Control method of unmanned vehicle in multi-axle steering mode

Abstract

The invention relates to a multi-axle steering mode unmanned vehicle control method and device, electronic equipment and a storage medium. The method comprises the following steps: establishing a multi-shaft unmanned vehicle steering model according to an Ackerman steering principle; based on the multi-axle unmanned vehicle steering model, calculating a steering angle relation of each wheel of the multi-axle unmanned vehicle, and generating a relational expression of an equivalent axle distance, an equivalent front wheel steering angle and corresponding steering angles of all rotating wheels; acquiring and calculating an equivalent wheelbase and an equivalent front wheel steering angle of the multi-axle unmanned vehicle through a preset experimental model; and establishing a multi-axle steering vehicle adaptive control parameter corresponding table including a steering mode, an equivalent wheelbase, an equivalent front wheel steering angle and a control parameter based on a universal kinematic model equivalent steering mode of the multi-axle unmanned vehicle. The adaptive control algorithm of the multi-axle steering unmanned vehicle can be adapted to different steering modes, and the control precision of the multi-axle steering vehicle is improved.

Description

Technical Field

[0001] This disclosure relates to the field of multi-axle unmanned vehicle control, and more specifically, to a control method, apparatus, electronic device, and computer-readable storage medium for a multi-axle steering mode unmanned vehicle. Background Technology

[0002] When a multi-axle unmanned vehicle is in motion, it needs to perform path planning based on environmental information and the vehicle model to control the vehicle to travel along the planned trajectory. The motion control module receives trajectory information including waypoints, speed, and steering mode from the upstream planning module, and, combined with the vehicle's status, the controller calculates the control inputs for each actuator.

[0003] Existing autonomous vehicle control methods are mostly designed for ordinary two-axle passenger vehicles, conforming to the Ackermann steering model when turning. However, for multi-axle steering vehicles, directly increasing the vehicle model's geometric parameters and using a controller from an ordinary two-axle autonomous vehicle will result in a change in the steering center position due to the wheel angles of the steering axle. This makes it impossible for the multi-axle steering vehicle to conform to the Ackermann steering model as it does for two-axle autonomous vehicles. Therefore, when using existing methods to control the motion of multi-axle steering vehicles, steering failures often occur due to excessive lateral control errors, failing to achieve good control results.

[0004] Motion control of multi-axle steering vehicles is an important research topic. Because the turning radius and turning center position of the motion model differ between different steering modes of multi-axle steering vehicles, and the trajectory information of the planning module cannot be preset, it is necessary to generalize the steering model of multi-axle steering vehicles to avoid steering failures caused by mismatch between the kinematic model and the controller model. This requires establishing a correspondence table of lateral control parameters for different steering modes, achieving adaptive control under different steering models, and realizing precise and stable lateral control of multi-axle steering unmanned vehicles.

[0005] The motion control module of a multi-axle steering unmanned vehicle receives trajectory information, including path points, speed, and steering mode, from the planning module. Currently, the common industry approach is to directly increase the geometric parameters of the vehicle model, simplifying the multi-axle vehicle to a two-axle vehicle to adapt the control algorithm. Furthermore, this approach cannot adaptively adjust control parameters after switching steering modes. These two points result in insufficient control precision for multi-axle steering vehicles, hindering accurate trajectory tracking.

[0006] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this disclosure is to provide a control method, apparatus, electronic device, and computer-readable storage medium for a multi-axis steering mode unmanned vehicle, thereby overcoming, at least to some extent, one or more problems caused by limitations and defects in related technologies.

[0008] According to one aspect of this disclosure, a control method for a multi-axis steering mode unmanned vehicle is provided, comprising:

[0009] Based on the Ackermann steering principle, a multi-axis unmanned vehicle steering model is established.

[0010] Based on the multi-axis unmanned vehicle steering model, the steering angle relationship of each wheel of the multi-axis unmanned vehicle is calculated, and the relationship between the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels is generated.

[0011] The equivalent wheelbase and equivalent front wheel angle of the multi-axis unmanned vehicle were collected and calculated using a pre-set experimental model.

[0012] Based on the generalized kinematic model, the steering mode of the multi-axle unmanned vehicle is equivalent, and a correspondence table of adaptive control parameters for multi-axle steering vehicles, including steering mode, equivalent wheelbase, equivalent front wheel angle and control parameters, is established.

[0013] In one exemplary embodiment of this disclosure, the method further includes:

[0014] Based on the Ackermann steering principle, a steering model for a multi-axle unmanned vehicle is established, and expressions for the steering angles corresponding to all rotating wheels of the multi-axle unmanned vehicle are generated.

[0015] In one exemplary embodiment of this disclosure, the preset experimental model of the method further includes:

[0016] An orientation and positioning module is installed on the multi-axis unmanned vehicle. The geometric center point of the multi-axis unmanned vehicle is used as the target point, and the position information of the multi-axis unmanned vehicle is recorded by inertial navigation.

[0017] The steering wheel angle of the multi-axis unmanned vehicle is fixed at a preset angle, and the multi-axis unmanned vehicle is controlled to perform circular motion on a flat road surface to collect point cloud data of the geometric center point of the multi-axis unmanned vehicle.

[0018] In one exemplary embodiment of this disclosure, the method further includes:

[0019] Based on the position information of the multi-axis unmanned vehicle and the point cloud data of the geometric center point of the multi-axis unmanned vehicle, the motion trajectory and trajectory radius of the multi-axis unmanned vehicle are generated after fitting by the RANSAC algorithm.

[0020] In one exemplary embodiment of this disclosure, the method further includes:

[0021] Based on the motion trajectory and trajectory radius of the multi-axis unmanned vehicle, the equivalent wheelbase and equivalent front wheel steering angle of the multi-axis unmanned vehicle under the generalized motion model are calculated.

[0022] In one exemplary embodiment of this disclosure, the control parameters of the multi-axis unmanned vehicle include prediction time-domain parameters, control time-domain parameters, state error weight matrix, and control input weight matrix.

[0023] In one exemplary embodiment of this disclosure, the method further includes:

[0024] Based on the corresponding table of adaptive control parameters for multi-axis steering vehicles, the steering mode and control parameters corresponding to the preset path point are selected to complete the control of the unmanned vehicle in multi-axis steering mode.

[0025] In one aspect of this disclosure, a control device for a multi-axis steering mode unmanned vehicle is provided, comprising:

[0026] The steering model modeling module is used to build a multi-axis unmanned vehicle steering model based on the Ackermann steering principle.

[0027] The steering angle correspondence module is used to calculate the steering angle relationship of each wheel of the multi-axle unmanned vehicle based on the steering model of the multi-axle unmanned vehicle, and generate the relationship between the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels.

[0028] The experimental data acquisition module is used to acquire and calculate the equivalent wheelbase and equivalent front wheel angle of the multi-axis unmanned vehicle through a preset experimental model.

[0029] The parameter mapping table generation module is used to establish a multi-axis steering vehicle adaptive control parameter mapping table that includes steering mode, equivalent wheelbase, equivalent front wheel angle and control parameters, based on the generalized kinematic model to equivalently represent the steering mode of the multi-axis unmanned vehicle.

[0030] In one aspect of this disclosure, an electronic device is provided, comprising:

[0031] Processor; and

[0032] A memory storing computer-readable instructions that, when executed by the processor, implement the method according to any one of the preceding claims.

[0033] In one aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method according to any one of the preceding claims.

[0034] An exemplary embodiment of this disclosure provides a control method for a multi-axle steering mode unmanned vehicle. The method includes: establishing a multi-axle unmanned vehicle steering model based on the Ackermann steering principle; calculating the steering angle relationship of each wheel of the multi-axle unmanned vehicle based on the steering model, generating a relationship between the equivalent wheelbase, equivalent front wheel angle, and the corresponding steering angles of all rotating wheels; collecting and calculating the equivalent wheelbase and equivalent front wheel angle of the multi-axle unmanned vehicle through a preset experimental model; and establishing a correspondence table of adaptive control parameters for the multi-axle steering vehicle, including the steering mode, equivalent wheelbase, equivalent front wheel angle, and control parameters, based on a generalized kinematic model to represent the steering mode of the multi-axle unmanned vehicle. This disclosure can adapt to adaptive control algorithms for multi-axle steering unmanned vehicles with different steering modes, improving the control accuracy of multi-axle steering vehicles.

[0035] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0036] The above and other features and advantages of this disclosure will become more apparent from the detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0037] Figure 1 A flowchart is shown below illustrating a control method for a multi-axis steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure;

[0038] Figure 2 A schematic diagram of a multi-axle vehicle steering mode is shown, illustrating a control method for a multi-axle steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure.

[0039] Figure 3 A diagram of a six-axle vehicle Ackerman steering model is shown, illustrating a control method for a multi-axle steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure.

[0040] Figure 4 A six-axle vehicle calibration model diagram is shown, illustrating a control method for a multi-axle steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure.

[0041] Figure 5 A vehicle trajectory acquisition diagram is shown for a control method of a multi-axis steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure;

[0042] Figure 6 A diagram showing the geometric relationship between point A and the inner front wheel of a control method for a multi-axis steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure is provided.

[0043] Figure 7A schematic block diagram of a control device for a multi-axis steering mode unmanned vehicle according to an exemplary embodiment of the present disclosure is shown.

[0044] Figure 8 A block diagram of an electronic device according to an exemplary embodiment of the present disclosure is shown schematically;

[0045] Figure 9 The illustration shows a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0046] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0047] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced without one or more of the specific details described, or other methods, components, materials, apparatuses, steps, etc., can be employed. In other instances, well-known structures, methods, apparatuses, implementations, materials, or operations are not shown or described in detail to avoid obscuring various aspects of this disclosure.

[0048] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, or in one or more software-hardened modules, or in different network and / or processor devices and / or microcontroller devices.

[0049] In this example embodiment, a control method for a multi-axis steering mode unmanned vehicle is first provided; refer to Figure 1 As shown, the control method for a multi-axis steering mode unmanned vehicle may include the following steps:

[0050] Step S110: Based on the Ackermann steering principle, establish a multi-axis unmanned vehicle steering model;

[0051] Step S120: Based on the multi-axle unmanned vehicle steering model, calculate the steering angle relationship of each wheel of the multi-axle unmanned vehicle, and generate the relationship between the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels.

[0052] Step S130: Using a preset experimental model, collect and calculate the equivalent wheelbase and equivalent front wheel angle of the multi-axle unmanned vehicle;

[0053] Step S140: Based on the generalized kinematic model, the steering mode of the multi-axle unmanned vehicle is equivalent, and a correspondence table of adaptive control parameters for multi-axle steering vehicles, including steering mode, equivalent wheelbase, equivalent front wheel angle and control parameters, is established.

[0054] An exemplary embodiment of this disclosure provides a control method for a multi-axle steering mode unmanned vehicle. The method includes: establishing a multi-axle unmanned vehicle steering model based on the Ackermann steering principle; calculating the steering angle relationship of each wheel of the multi-axle unmanned vehicle based on the steering model, generating a relationship between the equivalent wheelbase, equivalent front wheel angle, and the corresponding steering angles of all rotating wheels; collecting and calculating the equivalent wheelbase and equivalent front wheel angle of the multi-axle unmanned vehicle through a preset experimental model; and establishing a correspondence table of adaptive control parameters for the multi-axle steering vehicle, including the steering mode, equivalent wheelbase, equivalent front wheel angle, and control parameters, based on a generalized kinematic model to represent the steering mode of the multi-axle unmanned vehicle. This disclosure can adapt to adaptive control algorithms for multi-axle steering unmanned vehicles with different steering modes, improving the control accuracy of multi-axle steering vehicles.

[0055] The control method of a multi-axis steering mode unmanned vehicle in this example embodiment will be further described below.

[0056] Example 1:

[0057] In step S110, a multi-axis unmanned vehicle steering model can be established based on the Ackermann steering principle.

[0058] In this example embodiment, the method further includes:

[0059] Based on the Ackermann steering principle, a steering model for a multi-axle unmanned vehicle is established, and expressions for the steering angles corresponding to all rotating wheels of the multi-axle unmanned vehicle are generated.

[0060] In step S120, based on the multi-axle unmanned vehicle steering model, the steering angle relationship of each wheel of the multi-axle unmanned vehicle can be calculated, and the relationship between the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels can be generated.

[0061] In this example embodiment, the preset experimental model of the method further includes:

[0062] An orientation and positioning module is installed on the multi-axis unmanned vehicle. The geometric center point of the multi-axis unmanned vehicle is used as the target point, and the position information of the multi-axis unmanned vehicle is recorded by inertial navigation.

[0063] The steering wheel angle of the multi-axis unmanned vehicle is fixed at a preset angle, and the multi-axis unmanned vehicle is controlled to perform circular motion on a flat road surface to collect point cloud data of the geometric center point of the multi-axis unmanned vehicle.

[0064] In step S130, the equivalent wheelbase and equivalent front wheel angle of the multi-axle unmanned vehicle can be collected and calculated using a preset experimental model.

[0065] In this example embodiment, the method further includes:

[0066] Based on the position information of the multi-axis unmanned vehicle and the point cloud data of the geometric center point of the multi-axis unmanned vehicle, the motion trajectory and trajectory radius of the multi-axis unmanned vehicle are generated after fitting by the RANSAC algorithm.

[0067] In this example embodiment, the method further includes:

[0068] Based on the motion trajectory and trajectory radius of the multi-axis unmanned vehicle, the equivalent wheelbase and equivalent front wheel steering angle of the multi-axis unmanned vehicle under the generalized motion model are calculated.

[0069] In step S140, the steering mode of the multi-axle unmanned vehicle can be equivalent to that of the generalized kinematic model, and a correspondence table of adaptive control parameters for multi-axle steering vehicles, including steering mode, equivalent wheelbase, equivalent front wheel angle and control parameters, can be established.

[0070] In this example embodiment, the control parameters of the multi-axis unmanned vehicle include prediction time-domain parameters, control time-domain parameters, state error weight matrix, and control input weight matrix.

[0071] In this example embodiment, the method further includes:

[0072] Based on the corresponding table of adaptive control parameters for multi-axis steering vehicles, the steering mode and control parameters corresponding to the preset path point are selected to complete the control of the unmanned vehicle in multi-axis steering mode.

[0073] In the embodiments of this example, this disclosure establishes a generalized kinematic model for multi-axis steering vehicles, utilizes equivalent steering relationships to construct the correspondence between turning radius and turning center position under different steering modes, designs an adaptive control algorithm that takes into account different steering modes under this model, realizes the switching of control parameters under different steering modes, and improves the control accuracy of multi-axis steering unmanned vehicles.

[0074] Example 2:

[0075] In this example embodiment, Figure 2A bicycle motion model of the vehicle is presented. Due to its simplicity and ability to handle low-speed conditions, the bicycle motion model is widely used in motion control for autonomous driving. Typically, the position of its steering center is fixed. The front steering wheel is represented by a wheel located at point A, and the rear steering wheel by a wheel located at point B. This model assumes that both front and rear wheels can be steered. When only one front wheel can be steered, the rear wheel steering angle can be set to 0. The distances between the vehicle's turning center and points A and B are additive, and the vehicle's wheelbase is l = l r +l f .

[0076] However, for multi-axle steering vehicles, since the steering center position changes continuously within a certain range with the steering angle, directly limiting the steering mode of multi-axle vehicles and using a bicycle model for equivalence will affect the accuracy of the motion control algorithm.

[0077] The generalized kinematic model for multi-axle steering vehicles is based on the bicycle model. By calculating the equivalent steering center, turning radius, and wheelbase, it is simplified to the corresponding bicycle model. Examples include three-axle front-axle steering vehicles and four-axle dual-front-axle steering vehicles.

[0078] Taking a six-axle, dual-front-axle steering vehicle as an example, ignoring the change in the ground friction coefficient during vehicle operation, a multi-axle vehicle steering model can be established using the Ackermann steering principle to obtain the steering angle of each wheel. For a six-axle, dual-front-axle steering vehicle, a model is established as follows... Figure 3 The Ackermann steering model.

[0079] For ease of intuitive understanding, let's first assume an equivalent steering rear axle as follows: Figure 3 As shown, L1 will be calibrated later. Based on the geometric relationships in the diagram, the relationship between the steering wheel angles can be obtained:

[0080]

[0081] Wheels on the same side have different axles of rotation, and their rotation angles satisfy the following relationship:

[0082]

[0083] Combining the above two equations, we can obtain the relationship between the angles of the steering wheels and the relationship between the first steering wheel angle and the steering wheel angle:

[0084]

[0085] In the formula:

[0086] D – Distance between the landing points of the two main pins;

[0087] δ ir—The rotation angle of the inner wheel of the i-th axle of the car (i = 1, 2, ...);

[0088] δ il —The rotation angle of the outer wheel of the i-th axle of the car (i = 1, 2, ...);

[0089] L i —The distance from the i-th axis to the steering centerline;

[0090] δ sw It's the steering wheel angle, i sw It is the steering ratio, so the steering angle of each steering wheel can be obtained by the steering wheel angle.

[0091] like Figure 4 As shown, point B is the rear axle center of the generalized kinematic model. At this point, it is only necessary to calibrate the equivalent wheelbase L1 and the equivalent front wheel steering angle δ. A Then you can get the corresponding rotation angles of all rotating wheels.

[0092] Using the orientation and positioning module installed on the vehicle, point A is selected as the target point, and its position information is recorded via inertial navigation. The vehicle's steering wheel angle is fixed at a certain angle, and the vehicle is manipulated to perform circular motion on a flat road surface, collecting point cloud data of point A. A trajectory diagram is shown below. Figure 5 As shown.

[0093] The fitted trajectory and radius R are obtained using the RANSAC algorithm. a Given the maximum steering angle δ1 on the inner side of the front wheel, through Figure 6 The geometric relationship shown can be used to obtain L1, which is the position of the turning centerline.

[0094] L1, the position of the turning centerline, can be obtained using the following formula:

[0095]

[0096] This leads to the equivalent wheelbase L1 and equivalent front wheel steering angle δ of a multi-axle steering vehicle under the generalized motion model. A .

[0097] Model predictive control algorithms commonly used for the lateral control of autonomous vehicles mainly involve model parameters and control parameters. The model parameters primarily include the vehicle's wheelbase L and the front wheel steering angle range δ; the control model mainly includes the prediction time-domain parameter N. p Controlling time-domain parameter N c The state error weight matrix Q and the control input weight matrix R.

[0098] After the different steering modes of a multi-axle steering vehicle are equivalent through a generalized kinematic model, a corresponding parameter table can be established according to the steering mode, equivalent wheelbase, and other information. This facilitates the automatic selection of the corresponding model parameters and control parameters for different steering modes when path information is received, as shown in Table 1.

[0099] Table 1. Correspondence of Adaptive Control Parameters for Multi-Axle Steering Vehicles

[0100]

[0101] In the table, the steering mode value represents the type of steering mode corresponding to multi-axle steering vehicles.

[0102] The trajectory information provided by the planning module includes path points of a trajectory segment, the steering mode corresponding to different path points, and the corresponding velocity v and acceleration v. When controlling a multi-axle steering unmanned vehicle, the control algorithm selects the appropriate adaptive parameters for the corresponding steering mode from the multi-axle steering vehicle adaptive control parameter correspondence table based on the trajectory information mode, thereby improving the control accuracy of the multi-axle steering vehicle.

[0103] It should be noted that although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0104] Furthermore, in this example embodiment, a control device for a multi-axis steering mode unmanned vehicle is also provided. (Refer to...) Figure 7 As shown, the control device 200 for a multi-axis steering mode unmanned vehicle may include: a steering model modeling module 210, a steering angle correspondence module 220, an experimental data acquisition module 230, and a parameter correspondence table generation module 240. Wherein:

[0105] Steering model modeling module 210 is used to establish a multi-axis unmanned vehicle steering model based on the Ackermann steering principle;

[0106] The steering angle corresponding module 220 is used to calculate the steering angle relationship of each wheel of the multi-axle unmanned vehicle based on the steering model of the multi-axle unmanned vehicle, and generate the relationship between the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels.

[0107] The experimental data acquisition module 230 is used to acquire and calculate the equivalent wheelbase and equivalent front wheel angle of the multi-axis unmanned vehicle through a preset experimental model.

[0108] The parameter mapping table generation module 240 is used to establish a multi-axis steering vehicle adaptive control parameter mapping table that includes steering mode, equivalent wheelbase, equivalent front wheel angle and control parameters, based on the generalized kinematic model to equivalence the steering mode of the multi-axis unmanned vehicle.

[0109] The specific details of the control device module for each of the above-mentioned multi-axle steering mode unmanned vehicles have been described in detail in the corresponding control method for a multi-axle steering mode unmanned vehicle, so they will not be repeated here.

[0110] It should be noted that although several modules or units of a control device 200 for a multi-axis steering mode unmanned vehicle have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0111] Furthermore, in an exemplary embodiment of this disclosure, an electronic device capable of implementing the above-described method is also provided.

[0112] Those skilled in the art will understand that various aspects of the present invention can be implemented as systems, methods, or program products. Therefore, various aspects of the present invention can be specifically implemented as entirely hardware embodiments, entirely software embodiments (including firmware, microcode, etc.), or embodiments combining hardware and software aspects, collectively referred to herein as “circuit,” “module,” or “system.”

[0113] The following reference Figure 8 To describe an electronic device 500 according to such an embodiment of the present invention. Figure 8 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0114] like Figure 8 As shown, the electronic device 500 is manifested in the form of a general-purpose computing device. The components of the electronic device 500 may include, but are not limited to: at least one processing unit 510, at least one storage unit 520, a bus 530 connecting different system components (including storage unit 520 and processing unit 510), and a display unit 540.

[0115] The storage unit stores program code that can be executed by the processing unit 510, causing the processing unit 510 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 510 can perform actions such as... Figure 1Steps S110 to S140 are shown in the diagram.

[0116] Storage unit 520 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 5201 and / or cache memory 5202, and may further include a read-only memory (ROM) 5203.

[0117] Storage unit 520 may also include a program / utility 5204 having a set (at least one) program module 5205, such program module 5205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0118] Bus 530 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0119] Electronic device 500 can also communicate with one or more external devices 570 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 500, and / or with any device that enables electronic device 500 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 550. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 560. As shown, network adapter 560 communicates with other modules of electronic device 500 via bus 530. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0120] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0121] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section above.

[0122] refer to Figure 9 As shown, a program product 600 for implementing the above-described method according to an embodiment of the present invention is described. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0123] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0124] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0125] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0126] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0127] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0128] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.

[0129] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A control method of a multi-axle steering mode unmanned vehicle, characterized by, The method comprises: According to Ackerman steering principle, a multi-axle unmanned vehicle steering model is established; Based on the multi-axle unmanned vehicle steering model, the steering angle relationship of each wheel of the multi-axle unmanned vehicle is calculated, and a relationship formula of the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels is generated; Through a preset experimental model, the equivalent wheelbase and the equivalent front wheel steering angle of the multi-axle unmanned vehicle are collected and calculated; Based on the universal kinematic model, the steering mode of the multi-axle unmanned vehicle is equivalent, and a multi-axle steering vehicle adaptive control parameter corresponding table containing the steering mode, the equivalent wheelbase, the equivalent front wheel steering angle and the control parameter is established.

2. The method of claim 1, wherein, The method further comprises: According to Ackerman steering principle, a multi-axle unmanned vehicle steering model is established, and an expression of the corresponding steering angle of all rotating wheels of the multi-axle unmanned vehicle is generated.

3. The method of claim 1, wherein, The preset experimental model of the method further comprises: A directional positioning module is installed on the multi-axle unmanned vehicle, the geometric center point of the multi-axle unmanned vehicle is taken as a target point, and the position information of the multi-axle unmanned vehicle is recorded by inertial navigation; The steering wheel angle of the multi-axle unmanned vehicle is fixed at a preset angle, the multi-axle unmanned vehicle is controlled to perform circular motion on a flat road surface, and the point cloud data of the geometric center point of the multi-axle unmanned vehicle is collected.

4. The method of claim 3, wherein, The method further comprises: Based on the position information of the multi-axle unmanned vehicle and the point cloud data of the geometric center point of the multi-axle unmanned vehicle, the motion trajectory and the trajectory radius of the multi-axle unmanned vehicle are generated after being fitted by a RANSAC algorithm.

5. The method of claim 4, wherein, The method further comprises: Based on the motion trajectory and the trajectory radius of the multi-axle unmanned vehicle, the equivalent wheelbase and the equivalent front wheel steering angle of the multi-axle unmanned vehicle under the universal kinematic model are calculated.

6. The method of claim 1, wherein, In the method, the control parameters of the multi-axle unmanned vehicle include prediction time domain parameters, control time domain parameters, state error weight matrix and control input weight matrix.

7. The method of claim 1, wherein, The method further comprises: Based on the multi-axle steering vehicle adaptive control parameter corresponding table, the steering mode and the control parameter corresponding to a preset path point are selected, and the control of the multi-axle steering mode unmanned vehicle is completed.

8. A control device of a multi-axle steering mode unmanned vehicle, characterized by, The device comprises: A steering model modeling module is configured to establish a multi-axle unmanned vehicle steering model according to Ackerman steering principle; A steering angle corresponding module is configured to calculate the steering angle relationship of each wheel of the multi-axle unmanned vehicle based on the multi-axle unmanned vehicle steering model, and generate a relationship formula of the equivalent wheelbase, the equivalent front wheel steering angle and the corresponding steering angle of all rotating wheels; An experimental collection module is configured to collect and calculate the equivalent wheelbase and the equivalent front wheel steering angle of the multi-axle unmanned vehicle through a preset experimental model; A parameter corresponding table generation module is configured to establish a multi-axle steering vehicle adaptive control parameter corresponding table containing the steering mode, the equivalent wheelbase, the equivalent front wheel steering angle and the control parameter based on the universal kinematic model equivalent to the steering mode of the multi-axle unmanned vehicle.

9. An electronic device, comprising: The device comprises A processor; and A memory having computer readable instructions stored thereon, wherein the computer readable instructions are executed by the processor to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, a computer program stored thereon, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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