Intelligent Unmanned Vehicle Formation Reconfiguration Control Method and Device Based on Adaptive Potential Field

Abstract

This invention provides a method and apparatus for intelligent unmanned vehicle (UAV) platooning reconfiguration control based on an adaptive potential field, relating to the field of intelligent UAV platooning control technology. The method includes: acquiring environmental information and the state information of the intelligent UAV platoon; generating a critical adaptive potential field threshold through an adaptive potential field and calculating the desired position of each intelligent UAV in the platoon; generating continuous and bounded desired trajectory information using a fixed-time nonlinear filter; and using an anti-saturation fixed-time sliding mode controller to control each intelligent UAV to track the desired trajectory information within a fixed time period under actuator saturation, thereby achieving intelligent UAV platooning reconfiguration and fixed-time convergence control. This invention is applicable to scenarios such as intelligent transportation, environmental detection, and rescue operations, and can realize real-time platooning reconfiguration, obstacle avoidance, and fixed-time convergence control of intelligent UAV platoons in dynamic environments.

Description

Technical Field

[0001] This invention relates to the field of intelligent unmanned vehicle platooning control technology, and in particular to an intelligent unmanned vehicle platooning reconfiguration control method and device based on an adaptive potential field. Background Technology

[0002] AIV (Autonomous Intelligent Vehicle) formation control is widely used in civilian and military fields due to its efficiency, economy, and scalability. Formation control mainly includes two aspects: formation shape control and formation maneuver control. Formation maneuver control requires multiple AIVs to perform geometric transformations such as translation, rotation, and scaling as a whole to adapt to mission requirements in complex environments, such as obstacle avoidance and passage through narrow passages. Traditional formation control methods (such as displacement, distance, or orientation-based methods) have limitations: displacement-based methods support time-varying translation but struggle to adjust scale or orientation; distance-based methods can achieve translation and rotation but face challenges in scale control; orientation-based methods are suitable for translation and scaling but ineffective for orientation control. Although some research has combined affine transformations with complex Laplacian operators to support multidimensional transformations, these methods suffer from leader redundancy (requiring further research to address this issue). One agent is used for Problems such as limited dimensional control, limited environmental adaptability (due to linear transformation), and high computational load restrict its deployment in real-time obstacle response scenarios.

[0003] Safety Potential Field (SPF) offers a novel approach to formation control due to its environmental adaptability, low computational complexity, and ease of use. However, existing SPF methods are mostly designed for single-vehicle path planning or risk perception, lacking formation-level attributes, which can easily lead to internal collisions or disorder. Furthermore, their environmental adaptability is not fully utilized, making it difficult to achieve real-time flexible formation reconfiguration in dynamic environments. In addition, actuator saturation in the underlying tracking control affects real-time performance, and the convergence time of traditional asymptotic or finite-time control methods depends on the initial state. While fixed-time stability theory can guarantee convergence time independent of the initial state, its anti-saturation capability needs improvement. Therefore, how to construct an AIV formation control method to achieve real-time formation reconfiguration and fixed-time convergence control under dynamic environments and actuator saturation is a core problem that urgently needs to be solved by engineers in this field. Summary of the Invention

[0004] To address the technical challenges of slow real-time performance, environmental adaptability, and convergence speed in existing formation reconfiguration methods for dynamic environments, this invention provides an intelligent unmanned vehicle formation reconfiguration control method and apparatus based on an adaptive potential field. The technical solution is as follows: On the one hand, an intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field is provided. This method is implemented by an intelligent unmanned vehicle formation reconfiguration control device and includes: S1. Obtain environmental information and the state information of the intelligent unmanned vehicle formation, generate a critical adaptive potential field threshold through an adaptive potential field, and calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0005] S2. Design a fixed-time nonlinear filter based on the desired position, and use the fixed-time nonlinear filter to generate continuous and bounded desired trajectory information.

[0006] S3. Design an anti-saturation fixed-time sliding mode controller. The anti-saturation fixed-time sliding mode controller is used to control each intelligent unmanned vehicle to track the desired trajectory information within a fixed time when the actuator is saturated.

[0007] S4. Intelligent unmanned vehicle formation reconstruction and fixed-time convergence control are realized based on adaptive potential field, fixed-time nonlinear filter and anti-saturation fixed-time sliding mode controller.

[0008] Optionally, S1 includes: S11. Obtain environmental information and the status information of the intelligent unmanned vehicle platoon, and establish a 3-DOF intelligent unmanned vehicle model for each intelligent unmanned vehicle in the platoon.

[0009] S12. Based on the intelligent unmanned vehicle model, establish an adaptive potential field in polar coordinates, wherein the adaptive potential field has formation-level attributes and environmental adaptation capabilities.

[0010] S13. Based on the adaptive potential field, set the critical adaptive potential field threshold.

[0011] S14. Calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0012] Alternatively, the adaptive potential field is as shown in equation (1): (1) In the formula, Represents the safety potential field value. This represents the equivalent mass of the formation with the leader as the center. This represents the formation scaling factor. Denotes undetermined coefficients. Indicates formation acceleration. This represents the angle between points surrounding the formation and the formation's center of mass. This represents a pseudo-distance.

[0013] Alternatively, the fixed-time nonlinear filter in S2 is as shown in equation (2) below: (2) In the formula, Indicates the desired speed. , This represents the state of a fixed-time nonlinear filter. Indicates the expected acceleration. Indicates the initial desired position. Indicates the initial reference position. Indicates the initial expected velocity. This indicates the initial reference speed.

[0014] Optionally, the anti-saturation fixed-time sliding mode controller in S3 is as shown in equation (3) below: (3) In the formula, Indicates the control quantity. Represents the control matrix. , Indicates the first Tracking error of intelligent unmanned vehicles , , , Indicates the sliding surface. , , Represents auxiliary state variables. Represent a positive number. Represents the control matrix The largest eigenvalue, It represents the expected acceleration.

[0015] On the other hand, an intelligent unmanned vehicle formation reconfiguration control device based on an adaptive potential field is provided. This device is applied to the intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field. The device includes: The adaptive potential field design module is used to acquire environmental information and the state information of the intelligent unmanned vehicle formation. It generates a critical adaptive potential field threshold through the adaptive potential field and calculates the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0016] The fixed-time nonlinear filter design module is used to design a fixed-time nonlinear filter based on the desired position, and to generate continuous and bounded desired trajectory information using the fixed-time nonlinear filter.

[0017] The anti-saturation fixed-time sliding mode controller design module is used to design an anti-saturation fixed-time sliding mode controller. This controller is used to control each intelligent unmanned vehicle to track the desired trajectory information within a fixed time when the actuator is saturated.

[0018] The output module is used to realize intelligent unmanned vehicle formation reconstruction and fixed-time convergence control based on an adaptive potential field, a fixed-time nonlinear filter, and an anti-saturation fixed-time sliding mode controller.

[0019] Optionally, the adaptive potential field design module is further used for: S11. Obtain environmental information and the status information of the intelligent unmanned vehicle platoon, and establish a 3-DOF intelligent unmanned vehicle model for each intelligent unmanned vehicle in the platoon.

[0020] S12. Based on the intelligent unmanned vehicle model, establish an adaptive potential field in polar coordinates; the adaptive potential field has formation-level attributes and environmental adaptation capabilities.

[0021] S13. Based on the adaptive potential field, set the critical adaptive potential field threshold.

[0022] S14. Calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0023] Alternatively, the adaptive potential field is as shown in equation (1): (1) In the formula, Represents the safety potential field value. This represents the equivalent mass of the formation with the leader as the center. This represents the formation scaling factor. Denotes undetermined coefficients. Indicates formation acceleration. This represents the angle between points surrounding the formation and the formation's center of mass. This represents a pseudo-distance.

[0024] Alternatively, a fixed-time nonlinear filter can be used, as shown in equation (2) below: (2) In the formula, Indicates the desired speed. , This represents the state of a fixed-time nonlinear filter. Indicates the expected acceleration. Indicates the initial desired position. Indicates the initial reference position. Indicates the initial expected velocity. This indicates the initial reference speed.

[0025] Optionally, the anti-saturation fixed-time sliding mode controller is as shown in equation (3): (3) In the formula, Indicates the control quantity. Represents the control matrix. , Indicates the first Tracking error of intelligent unmanned vehicles , , , Indicates the sliding surface. , , Represents auxiliary state variables. Represent a positive number. Represents the control matrix The largest eigenvalue, It represents the expected acceleration.

[0026] On the other hand, an intelligent unmanned vehicle formation reconfiguration control device is provided, the intelligent unmanned vehicle formation reconfiguration control device comprising: a processor; a memory, the memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods in the above-described intelligent unmanned vehicle formation reconfiguration control methods based on adaptive potential fields.

[0027] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored in the storage medium, the at least one instruction being loaded and executed by a processor to implement any of the methods in the above-described intelligent unmanned vehicle formation reconfiguration control method based on adaptive potential field.

[0028] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: This invention proposes a formation reconfiguration control method based on an adaptive potential field. Compared to traditional potential field methods, this method has the following advantages: First, the adaptive potential field possesses formation-level properties and environmental adaptability, achieving real-time obstacle avoidance and flexible reconfiguration through potential field contour deformation, overcoming the shortcomings of traditional potential field methods in formation applications. Second, a continuous and bounded trajectory information is generated through a fixed-time nonlinear filter, bridging the upper-level formation commands and the lower-level tracking control. Finally, the designed anti-saturation fixed-time sliding mode controller solves the actuator saturation problem, ensuring fixed-time convergence and real-time performance. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of an intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field provided by an embodiment of the present invention; Figure 2 This is a flowchart of the formation reconfiguration control based on an adaptive potential field provided in an embodiment of the present invention; Figure 3 This is a simulation diagram of the intelligent unmanned vehicle formation reconfiguration control process based on an adaptive potential field provided in an embodiment of the present invention; Figure 4 This is a block diagram of an intelligent unmanned vehicle formation reconfiguration control device based on an adaptive potential field, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of an intelligent unmanned vehicle formation reconfiguration control device provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0032] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0033] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0034] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0035] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0036] This invention provides an intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field. This method can be implemented by an intelligent unmanned vehicle formation reconfiguration control device, which can be a terminal or a server. Figure 1The flowchart shown is for an intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field. The processing flow of this method may include the following steps: S1. Obtain environmental information and the state information of the intelligent unmanned vehicle formation, generate a critical adaptive potential field threshold through an adaptive potential field, and calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0037] In one feasible implementation, step S1 takes environmental information and formation status information as input and generates potential field contour lines through adaptive potential field to adjust the formation in real time.

[0038] Specifically, step S1 above may include the following steps S11-S14: S11. Obtain environmental information and the status information of the intelligent unmanned vehicle platoon, and establish a 3-DOF intelligent unmanned vehicle model for each intelligent unmanned vehicle in the platoon.

[0039] In one feasible implementation, a 3-DOF AIV model is established, with the following expression: (1) in, Indicates the first Location status of the AIV vehicle. Indicates the first The derivative of the position state of the AIV. Indicates the first The longitudinal position of the AIV Indicates the first The lateral position of the AIV. Indicates the first The attitude angle of the AIV Indicates the first The speed status of the AIV vehicle Indicates the first The longitudinal speed of the AIV Indicates the first The lateral speed of the AIV Indicates the first The yaw rate of the AIV Indicates matrix transpose. Indicates the first The derivative of the speed state of the AIV. The rotation matrix is ​​expressed as: (2) The control matrix is ​​expressed as: (3) in, Indicates the first The quality of the AIV vehicle , and Represents the coefficients related to the state. Indicates wheelbase. Indicates circling The mass inertia matrix of the axis The control variable is expressed as: (4) and These are the upper and lower bounds that the actuator can output, respectively. Indicates the control quantity.

[0040] S12. Based on the intelligent unmanned vehicle model, establish an adaptive potential field in polar coordinates.

[0041] Alternatively, an adaptive potential field can be used, as shown in equation (5): (5) In the formula, Represents the safety potential field value. This represents the equivalent mass of the formation with the leader as the center. This represents the formation scaling factor. and Denotes undetermined coefficients. Indicates formation acceleration. This represents the angle between points surrounding the formation and the formation's center of mass. This represents a pseudo-distance.

[0042] The expression is: (6) Indicates the scaling sensitivity factor. This indicates the nominal size of the formation, that is, the nominal number of AIVs within the formation. This indicates the actual size of the formation, that is, the actual number of AIVs within the formation.

[0043] The expression is: (7) This represents the distance between points surrounding the formation and the formation's center of mass. Indicates speed sensitivity factor, Denotes undetermined coefficients. Indicates formation speed. The environmental constraint coefficient is expressed as follows: (8) Indicates the channel width. This indicates the minimum channel width that the current formation is allowed to pass through. This indicates a sensitivity factor to environmental constraints.

[0044] S13. Based on the adaptive potential field, set the critical adaptive potential field threshold.

[0045] In one feasible implementation, based on step S12, a critical adaptive potential field threshold is set. That is, the contour lines of the potential field.

[0046] S14. Calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0047] In one feasible implementation, the first threshold is calculated. Desired position of AIV in formation The expression is: (9) in, Indicates the first The expected longitudinal position of the AIV Indicates the first The expected lateral position of an AIV. and Indicates the position of the formation's center of mass. Indicates the first The expected distance of a vehicle AIV relative to the center of gravity of the formation. Indicates the first From the perspective of the expected center of gravity of each AIV relative to the formation, Indicates the heading angle of the formation.

[0048] S2. Design a fixed-time nonlinear filter based on the desired position, and use the fixed-time nonlinear filter to generate continuous and bounded desired trajectory information.

[0049] In one feasible implementation, a fixed-time nonlinear filter is designed based on the desired position to generate continuous and bounded desired trajectory information, and then this information is input into the underlying tracking control in step S3.

[0050] The fixed-time nonlinear filter is shown in equation (10) below: (10) In the formula, Indicates the desired speed. , This represents the state of a fixed-time nonlinear filter. Indicates the expected acceleration. Indicates the initial desired position. Indicates the initial reference position. Indicates the initial expected velocity. This indicates the initial reference speed.

[0051] Compared with existing linear filters, the fixed-time nonlinear filter designed in this invention has the following three advantages: fixed-time convergence, continuous and bounded output, and ease of application.

[0052] S3. Design an anti-saturation fixed-time sliding mode controller. The anti-saturation fixed-time sliding mode controller is used to control each intelligent unmanned vehicle to track the desired trajectory information within a fixed time when the actuator is saturated.

[0053] In one feasible implementation, an anti-saturation fixed-time sliding mode controller is designed to ensure that each AIV can quickly and accurately track the desired trajectory generated in step S2 within a fixed time when the actuator is saturated.

[0054] The anti-saturation fixed-time sliding mode controller is shown in equation (11) below: (11) In the formula, Indicates the control quantity. Represents the control matrix. , Indicates the first Tracking error of intelligent unmanned vehicles , , , Indicates the sliding surface. , , Represents auxiliary state variables. Let a positive number satisfy... , This represents the difference in control quantity before and after actuator saturation. Represents the control matrix The largest eigenvalue, Indicates the expected acceleration. Represents a symbolic function. , express The Each element.

[0055] and The expression is: (12) Auxiliary state variables To prevent actuator saturation, the following formula can be used: (13) Sliding surface The expression is: (14) Compared to existing fixed-time controllers, the anti-saturation fixed-time sliding mode controller designed in this invention has the following two advantages: it solves the problem of limited control input and possesses transferability. By employing auxiliary state variables, the control input is limited within the physical saturation boundary. Furthermore, the parameters in the AIV model may differ significantly between different agents. This indicates that the anti-saturation fixed-time sliding mode controller designed in this invention is applicable to both homogeneous and heterogeneous AIV formations, greatly expanding its applicability in practical scenarios.

[0056] S4. Intelligent unmanned vehicle formation reconstruction and fixed-time convergence control are realized based on adaptive potential field, fixed-time nonlinear filter and anti-saturation fixed-time sliding mode controller.

[0057] In one feasible implementation, steps S1 to S3 are iterated based on environmental information and formation state information during the AIV formation process, thereby achieving formation reconstruction and fixed-time convergence control.

[0058] This invention achieves adaptive reconstruction of formation-level attributes and environment through an adaptive potential field. Combined with a fixed-time nonlinear filter and an anti-saturation fixed-time sliding mode controller, it realizes real-time reconstruction and fixed-time convergence control of AIV formations.

[0059] like Figure 2 As shown, this invention first designs an adaptive potential field with formation-level attributes and environmental adaptability. The formation configuration is dynamically adjusted by the potential field strength to achieve obstacle avoidance and passage through narrow passages. Second, a fixed-time nonlinear filter is developed to generate continuous and bounded desired trajectory information (position, velocity, acceleration), connecting upper-level formation commands with lower-level tracking control. Third, an anti-saturation fixed-time sliding mode controller is designed to ensure that each AIV tracks the desired trajectory quickly and accurately within a fixed time. Finally, numerical simulations verify the effectiveness of this method in complex scenarios (such as narrow passages and sharp turns), as shown in the simulation diagram. Figure 3 As shown, Figure 2 In It is the position of the formation leader (that is, the position of the formation's center of mass), which is determined by... and The invention overcomes the shortcomings of traditional potential field methods, such as lack of formation coordination, insufficient environmental adaptability, and dependence of control convergence on the initial state, and significantly improves the flexibility, robustness, and practicality of formation reconstruction.

[0060] This invention proposes an adaptive potential field-based formation reconfiguration control method to overcome problems such as poor environmental adaptability, low real-time performance, and slow convergence speed in formation control. Compared with traditional potential field methods, this method has the following advantages: First, the adaptive potential field possesses formation-level attributes and environmental adaptability, achieving real-time obstacle avoidance and flexible reconfiguration through potential field contour deformation, overcoming the shortcomings of traditional potential field methods in formation applications. Second, a continuous and bounded trajectory information is generated through a fixed-time nonlinear filter, connecting the upper-level formation commands with the lower-level tracking control. Finally, the designed anti-saturation fixed-time sliding mode controller solves the actuator saturation problem, ensuring fixed-time convergence and real-time performance.

[0061] Figure 4 This is a block diagram illustrating an intelligent unmanned vehicle formation reconfiguration control device based on an adaptive potential field, according to an exemplary embodiment. This device is used in an intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field. (Refer to...) Figure 4 The device includes an adaptive potential field design module 310, a fixed-time nonlinear filter design module 320, an anti-saturation fixed-time sliding mode controller design module 330, and an output module 340. Among them: The adaptive potential field design module 310 is used to acquire environmental information and the state information of the intelligent unmanned vehicle formation, generate a critical adaptive potential field threshold through the adaptive potential field, and calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

[0062] The fixed-time nonlinear filter design module 320 is used to design a fixed-time nonlinear filter based on the desired position and to generate continuous and bounded desired trajectory information using the fixed-time nonlinear filter.

[0063] The anti-saturation fixed-time sliding mode controller design module 330 is used to design an anti-saturation fixed-time sliding mode controller. The anti-saturation fixed-time sliding mode controller is used to control each intelligent unmanned vehicle to track the desired trajectory information within a fixed time when the actuator is saturated.

[0064] Output module 340 is used to realize intelligent unmanned vehicle formation reconstruction and fixed-time convergence control based on adaptive potential field, fixed-time nonlinear filter and anti-saturation fixed-time sliding mode controller.

[0065] This invention proposes a formation reconfiguration control method based on an adaptive potential field. Compared to traditional potential field methods, this method has the following advantages: First, the adaptive potential field possesses formation-level attributes and environmental adaptability, achieving real-time obstacle avoidance and flexible reconfiguration through potential field contour deformation, overcoming the shortcomings of traditional potential field methods in formation applications. Second, it generates continuous and bounded trajectory information through a fixed-time nonlinear filter, connecting upper-level formation commands with lower-level tracking control. Finally, the designed anti-saturation fixed-time sliding mode controller solves the actuator saturation problem, ensuring fixed-time convergence and real-time performance.

[0066] Figure 5 This is a schematic diagram of the structure of an intelligent unmanned vehicle formation reconfiguration control device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the intelligent unmanned vehicle platoon reconfiguration control equipment may include the above-mentioned Figure 4 The illustrated intelligent unmanned vehicle formation reconfiguration control device is based on an adaptive potential field. Optionally, the intelligent unmanned vehicle formation reconfiguration control device 410 may include a first processor 2001.

[0067] Optionally, the intelligent unmanned vehicle platoon reconfiguration control device 410 may also include a memory 2002 and a transceiver 2003.

[0068] The first processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0069] The following is combined Figure 5 A detailed introduction to each component of the intelligent unmanned vehicle platoon reconfiguration control device 410 is provided below: The first processor 2001 is the control center of the intelligent unmanned vehicle formation reconfiguration control device 410. It can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0070] Optionally, the first processor 2001 can execute various functions of the intelligent unmanned vehicle formation reconfiguration control device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0071] In a specific implementation, as one example, the first processor 2001 may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 are shown in the diagram.

[0072] In a specific implementation, as one example, the intelligent unmanned vehicle platoon reconfiguration control device 410 may also include multiple processors, such as... Figure 5 The first processor 2001 and the second processor 2004 are shown in the diagram. Each of these processors can be a single-core processor or a multi-core processor. Here, a processor can refer to one or more devices, circuits, and / or processing cores used to process data (such as computer program instructions).

[0073] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the first processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0074] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or may exist independently and be connected to the interface circuit of the intelligent unmanned vehicle platooning reconfiguration control device 410. Figure 5 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0075] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0076] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 5 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0077] Optionally, the transceiver 2003 can be integrated with the first processor 2001, or it can exist independently and be connected to the interface circuit of the intelligent unmanned vehicle platooning reconfiguration control device 410. Figure 5 (Not shown in the image) is coupled to the first processor 2001, and this embodiment of the invention does not specifically limit this.

[0078] It should be noted that, Figure 5 The structure of the intelligent unmanned vehicle formation reconfiguration control device 410 shown does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0079] Furthermore, the technical effects of the intelligent unmanned vehicle formation reconfiguration control device 410 can be referenced from the technical effects of the intelligent unmanned vehicle formation reconfiguration control method based on adaptive potential field described in the above method embodiments, and will not be repeated here.

[0080] It should be understood that the first processor 2001 in the embodiments of the present invention may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0081] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0082] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0083] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0084] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0085] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0086] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0087] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0088] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0089] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0090] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0091] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0092] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent unmanned vehicle formation reconfiguration control based on adaptive potential field, characterized in that, The method includes: S1. Obtain environmental information and the status information of the intelligent unmanned vehicle formation, set the critical adaptive potential field threshold through the adaptive potential field, and calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold. S2. Design a fixed-time nonlinear filter based on the desired position, and use the fixed-time nonlinear filter to generate continuous and bounded desired trajectory information; S3. Design an anti-saturation fixed-time sliding mode controller. The anti-saturation fixed-time sliding mode controller is used to control each intelligent unmanned vehicle to track the desired trajectory information within a fixed time when the actuator is saturated. S4. Intelligent unmanned vehicle formation reconstruction and fixed-time convergence control are realized based on adaptive potential field, fixed-time nonlinear filter and anti-saturation fixed-time sliding mode controller; The adaptive potential field is shown in equation (5) below: （5） In the formula, Represents the safety potential field value. This represents the equivalent mass of the formation with the leader as the center. This represents the formation scaling factor. and Denotes undetermined coefficients. Indicates formation acceleration. This represents the angle between points surrounding the formation and the formation's center of mass. Indicates pseudo-distance; The expected position of each intelligent unmanned vehicle in the formation is calculated based on the critical adaptive potential field threshold, expressed as follows: （9） In the formula, Indicates the first The desired longitudinal position of an intelligent unmanned vehicle. Indicates the first The expected lateral position of an intelligent unmanned vehicle. and Indicates the position of the formation's center of mass. Indicates the first The expected distance of each intelligent unmanned vehicle relative to the center of mass of the formation. Indicates the first From the expected perspective of the center of mass of an intelligent unmanned vehicle relative to the formation, Indicates the heading angle of the formation.

2. The intelligent unmanned vehicle formation reconfiguration control method based on adaptive potential field according to claim 1, characterized in that, S1 includes: S11. Obtain environmental information and the status information of the intelligent unmanned vehicle platoon, and establish a 3-DOF intelligent unmanned vehicle model for each intelligent unmanned vehicle in the platoon. S12. Based on the intelligent unmanned vehicle model, establish an adaptive potential field in polar coordinates, wherein the adaptive potential field has formation-level attributes and environmental adaptation capabilities. S13. Based on the adaptive potential field, set the critical adaptive potential field threshold; S14. Calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

3. The intelligent unmanned vehicle formation reconfiguration control method based on adaptive potential field according to claim 1, characterized in that, The fixed-time nonlinear filter in S2 is shown in equation (2) below: （2） In the formula, Indicates the desired speed. , This represents the state of a fixed-time nonlinear filter. Indicates the expected acceleration. Indicates the initial desired position. Indicates the initial reference position. Indicates the initial expected velocity. This indicates the initial reference speed.

4. The intelligent unmanned vehicle formation reconfiguration control method based on adaptive potential field according to claim 1, characterized in that, The anti-saturation fixed-time sliding mode controller in S3 is shown in the following equation (3): （3） In the formula, Indicates the control quantity. Represents the control matrix. , Indicates the first Tracking error of intelligent unmanned vehicles , , , Indicates the sliding surface. , , Represents auxiliary state variables. To represent a positive number, Represents the control matrix The largest eigenvalue, It represents the expected acceleration.

5. An intelligent unmanned vehicle formation reconfiguration control device based on an adaptive potential field, wherein the intelligent unmanned vehicle formation reconfiguration control device based on an adaptive potential field is used to implement the intelligent unmanned vehicle formation reconfiguration control method based on an adaptive potential field as described in any one of claims 1-4, characterized in that, The device includes: The adaptive potential field design module is used to acquire environmental information and the state information of the intelligent unmanned vehicle formation. It sets a critical adaptive potential field threshold through the adaptive potential field and calculates the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold. The fixed-time nonlinear filter design module is used to design a fixed-time nonlinear filter based on the desired position and to generate continuous and bounded desired trajectory information using the fixed-time nonlinear filter. The anti-saturation fixed-time sliding mode controller design module is used to design an anti-saturation fixed-time sliding mode controller, which controls each intelligent unmanned vehicle to track the desired trajectory information within a fixed time when the actuator is saturated. The output module is used to realize intelligent unmanned vehicle formation reconstruction and fixed-time convergence control based on an adaptive potential field, a fixed-time nonlinear filter, and an anti-saturation fixed-time sliding mode controller. The adaptive potential field is shown in equation (5) below: （5） In the formula, Represents the safety potential field value. This represents the equivalent mass of the formation with the leader as the center. This represents the formation scaling factor. and Denotes undetermined coefficients. Indicates formation acceleration. This represents the angle between points surrounding the formation and the formation's center of mass. Indicates pseudo-distance; The expected position of each intelligent unmanned vehicle in the formation is calculated based on the critical adaptive potential field threshold, expressed as follows: （9） In the formula, Indicates the first The desired longitudinal position of an intelligent unmanned vehicle. Indicates the first The expected lateral position of an intelligent unmanned vehicle. and Indicates the position of the formation's center of mass. Indicates the first The expected distance of each intelligent unmanned vehicle relative to the center of mass of the formation. Indicates the first From the expected perspective of the center of mass of an intelligent unmanned vehicle relative to the formation, Indicates the heading angle of the formation.

6. The intelligent unmanned vehicle formation reconfiguration control device based on adaptive potential field according to claim 5, characterized in that, The acquisition of environmental information and the state information of the intelligent unmanned vehicle platoon, setting a critical adaptive potential field threshold through an adaptive potential field, and calculating the expected position of each intelligent unmanned vehicle in the platoon based on the critical adaptive potential field threshold includes: S11. Obtain environmental information and the status information of the intelligent unmanned vehicle platoon, and establish a 3-DOF intelligent unmanned vehicle model for each intelligent unmanned vehicle in the platoon. S12. Based on the intelligent unmanned vehicle model, establish an adaptive potential field in polar coordinates, wherein the adaptive potential field has formation-level attributes and environmental adaptation capabilities. S13. Based on the adaptive potential field, set the critical adaptive potential field threshold; S14. Calculate the expected position of each intelligent unmanned vehicle in the formation based on the critical adaptive potential field threshold.

7. An intelligent unmanned vehicle platoon reconfiguration control device, characterized in that, The intelligent unmanned vehicle platoon reconfiguration control equipment includes: processor; A memory storing computer-readable instructions that, when executed by the processor, implement the method as described in any one of claims 1 to 4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains program code that can be invoked by a processor to execute the method as described in any one of claims 1 to 4.

Images