An unmanned vehicle formation obstacle avoidance and connection maintenance control method, device and medium

Abstract

The present disclosure provides a unmanned vehicle formation obstacle avoidance and connection maintenance control method, device and system, the method comprises establishing the kinematic model of each unmanned vehicle; the relative distance and relative angle between each unmanned vehicle and its pilot, and the distance error and azimuth error are designed; the unmanned vehicle collision avoidance mechanism and the connection maintenance mechanism are designed through the artificial potential field function; according to the defined distance error and azimuth error, the finite time flexible performance function control method is used to define the unmanned vehicle distance error boundary; the error conversion function is used to convert the constrained error into the unconstrained error; the unmanned vehicle controller based on obstacle Lyapunov function is designed; according to the designed controller, the established kinematic model of unmanned vehicle is simulated to determine the system stability. The present application can automatically avoid obstacles and avoid collision with other unmanned vehicles when the unmanned vehicle formation moves to maintain communication connection or encounters obstacles.

Description

Technical Field

[0001] This invention relates to the field of unmanned vehicle control technology, and in particular to a method, device and system for unmanned vehicle formation obstacle avoidance and connection maintenance control. Background Technology

[0002] In recent years, autonomous vehicle (RV) platooning control has attracted increasing attention from scholars, as it can be used to perform various tasks such as environmental monitoring, search and rescue. Common platooning control methods include the leader-follower method, behavior-based methods, and virtual structure methods. Most existing RV platooning control systems operate in environments assuming no obstacles. However, due to the complexity of real-world environments, obstacles are unavoidable. Therefore, considering collision avoidance during platooning is of great practical significance. During movement, each RV must ensure collision avoidance with obstacles and other RVs to guarantee the safety of the overall platooning.

[0003] Existing methods for obstacle avoidance include artificial potential field methods and model prediction methods. Furthermore, since the communication range of the communication equipment installed on autonomous vehicles is limited, the formation may break when the distance between vehicles exceeds the communication range during formation movement. Additionally, due to the requirements of the task or constraints in the formation movement environment, the entire formation movement is required to meet certain performance requirements. The control method commonly used to achieve the desired performance is Preset Performance Control (PPC), which allows the system state to change within a user-defined performance function range, adjusting performance indicators such as convergence rate, overshoot, and steady-state error through an error performance function. However, because the performance boundary of the PPC method is fixed, if the autonomous vehicle formation adapts to environmental constraints during movement, such as when avoiding obstacles, the desired formation error may approach or even exceed the performance function boundary, leading to controller singularity and the inability of the autonomous vehicles to continue the task. Moreover, for some special tasks, it is often desired that the autonomous vehicles can quickly form the desired formation from a random initial state. Existing control methods include finite-time control and fixed-time control, but their design process is relatively cumbersome and complex. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a method, device and system for unmanned vehicle formation obstacle avoidance and connection maintenance control.

[0005] This specification provides one or more embodiments of an unmanned vehicle platooning obstacle avoidance and connectivity maintenance control method, including:

[0006] Establish kinematic models for each unmanned vehicle;

[0007] Design the relative distance and relative angle between each unmanned vehicle and its navigator, as well as the distance error and azimuth error;

[0008] Design collision avoidance and connectivity maintenance mechanisms for autonomous vehicles using artificial potential field functions;

[0009] The distance error boundary of the unmanned vehicle is defined based on the defined distance error and azimuth error, and a finite-time flexible performance function control method is used.

[0010] The constrained error is transformed into an unconstrained error using an error transformation function.

[0011] Design an autonomous vehicle controller based on the obstacle Lyapunov function;

[0012] Based on the designed controller, simulations are performed on the established kinematic model of the unmanned vehicle to determine the system stability.

[0013] Furthermore, the design includes the relative distance and relative angle between each unmanned vehicle and its navigator, as well as the distance error and azimuth error, wherein...

[0014] The relative distance and relative angle between the autonomous vehicle i and its navigator L are as follows:

[0015]

[0016] θ i (t)=atan2(y L -y i x L -x i Formula 1;

[0017] The distance error and azimuth error between the autonomous vehicle i and its navigator L are as follows:

[0018] e di (t)=d i (t)-d *

[0019] e βi (t)=β i (t)-β * Formula 2;

[0020] Where, d i (t) represents the relative distance, θ i (t) represents the relative angle, and atan2 is the arctangent function in the four quadrants; d is the azimuth angle of the autonomous vehicle i relative to its navigator L; * β * These represent the desired distance and azimuth between the navigator L and the target navigator L, respectively.

[0021] Furthermore, the design of the autonomous vehicle collision avoidance mechanism and connection maintenance mechanism through the artificial potential field function specifically includes:

[0022] Collision avoidance mechanisms in unmanned workshops, collision avoidance mechanisms between unmanned vehicles and obstacles, and communication connection maintenance mechanisms in unmanned workshops.

[0023] Furthermore, the collision avoidance mechanism of the unmanned workshop, the collision avoidance mechanism between the unmanned vehicle and obstacles, and the communication connection maintenance mechanism of the unmanned workshop are as follows:

[0024] The artificial potential field function for collision avoidance in unmanned workshops is as follows:

[0025]

[0026]

[0027] In the formula, ||p ij || represents the distance between the i-th autonomous vehicle and the j-th autonomous vehicle, R a To achieve the maximum detection range of the potential field in an unmanned workshop, r a For the limit collision avoidance range of the unmanned factory, when ||p ij || Less than R a The collision avoidance mechanism will be activated when ||p ij || Less than r a There is a risk of collision;

[0028] The artificial potential field function between the autonomous vehicle and the obstacle is selected as follows:

[0029]

[0030] In the formula, ||p ik || represents the distance between the i-th autonomous vehicle and obstacle k, R is the distance between them. o To maximize the detection range of the potential field for collision avoidance between autonomous vehicles and obstacles, r o The range for minimizing collisions between autonomous vehicles and obstacles;

[0031] The artificial potential field function for maintaining the communication connection in the unmanned workshop is selected as follows:

[0032]

[0033] In the formula, when ||p ij || Greater than R m At that time, the potential field function acts to keep it out of the potential field range to ensure the communication connection, R max This represents the maximum communication range for autonomous vehicles.

[0034] Furthermore, the finite-time flexible performance function control method defines the distance error boundary of the unmanned vehicle as follows:

[0035]

[0036]

[0037] E di (t)= e di (t)+σ di，l (t);

[0038] In the formula, E di (t) represents the upper and lower distance error boundaries, σ di，u (t), σ di，l (t) respectively correspond to e di The modified signal of (t), e di (t) is the upper and lower boundary performance function, σ di，u (t), σ di，l (t) is generated by the following auxiliary system:

[0039]

[0040]

[0041] In the formula, a i b i The design parameters are normal values; S i To determine the safety variables related to the potential energy function value when an autonomous vehicle performs collision avoidance and connection holding operations, design a method for handling S... i If S is greater than 0, then i，u =S i Otherwise S i，u =0; S i，l =S i，u -S i .

[0042] Furthermore, it also includes a finite-time performance function that ensures the formation tracking error converges to a predetermined range within a finite time, as shown in the following formula:

[0043]

[0044] in, The initial value of the performance function. For time greater than or equal to T fsteady-state value at time T f To determine the convergence time, at time t = T f When the tracking error converges to the predefined boundary, the tracking error will eventually converge to the boundary.

[0045] Furthermore, it also includes the following steps:

[0046] The constrained distance and azimuth errors are converted into unconstrained distance and azimuth errors using the error transformation function T(·), specifically as follows:

[0047]

[0048]

[0049] Among them, let z di about E di (t), e di The partial derivative of (t) is denoted as P. Edi，u P Edi，l P edi , let z βi about e βi (t), e βi The partial derivative of (t) is denoted as P. eβi，u P eβi，l P eβi .

[0050] Furthermore, the design of the autonomous vehicle controller based on the obstacle Lyapunov function is specifically as follows:

[0051] The barrier Lyapunov function is:

[0052]

[0053] The controller is designed as follows:

[0054]

[0055]

[0056] Where, k i1 k i2 For design parameters, v L , These are the leader's linear velocity and heading angle, respectively.

[0057] By ensuring that the azimuth error constraint always holds and by reasonably selecting the desired azimuth angle, The value is not zero, meaning that the denominator of the relevant part of the controller is not zero, and P exists.eβi ≠0, P edi ≠0.

[0058] This specification provides one or more embodiments of a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the unmanned vehicle platooning obstacle avoidance and connectivity maintenance control method as described above.

[0059] This specification provides one or more embodiments of a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the unmanned vehicle platooning obstacle avoidance and connectivity maintenance control method described above.

[0060] This invention provides a method, device, and system for unmanned vehicle (UAV) formation obstacle avoidance and connection maintenance control. The scheme employs a leader-follower method for formation control. First, a kinematic model of the UAV is established. Then, to achieve collision avoidance and communication connection maintenance among the UAVs, an artificial potential field function is introduced to establish collision avoidance and connection maintenance mechanisms. When a UAV enters the potential field range, the potential field function drives the UAV to leave the potential field range. Furthermore, a finite-time flexible performance function control method is used to set the distance error boundary for the UAVs, enabling the formation tracking error to quickly converge within a pre-set range within a finite time. Simultaneously, a balance is achieved between collision avoidance, communication connection maintenance, and performance requirements during the UAV formation process. This ensures that collision avoidance and communication connection maintenance are achieved throughout the entire UAV formation process while also meeting the performance requirements of the entire formation movement. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in one or more embodiments of this specification or in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 A flowchart of an unmanned vehicle platooning obstacle avoidance and connection maintenance control method provided for one or more embodiments of this specification;

[0063] Figure 2 This is the relative pose diagram of the navigator and follower provided in this embodiment;

[0064] Figure 3 This is the relative pose diagram of the unmanned vehicle and obstacles provided in this embodiment;

[0065] Figure 4 This is a schematic diagram illustrating the maintenance of communication connections in an unmanned workshop, as provided in this embodiment.

[0066] Figure 5 This is a schematic diagram showing the effect comparison between using a standard preset performance function and a finite-time flexible performance function in this embodiment;

[0067] Figure 6 This is a schematic diagram of the structure of a computer provided for one or more embodiments of this specification. Detailed Implementation

[0068] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this invention.

[0069] The present invention will now be described in detail with reference to specific embodiments and accompanying drawings.

[0070] Method Implementation Examples

[0071] According to embodiments of the present invention, a method for obstacle avoidance and connection maintenance control in unmanned vehicle platooning is provided, such as... Figure 1 The diagram shown is a flowchart of the unmanned vehicle platooning obstacle avoidance and connection maintenance control method provided in this embodiment. According to an embodiment of the present invention, an unmanned vehicle platooning obstacle avoidance and connection maintenance control method includes:

[0072] S1. Establish the kinematic models of each unmanned vehicle;

[0073] S2. Design the relative distance and relative angle between each unmanned vehicle and its navigator, as well as the distance error and azimuth error;

[0074] S3. Design collision avoidance and connection maintenance mechanisms for unmanned vehicles using artificial potential field functions;

[0075] S4. Based on the defined distance error and azimuth error, the finite-time flexible performance function control method is used to define the distance error boundary of the unmanned vehicle;

[0076] S5. Use the error transformation function to transform constrained errors into unconstrained errors; that is, transform distance error and azimuth error into unconstrained errors.

[0077] Specifically, the Preset Performance Control (PPC) method is used to convert the previously defined distance and azimuth errors (which are limited to a certain area using the PPC method) into an unconstrained error through an error transformation function, that is, the value ranges from negative infinity to positive infinity.

[0078] S6. Design an unmanned vehicle controller based on obstacle Lyapunov functions;

[0079] S7. Based on the designed controller, perform simulation on the established kinematic model of the unmanned vehicle to determine the system stability.

[0080] This embodiment addresses the issue of unmanned vehicle (UAV) formation movement, enabling automatic obstacle avoidance and collision prevention when maintaining communication connections or encountering obstacles. It provides a UAV formation obstacle avoidance and connection maintenance control method. This design employs a leader-follower approach for formation control. First, a kinematic model of the UAV is established. Then, to achieve collision avoidance and communication connection maintenance, an artificial potential field function is introduced to establish collision avoidance and connection maintenance mechanisms. When a UAV enters the potential field range, the potential field function drives it out of the range. A finite-time flexible performance function control method is used to set the distance error boundary for the UAV, enabling the formation tracking error to converge rapidly within a pre-defined range within a finite time. This achieves a balance between collision avoidance, communication connection maintenance, and performance requirements during UAV formation movement, ensuring both collision avoidance and communication connection maintenance while meeting the overall performance requirements of the formation movement.

[0081] In some embodiments, in step S1, a kinematic model of a single autonomous vehicle is first established, and the kinematic model of the i-th autonomous vehicle can be represented as:

[0082]

[0083]

[0084]

[0085] In the formula, v i Let ω be the linear velocity of the autonomous vehicle. i Angular velocity, This is the heading angle.

[0086] In this embodiment, the relative distance and relative angle between each unmanned vehicle and its navigator are designed, along with distance error and azimuth error, with reference to... Figure 2 As shown, Figure 2The relative pose diagram of the navigator L and the follower i (hereinafter referred to as autonomous vehicle i) provided in this embodiment, wherein,

[0087] The relative distance and relative angle between the autonomous vehicle i and its navigator L are as follows:

[0088]

[0089] θ i (t)=atan2(y L -y i x L -x i Formula 2;

[0090] The distance error and azimuth error between the autonomous vehicle i and its navigator L are as follows:

[0091] e di (t)=d i (t)-d *

[0092] e βi (t)=β i (t)-β * Formula 3;

[0093] Where, d i (t) represents the relative distance, θ i (t) represents the relative angle, and atan2 is the arctangent function in the four quadrants; d is the azimuth angle of the autonomous vehicle i relative to its navigator L; * β * These represent the desired distance and azimuth angle between the navigator L and the target navigator L, respectively. L y L () represents the position of the leader.

[0094] In one embodiment, in order to ensure collision avoidance and communication connection maintenance during the entire unmanned vehicle platooning process, this embodiment uses an artificial potential field function to design an unmanned vehicle collision avoidance mechanism and a connection maintenance mechanism, including a collision avoidance mechanism between unmanned vehicles, a collision avoidance mechanism between unmanned vehicles and obstacles, and a communication connection maintenance mechanism between unmanned vehicles.

[0095] Preferably, the artificial potential field function for collision avoidance in unmanned workshops is as follows:

[0096]

[0097]

[0098] In the formula, ||p ij || represents the distance between the i-th autonomous vehicle and the j-th autonomous vehicle, Ra To achieve the maximum detection range of the potential field in an unmanned workshop, r a For the limit collision avoidance range of the unmanned factory, when ||p ij || Less than R a The collision avoidance mechanism will be activated when ||p ij || Less than r a There is a risk of collision.

[0099] Figure 3 For the relative pose diagram of the unmanned vehicle i and the obstacle k provided in this embodiment, the artificial potential field function between the unmanned vehicle and the obstacle is selected as follows:

[0100]

[0101] In the formula, ||p ik || represents the distance between the i-th autonomous vehicle and obstacle k, R is the distance between them. o To maximize the detection range of the potential field for collision avoidance between autonomous vehicles and obstacles, r o This defines the range for minimizing collisions between autonomous vehicles and obstacles.

[0102] In this embodiment, refer to Figure 3 As shown, through R o (R a R represents the detection range of the potential field function. When the distance between the autonomous vehicle and the obstacle (or other autonomous vehicles) is less than R... o (R a When r is in the range of 0, the potential field function takes effect. o (r a () indicates the collision avoidance range, when the distance between the autonomous vehicle and the obstacle (or other autonomous vehicles) is less than r. o (r a When the value is 0, it indicates that there is a very high probability that the driverless car will collide.

[0103] refer to Figure 4 This is a schematic diagram for maintaining a communication connection. When the distance between driverless vehicles i and j is greater than R... m At that time, the potential field function reduces the distance between unmanned vehicles i and j, keeping them within communication range, R max This represents the maximum communication range between the two unmanned vehicles.

[0104] The artificial potential field function for maintaining the communication connection in the unmanned workshop is selected as follows:

[0105]

[0106] In the formula, when ||p ij || Greater than R m At that time, the potential field function acts to keep it out of the potential field range to ensure the communication connection, R maxTo limit the communication range of autonomous vehicles, it is also necessary to reasonably set the boundary range of each potential field function to ensure that the autonomous vehicle formation can move normally.

[0107] In some embodiments, in the prior art, the standard PPC method is to measure the formation tracking error e i (t) is restricted to variation within a predefined boundary:

[0108]

[0109] in, e i (t) represents the upper and lower boundary performance functions of the formation tracking error, respectively. These are smooth, bounded functions designed by the user that decay positively over time. An error transformation function is then used to convert the constrained error into an unconstrained error for subsequent controller design. However, it's important to note that these boundary performance functions are fixed and cannot change with the error. For example, when considering the autonomous vehicle encountering obstacles during formation movement, the formation tracking error may exceed the above performance boundary limits due to obstacle avoidance. Figure 5 The diagram shown is a comparison of the effects of using a standard preset performance function and a finite-time flexible performance function provided in this embodiment. It can be seen that using the standard preset performance control method will break the performance boundary limit, which will have undesirable consequences for the system.

[0110] Therefore, to address the balance between unmanned vehicle (UAV) platooning collision avoidance or communication connection maintenance and its preset performance, this embodiment adds a non-negative modification signal to the upper and lower boundary terms of the platooning tracking error. This modification signal is related to UAV platooning collision avoidance and communication connection maintenance. When the UAV performs collision avoidance or communication connection maintenance operations, the modification signal can reduce performance limitations and increase the performance function boundary. After the UAV completes the operation, the original performance limitations are restored. In step S4 of this embodiment, the finite-time flexible performance function control method is used to define the UAV distance error boundary as follows:

[0111]

[0112]

[0113] E di (t)= e di (t)+σ di，l (t) Equation 8;

[0114] In the formula, Edi (t) represents the upper and lower distance error boundaries, σ di，u (t), σ di，l (t) respectively correspond to e di The modified signal of (t), e di (t) is the upper and lower boundary performance function, σ di，u (t), σ di，l (t) is generated by the following auxiliary system:

[0115]

[0116]

[0117] In the formula, a i b i The design parameters are normal values; S i This is a safety variable related to the potential energy function value when the autonomous vehicle performs collision avoidance and connection maintenance operations. To ensure that the lower boundary of the error increases when the tracking error is less than 0, and the upper boundary of the error increases when the tracking error is greater than 0, this is designed so that when S... i If S is greater than 0, then i，u =S i Otherwise S i，u =0; S i，l =S i，u -S i Here, N represents the number of follower autonomous vehicles. o The number of obstacles.

[0118]

[0119] In this embodiment, the distance error is first defined using a flexible performance function control method, and then a finite-time performance function is employed (standard performance functions do not have the property of finite-time convergence). By combining these two aspects, a finite-time flexible performance function control method is formed.

[0120] Preferably, in this embodiment, in order to achieve convergence of the formation tracking error to a predetermined range within a finite time, a new finite-time performance function is proposed, as follows:

[0121]

[0122] in, The initial value of the performance function. For time greater than or equal to T f steady-state value at time T f To determine the convergence time, at time t = T fWhen the tracking error converges to the predefined boundary, the tracking error will eventually converge to the boundary.

[0123] Similarly, the azimuth error boundary is defined as

[0124]

[0125] To avoid controller singularity issues, the azimuth error uses a finite-time performance function control method to define the error boundary. That is, the performance function is a finite-time performance function; the azimuth error does not use finite-time flexible performance control, but rather finite-time performance control. Therefore, the upper and lower boundaries of the azimuth error contain only one term: the upper and lower boundary performance functions. Here, the upper and lower boundaries of the error and the upper and lower boundary performance functions are equivalent, meaning the same thing. To avoid confusion in the error transformation function, we uniformly use the upper and lower boundaries of the error to represent them; where, e βi (t) represents the upper and lower boundaries of the azimuth error, respectively.

[0126] in, e di (t)=δ did ρ i , e βi (t)=δ βi，l ρ i Where δ di，u δ di，l δ βi，u δ βi，l These are the design parameters, and these are the normal values. By properly adjusting these parameters, the magnitude of the performance function can be adjusted, and the initial error can be kept within the upper and lower boundaries of the initial error.

[0127] Next, the constrained distance error and azimuth error are converted into unconstrained distance error and azimuth error using the error transformation function T(·).

[0128]

[0129] The error transformation function is selected as follows:

[0130]

[0131] Here z i This represents the unconstrained error after conversion. e i These represent the upper and lower boundaries of the error, respectively.

[0132] Then, the constrained distance and azimuth errors defined above are converted into unconstrained errors, i.e.

[0133]

[0134]

[0135] For ease of expression, let z be... di about E di (t), e di The partial derivative of (t) is denoted as P. Edi，u P Edi，l P edi , let z βi about e βi (t), e βi The partial derivative of (t) is denoted as P. eβi，u P eβi，l P eβi .

[0136] Define the barrier Lyapunov function as:

[0137]

[0138] The controller is designed as follows:

[0139]

[0140]

[0141] Where, k i1 k i2 All design parameters are within normal ranges. L , These represent the leader's linear velocity and heading angle, respectively. Note that the azimuth error has already been constrained, so by ensuring the azimuth error constraint always holds and by appropriately selecting the desired azimuth angle, it can be achieved... The value is not zero, meaning that the denominator of the relevant part of the controller is not zero, and P exists. eβi ≠0, P edi ≠0, meaning the controller singularity problem has been resolved.

[0142] like Figure 6As shown, the present invention also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the unmanned vehicle platooning obstacle avoidance and connectivity maintenance control method described in the above embodiments, or when the computer program is executed by a processor, it implements the unmanned vehicle platooning obstacle avoidance and connectivity maintenance control method described in the above embodiments. When the computer program is executed by the processor, it implements the following method steps:

[0143] S1. Establish the kinematic models of each unmanned vehicle;

[0144] S2. Design the relative distance and relative angle between each unmanned vehicle and its navigator, as well as the distance error and azimuth error;

[0145] S3. Design collision avoidance and connection maintenance mechanisms for unmanned vehicles using artificial potential field functions;

[0146] S4. Based on the defined distance error and azimuth error, the finite-time flexible performance function control method is used to define the distance error boundary of the unmanned vehicle;

[0147] S5. Use the error transformation function to transform constrained errors into unconstrained errors;

[0148] S6. Design an unmanned vehicle controller based on obstacle Lyapunov functions;

[0149] S7. Based on the designed controller, perform simulation on the established kinematic model of the unmanned vehicle to determine the system stability.

[0150] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0151] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and the contents not described in detail in the specification of the present invention are well known to those skilled in the art.

Claims

1. A method for obstacle avoidance and connection maintenance control in unmanned vehicle platooning, characterized in that, include: Establish kinematic models for each unmanned vehicle; Design the relative distance and relative angle between each unmanned vehicle and its navigator, as well as the distance error and azimuth error; Design collision avoidance and connectivity maintenance mechanisms for autonomous vehicles using artificial potential field functions; The distance error boundary of the unmanned vehicle is defined based on the defined distance error and azimuth error, and a finite-time flexible performance function control method is used. The constrained error is transformed into an unconstrained error using an error transformation function. Design an autonomous vehicle controller based on the obstacle Lyapunov function; the specific design of the autonomous vehicle controller based on the obstacle Lyapunov function is as follows: The barrier Lyapunov function is: ； The artificial potential field function for collision avoidance in unmanned workshops. Let be the artificial potential field function between the autonomous vehicle and the obstacle. The artificial potential field function maintained for communication connections in an unmanned workshop; The controller is designed as follows: ； ； in, For design parameters, , These are the leader's linear velocity and heading angle, respectively. For safety variables related to the potential energy function value when an autonomous vehicle performs collision avoidance and connection maintenance operations; 、 They correspond to as Modification signal; These correspond to the upper and lower boundary performance functions, respectively. 、 The design parameters are normal values; when If greater than 0, then ,otherwise ; The desired azimuth angle between the navigator L and the target azimuth. Using the error transformation function T( The constrained distance and azimuth errors are converted into unconstrained distance and azimuth errors, specifically as follows: ； ； Among them, let about , , The partial derivative is denoted as , , ,make about , , The partial derivative is denoted as , , ; autonomous vehicles i The distance error and azimuth error between it and its navigator L; By ensuring that the azimuth error constraint always holds and by reasonably selecting the desired azimuth angle, Non-zero means ensuring that the denominator of the relevant part of the controller is not zero, and that it exists. , ; Based on the designed controller, simulations are performed on the established kinematic model of the unmanned vehicle to determine the system stability; The design specifies the relative distance and angle between each unmanned vehicle and its navigator, as well as the distance error and azimuth error. driverless car i The relative distance and relative angle between it and its navigator L are as follows: ； driverless car i The distance error and azimuth error between it and its navigator L are as follows: ； in, The distance is relative. Relative angle, It is the arctangent function in the four quadrants; For driverless cars i The bearing relative to its navigator L; 、 These represent the desired distance and azimuth between the navigator L and the target navigator L, respectively.

2. The unmanned vehicle platooning obstacle avoidance and connection maintenance control method as described in claim 1, characterized in that, The design of the autonomous vehicle collision avoidance mechanism and connection maintenance mechanism through artificial potential field function specifically includes: Collision avoidance mechanisms in unmanned workshops, collision avoidance mechanisms between unmanned vehicles and obstacles, and communication connection maintenance mechanisms in unmanned workshops.

3. The unmanned vehicle platooning obstacle avoidance and connection maintenance control method as described in claim 2, characterized in that, The collision avoidance mechanism of the unmanned workshop, the collision avoidance mechanism between the unmanned vehicle and obstacles, and the communication connection maintenance mechanism of the unmanned workshop are as follows: The artificial potential field function for collision avoidance in unmanned workshops is as follows: ； ； In the formula, No. i The driverless car and the first j The distance between unmanned workshops This represents the maximum detection range of the potential field in an unmanned workshop. For the extreme collision avoidance range of unmanned workshops, when Less than The collision avoidance mechanism will be activated when... Less than There is a risk of collision; The artificial potential field function between the autonomous vehicle and the obstacle is selected as follows: ； In the formula, For the first i driverless car and obstacles k The distance between, The maximum detection range of the potential field to avoid collisions between autonomous vehicles and obstacles. The range for minimizing collisions between autonomous vehicles and obstacles; The artificial potential field function for maintaining the communication connection in the unmanned workshop is selected as follows: ； In the formula, when Greater than At that time, the potential field function causes the unmanned vehicle to leave the potential field range, ensuring communication connection. This represents the maximum communication range for autonomous vehicles.

4. The unmanned vehicle platooning obstacle avoidance and connection maintenance control method as described in claim 3, characterized in that, The finite-time flexible performance function control method is used to define the distance error boundary of the unmanned vehicle as follows: ； ； ； In the formula, These correspond to the upper and lower distance error boundaries, respectively. 、 They correspond to as Modified signal, These correspond to the upper and lower boundary performance functions, respectively. 、 It is generated by the following auxiliary systems: ； ； ； In the formula, N For the number of follower driverless cars, The number of obstacles, 、 The design parameters are normal values; To avoid collisions and maintain safety variables related to connection retention, design when If greater than 0, then ,otherwise Define the azimuth error boundary as: ； In the formula, 、 These are the upper and lower boundaries of the azimuth error, respectively.

5. The unmanned vehicle platooning obstacle avoidance and connection maintenance control method as described in claim 1, characterized in that, Also includes: The finite-time performance function, under given conditions, ensures that the formation tracking error converges to a predetermined range within a finite time, as shown in the following equation: in, The initial value of the performance function. For time greater than or equal to steady-state value at that time To converge the time, in time When the tracking error converges to the predefined boundary, the tracking error will eventually converge to the boundary.

6. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the unmanned vehicle platooning obstacle avoidance and connection maintenance control method as described in any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the unmanned vehicle platooning obstacle avoidance and connection maintenance control method as described in any one of claims 1 to 5.

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