Multi-vehicle collaborative trajectory planning method, system and device for heavy-load cargo transportation

Abstract

The present application discloses a multi-vehicle collaborative trajectory planning method, system and equipment for heavy-load cargo transportation, which relates to the field of autonomous driving motion planning. The method includes: constructing a load subsystem dynamics model and a multi-vehicle subsystem dynamics model; constructing constraints; constructing a trajectory optimization problem of the load subsystem based on the load subsystem dynamics model and the load subsystem safety obstacle avoidance constraints, and generating a driving trajectory of the load subsystem; generating a reference driving trajectory of the multi-vehicle subsystem based on the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem; constructing a trajectory optimization problem of the multi-vehicle subsystem based on the multi-vehicle subsystem dynamics model and constraints, optimizing the reference driving trajectory of the multi-vehicle subsystem for heavy-load cargo transportation, generating an optimized driving trajectory of the multi-vehicle subsystem, and realizing multi-vehicle collaborative transportation of heavy-load cargo. The present application improves the transportation efficiency and driving safety of heavy-load cargo.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving motion planning, and in particular to a multi-vehicle collaborative trajectory planning method, system and device for heavy-load cargo transportation. Background Art

[0002] With the rapid advancement of modern industry and infrastructure construction, the demand for the transportation of oversized and heavy goods continues to rise. The traditional mode of transporting large equipment often requires the equipment to be disassembled and transported in batches. This mode is not only time-consuming, labor-intensive and costly, but also adds additional technical complexity. Although there are special heavy-duty transport vehicles for the overall transportation of equipment, this method has low reusability and is difficult to repair once a vehicle breaks down. Therefore, the development of a multi-vehicle collaborative transportation system for heavy-duty cargo has become a key technology to meet the challenges of large equipment transportation.

[0003] At present, the trajectory of the cooperative transportation system mainly relies on manual control, which undoubtedly brings challenges to driving safety and traffic efficiency. In-depth research on the trajectory planning of the heavy-load multi-vehicle cooperative transportation system is the key to ensuring the safe and efficient operation of the entire transportation system. However, most of the related research focuses on single-vehicle systems or multi-agent systems without physical connections, while for transportation systems with special physical connection structures, the research on trajectory planning is relatively scarce. Summary of the invention

[0004] The purpose of this application is to provide a multi-vehicle collaborative trajectory planning method, device, equipment, medium and product for heavy-load cargo transportation, which can improve the efficiency and safety of heavy-load cargo transportation.

[0005] To achieve the above objectives, this application provides the following solutions.

[0006] In the first aspect, the present application provides a multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation, including: constructing a total system dynamics model based on Newton's laws of motion and the law of conservation of angular momentum; the total system dynamics model includes a load subsystem dynamics model and a multi-vehicle subsystem dynamics model; the load subsystem dynamics model is the dynamics model of the load subsystem in the total system, and the multi-vehicle subsystem dynamics model is the dynamics model of the multi-vehicle subsystem in the total system; the multi-vehicle subsystem includes: multiple vehicles for heavy-load cargo transportation; based on the state quantity of the load subsystem, the state quantity of the multi-vehicle subsystem and the state quantity of the obstacle, constructing constraints; the constraints include: multi-vehicle subsystem collaborative motion constraints, multi-vehicle system driving stability constraints and system safety obstacle avoidance constraints; the system safety obstacle avoidance constraints include: load subsystem safety Full obstacle avoidance constraints and multi-vehicle subsystem safe obstacle avoidance constraints; constructing the optimization problem of the load subsystem based on the load subsystem dynamics model and the load subsystem safe obstacle avoidance constraints; optimizing the reference path of heavy-load cargo transportation through the optimization problem of the load subsystem to generate the driving trajectory of the load subsystem; based on the driving trajectory of the load subsystem, generating the reference driving trajectory of the multi-vehicle subsystem according to the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem; constructing the distributed optimization problem of the multi-vehicle subsystem based on the multi-vehicle subsystem dynamics model and the constraints; optimizing the reference driving trajectory of the multi-vehicle subsystem through the optimization problem of the multi-vehicle subsystem to generate the optimized driving trajectory of the multi-vehicle subsystem; the optimized driving trajectory of the multi-vehicle subsystem is the multi-vehicle collaborative trajectory of heavy-load cargo transportation.

[0007] Optionally, the expression of the load subsystem dynamics model is as follows.

[0008] .

[0009] .

[0010] .

[0011] .

[0012] .

[0013] .

[0014] in, is the load subsystem in the global coordinate system X The rate of change of the axis coordinates; is the longitudinal velocity of the load subsystem in the inertial coordinate system; is the heading angle of the load subsystem; is the lateral velocity of the load subsystem in the inertial coordinate system; is the load subsystem in the global coordinate system Y The rate of change of the axis coordinates; is the rate of change of the heading angle of the load subsystem; is the heading angular velocity of the load subsystem; is the rate of change of the longitudinal velocity of the load subsystem in the inertial coordinate system; is the mass of the load; is the rate of change of the lateral velocity of the load subsystem in the inertial coordinate system; For the load subsystem i Longitudinal contact forces on the joints of heavy-duty cargo transport vehicles; For the load subsystem i Transverse contact forces on the joints of heavy-duty cargo transport vehicles; is the yaw moment of inertia of the load subsystem; For the i The longitudinal coordinates of the connectors of a heavy-duty cargo transport vehicle in the inertial coordinate system of the load subsystem; For the i The lateral coordinates of the connector of a heavy-duty cargo transport vehicle in the inertial coordinate system of the load subsystem; , The number of heavy-load cargo transport vehicles.

[0015] Optionally, in the multi-vehicle system dynamics model, i The expression of the dynamic model of a heavy-load cargo transport vehicle is shown below.

[0016] .

[0017] .

[0018] .

[0019] .

[0020] .

[0021] .

[0022] in, For the i A heavy-load cargo transport vehicle in the global coordinate system X The rate of change of the axis coordinates; For the i The longitudinal velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For thei The heading angle of a heavy-load cargo transport vehicle; For the i The lateral velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i A heavy-load cargo transport vehicle in the global coordinate system Y The rate of change of the axis coordinates; For the i The rate of change of the heading angle of a heavy-load cargo transport vehicle; For the i The heading angular velocity of a heavy-load cargo transport vehicle; For the i The rate of change of the longitudinal velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The driving force of a heavy-load cargo transport vehicle; For the i The mass of a heavy-duty cargo transport vehicle; For the i The longitudinal planar contact force transmitted by the connector of a heavy-duty cargo transport vehicle; For the i The lateral force of the rear tires of a heavy-load cargo transport vehicle; For the i The rate of change of the lateral velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The lateral force of the front tires of a heavy-load cargo transport vehicle; For the i The front wheel turning angle of a heavy-duty cargo transport vehicle; For the i Transverse planar contact forces transmitted by the connectors of a heavy-duty cargo transport vehicle; For the i The rate of change of the heading angular velocity of a heavy-load cargo transport vehicle; For the i Yaw moment of inertia of a heavy-load cargo transport vehicle; is the distance from the front axle to the center of mass of the vehicle; is the distance from the rear axle to the center of mass.

[0023] Optionally, the expression of the cooperative motion constraint of the multi-vehicle subsystem is as follows.

[0024] .

[0025] in, For the i The global position of a heavy-load cargo transport vehicle; For the i+1 global location for a heavy cargo transport vehicle; For the i A heavy-duty cargo transport vehicle connector and i +1 minimum value for the distance between connectors of heavy cargo transport vehicles; For the i A heavy-duty cargo transport vehicle connector and i +1 Maximum limit on the distance between connectors of heavy cargo transport vehicles.

[0026] Optionally, the expression of the driving stability constraint of the multi-vehicle subsystem is as follows.

[0027] .

[0028] in, For the i The minimum boundary value of the heading angular velocity constraint of a heavy-load cargo transport vehicle; For the i The heading angular velocity of a heavy-load cargo transport vehicle; For the i The maximum bound of the heading angular velocity constraint of a heavy-load cargo transport vehicle; For the i The minimum value of the side slip angle constraint at the center of mass of a heavy-duty cargo transport vehicle; For the i The sideslip angle of the center of mass of a heavy-duty cargo transport vehicle; For the i The maximum value of the center of mass sideslip angle constraint of a heavy-duty cargo transport vehicle.

[0029] Optionally, the expression of the system safety obstacle avoidance constraint is as follows.

[0030] .

[0031] .

[0032] in, is the vector of the polyhedron of the load subsystem; and is the optimized dual variable of the load subsystem; For the l The polyhedron matrix of obstacles; is the global position vector of the load subsystem; For the l The vectors of the obstacle polyhedron; is the obstacle avoidance relaxation variable of the load subsystem; It is the minimum safety distance threshold for the payload subsystem to avoid obstacles; For the i The vector of the polyhedron of a heavy-duty cargo transport vehicle; and For the i The dual variable for the optimization of a heavy-duty cargo transport vehicle; For the i The global position vector of a heavy-duty cargo transport vehicle; For the i Obstacle avoidance relaxation variables for heavy-duty cargo transport vehicles; For the i The minimum safe distance threshold for obstacle avoidance of a vehicle.

[0033] Optionally, the optimization problem of the load subsystem is expressed as follows.

[0034] .

[0035] st

[0036] .

[0037] .

[0038] .

[0039] .

[0040] in, is the state quantity of the load subsystem The cost function of is the control input of the load subsystem The cost function of is the slack variable of the load subsystem The cost function of ; st is the constraint limit; For the k The state quantity of the load subsystem at time +1; is the dynamic model of the load subsystem; For the k The state quantity of the load subsystem at the moment; For the k The control input of the load subsystem at time t; is the feasible set of state quantities of the load subsystem; is the feasible set of control inputs for the load subsystem.

[0041] Optionally, the expression of the optimization problem of the multi-vehicle subsystem is as follows.

[0042] .

[0043] st

[0044] .

[0045] .

[0046] .

[0047] .

[0048] .

[0049] in, For the k Moment i The state quantity of a heavy-load cargo transport vehicle ; For the k Moment i The reference state quantity of a heavy-load cargo transport vehicle; For the i The penalty matrix of the state quantity of a heavy-load cargo transport vehicle; For the k Moment i Control input for heavy-duty cargo transport vehicles ; For the i The penalty matrix of the control input of the heavy-duty cargo transport vehicle; For the i A heavy-duty cargo transport vehicle and l Obstacle avoidance relaxation variables between obstacles ; For the i The penalty matrix of the obstacle avoidance slack variables of a heavy-duty cargo transport vehicle; st is the constraint limit; For the k +1 moment i The state quantity of a heavy-load cargo transport vehicle; For the i A heavy-duty cargo transport vehicle dynamics model; For the i The feasible set of states of heavy-duty cargo transport vehicles; For the i A feasible set of control inputs for heavy-duty cargo transport vehicles; The feasible domain of multi-vehicle coordinated motion constraints for heavy-load cargo transport vehicles.

[0050] In the second aspect, the present application provides a multi-vehicle collaborative trajectory planning system for heavy-load cargo transportation, including: a model building module, used to construct a total system dynamics model according to Newton's laws of motion and the law of conservation of angular momentum; the total system dynamics model includes a load subsystem dynamics model and a multi-vehicle subsystem dynamics model; the load subsystem dynamics model is the dynamics model of the load subsystem in the total system, and the multi-vehicle subsystem dynamics model is the dynamics model of the multi-vehicle subsystem in the total system; the multi-vehicle subsystem includes: multiple vehicles for heavy-load cargo transportation; a constraint building module, used to construct constraint conditions based on the state quantity of the load subsystem, the state quantity of the multi-vehicle subsystem and the state quantity of the obstacle; the constraint conditions include: multi-vehicle subsystem collaborative motion constraints, multi-vehicle subsystem driving stability constraints and system safety obstacle avoidance constraints; the system safety obstacle avoidance constraints include: load subsystem safety obstacle avoidance constraints and A multi-vehicle subsystem safety obstacle avoidance constraint; a multi-vehicle subsystem trajectory generation module, which is used to construct an optimization problem of the load subsystem based on the load subsystem dynamics model and the load subsystem safety obstacle avoidance constraint; the reference path of heavy-load cargo transportation is optimized through the optimization problem of the load subsystem to generate a driving trajectory of the load subsystem; based on the driving trajectory of the load subsystem, a reference driving trajectory of the multi-vehicle subsystem is generated according to the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem; a multi-vehicle subsystem trajectory optimization module, which is used to construct a distributed optimization problem of the multi-vehicle subsystem based on the multi-vehicle subsystem dynamics model and the constraints, using a distributed model predictive control method; the reference driving trajectory of the multi-vehicle subsystem is optimized through the optimization problem of the multi-vehicle subsystem to generate an optimized driving trajectory of the multi-vehicle subsystem; the optimized driving trajectory of the multi-vehicle system is a multi-vehicle collaborative trajectory for heavy-load cargo transportation.

[0051] In a third aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation described in any one of the above-mentioned methods.

[0052] According to the specific embodiments provided in this application, this application has the following technical effects: This application provides a multi-vehicle collaborative trajectory planning method, system and equipment for heavy-load cargo transportation. By analyzing the contact force coupling relationship between multi-vehicle subsystems and the dynamic characteristics of the load subsystem, a more accurate load subsystem dynamics model and multi-vehicle subsystem dynamics model are constructed, which can fully reflect the mutual influence between the multi-vehicle subsystems and the overall dynamic behavior of the load subsystem during multi-vehicle collaborative transportation, analyze the dynamic coupling characteristics between the load subsystem and the multi-vehicle subsystem, and improve the integrity of the system model; through the state quantity of the load subsystem, the state quantity of the multi-vehicle system The state quantities of the load subsystem and the state quantities of the obstacles are used to construct constraint conditions, which ensures the safety of heavy-loaded goods during transportation. According to the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem, the trajectory coordination among multiple vehicles is also realized, which meets the requirements of safe driving of the transportation system in complex environments. Based on the constructed dynamics model and constraint conditions of the multi-vehicle subsystem, the distributed model predictive control method is adopted to construct the optimization problem of the multi-vehicle subsystem, and the reference driving trajectory of multiple vehicles for heavy-loaded goods transportation is optimized. The optimized driving trajectory of the multi-vehicle subsystem is generated, and the decomposition of the overall trajectory into the solution of multiple vehicle trajectories is realized, which improves the efficiency and safety of transporting heavy-loaded goods. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A flowchart of a multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation provided in one embodiment of the present application.

[0055] Figure 2 A schematic diagram of a two-vehicle collaborative trajectory planning framework for heavy-load cargo transportation provided in one embodiment of the present application.

[0056] Figure 3 for Figure 2 Schematic diagram of the hierarchical distributed trajectory planning framework.

[0057] Figure 4 for Figure 2 Schematic diagram of the cooperative coupling dynamics relationship between the two vehicles.

[0058] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application.

[0059] Figure numbers: 1-system dynamic coupling model, 2-safe operation feasible domain, 3-layered distributed trajectory optimization, 11-load subsystem dynamics model, 12-multi-vehicle subsystem dynamics model, 21-system safety obstacle avoidance constraints, 22-multi-vehicle subsystem collaborative motion constraints, 23-multi-vehicle driving stability constraints, 31-load subsystem optimization problem, 31-multi-vehicle subsystem optimization problem, 33-optimized driving trajectory of multi-vehicle subsystem. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0061] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0062] In an exemplary embodiment, Figure 1 As shown, a multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation is provided, and the method includes the following steps.

[0063] Step 1: Construct the overall system dynamics model based on Newton’s laws of motion and the law of conservation of angular momentum.

[0064] Among them, the overall system dynamics model includes the load subsystem dynamics model and the multi-vehicle subsystem dynamics model; the load subsystem dynamics model is the dynamics model of the load subsystem in the overall system, and the multi-vehicle subsystem dynamics model is the dynamics model of the multi-vehicle subsystem in the overall system; the multi-vehicle subsystem includes: multiple vehicles for heavy-load cargo transportation.

[0065] Step 2: Construct constraints based on the state quantities of the load subsystem, the multi-vehicle subsystem, and the obstacles.

[0066] Among them, the constraints include: multi-vehicle subsystem collaborative motion constraints, multi-vehicle subsystem driving stability constraints and system safety obstacle avoidance constraints; the system safety obstacle avoidance constraints include: load subsystem safety obstacle avoidance constraints and multi-vehicle subsystem safety obstacle avoidance constraints.

[0067] Step 3: Based on the payload subsystem dynamics model and the payload subsystem safety obstacle avoidance constraints, construct the optimization problem of the payload subsystem.

[0068] Step 4: Optimize the reference path of heavy-load cargo transportation through the optimization problem of the load subsystem to generate the driving trajectory of the load subsystem;

[0069] Step 5: Based on the driving trajectory of the load subsystem and the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem, a reference driving trajectory of the multi-vehicle subsystem is generated.

[0070] Step 6: Based on the multi-vehicle subsystem dynamics model and constraints, the distributed model predictive control method is used to construct the optimization problem of the multi-vehicle subsystem.

[0071] Step 7: Optimize the reference driving trajectory of the multi-vehicle subsystem through the optimization problem of the multi-vehicle subsystem to generate the optimized driving trajectory of the multi-vehicle subsystem; the optimized driving trajectory of the multi-vehicle subsystem is the multi-vehicle collaborative trajectory for heavy-load cargo transportation.

[0072] In a specific embodiment, the expression of the load subsystem dynamics model in step 1 is as follows.

[0073] (1)

[0074] (2)

[0075] (3)

[0076] (4)

[0077] (5)

[0078] (6)

[0079] in, is the load subsystem in the global coordinate system X The rate of change of the axis coordinates; is the longitudinal velocity of the load subsystem in the inertial coordinate system; is the heading angle of the load subsystem; is the lateral velocity of the load subsystem in the inertial coordinate system; is the load subsystem in the global coordinate system Y The rate of change of the axis coordinates; is the rate of change of the heading angle of the load subsystem; is the heading angular velocity of the load subsystem; is the rate of change of the longitudinal velocity of the load subsystem in the inertial coordinate system; is the mass of the load; is the rate of change of the lateral velocity of the load subsystem in the inertial coordinate system; For the load subsystem i Longitudinal contact forces on the joints of heavy-duty cargo transport vehicles; For the load subsystem i Transverse contact forces on the joints of heavy-duty cargo transport vehicles; is the yaw moment of inertia of the load subsystem; For the i The longitudinal coordinates of the connectors of a heavy-duty cargo transport vehicle in the inertial coordinate system of the load subsystem; For the i The lateral coordinates of the connector of a heavy-duty cargo transport vehicle in the inertial coordinate system of the load subsystem; , The number of heavy-load cargo transport vehicles.

[0080] In a specific embodiment, in the multi-vehicle subsystem dynamics model in step 1, i The expression of the dynamic model of a heavy-load cargo transport vehicle is shown below.

[0081] (7)

[0082] (8)

[0083] (9)

[0084] (10)

[0085] (11)

[0086] (12)

[0087] in, For the i A heavy-load cargo transport vehicle in the global coordinate system X The rate of change of the axis coordinates; For the i The longitudinal velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The heading angle of a heavy-load cargo transport vehicle; For the i The lateral velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i A heavy-load cargo transport vehicle in the global coordinate system Y The rate of change of the axis coordinates; For the i The rate of change of the heading angle of a heavy-load cargo transport vehicle; For the i The heading angular velocity of a heavy-load cargo transport vehicle; For the iThe rate of change of the longitudinal velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The driving force of a heavy-load cargo transport vehicle; For the i The mass of a heavy-duty cargo transport vehicle; For the i The longitudinal planar contact force transmitted by the connector of a heavy-duty cargo transport vehicle; For the i The lateral force of the rear tires of a heavy-load cargo transport vehicle; For the i The rate of change of the lateral velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The lateral force of the front tires of a heavy-load cargo transport vehicle; For the i The front wheel turning angle of a heavy-duty cargo transport vehicle; For the i Transverse planar contact forces transmitted by the connectors of a heavy-duty cargo transport vehicle; For the i The rate of change of the heading angular velocity of a heavy-load cargo transport vehicle; For the i Yaw moment of inertia of a heavy-load cargo transport vehicle; is the distance from the front axle to the center of mass of the vehicle; is the distance from the rear axle to the center of mass.

[0088] Specifically, Figure 2-Figure 3 As shown, the multi-vehicle subsystem takes two vehicles as an example, namely vehicle 1 subsystem and vehicle 2 subsystem. According to Newton's laws of motion and the law of conservation of angular momentum in the inertial coordinate system, a system dynamic coupling model 1 is established. The system dynamic coupling model 1 includes: a load subsystem dynamic model 11 and a multi-vehicle subsystem dynamic model 12. The system dynamic coupling model 1 clarifies the dynamic coupling relationship between the multi-subsystems. , and The expression is as follows.

[0089] (13)

[0090] (14)

[0091] (15)

[0092] in, is the longitudinal contact force received by the load subsystem at the connection point of the first heavy-load cargo transport vehicle; is the longitudinal contact force received by the load subsystem at the connection point of the second heavy-load cargo transport vehicle; is the lateral contact force received by the load subsystem at the connection point of the first heavy-load cargo transport vehicle; is the lateral contact force received by the load subsystem at the connection point of the second heavy-load cargo transport vehicle. The distance between the connectors of two heavy-duty cargo transport vehicles.

[0093] Multi-vehicle system dynamics model and The expression in Figure 4 is shown below.

[0094] (16)

[0095] (17)

[0096] in, is the lateral force of the rear tire of the first heavy-load cargo transport vehicle; is the lateral force of the left rear tire of the first heavy-load cargo transport vehicle; is the lateral force of the right rear tire of the first heavy-load cargo transport vehicle; is the lateral force of the front tire of the first heavy-load cargo transport vehicle; is the lateral force of the left front tire of the first heavy-load cargo transport vehicle; is the lateral force of the right front tire of the first heavy-load cargo transport vehicle.

[0097] Multi-vehicle system dynamics model and The expression is as follows.

[0098] (18)

[0099] in, For the i The deviation between the heading angle of the heavy-load cargo transport vehicle and the heading angle of the load, . Twiddle Factor The influence of the relative posture change between the load and the vehicle system on the force transmission direction is reflected in the form of a matrix. The load subsystem is subjected to the first i The longitudinal contact force of a heavy-duty cargo transport vehicle, The load subsystem is subjected to the first i The lateral contact force of a heavy-load cargo transport vehicle.

[0100] Longitudinal acceleration of the load subsystem It can be expressed as follows.

[0101] (19)

[0102] The longitudinal acceleration of the load subsystem will cause changes in the vertical force at the A and B connectors, which in turn affects the distribution of vertical forces in multiple subsystems. and It can be expressed as follows.

[0103] (20)

[0104] (twenty one)

[0105] in, is the vertical force of the load subsystem at connector A; is the vertical force of the load subsystem at connector B; is the acceleration due to gravity; is the vertical distance from the center of gravity of the load subsystem to the connector.

[0106] The rolling resistance of the vehicle subsystem is related to the vertical force. The rolling resistance can be expressed as shown below.

[0107] (twenty two)

[0108] (twenty three)

[0109] in, is the rolling resistance coefficient; is the rolling resistance experienced by the first heavy-load cargo transport vehicle; is the rolling resistance experienced by the second heavy-load cargo transport vehicle; is the mass of the first heavy-load cargo transport vehicle; The mass of the second heavy-duty cargo transport vehicle.

[0110] In a specific embodiment, the expression of the multi-vehicle subsystem cooperative motion constraint in step 2 is as follows.

[0111] (twenty four)

[0112] in, For the i A heavy-duty cargo transport vehicle connector and i +1 distance between connectors for heavy cargo transport vehicles; is the small plane displacement degree of freedom; For the i The global position of a heavy-load cargo transport vehicle; For thei +1 global location for a heavy cargo transport vehicle; Change the sign of the formula; For the i A heavy-duty cargo transport vehicle connector and i +1 minimum value for the distance between connectors of heavy cargo transport vehicles; For the i A heavy-duty cargo transport vehicle connector and i +1 Maximum limit on the distance between connectors of heavy cargo transport vehicles.

[0113] In a specific embodiment, the expression of the multi-vehicle driving stability constraint in step 2 is as follows.

[0114] (25)

[0115] in, For the i The minimum boundary value of the heading angular velocity constraint of a heavy-load cargo transport vehicle; For the i The heading angular velocity of a heavy-load cargo transport vehicle; For the i The maximum bound of the heading angular velocity constraint of a heavy-load cargo transport vehicle; For the i The minimum value of the side slip angle constraint at the center of mass of a heavy-duty cargo transport vehicle; For the i The sideslip angle of the center of mass of a heavy-duty cargo transport vehicle; For the i The maximum value of the center of mass sideslip angle constraint of a heavy-duty cargo transport vehicle.

[0116] In a specific embodiment, the expression of the system safety obstacle avoidance constraint in step 2 is as follows.

[0117] (26)

[0118] (27)

[0119] in, is the vector of the polyhedron of the load subsystem; and is the optimized dual variable of the load subsystem; For the l The polyhedron matrix of obstacles; is the global position vector of the load subsystem; For the l The vectors of the obstacle polyhedron; is the obstacle avoidance relaxation variable of the load subsystem; It is the minimum safety distance threshold for the payload subsystem to avoid obstacles; For the i The vector of the polyhedron of a heavy-duty cargo transport vehicle; and For the i The dual variable for the optimization of a heavy-duty cargo transport vehicle; For the i The global position vector of a heavy-duty cargo transport vehicle; For the i Obstacle avoidance relaxation variables for heavy-duty cargo transport vehicles; For the i The minimum safe distance threshold for obstacle avoidance of a vehicle.

[0120] Specifically, Figure 2-Figure 3 As shown, the multi-vehicle subsystem takes two vehicles as an example to construct the safe motion feasible domain of the multi-vehicle cooperative transportation system, including: system safety obstacle avoidance constraints 21, multi-vehicle subsystem cooperative motion constraints 22 and multi-vehicle driving stability constraints 23.

[0121] Constructing the coordinated motion constraints of multiple vehicle subsystems 22: There is a special physical topology between heavy cargo and vehicles. The two vehicle subsystems are physically connected, so the distance between multiple vehicles must be guaranteed during the motion process.

[0122] (28)

[0123] in, For the i The global position of a heavy-load cargo transport vehicle; No. i +1 global location for a heavy cargo transport vehicle; Represents the Euclidean norm, which is used to measure the size and length of a vector in Euclidean space.

[0124] Due to the connection between the load subsystem and the vehicle subsystem, small plane displacement degrees of freedom are allowed , construct the cooperative motion constraints of the multi-vehicle subsystem in equation (24).

[0125] Distributed optimization is difficult to satisfy the coordinated motion of multiple vehicles by constructing a global constraint. It is necessary to solve the coordinated motion of multiple vehicles through the collaborative optimization of multiple vehicle subsystems. The Alternating Direction Method of Multipliers (ADMM) algorithm is introduced to solve the above problem. The expression of the optimization problem of the coordinated motion of multiple vehicle subsystems is shown below.

[0126] (29)

[0127]

[0128] (30)

[0129] (31)

[0130] in, For the i Optimization variables for heavy-duty cargo transport vehicles; For the i +1 optimization variable for heavy-duty cargo transport vehicles; st is a constraint. . is the localization matrix for extracting the position vector. For vehicles i The relevant objective function; For vehicles i +1 for the objective function. and As shown below.

[0131] (32)

[0132] (33)

[0133] The expression of the augmented Lagrangian function for the multi-vehicle system cooperative motion optimization problem is shown below.

[0134] (34)

[0135] in, , is the dual variable. , To augment the Lagrangian parameters, each iteration of the multi-vehicle cooperative motion using the ADMM optimization method consists of three steps.

[0136] Step 1: Update .

[0137] (35)

[0138] Step 2: Update .

[0139] (36)

[0140] Step 3: Update the dual variables and .

[0141] (37)

[0142] (38)

[0143] Through continuous optimization, iterative updates, the coordinated movement of multiple vehicle subsystems can be achieved.

[0144] Specifically, the multi-vehicle driving stability constraint 23 The expression is as follows.

[0145] (39)

[0146] The center of mass sideslip angle boundary is dynamically changing, and the boundary expression is as follows.

[0147] (40)

[0148] in, is the maximum lateral force peak of the rear wheel; For the i The absolute value of the minimum or maximum boundary value of the sideslip angle constraint at the center of mass of a heavy-duty cargo transport vehicle. For the i The distance from the rear axle to the center of mass of a heavy-duty cargo transport vehicle.

[0149] use Construct a safe driving envelope for the multi-vehicle subsystem planning trajectory to ensure the stability of the optimized trajectory.

[0150] Specifically, the load subsystem safety obstacle avoidance constraint 21: Obstacle avoidance is an important part of trajectory planning to ensure that the system can travel safely without collision. In order to ensure the safety and passability of the planned trajectory, geometric polyhedrons are used to describe the outlines of each subsystem. Polyhedron model of the load subsystem , No. Polyhedral Model of Vehicle Subsystems and Polyhedron model of obstacles The expression is as follows.

[0151] (41)

[0152] (42)

[0153] (43)

[0154] in, is the rotation matrix of the heading angle of the payload subsystem; is the position vector of the load subsystem; is the set of polyhedrons corresponding to the initial position of the load subsystem; is the position variable describing the polyhedron of the load subsystem; r is the polyhedron dimension; for r dimensional real numbers; is the matrix of the polyhedron of the load subsystem; For a cone The generalized inequality of is the vector of the corresponding polyhedron of the load subsystem; For the i The rotation matrix of the heading angle of a heavy-duty cargo transport vehicle; For the i A set of polyhedra corresponding to the initial positions of heavy-load cargo transport vehicles; For the i The position vector of a heavy-load cargo transport vehicle; To describe the i Position variables of the heavy-duty cargo transport vehicle polyhedrons; For the i The matrix of the polyhedrons of heavy-duty cargo transport vehicles; For the i The vector of the polyhedron corresponding to each heavy-duty cargo transport vehicle; To describe the l The position variables of the obstacle polyhedrons; For the l The matrix of the polyhedrons of obstacles; For the l The vectors of the polyhedron corresponding to the obstacles. Assume and It is smooth. . is the number of obstacles.

[0155] In order to achieve collision-free driving, an obstacle avoidance method based on signed distance is adopted. The expression of obstacle avoidance constraint is shown below.

[0156] (44)

[0157] (45)

[0158] in, is the distance function; It is the minimum safety distance threshold for the payload subsystem to avoid obstacles; For the i The minimum safe distance threshold for heavy-load cargo transport vehicles to avoid obstacles.

[0159] However, this distance function is usually non-convex and non-differentiable. To solve this problem, the constraint is reformulated into a convex form using convex optimization theory. The expression of the dual optimization obstacle avoidance constraint of the load subsystem is shown below.

[0160] (46)

[0161] in, and is the dual variable of the load subsystem; For another cone The generalized inequality of .

[0162] Since each subsystem and obstacle are modeled as a polyhedron, in order to ensure the feasibility of the optimization problem and the continuity of the planned trajectory, the obstacle avoidance relaxation variable is introduced , , is the maximum value of the slack variable to prevent intrusion into obstacles.

[0163] By simplifying the above formula, the safety obstacle avoidance constraint of the load subsystem is obtained 21. The dual optimization method with constrained slack variables is used to construct the safety obstacle avoidance constraint of the heavy-load multi-vehicle cooperative transportation system, which can effectively adjust the degree of relaxation of the obstacle avoidance constraint, so that the cooperative transportation system can take into account both safety and flexibility in the obstacle avoidance process.

[0164] In a specific embodiment, the optimization problem of the load subsystem in step 3 is expressed as follows.

[0165] (47)

[0166] st

[0167] (48)

[0168] (49)

[0169] (50)

[0170] (51)

[0171] in, is the state quantity of the load subsystem The cost function of is the control input of the load subsystem The cost function of is the slack variable of the load subsystem The cost function of ; st is the constraint limit; For the kThe state quantity of the load subsystem at time +1; is the dynamic model of the load subsystem; For the k The state quantity of the load subsystem at the moment; For the k The control input of the load subsystem at time t; is the feasible set of state quantities of the load subsystem; is the feasible set of control inputs for the load subsystem.

[0172] In a specific embodiment, the expression of the optimization problem of the multi-vehicle subsystem in step 6 is as follows.

[0173] (52)

[0174] st

[0175] (53)

[0176] (54)

[0177] (55)

[0178] (56)

[0179] (57)

[0180] in, For the k Moment i The state quantity of a heavy-load cargo transport vehicle ; For the k Moment i The reference state quantity of a heavy-load cargo transport vehicle; For the i The penalty matrix of the state quantity of a heavy-load cargo transport vehicle; For the k Moment i Control input for heavy-duty cargo transport vehicles ; For the i The penalty matrix of the control input of the heavy-duty cargo transport vehicle; For the i A heavy-duty cargo transport vehicle and l Obstacle avoidance relaxation variables between obstacles ; For the i The penalty matrix of the obstacle avoidance slack variables of a heavy-duty cargo transport vehicle; st is the constraint limit; For the k +1 moment i The state quantity of a heavy-load cargo transport vehicle; For the i Dynamic model of a heavy-duty cargo transport vehicle; For the i The feasible set of states of heavy-duty cargo transport vehicles; For the i A feasible set of control inputs for heavy-duty cargo transport vehicles; The feasible domain of multi-vehicle coordinated motion constraints for heavy-load cargo transport vehicles.

[0181] Specifically, Figure 2-Figure 3 As shown, taking two vehicles as an example, the multi-vehicle subsystem adopts the method of hierarchical distributed trajectory optimization 3 to construct the optimization problem 31 of the load subsystem and the optimization problem 32 of the multi-vehicle subsystem, and obtains the optimized driving trajectory 33 of the multi-vehicle subsystem.

[0182] Figure 3 The input of the hierarchical distributed trajectory planning framework is sensor data and reference path. Among them, sensor data uses sensors such as GNSS (Global Navigation Satellite System) / IMU (Inertial Measurement Unit) to obtain sensor data, that is, the state information of the system, and the reference path provides reference information for trajectory planning. In the upper part, the load subsystem considers the transportation needs of the heavy-loaded multi-vehicle cooperative transportation system, and combines the load subsystem dynamics model and constraints to construct the optimization problem of the load subsystem. In the lower part, the dynamic feasibility and driving stability of vehicle 1 subsystem and vehicle 2 subsystem are fully considered to generate the optimized trajectory of vehicle 1 and vehicle 2. The output is the optimized driving trajectory of the multi-vehicle subsystem. Figure 3 V2V (Vehicle-to-Vehicle) refers to the communication technology between vehicles, allowing direct data exchange and communication between multiple vehicles.

[0183] Construct the optimization problem of the load subsystem to achieve the driving goal of the heavy-load multi-vehicle cooperative transportation system. The optimization problem consists of a dynamic model, safety constraints, and a cost function. The expression of the dynamic model of the load subsystem is shown below.

[0184] (58)

[0185] in, For the k The state quantity of the load subsystem at time +1; is the dynamic model of the load subsystem; For the k The state quantity of the load subsystem at the moment; For the k The control input of the load subsystem at time t.

[0186] To further ensure the feasibility and safety of the motion, it is necessary to construct constraints on the motion space and control input, and their expressions are as follows.

[0187] (59)

[0188] (60)

[0189] in, is the feasible set of state quantities of the load subsystem; is the feasible set of control inputs for the load subsystem.

[0190] The cost function of the optimization problem of the payload subsystem contains the deviation of the planned trajectory from the global reference path and the penalty term for the control input. In addition, to ensure the smoothness of the planned trajectory of the payload subsystem, the obstacle avoidance constraint is modeled as a soft constraint. Therefore, the cost function contains the slack variable The specific cost function is shown below.

[0191] (61)

[0192] (62)

[0193] (63)

[0194] in, is the state quantity of the load subsystem The cost function of is the length of the prediction time domain; For the k The state quantity of the load subsystem at the moment; For the k The reference state vector of the load subsystem at time t; is the state quantity of the load subsystem The penalty matrix of is the control input of the load subsystem The cost function of For the k The control input of the load subsystem at time t; is the control input of the load subsystem The penalty matrix of is the slack variable of the load subsystem The cost function of is the slack variable of the load subsystem The penalty matrix.

[0195] Combining the above load subsystem dynamics model, constraints and cost function, the expression of the optimization problem of the load subsystem is constructed. Solving the optimization problem of the load subsystem can obtain the driving trajectory of the load subsystem , thus meeting the transportation needs of the system. However, the operation of the system is realized by driving the multi-vehicle subsystem. The load trajectory obtained by the optimization problem does not consider the dynamic feasibility of the multi-vehicle subsystem. Therefore, it is necessary to plan the driving trajectory of the multi-vehicle subsystem to realize the dynamic feasibility of the heavy-loaded cargo multi-vehicle cooperative transportation system.

[0196] Constructing the optimization problem of multiple vehicle subsystems: The upper load subsystem can plan the driving trajectory of the load subsystem ,At the same time, combined with the physical connection relationship between the load subsystem and the multi-vehicle subsystem, the expression of the reference trajectory of the multi-vehicle subsystem can be obtained as shown below.

[0197] (64)

[0198] (65)

[0199] in, is the vehicle 1 subsystem in the global coordinate system X Axis reference coordinates; is the vehicle 1 subsystem in the global coordinate system Y Axis reference coordinates; is the load subsystem in the global coordinate system X Axis coordinates; is the load subsystem in the global coordinate system Y Axis coordinates; is the vehicle 2 subsystem in the global coordinate system X Axis reference coordinates; is the vehicle 2 subsystem in the global coordinate system Y Axis reference coordinates.

[0200] Position vector and Used to construct reference driving trajectory for vehicle 1 subsystem The driving trajectory of the vehicle 2 subsystem . is the trajectory of the load subsystem Obtained from.

[0201] Combining the multi-vehicle subsystem dynamics model and constraints, the optimization problem of the multi-vehicle subsystem is constructed. Solving the optimization problem of the multi-vehicle subsystem can plan the optimal trajectory sequence of the multi-vehicle subsystem that meets the vehicle dynamics and actuator constraints. In addition, the distributed optimization solution decomposes the multi-vehicle trajectory optimization problem into small sub-problems, which can be solved in parallel.

[0202] The multi-vehicle collaborative trajectory planning method, system and device proposed in this application for heavy-load cargo transportation have the following advantages:

[0203] (1) For heavy-load cargo with special topological structures, hierarchical distributed trajectory planning for multi-vehicle collaborative transportation is carried out. In complex driving environments, safe and collaborative trajectories are planned, which improves the efficiency of transporting heavy-load cargo.

[0204] (2) By analyzing the contact force coupling relationship between multiple subsystems and the dynamic characteristics of the load subsystem, a more accurate system coupling dynamics model was constructed. This model fully reflects the mutual influence between the subsystems and the dynamic behavior of the system as a whole during the collaborative transportation process. A safe motion feasible domain of a heavy-loaded multi-vehicle collaborative transportation system was constructed, including obstacle avoidance safety, motion coordination, and driving stability analysis. This feasible domain not only ensures the driving safety of each vehicle during the transportation process, but also realizes the trajectory coordination between multiple vehicles, meeting the requirements for safe driving of the transportation system in a complex environment.

[0205] (3) Aiming at the special topological structure of the heavy-load multi-vehicle cooperative transportation system, an innovative hierarchical distributed trajectory planning framework is proposed. The hierarchical approach can achieve a balance between the global transportation goal of the system and the local optimization performance of each subsystem. Through hierarchical planning, the system can comprehensively consider the overall optimal path planning and the personalized needs of each subsystem, thereby improving the overall operation efficiency and adaptability of the system. At the same time, the distributed model control strategy decomposes the complex optimization objectives into multiple manageable subtasks, which can effectively improve the real-time performance of trajectory planning.

[0206] In an exemplary embodiment, a computer device is provided, which provides a multi-vehicle collaborative trajectory planning system for heavy-load cargo transportation, including the following modules.

[0207] The model building module is used to construct the overall system dynamics model according to Newton's laws of motion and the law of conservation of angular momentum; the overall system dynamics model includes the load subsystem dynamics model and the multi-vehicle subsystem dynamics model; the load subsystem dynamics model is the dynamics model of the load subsystem in the overall system, and the multi-vehicle subsystem dynamics model is the dynamics model of the multi-vehicle subsystem in the overall system; the multi-vehicle subsystem includes: multiple vehicles for heavy-load cargo transportation.

[0208] The constraint construction module is used to construct constraint conditions based on the state quantities of the load subsystem, the state quantities of the multi-vehicle subsystem and the state quantities of the obstacles; the constraint conditions include: multi-vehicle subsystem collaborative motion constraints, multi-vehicle subsystem driving stability constraints and system safety obstacle avoidance constraints; system safety obstacle avoidance constraints include: load subsystem safety obstacle avoidance constraints and multi-vehicle subsystem safety obstacle avoidance constraints.

[0209] The multi-vehicle subsystem trajectory generation module is used to construct an optimization problem of the load subsystem based on the load subsystem dynamics model and the safety obstacle avoidance constraints of the load subsystem; optimize the reference path of heavy-load cargo transportation through the optimization problem of the load subsystem to generate a driving trajectory of the load subsystem; based on the driving trajectory of the load subsystem, according to the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem, generate a reference driving trajectory of the multi-vehicle subsystem.

[0210] The multi-vehicle subsystem trajectory optimization module is used to construct the optimization problem of the multi-vehicle subsystem based on the multi-vehicle subsystem dynamics model and constraints, using the distributed model predictive control method; the reference driving trajectory of the multi-vehicle subsystem is optimized through the optimization problem of the multi-vehicle subsystem to generate the optimized driving trajectory of the multi-vehicle subsystem; the optimized driving trajectory of the multi-vehicle subsystem is the multi-vehicle collaborative trajectory for heavy-load cargo transportation.

[0211] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store optimized driving trajectory data of a multi-vehicle subsystem. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a multi-vehicle collaborative trajectory planning method for heavy-duty cargo transportation is implemented.

[0212] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components. In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0213] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0214] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation, characterized in that: The multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation includes: According to Newton's laws of motion and the law of conservation of angular momentum, a total system dynamics model is constructed; the total system dynamics model includes a load subsystem dynamics model and a multi-vehicle subsystem dynamics model; the load subsystem dynamics model is a dynamics model of the load subsystem in the total system, and the multi-vehicle subsystem dynamics model is a dynamics model of the multi-vehicle subsystem in the total system; the multi-vehicle subsystem includes: multiple vehicles for heavy-load cargo transportation; Based on the state quantity of the load subsystem, the state quantity of the multi-vehicle subsystem and the state quantity of the obstacle, the constraint conditions are constructed; the constraint conditions include: the multi-vehicle subsystem coordinated motion constraint, the multi-vehicle subsystem driving stability constraint and the system safety obstacle avoidance constraint; the system safety obstacle avoidance constraint includes: the load subsystem safety obstacle avoidance constraint and the multi-vehicle subsystem safety obstacle avoidance constraint; Based on the load subsystem dynamics model and the load subsystem safety obstacle avoidance constraints, construct an optimization problem of the load subsystem; The reference path of heavy-load cargo transportation is optimized by the optimization problem of the load subsystem to generate a driving trajectory of the load subsystem; Based on the driving trajectory of the load subsystem and the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem, a reference driving trajectory of the multi-vehicle subsystem is generated; Based on the multi-vehicle subsystem dynamics model and the constraints, a distributed model predictive control method is used to construct the optimization problem of the multi-vehicle subsystem; The reference driving trajectory of the multi-vehicle subsystem is optimized through the optimization problem of the multi-vehicle subsystem to generate an optimized driving trajectory of the multi-vehicle subsystem; the optimized driving trajectory of the multi-vehicle subsystem is a multi-vehicle collaborative trajectory for heavy-load cargo transportation.

2. The multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to claim 1 is characterized in that: The expression of the load subsystem dynamics model is: ； ； ； ； ； ； in, is the load subsystem in the global coordinate system X The rate of change of the axis coordinates; is the longitudinal velocity of the load subsystem in the inertial coordinate system; is the heading angle of the payload subsystem; is the lateral velocity of the load subsystem in the inertial coordinate system; is the load subsystem in the global coordinate system Y The rate of change of the axis coordinates; is the rate of change of the heading angle of the load subsystem; is the heading angular velocity of the load subsystem; is the rate of change of the longitudinal velocity of the load subsystem in the inertial coordinate system; is the mass of the load; is the rate of change of the lateral velocity of the load subsystem in the inertial coordinate system; For the load subsystem i Longitudinal contact forces on the joints of heavy-duty cargo transport vehicles; For the load subsystem i Transverse contact forces on the joints of heavy-duty cargo transport vehicles; is the yaw moment of inertia of the load subsystem; For the i The longitudinal coordinates of the connectors of a heavy-duty cargo transport vehicle in the inertial coordinate system of the load subsystem; For the i The lateral coordinates of the connector of a heavy-duty cargo transport vehicle in the inertial coordinate system of the load subsystem; , The number of heavy-load cargo transport vehicles.

3. The multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to claim 1 is characterized in that: In the multi-vehicle system dynamics model, i The expression of the dynamic model of a heavy-load cargo transport vehicle is: ； ； ； ； ； ； in, For the i A heavy-load cargo transport vehicle in the global coordinate system X The rate of change of the axis coordinates; For the i The longitudinal velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The heading angle of a heavy-load cargo transport vehicle; For the i The lateral velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i A heavy-load cargo transport vehicle in the global coordinate system Y The rate of change of the axis coordinates; For the i The rate of change of the heading angle of a heavy-load cargo transport vehicle; For the i The heading angular velocity of a heavy-load cargo transport vehicle; For the i The rate of change of the longitudinal velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The driving force of a heavy-load cargo transport vehicle; For the i The mass of a heavy-duty cargo transport vehicle; For the i The longitudinal planar contact force transmitted by the connector of a heavy-duty cargo transport vehicle; For the i The lateral force of the rear tires of a heavy-load cargo transport vehicle; For the i The rate of change of the lateral velocity of a heavy-load cargo transport vehicle in the inertial coordinate system; For the i The lateral force of the front tires of a heavy-load cargo transport vehicle; For the i The front wheel turning angle of a heavy-duty cargo transport vehicle; For the i Transverse planar contact forces transmitted by the connectors of a heavy-duty cargo transport vehicle; For the i The rate of change of the heading angular velocity of a heavy-load cargo transport vehicle; For the i Yaw moment of inertia of a heavy-load cargo transport vehicle; is the distance from the front axle to the center of mass of the vehicle; is the distance from the rear axle to the center of mass.

4. The multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to claim 1 is characterized in that: The expression of the cooperative motion constraint of the multi-vehicle subsystem is: ； in, For the i The global position of a heavy-load cargo transport vehicle; For the i +1 global location for a heavy cargo transport vehicle; For the i A heavy-duty cargo transport vehicle connector and i +1 minimum value for the distance between connectors of heavy cargo transport vehicles; For the i A heavy-duty cargo transport vehicle connector and i +1 Maximum limit on the distance between connectors of heavy cargo transport vehicles.

5. The multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to claim 1 is characterized in that: The expression of the driving stability constraint of the multi-vehicle subsystem is: ； in, For the i The minimum bound of the heading angular velocity constraint of a heavy-load cargo transport vehicle; For the i The heading angular velocity of a heavy-load cargo transport vehicle; For the i The maximum bound of the heading angular velocity constraint of a heavy-load cargo transport vehicle; For the i The minimum value of the side slip angle constraint at the center of mass of a heavy-duty cargo transport vehicle; For the i The sideslip angle of the center of mass of a heavy-duty cargo transport vehicle; For the i The maximum value of the center of mass sideslip angle constraint of a heavy-duty cargo transport vehicle.

6. The multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to claim 1 is characterized in that: The expression of the system safety obstacle avoidance constraint is: ； ； in, is the vector of the polyhedron of the load subsystem; and is the optimized dual variable of the load subsystem; For the l The polyhedron matrix of obstacles; is the global position vector of the load subsystem; For the l The vectors of the obstacle polyhedrons; is the obstacle avoidance relaxation variable of the load subsystem; It is the minimum safety distance threshold for the payload subsystem to avoid obstacles; For the i The vector of the polyhedron of a heavy-duty cargo transport vehicle; and For the i The dual variable for the optimization of a heavy-duty cargo transport vehicle; For the i The global position vector of a heavy-duty cargo transport vehicle; For the i Obstacle avoidance relaxation variables for heavy-duty cargo transport vehicles; For the i The minimum safe distance threshold for obstacle avoidance of a vehicle.

7. The multi-vehicle coordinated trajectory planning method for heavy-load cargo transportation according to claim 6 is characterized in that: The optimization problem of the load subsystem is expressed as: ； st ； ； ； ； in, is the state quantity of the load subsystem The cost function of is the control input of the load subsystem The cost function of is the slack variable of the load subsystem The cost function of ; st is the constraint limit; For the k The state quantity of the load subsystem at time +1; is the dynamic model of the load subsystem; For the k The state quantity of the load subsystem at the moment; For the k The control input of the load subsystem at time t; is the feasible set of state quantities of the load subsystem; is the feasible set of control inputs for the load subsystem.

8. The multi-vehicle coordinated trajectory planning method for heavy-load cargo transportation according to claim 6 is characterized in that: The optimization problem of the multi-vehicle subsystem is expressed as: ； st ； ； ； ； ； in, For the k Moment i The state quantity of a heavy-load cargo transport vehicle ; For the k Moment i The reference state quantity of a heavy-load cargo transport vehicle; For the i The penalty matrix of the state quantity of a heavy-load cargo transport vehicle; For the k Moment i Control input for heavy-duty cargo transport vehicles ; For the i The penalty matrix of the control input of the heavy-duty cargo transport vehicle; For the i A heavy-duty cargo transport vehicle and l Obstacle avoidance relaxation variables between obstacles ; For the i The penalty matrix of the obstacle avoidance slack variables of a heavy-duty cargo transport vehicle; st is the constraint limit; For the k +1 moment i The state quantity of a heavy-load cargo transport vehicle; For the i A heavy-duty cargo transport vehicle dynamics model; For the i The feasible set of states of heavy-duty cargo transport vehicles; For the i A feasible set of control inputs for heavy-duty cargo transport vehicles; The feasible domain of multi-vehicle coordinated motion constraints for heavy-load cargo transport vehicles.

9. A multi-vehicle collaborative trajectory planning system for heavy-load cargo transportation, characterized in that: The multi-vehicle collaborative trajectory planning system for heavy-load cargo transportation is applied to the multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to any one of claims 1 to 8, and the multi-vehicle collaborative trajectory planning system for heavy-load cargo transportation includes: The model building module is used to build a total system dynamics model according to Newton's laws of motion and the law of conservation of angular momentum; the total system dynamics model includes a load subsystem dynamics model and a multi-vehicle subsystem dynamics model; the load subsystem dynamics model is a dynamics model of the load subsystem in the total system, and the multi-vehicle subsystem dynamics model is a dynamics model of the multi-vehicle subsystem in the total system; the multi-vehicle subsystem includes: multiple vehicles for heavy-load cargo transportation; A constraint construction module is used to construct constraint conditions based on the state quantity of the load subsystem, the state quantity of the multi-vehicle subsystem and the state quantity of the obstacle; the constraint conditions include: multi-vehicle subsystem coordinated motion constraint, multi-vehicle subsystem driving stability constraint and system safety obstacle avoidance constraint; the system safety obstacle avoidance constraint includes: load subsystem safety obstacle avoidance constraint and multi-vehicle subsystem safety obstacle avoidance constraint; The multi-vehicle subsystem trajectory generation module is used to construct an optimization problem of the load subsystem based on the load subsystem dynamics model and the load subsystem safety obstacle avoidance constraints; optimize the reference path of heavy-load cargo transportation through the optimization problem of the load subsystem to generate a driving trajectory of the load subsystem; based on the driving trajectory of the load subsystem, according to the kinematic coupling relationship between the load subsystem and the multi-vehicle subsystem, generate a reference driving trajectory of the multi-vehicle subsystem; The multi-vehicle subsystem trajectory optimization module is used to construct the optimization problem of the multi-vehicle subsystem based on the multi-vehicle subsystem dynamics model and the constraints, using a distributed model predictive control method; optimize the reference driving trajectory of the multi-vehicle subsystem through the optimization problem of the multi-vehicle subsystem to generate an optimized driving trajectory of the multi-vehicle subsystem; the optimized driving trajectory of the multi-vehicle subsystem is a multi-vehicle collaborative trajectory for heavy-load cargo transportation.

10. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multi-vehicle collaborative trajectory planning method for heavy-load cargo transportation according to any one of claims 1-8.

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