unit is equipped with a power monitoring device. While controlling and protecting the power supply to all electrical equipment, it also transmits power consumption data and battery status to the operating computing unit via a communication unit.

[0226] The communication unit is used for real-time communication between the master vehicle, slave vehicle, and load via wired or wireless means. Specifically, the communication unit transmits the angle information and environmental perception data collected by the slave vehicle information sensing unit, the real-time operating status information of the slave vehicle execution unit, and the location information obtained by the load positioning unit to the master vehicle's operation calculation unit, and sends the control commands generated by the master vehicle's operation calculation unit to the slave vehicle execution unit through the communication unit.

[0227] The unmanned dual-vehicle cooperative transportation system designed based on the trajectory tracking control method of this invention integrates a running calculation unit, a positioning unit, an information sensing unit, an execution unit, a power supply unit, and a communication unit into one unit. The system composition is simplified, which can reduce costs while ensuring efficient collaborative work between the units.

[0228] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A trajectory tracking and control method for an unmanned dual-vehicle cooperative transportation system, characterized in that, include: S1: Based on the geometric constraints, motion constraints and Euler-Lagrange equations of the system, a dynamic model of the system is obtained; The specific steps include: S101: Obtain geometric constraints based on the positional relationship between the load and the carrier in the system; The system includes a load and a carrier, with carrier 1 and carrier 2 following carrier 1; the coordinate system and key nodes are defined as follows: It is an inertial coordinate system; For a connected coordinate system, the subscript... These are used to represent the load, carrier 1, and carrier 2, respectively, in a solid coordinate system. Indicates the vertical direction of the object. This indicates the horizontal direction of the object, which includes the load, carrier 1, and carrier 2. , , These represent the object's center point, center of mass, and heading angle, respectively; the center point of the carrier is the midpoint of the line connecting the front and rear axle centers; the pose information of the load, carrier 1, and carrier 2 can be used... To represent, or use To indicate; to make , ; Based on the load and the positional relationship between the two vehicles, the geometric constraint equations of the dual-vehicle cooperative transportation system are obtained as follows: in, , and These represent the distances from hinge point 1 to the load's center of mass, hinge point 1 to the center of mass of carrier 1, and hinge point 2 to the center of mass of carrier 2, respectively; hinge point 1 is the center point of carrier 1; hinge point 2 is the center point of carrier 2; the load is a uniform rectangular rigid body, and its center point and center of mass are both the midpoints of the line connecting hinge point 1 and hinge point 2. The system is subject to four independent constraints represented by the analytical form of generalized coordinates in the above equation; therefore, the generalized coordinates of the system's nine degrees of freedom are... Capable of using 5 independent generalized coordinates express; Taking the second derivative of the geometric constraint equation, we obtain the following formula: in, ； ； A single dot on the parameter indicates the first derivative, and two dots on the parameter indicate the second derivative. S102: Obtain motion constraints based on the motion state of the load and carrier in the system; Let the speed at the load center point be... Components, velocity The components and angular velocities are respectively , and ; velocity at the center point of carrier 1 Components, velocity The components and angular velocities are respectively , and ; velocity of the center point of carrier 2 Components, velocity The components and angular velocities are respectively , and State vector The system's motion constraint equations are: in, ； Based on the geometric relationship between the centroid and the center point of the object, the transformation relationship between the centroid and the center point is obtained as follows: and ; Differentiating the above equation, we get in, and ; It is a 3-order identity matrix; S103: Combining the geometric constraints, the motion constraints, and the Euler-Lagrange equations, we obtain the system dynamics model; The kinetic energy expression of the system with the center of mass as the reference point is: Among them, subscript These are used to represent the load, carrier 1, and carrier 2, respectively. and These represent the longitudinal and transverse components of the center-of-mass velocity in the inertial coordinate system, respectively. Angular velocity; For quality; It is the moment of inertia; Combining the geometric constraints, the kinematic constraints, and the Euler-Lagrange equations, the dynamic model of the system is derived as follows: in, ； ； ； ； Represents a 3rd-order zero matrix; virtual control quantity middle, , and These represent the longitudinal driving force, lateral driving force, and torque of carrier 1, respectively. , and These represent the longitudinal driving force, lateral driving force, and torque of carrier 2, respectively. It is a Lagrange multiplier; make ,get ; The simplified system dynamics equations are as follows: in, ； ； ； Assuming the above parameters consist of a nominal part and a time-varying uncertain part, the parameter matrix can be decomposed as follows: in, Indicates time; Indicates the system status; This indicates system uncertainty; , and They are respectively , and The nominal part; , and They are respectively , and The uncertain part; make ； ； ； Therefore, we obtain in, Represents the identity matrix; S2: Based on the system dynamics model, construct an adaptive robust controller; the control terms of the adaptive robust controller include a nominal system control term, a feedback term, and an adaptive term; S3: The control terms of the adaptive robust controller are allocated to each carrier of the system through a stress optimization algorithm to achieve driving force distribution.

2. The method according to claim 1, characterized in that, S2 specifically includes: S201: Based on the actual position and desired trajectory of the system, calculate the actual error in trajectory tracking and establish trajectory tracking constraint equations; S202: Based on the system dynamics model and the trajectory tracking constraint equation, the nominal system control term is obtained; S203: Construct feedback and adaptive terms.

3. The method according to claim 2, characterized in that, S201 specifically includes: heading angle error Defined as the heading angle of an object With desired heading angle The difference; the positional error Defined as the distance from the load centroid to the desired trajectory point, where, and Let the longitudinal and lateral distances be the distances from the load centroid to the desired trajectory point; then the actual error in trajectory tracking is specifically expressed as: in, Represents system state variables; Indicates the desired state. Represents the coordinates of the points on the desired trajectory; Differentiating the above equation yields in, Represents the longitudinal component of the object's velocity at the desired trajectory point; This represents the curvature at the desired trajectory point; The following equality constraints are imposed on the system dynamics model: in, , is a constant and Rewriting the above equation in matrix form, we get... in, ； ； for abbreviation, for abbreviation; Convert the above expression to a time-related expression The second derivative form is obtained as follows: in, ， for The abbreviation of .

4. The method according to claim 3, characterized in that, S202 specifically includes: definition Then the formula for the nominal dynamic system can be written in the following form: The derived formula for the second-order equality constraint is described as follows: in, ; Through derivation, we obtain definition , Substituting into the above formula, we get The constraint control quantity that meets the requirements is: in, It is any vector that satisfies the order; The designed adaptive robust controller is as follows: in, , and These represent the nominal system control term, feedback term, and adaptive term, respectively; and .

5. The method according to claim 4, characterized in that, In S203, the step of constructing the feedback item specifically includes: Define the system constraint following error as: ； The feedback items are as follows: in, ; ; It is an adjustable parameter and .

6. The method according to claim 5, characterized in that, In S203, the step of constructing the adaptive term specifically includes: Make the following assumptions: Assumption 1: Let There are unknown constants. ,make in, Representation matrix eigenvalues; Assumption 2: There exists an unknown constant vector. and a known function , so that: Assumption 3: Based on Assumption 2, for each There is a function Able to Compared to Linear decomposition, as shown below: in These are adaptive coefficients, and they satisfy... ; and , express and These two parameters have no special meaning and are used for adjustment. The equation of change; Based on the above assumptions, for a given boundary constant ,have in, ； 。 7. The method according to claim 6, characterized in that, S3 specifically includes: make Then there is in, For any vector satisfying the order; Taking the minimum stress on the load as the objective of load distribution optimization, the objective function is as follows: in, The cost function represents the external force acting on the load; This is the net force acting on the load in the X direction of the inertial coordinate system; This is the net force acting on the load in the Y direction of the inertial coordinate system; and These are the horizontal and vertical decomposition matrices of the driving force for carrier 1 and carrier 2, respectively. and These represent the external resistance experienced by carrier 1 and carrier 2, respectively.

8. The method according to claim 7, characterized in that, The optimization problem in S3 is solved using quadratic programming.

9. An unmanned dual-vehicle cooperative transportation system, characterized in that, The system, which operates according to any one of claims 1-8, is based on a master vehicle, slave vehicles, and a load, and includes: a computing unit, a positioning unit, an information sensing unit, an execution unit, a power supply unit, and a communication unit. The operation computing unit is located in the main vehicle and is used to process data from other units, solve control quantities through controller algorithms, and issue instructions to the execution unit. The operation computing unit receives data from other units including: the main vehicle-load angle information, main vehicle obstacle and image information obtained through the main vehicle information sensing unit, the slave vehicle-load angle information, slave vehicle obstacle and image information obtained through the slave vehicle information sensing unit through the communication unit, and the load coordinate position, negative acceleration and angular velocity information obtained through the positioning unit through the communication unit. The positioning unit is located on the load and is used to determine the load's position and heading information, including a GPS global positioning system and an inertial measurement unit; The information perception unit includes lidar, cameras, millimeter-wave radar, and angle sensors located on the master vehicle and slave vehicle, respectively. The lidar, cameras, and millimeter-wave radar are used to identify and track static and dynamic obstacles around the cooperative transportation system. The angle sensors are located at the hinges between the master vehicle and the load, and between the slave vehicle and the load, respectively, to sense the rotation angle of the master vehicle and slave vehicle relative to the load. The lidar is located at the front of the master vehicle and the rear of the slave vehicle, the cameras are located on all four sides of the master vehicle and slave vehicle, and the millimeter-wave radar is located at the front left and right ends of the master vehicle and the rear left and right ends of the slave vehicle. The cameras provide real-time images for background monitoring. The lidar located on top of the vehicle provides a longer detection range and a wider field of view. The millimeter-wave radar located at the front of the master vehicle and the rear of the slave vehicle detects whether there are vehicles or obstacles in the surrounding area, providing support for front and rear collision warnings and close-range protection. The execution unit executes the instructions of the computing unit and controls the movement and steering of the master vehicle and slave vehicle through the drive system and steering system. The drive system includes a motor control module and a braking control module, wherein the motor control module has two modes: braking and driving. In driving mode, the motor control module adjusts the power supply voltage and frequency of the motor to achieve precise control of the motor speed and steering. In braking mode, the execution unit coordinates the motor control module and the braking control module, and uses the motor braking module to efficiently convert the kinetic energy of the carrier into electrical energy and feed it back to the battery of the power supply unit. The power supply unit is used to provide the energy required by each unit of the dual-vehicle cooperative transportation system. The power supply unit is installed inside the carrier and the load respectively, and is equipped with a power monitoring device. While controlling and protecting the power supply of all electrical equipment, it transmits power consumption data and battery status to the operation computing unit through the communication unit. The communication unit is used to connect the master vehicle, slave vehicle and load for real-time communication via wired or wireless means. The communication unit transmits the angle information and environmental perception data collected by the slave vehicle information sensing unit, the real-time operating status information of the slave vehicle execution unit and the position information obtained by the load positioning unit to the master vehicle's operation calculation unit, and sends the control commands generated by the master vehicle's operation calculation unit to the slave vehicle execution unit through the communication unit.

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