Vehicle control method, system, device and storage medium for multi-vehicle coordinated transportation

Abstract

The embodiments of the present application provide a vehicle control method, system, device and storage medium for multi-vehicle cooperative transportation, which belongs to the field of vehicle cooperative control technology. The method constructs a first dynamic model of an independently steering unmanned vehicle, a second forward dynamic model of a multi-vehicle cooperative transportation system, and a motion state quantity relationship model between the system and the unmanned vehicle based on a hierarchical modeling strategy, and then determines the system transportation trajectory according to the first dynamic model and the target position. Then, a model predictive controller algorithm is used to determine the system control quantity according to the second forward dynamic model and the system transportation trajectory, and the vehicle control quantity of the unmanned vehicle is determined according to the system control quantity and the motion state control quantity relationship model, and the unmanned vehicle is controlled according to the vehicle control quantity. The present application can simplify the vehicle control process of multi-vehicle cooperative transportation and improve the coordination between the unmanned vehicle and the goods.

Description

Technical Field

[0001] The present application relates to the field of vehicle cooperative control technology, and in particular to a vehicle control method, system, device and storage medium for multi-vehicle cooperative transportation. Background Art

[0002] The transportation of oversized cargo plays an irreplaceable role in numerous sectors, including construction, energy, and manufacturing. Given the limited carrying capacity of individual vehicles, the transportation of oversized cargo is evolving towards multi-vehicle collaborative transport. Current multi-vehicle collaborative transport involves multiple mobile robots working together, relying primarily on human coordination and resulting in low efficiency. To improve transportation efficiency, collaborative transport methods that rely on communication and interaction between agents have emerged. However, this interactive communication collaborative transport method has complex control processes and lacks consideration for the interaction between cargo and agents, resulting in poor coordination between agents and cargo. Summary of the Invention

[0003] The main purpose of the embodiments of this application is to propose a vehicle control method, system, equipment and storage medium for multi-vehicle collaborative transportation, aiming to simplify the vehicle control process of multi-vehicle collaborative transportation and improve the coordination between unmanned vehicles and cargo.

[0004] To achieve the above objectives, one aspect of an embodiment of the present application provides a vehicle control method for multi-vehicle coordinated transportation, comprising the following steps:

[0005] Obtaining a first dynamic model of an independently steering unmanned vehicle, a second forward dynamic model of a multi-vehicle cooperative transport system, and a motion state quantity relationship model between the system and the unmanned vehicle, constructed based on a hierarchical modeling strategy, wherein the multi-vehicle cooperative transport system includes cargo and multiple unmanned vehicles transporting the cargo;

[0006] determining a system transport trajectory based on the first dynamic model and the target position;

[0007] Determining a system control variable based on the second forward dynamics model and the system transport trajectory using a model predictive controller algorithm;

[0008] The vehicle control quantity of the unmanned vehicle is determined according to the relationship model between the system control quantity and the motion state control quantity, and the unmanned vehicle is controlled according to the vehicle control quantity.

[0009] In some embodiments, the first dynamics model includes a first forward dynamics model of the unmanned vehicle and a motion constraint of the unmanned vehicle, wherein the motion constraint of the unmanned vehicle is obtained by the following steps:

[0010] Get the maximum steering angle of the steering wheel of the unmanned vehicle;

[0011] Based on the turning radius model of the unmanned vehicle in the front and rear Ackerman steering modes, the minimum turning radius of the unmanned vehicle is determined according to the maximum steering angle, and the motion constraint of the unmanned vehicle is determined according to the minimum turning radius.

[0012] In some embodiments, the motion state control quantity relationship model is obtained by the following steps: determining the positional relationship between each unmanned vehicle and the cargo according to the distribution pattern of the multi-vehicle cooperative transportation system; determining the motion state quantity relationship model between the system and the unmanned vehicle according to the positional relationship. In some embodiments, determining the system transportation trajectory according to the first dynamic model and the target position includes the following steps: determining the system motion constraints of the multi-vehicle cooperative transportation system according to the first dynamic model; determining the system transportation trajectory according to the system motion constraints and the target position. In some embodiments, the use of a model predictive controller algorithm to determine the system control quantity according to the second forward dynamic model and the system transportation trajectory includes the following steps: constructing a relational expression of state variables and control variables according to the second forward dynamic model, wherein the state variables represent the system position and the system heading angle, and the control variables represent the longitudinal velocity, lateral velocity and angular velocity of the system; linearizing the relational expression near the reference point using a first-order Taylor expansion to obtain an error-based state space equation, which is expressed as: in, Represents the error between the current position and the reference position of the multi-vehicle cooperative transportation system; Represents the error between the desired control quantity and the actual control quantity of the multi-vehicle cooperative transportation system; discretizes the state space equation and performs control increment constraint processing to determine the state quantity prediction model in the prediction time domain; determines the system control quantity according to the system transportation trajectory and the state quantity prediction model. In some embodiments, the determination of the system control quantity according to the system transportation trajectory and the state quantity prediction model includes the following steps: constructing an objective function according to the reference state represented by the system transportation trajectory; solving the control increment in the state quantity prediction model according to the objective function to obtain the system control quantity. In some embodiments, the motion state control quantity relationship model is expressed as follows: Among them, v i represents the linear speed control value of the i-th unmanned vehicle, ω i represents the angular velocity control value of the i-th unmanned vehicle, r xi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the x-axis, r xi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the y-axis, ω b 、v bx 、v byAll represent system control quantities, namely the system angular velocity control quantity, the system lateral velocity control quantity and the system longitudinal velocity control quantity.

[0013] To achieve the above objectives, another aspect of the present application provides a vehicle control system for multi-vehicle coordinated transportation, comprising:

[0014] A first module is configured to obtain a first dynamic model of an independently steering unmanned vehicle constructed based on a hierarchical modeling strategy, a second forward dynamic model of a multi-vehicle coordinated transport system, and a motion state quantity relationship model between the system and the unmanned vehicle, wherein the multi-vehicle coordinated transport system includes cargo and multiple unmanned vehicles transporting the cargo;

[0015] A second module is used to determine the system transportation trajectory according to the first dynamic model and the target position;

[0016] A third module is configured to determine a system control variable based on the second forward dynamics model and the system transport trajectory using a model predictive controller algorithm;

[0017] The fourth module is used to determine the vehicle control quantity of the unmanned vehicle based on the relationship model between the system control quantity and the motion state control quantity, and control the unmanned vehicle based on the vehicle control quantity.

[0018] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, which includes a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory. When the program is executed by the processor, the method described in the above embodiment is implemented.

[0019] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the method described in the above embodiment.

[0020] The vehicle control method, system, device and storage medium for multi-vehicle cooperative transportation proposed in this application respectively construct a first dynamic model of an independently steering unmanned vehicle, a second forward dynamic model of the multi-vehicle cooperative transportation system, and a motion state quantity relationship model between the system and the unmanned vehicle based on a hierarchical modeling strategy, and then determine the system transportation trajectory according to the first dynamic model and the target position. Then, a model predictive controller algorithm is used to determine the system control quantity according to the second forward dynamic model and the system transportation trajectory, determine the vehicle control quantity of the unmanned vehicle according to the system control quantity and the motion state control quantity relationship model, and control the unmanned vehicle according to the vehicle control quantity. This application uses hierarchical modeling and calculates the vehicle control quantity based on the analysis of the overall trajectory and control quantity of the system. It has the characteristics of simple calculation, fast solution, and variable configuration. That is, this calculation method is highly flexible in scenarios where the system vehicle layout changes. At the same time, the vehicle control quantity is calculated based on the analysis of the overall trajectory and control quantity of the system, thereby improving the coordination between the unmanned vehicle and the cargo. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of a vehicle control method for multi-vehicle coordinated transportation provided by an embodiment of the present application;

[0022] Figure 2 is a side view of a multi-vehicle cooperative transportation system provided by an embodiment of the present application;

[0023] Figure 3 is a top view of a multi-vehicle cooperative transportation system provided by an embodiment of the present application;

[0024] Figure 4 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0026] It should be noted that although the system is divided into functional modules and the flowcharts illustrate a logical sequence, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowcharts. The terms "first," "second," and so on in the specification, claims, and drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0028] The embodiments of the present application provide a vehicle control method, system, device and storage medium for multi-vehicle collaborative transportation, aiming to simplify the vehicle control process of multi-vehicle collaborative transportation and improve the coordination between unmanned vehicles and cargo.

[0029] The vehicle control method, system, device and storage medium for multi-vehicle cooperative transportation provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the vehicle control method for multi-vehicle cooperative transportation in the embodiments of the present application is described.

[0030] The vehicle control method for multi-vehicle cooperative transportation provided in the embodiment of the present application relates to the field of artificial intelligence technology. The vehicle control method for multi-vehicle cooperative transportation provided in the embodiment of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application of the vehicle control method for multi-vehicle cooperative transportation, etc., but is not limited to the above forms.

[0031] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0032] Figure 1This is an optional flow chart of a vehicle control method for multi-vehicle cooperative transportation provided by an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S106.

[0033] Step S101: Obtaining a first dynamic model of an independently steering unmanned vehicle, a second forward dynamic model of a multi-vehicle coordinated transport system, and a motion state relationship model between the system and the unmanned vehicle, constructed based on a hierarchical modeling strategy. The multi-vehicle coordinated transport system includes cargo and multiple unmanned vehicles transporting the cargo.

[0034] Step S102, determining a system transport trajectory according to the first dynamic model and the target position;

[0035] Step S103, using a model predictive controller algorithm to determine the system control variable according to the second forward dynamics model and the system transportation trajectory;

[0036] Step S104: determining the vehicle control amount of the unmanned vehicle according to the relationship model between the system control amount and the motion state control amount, and controlling the unmanned vehicle according to the vehicle control amount.

[0037] In the steps S101 to S104 shown in the embodiment of the present application, a first dynamic model of an independently steering unmanned vehicle, a second forward dynamic model of a multi-vehicle cooperative transport system, and a motion state quantity relationship model between the system and the unmanned vehicle are respectively constructed through a hierarchical modeling strategy, and then the system transport trajectory is determined based on the first dynamic model and the target position. Then, a model predictive controller algorithm is used to determine the system control quantity based on the second forward dynamic model and the system transport trajectory, and the vehicle control quantity of the unmanned vehicle is determined based on the system control quantity and the motion state control quantity relationship model, and the unmanned vehicle is controlled based on the vehicle control quantity. The present application uses hierarchical modeling and calculates the vehicle control quantity based on the analysis of the overall trajectory and control quantity of the system. It has the characteristics of simple calculation, fast solution, and variable configuration, that is, this calculation method is highly flexible for scenarios where the system vehicle layout changes. At the same time, the vehicle control quantity is calculated based on the analysis of the overall trajectory and control quantity of the system, thereby improving the coordination between the unmanned vehicle and the cargo.

[0038] In step S101 of some embodiments, the multi-vehicle cooperative transport system can be composed of multiple modular four-wheel independent steering unmanned vehicles and goods to be transported, and the multiple unmanned vehicles cooperate to transport a large piece of cargo through a mechanical connection device with an angle sensor. The multi-vehicle cooperative transport system is modeled in layers. First, a forward kinematic model of the modular four-wheel independent steering unmanned vehicle (i.e., a first forward kinematic model) is established, and the motion constraints of the unmanned vehicle are established, which together with the first forward kinematic model form the first kinematic model of the unmanned vehicle; secondly, a kinematic model of the omnidirectional multi-vehicle cooperative transport system (i.e., a second forward kinematic model) is established; finally, based on the positional relationship between the modular four-wheel independent steering unmanned vehicle and the system, a relationship between the motion state quantity of the multi-vehicle cooperative transport system and the state quantity of each modular four-wheel independent steering unmanned vehicle is established, i.e., a motion state quantity relationship model. It can be understood that the system position can be represented by the position of the system center point or the position of the cargo center point.

[0039] In some embodiments, the first dynamics model in step S101 includes a first forward dynamics model of the unmanned vehicle and motion constraints of the unmanned vehicle.

[0040] The first forward kinematic model of the modular four-wheel independent steering unmanned vehicle is expressed as:

[0041]

[0042] Among them, x, y, and θ represent the position and heading angle of the unmanned vehicle in the global coordinate system, respectively; v and ω represent the linear velocity and angular velocity of the unmanned vehicle body coordinate system in the global coordinate system, respectively.

[0043] The motion constraints of the autonomous vehicle can be obtained through but not limited to the following steps:

[0044] Step S201, obtaining the maximum steering angle of the steering wheel of the unmanned vehicle;

[0045] Step S202 : Based on the turning radius model of the unmanned vehicle in the front and rear Ackerman steering modes, the minimum turning radius of the unmanned vehicle is determined according to the maximum steering angle, and the motion constraint of the unmanned vehicle is determined according to the minimum turning radius.

[0046] Specifically, the modular four-wheel independent steering (IWS) autonomous vehicle utilizes front-to-back Ackermann steering and pivoting. Compared to front-wheel Ackermann steering, the front-to-back Ackermann steering significantly reduces the vehicle's turning radius, improving its maneuverability and maneuverability on narrow roads.

[0047] The turning radius model of the modular four-wheel independent steering unmanned vehicle in the front and rear Ackerman steering modes is expressed as:

[0048]

[0049] Where R is the turning radius of the unmanned vehicle; L is the distance between the front and rear axles of the unmanned vehicle; δ f is the equivalent front wheel turning angle of the unmanned vehicle. The equivalent rear wheel turning angle of the unmanned vehicle is equal to the equivalent front wheel turning angle but in the opposite direction, that is, δ r =-δ f .

[0050] Due to the limitation of mechanical structure, the steering wheel of the unmanned vehicle has a maximum steering angle constraint, which is set as δ fmax , combined with the above turning radius model, it can be deduced that the minimum turning radius of the unmanned vehicle in the Ackerman steering mode (which can be used as the motion constraint of the unmanned vehicle) is:

[0051]

[0052] In another embodiment, the turning radius of the unmanned vehicle can be calculated by the absolute value of the ratio of the linear velocity and the angular velocity of the geometric center of the unmanned vehicle, as follows:

[0053]

[0054] when At this time, the wheel steering angle exceeds the maximum steering angle limit, and the unmanned vehicle enters the on-the-spot rotation mode.

[0055] In some embodiments, the second forward dynamics model of the multi-vehicle coordinated transportation system in step S101 is expressed as follows:

[0056]

[0057] Among them, x c 、y c ,θ c Represent the position and heading angle of the cargo geometric center point, v cx 、v cy 、ω c They represent the longitudinal velocity, lateral velocity and angular velocity of the cargo's geometric center respectively.

[0058] In some embodiments, the motion state control quantity relationship model in step S101 can be obtained by, but not limited to, the following steps:

[0059] Step S301, determining the positional relationship between each unmanned vehicle and the cargo according to the distribution pattern of the multi-vehicle coordinated transportation system;

[0060] Step S302: Determine a motion state relationship model between the system and the unmanned vehicle based on the position relationship.

[0061] Specifically, the multi-vehicle cooperative transport system can have a variety of vehicle distribution modes, for example, the vehicles are symmetrical with respect to the cargo or the vehicles are distributed around the cargo, etc. For example, referring to Figure 2 and Figure 3 Four unmanned vehicles 300 are cooperatively transporting cargo 100 via a mechanical connection device 200 equipped with an angle sensor. The four unmanned vehicles are distributed around the cargo. Based on the distribution pattern of the multi-vehicle cooperative transportation system, the positional relationship between each unmanned vehicle and the cargo center point is determined, and the motion state relationship model between the system and the unmanned vehicles can be determined. The motion state relationship model is used to characterize the correlation between the control quantity of the overall system and the control quantity of each individual vehicle. The motion state control quantity relationship model is expressed as follows:

[0062]

[0063] Among them, v i represents the linear speed control value of the i-th unmanned vehicle, ω i represents the angular velocity control value of the i-th unmanned vehicle, r xi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the x-axis, r xi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the y-axis, ω b 、v bx 、v by All represent system control quantities, namely the system angular velocity control quantity, the system lateral velocity control quantity and the system longitudinal velocity control quantity.

[0064] In step S102 of some embodiments, trajectory planning is performed on the system as a whole in combination with the target location to which the goods are to be transported, thereby generating a desired system transportation trajectory.

[0065] In some embodiments, step S102 may include but is not limited to steps S401 to S402:

[0066] Step S401, determining the system motion constraints of the multi-vehicle cooperative transportation system according to the first dynamics model;

[0067] Step S402: determining the system transport trajectory according to the system motion constraints and the target position.

[0068] Specifically, the motion constraints of the multi-vehicle cooperative transportation system are analyzed by combining the motion constraints of the unmanned vehicle in the first dynamic model. Then, the system transportation trajectory that satisfies the motion constraints of the multi-vehicle cooperative transportation system is generated based on the Cartesian cubic polynomial. The system transportation trajectory is shown in formulas (7-1) and (7-2):

[0069]

[0070] in, (x f ,y f ,θ f ) represents the desired target point, (x i ,y i ,θ i ) represents the current position of the geometric center of the cargo in the multi-vehicle cooperative transportation system; k i and k f are parameters for adjusting the shape of the generated trajectory; (x(s), y(s), θ(s)) represents the generated state trajectory.

[0071] In step S103 of some embodiments, the Model Predictive Control (MPC) algorithm is an advanced control strategy that controls the system by predicting the future behavior of the system and optimizing the control input. The core of the MPC algorithm is to periodically obtain the optimal control input sequence at each sampling moment based on the current system state information and future control objectives by solving a finite-time open-loop optimization problem, and implement the first control action of the sequence on the controlled object. The embodiment of the present application adopts a model predictive controller algorithm to determine the optimal system control quantity based on the second forward dynamics model of the entire system and the system transportation trajectory, so that the system can accurately track the system transportation trajectory to the target position.

[0072] In some embodiments, step S103 may also include but is not limited to steps S501 to S502:

[0073] Step S501: constructing a relational expression between state variables and control variables according to the second forward dynamics model, wherein the state variables represent the system position and the system heading angle, and the control variables represent the longitudinal velocity, lateral velocity, and angular velocity of the system;

[0074] Step S502 , linearizing the relational expression near the reference point using a first-order Taylor expansion to obtain an error-based state space equation;

[0075] Step S503, discretizing the state space equation and performing control increment constraint processing to determine a state quantity prediction model within the prediction time domain;

[0076] Step S504: determining the system control quantity according to the system transportation trajectory and the state quantity prediction model.

[0077] Specifically, based on the generated state trajectory (i.e., the system transportation trajectory), the geometric input that drives the multi-vehicle cooperative transportation system to move along the Cartesian path is derived, as shown in formulas (8-1) and (8-2):

[0078]

[0079] in, and They represent the speed and angular velocity of the multi-vehicle cooperative transportation system in the geometric space respectively.

[0080] Based on the above formulas (8-1) and (8-2), the time-varying expression of the multi-vehicle cooperative transportation system in the velocity space can be derived, as shown in formulas (9-1) and (9-2):

[0081]

[0082] The second forward dynamics model of the multi-vehicle cooperative transportation system in formula (5) is written as follows, and the relationship between the state variables and the control variables is obtained as follows:

[0083]

[0084] in, Represent the state variables of the multi-vehicle cooperative transportation system, namely the position and heading angle vectors; Represents the control variables of the system, namely the longitudinal velocity, lateral velocity and angular velocity of the system.

[0085] From the above formula, we can see that the kinematics of the multi-vehicle cooperative transportation system is a nonlinear strongly coupled system. We further use the first-order Taylor expansion to expand formula (10) at the reference point (x r ,u r ) is linearized near the surface and expanded as follows:

[0086]

[0087] Among them, It means that the function f(x,u) is r , u=u r The partial derivative of x, similarly, let It means that the function f(x,u) is r , u=u r The partial derivative of u at u, u r =[v cxd v cyd ω cd ] T =[v d cosθ c v d sinθ c ω d ] T .

[0088] Based on the above formula, the error-based state space equation can be obtained as follows:

[0089]

[0090] in, Represents the error between the current position and the reference position of the multi-vehicle cooperative transportation system; It represents the error between the expected control quantity and the actual control quantity of the multi-vehicle cooperative transportation system.

[0091] By discretizing the continuous-time state space equation of formula (12), the following formula can be derived:

[0092]

[0093] in, and Are parameter matrices, as follows:

[0094]

[0095]

[0096] Furthermore, in order to constrain the control increment within each sampling period, the incremental input model predictive control is adopted, and the formula can be derived as follows:

[0097]

[0098] Based on formula (16), a new state space equation can be constructed, as shown in formulas (17-1) and (17-2):

[0099]

[0100] The parameters of the new state space equation are expressed as follows:

[0101] Δu(k)=u(k)-u(k-1); (18)

[0102]

[0103] Set the control horizon of the model predictive control to N c , the prediction time domain is N p , where N c ≤N p , the output of the multi-vehicle cooperative transportation system in the prediction time domain can be derived and expressed in the form of a matrix, that is, the state quantity prediction model, as follows:

[0104] Y(k)=Ψ(k)ξ(k)+Θ(k)ΔU(k); (22)

[0105] The state quantity prediction model is as follows:

[0106]

[0107] Then the system control quantity is determined based on the system transportation trajectory and state quantity prediction model.

[0108] In some embodiments, determining the system control quantity according to the system transport trajectory and the state quantity prediction model in step S504 includes, but is not limited to, steps S601 to S602:

[0109] Step S601, constructing an objective function based on a reference state represented by the system transport trajectory;

[0110] Step S602: solving the control increment in the state quantity prediction model according to the objective function to obtain the system control quantity.

[0111] Specifically, since the control increment of the multi-vehicle cooperative transportation system is unknown, by designing a reasonable optimization objective function and solving it, the control sequence in the control time domain can be obtained.

[0112] The designed objective function is as follows:

[0113]

[0114] Where Q and R represent the weight matrices of trajectory tracking error and control input increment, respectively, and η re f(k+i|k) represents the reference state of the system transport trajectory representation.

[0115] Based on the objective function, the state quantity prediction model is optimized and solved to obtain the system control quantity of the multi-vehicle cooperative transportation system, which is denoted as u r =[v bx v by ω b ] T .

[0116] In step S104 of some embodiments, after obtaining the system control quantity of the overall system, the system control quantity is substituted into the motion state control quantity relationship model of the above formula (6) to obtain the vehicle control quantity of the corresponding unmanned vehicle. The unmanned vehicle is then controlled based on the vehicle control quantity to achieve efficient cargo transportation. It is understood that in a multi-vehicle cooperative transportation system, different unmanned vehicles have different positional relationships with the cargo center point. Different motion state control quantity relationship models need to be constructed based on these different positional relationships, and then the vehicle control quantity of each unmanned vehicle is calculated separately.

[0117] The present application also provides a vehicle control system for multi-vehicle coordinated transportation, including:

[0118] The first module is used to obtain a first dynamic model of an independently steering unmanned vehicle constructed based on a hierarchical modeling strategy, a second forward dynamic model of a multi-vehicle cooperative transportation system, and a motion state quantity relationship model between the system and the unmanned vehicle, wherein the multi-vehicle cooperative transportation system includes cargo and multiple unmanned vehicles transporting the cargo;

[0119] The second module is used to determine the system transportation trajectory according to the first dynamic model and the target position;

[0120] The third module is used to determine the system control quantity according to the second forward dynamics model and the system transportation trajectory by using a model predictive controller algorithm;

[0121] The fourth module is used to determine the vehicle control quantity of the unmanned vehicle based on the relationship model between the system control quantity and the motion state control quantity, and control the unmanned vehicle based on the vehicle control quantity.

[0122] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0123] The present application also provides an electronic device comprising: a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for enabling communication between the processor and the memory. When the program is executed by the processor, the aforementioned vehicle control method for multi-vehicle coordinated transportation is implemented. The electronic device may be any intelligent terminal, including a tablet computer and an in-vehicle computer.

[0124] See also Figure 4 , Figure 4 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0125] The processor 401 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0126] The memory 402 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 402 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program codes are stored in the memory 402 and are called by the processor 401 to execute the vehicle control method for multi-vehicle cooperative transportation in the embodiments of this application.

[0127] Input / output interface 403, used to implement information input and output;

[0128] Communication interface 404, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0129] Bus 405 , which transmits information between various components of the device (e.g., processor 401 , memory 402 , input / output interface 403 , and communication interface 404 );

[0130] The processor 401 , the memory 402 , the input / output interface 403 and the communication interface 404 are connected to each other in communication within the device via a bus 405 .

[0131] An embodiment of the present application also provides a storage medium, which is a computer-readable storage medium used for computer-readable storage. The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the above-mentioned vehicle control method for multi-vehicle cooperative transportation.

[0132] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0133] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0134] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0135] The system embodiment described above is merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0136] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0137] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0138] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0139] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the above units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0140] The units described above as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0142] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0143] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A vehicle control method for multi-vehicle coordinated transportation, characterized in that: The following steps are involved: Obtain a first dynamic model of an independently steering unmanned vehicle, a second forward dynamic model of a multi-vehicle coordinated transport system, and a motion state quantity relationship model between the system and the unmanned vehicle, constructed based on a hierarchical modeling strategy. The multi-vehicle coordinated transport system includes cargo and multiple unmanned vehicles transporting the cargo. The motion state control quantity relationship model is expressed as follows: Among them, v i represents the linear speed control value of the i-th unmanned vehicle, ω i represents the angular velocity control value of the i-th unmanned vehicle, r xi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the x-axis, r yi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the y-axis, ω b 、v bx 、v by All represent system control variables, namely system angular velocity control variable, system lateral velocity control variable and system longitudinal velocity control variable; determining a system transport trajectory based on the first dynamic model and the target position; Determining a system control variable based on the second forward dynamics model and the system transport trajectory using a model predictive controller algorithm; The vehicle control quantity of the unmanned vehicle is determined according to the relationship model between the system control quantity and the motion state control quantity, and the unmanned vehicle is controlled according to the vehicle control quantity.

2. The vehicle control method for multi-vehicle coordinated transportation according to claim 1, characterized in that: The first dynamics model includes a first forward dynamics model of the unmanned vehicle and a motion constraint of the unmanned vehicle, and the motion constraint of the unmanned vehicle is obtained by the following steps: Get the maximum steering angle of the steering wheel of the unmanned vehicle; Based on the turning radius model of the unmanned vehicle in the front and rear Ackerman steering modes, the minimum turning radius of the unmanned vehicle is determined according to the maximum steering angle, and the motion constraint of the unmanned vehicle is determined according to the minimum turning radius.

3. The vehicle control method for multi-vehicle coordinated transportation according to claim 1, characterized in that: The motion state control quantity relationship model is obtained by the following steps: Determine the positional relationship between each unmanned vehicle and the cargo based on the distribution pattern of the multi-vehicle cooperative transportation system; A motion state quantity relationship model between the system and the unmanned vehicle is determined based on the positional relationship.

4. The vehicle control method for multi-vehicle coordinated transportation according to claim 1, characterized in that: The method of using a model predictive controller algorithm to determine the system control variable according to the second forward dynamics model and the system transport trajectory includes the following steps: Constructing a relational expression between state variables and control variables according to the second forward dynamics model, wherein the state variables represent the system position and the system heading angle, and the control variables represent the longitudinal velocity, lateral velocity, and angular velocity of the system; The relational expression is linearized near the reference point using a first-order Taylor expansion to obtain an error-based state-space equation, which is expressed as: in, Represents the error between the current position and the reference position of the multi-vehicle cooperative transportation system; Represents the error between the expected control quantity and the actual control quantity of the multi-vehicle cooperative transportation system; Discretizing the state space equation and performing control increment constraint processing to determine a state quantity prediction model within a prediction time domain; The system control quantity is determined according to the system transportation trajectory and the state quantity prediction model.

5. The vehicle control method for multi-vehicle coordinated transportation according to claim 4, characterized in that: Determining the system control quantity according to the system transport trajectory and the state quantity prediction model includes the following steps: constructing an objective function based on a reference state represented by the transport trajectory of the system; The control increment in the state quantity prediction model is solved according to the objective function to obtain the system control quantity.

6. A vehicle control system for multi-vehicle coordinated transportation, characterized in that: include: The first module is used to obtain a first dynamic model of an independently steering unmanned vehicle constructed based on a hierarchical modeling strategy, a second forward dynamic model of a multi-vehicle coordinated transport system, and a motion state quantity relationship model between the system and the unmanned vehicle, wherein the multi-vehicle coordinated transport system includes cargo and multiple unmanned vehicles transporting the cargo; the motion state control quantity relationship model is expressed as follows: Among them, v i represents the linear speed control value of the i-th unmanned vehicle, ω i represents the angular velocity control value of the i-th unmanned vehicle, r xi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the x-axis, r yi represents the distance from the connection point between the i-th unmanned vehicle and the cargo to the geometric center of the cargo along the y-axis, ω b 、v bx 、v by All represent system control variables, namely system angular velocity control variable, system lateral velocity control variable and system longitudinal velocity control variable; A second module is used to determine the system transportation trajectory according to the first dynamic model and the target position; A third module is configured to determine a system control variable based on the second forward dynamics model and the system transport trajectory using a model predictive controller algorithm; The fourth module is used to determine the vehicle control quantity of the unmanned vehicle based on the relationship model between the system control quantity and the motion state control quantity, and control the unmanned vehicle based on the vehicle control quantity.

7. An electronic device, characterized in that: The electronic device includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing connection and communication between the processor and the memory. When the program is executed by the processor, the steps of the method described in any one of claims 1 to 5 are implemented.

8. A storage medium, which is a computer-readable storage medium and is used for computer-readable storage, characterized in that: The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of claims 1 to 5.

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