Anti-swing control method, device and equipment for unmanned vehicle and medium

Abstract

The invention provides an anti-swing control method, device, equipment and medium for an unmanned travelling crane, and the anti-swing control method comprises the steps: obtaining the operation parameters of the travelling crane in real time, the operation parameters comprising the rope length between the travelling crane and a load; fitting a rope length function according to the rope length of the operation parameters; constructing a swing periodic function according to the rope length function; solving the swing period function, and calculating the swing period of the load; the acceleration and deceleration duration of driving is calculated according to the swing period; and according to the operation parameters and the acceleration and deceleration duration, driving is controlled. Through the anti-swing control method, device and equipment for the unmanned vehicle and the medium provided by the invention, the swing of the load can be eliminated.

Description

Technical Field

[0001] The present invention relates to the industrial field, and in particular to an anti-sway control method, device, equipment and medium for an unmanned vehicle. Background Art

[0002] The crane is a key piece of equipment for material handling within the warehouse. It achieves precise positioning through the three-dimensional coordinated movement of the large truck, the trolley, and the load. The crane utilizes a flexible steel wire sling to connect to the load, effectively absorbing the impact energy during acceleration and braking while transmitting driving force, enhancing stability during transportation. This structural feature makes it a crucial support tool for efficient warehouse operations.

[0003] However, the low damping characteristics of the flexible connection lead to inertial oscillation of the load when the trolley and carriage change speeds. This uncontrolled oscillation not only increases wear on the mechanical structure but also poses a safety hazard of collision between the load and surrounding equipment. Traditional oscillation suppression relies on manual restarting and adjusting the equipment's motion state. This is not only complex but also significantly prolongs the operation cycle, limiting the overall operational efficiency of the depot. Therefore, there is room for improvement. Summary of the Invention

[0004] The object of the present invention is to provide an anti-sway control method, device, equipment and medium for an unmanned vehicle, which can eliminate the swing of the load.

[0005] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention provides an anti-sway control method for an unmanned vehicle, comprising:

[0007] Acquiring the operating parameters of the crane in real time, including the rope length between the crane and the load;

[0008] fitting a rope length function according to the rope length of the operating parameters;

[0009] constructing a swing period function according to the rope length function;

[0010] Solving the swing period function to calculate the swing period of the load;

[0011] Calculating the acceleration and deceleration time of the vehicle according to the swing period;

[0012] Driving is controlled according to the operating parameters and the acceleration / deceleration time.

[0013] In one embodiment of the present invention, the swing period function is expressed as: The rope length function is expressed as: Where a is the acceleration of the rope length change, l0 is the initial rope length at time t0, n is the number of swing cycles, g is the acceleration due to gravity, and t is time.

[0014] In one embodiment of the present invention, the step of calculating the acceleration and deceleration duration of the vehicle according to the swing period includes:

[0015] Based on a preset swing model, the acceleration and deceleration duration is calculated according to the swing period; the swing model represents the functional relationship between the acceleration and deceleration duration and the swing period.

[0016] In one embodiment of the present invention, the step of controlling the driving of the vehicle according to the operating parameters and the acceleration / deceleration duration includes:

[0017] Calculating a target speed of the vehicle based on the acceleration and running speed of the vehicle in the operating parameters, and the acceleration and deceleration time;

[0018] Driving is controlled according to the target speed and the acceleration / deceleration time.

[0019] In one embodiment of the present invention, the step of controlling the vehicle according to the target speed and the acceleration / deceleration duration includes:

[0020] Calculating the displacement deviation and the corresponding deviation change rate according to the running speed, the acceleration, and the acceleration / deceleration time;

[0021] Calculating overshoot risk data based on the displacement deviation and the corresponding deviation change rate using a pre-trained risk assessment model; the risk assessment model is a deep neural network model;

[0022] The driving is controlled according to a comparison result of the overshoot risk data and a corresponding risk threshold.

[0023] In one embodiment of the present invention, the step of calculating the displacement deviation and the corresponding deviation change rate based on the running speed, the acceleration, and the acceleration / deceleration time includes:

[0024] Calculating the corresponding target displacement of the vehicle according to the running speed, the acceleration, and the acceleration / deceleration time;

[0025] Calculating a displacement deviation between a displacement of the vehicle and a corresponding target displacement in the operating parameters;

[0026] According to the displacement deviation, a corresponding deviation change rate is calculated.

[0027] In one embodiment of the present invention, the step of controlling the driving of the vehicle according to the comparison result of the overshoot risk data and the corresponding risk threshold comprises:

[0028] Determine the overshoot risk data and the corresponding risk threshold:

[0029] When the overshoot risk data is less than a risk threshold, controlling the driving of the vehicle according to the target speed and the acceleration / deceleration duration;

[0030] Otherwise, a feedback value is calculated according to the acceleration by a pre-trained dynamic compensation model; the dynamic compensation model is a deep neural network model;

[0031] adjusting the target speed according to the feedback value;

[0032] The driving is controlled according to the adjusted target speed and the acceleration / deceleration time.

[0033] The present invention also discloses an anti-sway control device for an unmanned vehicle, comprising:

[0034] A data acquisition module, for acquiring operating parameters of the crane in real time, the operating parameters including the rope length between the crane and the load;

[0035] A function fitting module, used for fitting a rope length function according to the rope length of the operating parameter;

[0036] A function construction module, used for constructing a swing period function according to the rope length function;

[0037] A period calculation module, used to solve the swing period function and calculate the swing period of the load;

[0038] A duration calculation module, used to calculate the acceleration and deceleration duration of driving according to the swing period;

[0039] The speed control module is used to control the driving of the vehicle according to the operating parameters and the acceleration and deceleration time.

[0040] The present invention also discloses an electronic device, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the computer program, the steps of the anti-sway control method for the unmanned vehicle are implemented.

[0041] The present invention also discloses a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the anti-sway control method for the unmanned vehicle are implemented.

[0042] As described above, the present invention provides an anti-sway control method, device, equipment, and medium for unmanned vehicles. Through intelligent control, these methods effectively address the inefficiencies and safety hazards associated with load inertial swing in traditional vehicle operations. By actively suppressing load swing during the speed change phases of the trolley and carriage, the load quickly returns to zero and stops during uniform motion, eliminating the impact and wear on the mechanical structure caused by uncontrolled swing. Furthermore, by adaptively adjusting the acceleration strategy to replace the traditional repetitive start-stop mode, the operating cycle is significantly shortened.

[0043] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a flow chart of an anti-sway control method for an unmanned vehicle according to one embodiment of the present invention;

[0046] Figure 2 Schematic diagram of the swing angle and angular velocity in one embodiment of the present invention;

[0047] Figure 3 A schematic diagram of an anti-sway control device for an unmanned vehicle according to an embodiment of the present invention;

[0048] Figure 4 FIG. 1 is a schematic diagram of an electronic device according to an embodiment of the present invention.

[0049] In the figure: 100, data acquisition module; 200, function fitting module; 300, function construction module; 400, cycle calculation module; 500, duration calculation module; 600, speed control module; 10, electronic device; 11, memory; 12, processor. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] See also Figure 1The present invention discloses an anti-sway control method for an unmanned vehicle, which can be applied to an unmanned vehicle automatic operation system. When the unmanned vehicle automatic operation system transports steel coils in the storage area, due to the flexible connection between the load and the vehicle (such as a wire rope), the load will produce inertial swing due to the start and stop, acceleration and deceleration of the trolley / car. Swinging can lead to inaccurate load positioning, reduced operating efficiency, and even cause collision accidents. Traditional manual operation relies on repeated start and stop to eliminate swing, which is inefficient and relies on experience. Through automated control, the swing can be quickly suppressed, and the operating efficiency and control accuracy of the unmanned vehicle can be improved. The anti-sway control method may include: step S10, real-time acquisition of the operating parameters of the vehicle, the operating parameters including the rope length between the vehicle and the load, the acceleration of the vehicle, the operating speed of the vehicle, the displacement of the vehicle, etc.

[0052] In some embodiments, the operating parameters of the crane are a set of dynamic data used to describe the operating status of the crane system in real time. The operating parameters may include the real-time positions of the trolley, carriage, and load; the operating speeds of the trolley, carriage, and load; the accelerations of the trolley and carriage; the load's swing angle; the load's angular velocity; the length of the rope between the trolley and the load; and the like. The trolley can move horizontally along the track, and the carriage can move laterally along the trolley. The load's swing angle refers to the deflection angle of the rope relative to the vertical direction, and the angular velocity of the swing refers to the rate of change of the swing angle.

[0053] In some embodiments, the absolute or relative positions of the trolley and carriage on the track can be detected using positioning equipment (such as Gray lines, encoders, laser rangefinders, etc.) to obtain the real-time positions of the trolley, carriage, and load. The length of the rope between the trolley and the load can be measured using an encoder or laser rangefinder on the lifting mechanism. The swing angle of the load can be measured using a camera and a light source (reflector).

[0054] In some embodiments, the anti-sway control method may further include: step S20, fitting a rope length function according to the rope length of the operating parameter.

[0055] In some embodiments, the acceleration of the trolley and the trolley can be a constant value or can vary with time, which is not limited here. After obtaining the length of the rope, the rope length can be fitted. Assuming that the rope is contracted or lengthened with uniform acceleration, the rope length can be fitted to obtain a rope length function, which is expressed as: Where a represents the acceleration of the rope length change, and l0 represents the initial rope length at time t0.

[0056] In some embodiments, the anti-sway control method may further include: step S30, constructing a swing period function according to the rope length function.

[0057] In some embodiments, the operation of the vehicle is divided into the operation of the trolley and the carriage. The trolley moves along the ground track (the direction of movement of the trolley can be defined as the X-axis), and the trolley moves along the direction of the trolley track (the direction of movement of the trolley can be defined as the Y-axis). The lifting direction of the load can be defined as the Z-axis. There is a linkage relationship between the movement of the trolley (X-axis) and the load (Z-axis), which is mainly affected by the physical relationship between centripetal force and tangential velocity; there is a composite effect between the movement of the trolley (Y-axis) and the load (Z-axis), which jointly affect the lifting trajectory; when the trolley runs at a constant speed and the rope length changes, the change in rope length will cause the lifting trajectory to change in a spiral line. Among them, when the angular velocity of the load is 0, the system reaches a state of equilibrium. During operation, the graphical method cannot be directly applied to the solution of the actual control process. At this time, a numerical solution method is used to calculate the system stability point during the operation of the unmanned vehicle automatic operation system.

[0058] In some embodiments, a vehicle operation state model can be used to describe the dynamic characteristics of the vehicle-load system. The vehicle operation state model can be a high-order nonlinear differential equation. Due to the nonlinearity of the unmanned vehicle's automatic operation system (such as the centripetal force and rope length changes in circular motion) and the limitations of graphical methods, numerical methods (such as the Runge-Kutta method) are required to solve for the stable point. The vehicle operation state model can represent the functional relationship between the swing angle, angular velocity, rope length, and displacement.

[0059] In some embodiments, since the X- and Z-axis anti-sway control (for the trolley and load) is relatively independent from the Y- and Z-axis anti-sway control (for the trolley and load), they can be analyzed separately. To simplify the system analysis, the following assumptions are made: the suspension rope is rigid (no stretching), air resistance and wind forces are negligible, suspension rope damping is not considered, the load is abstracted as a point mass (ignoring shape and mass distribution), and the trolley and trolley motions are independent.

[0060] In some embodiments, the vehicle operation state model can be expressed as: Where θ represents the swing angle (including the swing angle of the load relative to the large vehicle and the swing angle of the load relative to the small vehicle); It is expressed as the first derivative of θ, that is, the angular velocity (including the angular velocity of the load relative to the trolley and the angular velocity of the load relative to the trolley); l(t) is the length of the rope; Expressed as the first derivative of l(t), that is, the rate of change of the rope length; It is expressed as the second-order derivative of θ; x represents the displacement of the vehicle (including the displacement of the large vehicle and the small vehicle), and the displacement can be calculated from the real-time position of the large vehicle and the small vehicle; It is expressed as the second-order derivative of x, that is, the acceleration of the big car and the acceleration of the small car; g is expressed as the acceleration due to gravity.

[0061] In some embodiments, in the vehicle operation state model, θ, is the unknown quantity, l(t), As a parameter variable, the vehicle operation state model can be regarded as a second-order differential equation with variable parameters θ. Assuming θ0≠0, l0≠0, Draw the numerical graph of the second-order differential equation solution graph as follows Figure 2 As shown, Figure 2 The horizontal axis is the angular velocity, and the vertical axis is the swing angle. Figure 2 As can be seen in the figure, as the rope length changes, the trajectory of the trolley and carriage changes in a spiral pattern during uniform speed operation. The image also shows that the angular velocity at the intersection of the image and the Y-axis is zero, indicating that the system has reached equilibrium. Graphical methods cannot be directly applied to solving the actual control process during operation. In this case, numerical solutions can be used to determine the stable point.

[0062] In some embodiments, when the rope length does not change, the crane operation state model can be solved to calculate the swing angle and angular velocity change functions, which can be expressed as: Where θ represents the swing angle, represents the angular velocity, represents the acceleration of the vehicle, g represents the acceleration due to gravity, l represents the rope length function, and t represents time. The swing angle and angular velocity can be periodic functions. In this case, the swing period function can be constructed based on the rope length function, which is expressed as:

[0063] In some embodiments, the anti-sway control method may further include: step S40, solving the swing period function to calculate the swing period of the load.

[0064] In some embodiments, in the preset state, that is, t=0, θ0=0, The driving state model can be solved. are all functions of time t, and the pendulum angle θ and angular velocity They are all periodic functions, so the oscillation periodic function can be approximately solved as follows: Where n represents the number of swing cycles. When the rope length remains unchanged, a=0, t0=0, at0=0, that is, the rope length does not change, and the swing period function can be simplified to The swing period function is consistent with the swing period of the swing angle, which can verify the correctness of the swing period function. Among them, when n=1, the swing period of the load can be calculated.

[0065] In some embodiments, the anti-sway control method may further include: step S50, calculating the acceleration and deceleration duration of the vehicle according to the swing period. Step S50 may include the following steps: calculating the acceleration and deceleration duration according to the swing period based on a preset swing model; the swing model represents the functional relationship between the acceleration and deceleration duration and the swing period.

[0066] In some embodiments, the example of a vehicle with variable speed is used to illustrate that the vehicle is uniformly accelerated at a constant acceleration, driving the load to swing. The load swing process can be divided into an initial stage, an acceleration stage, a deceleration stage, and a swing back stage. In the initial stage, the load moves from the initial position (swing angle θ = 0, angular velocity ) begins to swing toward the equilibrium angle; in the acceleration stage, the swing angle and angular velocity gradually increase until the equilibrium angle is reached, the angular velocity reaches the maximum value, but the swing angle does not reach the maximum value; in the deceleration stage, due to the angular velocity The swing angle continues to increase, but under the influence of the system's combined force (such as gravity and centripetal force), the angular velocity gradually decreases until the angular velocity At this time, the swing angle θ reaches its maximum value; in the swing-back stage, the load swings back to the initial position, and the process is symmetrical with the acceleration stage until the swing angle and angular velocity return to zero.

[0067] In some embodiments, the swing period T is determined by system characteristics (such as rope length l and gravity acceleration g) and is given by: When the speed change time t matches T, the swing energy is completely absorbed and the load returns to a stable state. That is, when the acceleration or deceleration time t of the trolley is an integer multiple of the swing period T (i.e. t = nT, n is a positive integer), the swing angle θ and the angular velocity Can return to zero. When the swing angle θ and angular velocity When the load returns to zero, if the trolley is moving at a constant speed, the load is no longer disturbed by acceleration, and sway is effectively suppressed. By controlling the trolley's acceleration and deceleration time t to an integer multiple of the sway period T, sway-free switching can be achieved, improving operating efficiency and safety.

[0068] In some embodiments, the anti-sway control method may further include: step S60, controlling the vehicle according to the operating parameters and the acceleration / deceleration duration. Step S60 may include the following steps: step S61, calculating the target speed of the vehicle according to the operating parameters, namely, the acceleration of the vehicle, the running speed of the vehicle, and the acceleration / deceleration duration.

[0069] In some embodiments, after the acceleration and deceleration time is calculated, the target speed of the vehicle can be calculated by obtaining the running speed and the acceleration and deceleration time. t It can be expressed as: t =v0+a m ×t, v0 can be expressed as the running speed, a mIt can be expressed as the acceleration of the vehicle, and t can be expressed as the acceleration and deceleration time.

[0070] In some embodiments, step S60 may further include the following steps: step S62, controlling the driving according to the target speed and the acceleration / deceleration time.

[0071] In some embodiments, after the target speed is calculated, open-loop control can be implemented based on the target speed and acceleration / deceleration durations. Open-loop control involves pre-setting control variables (acceleration / deceleration durations, preset target speed curves) without relying on real-time feedback. To improve control progress, feedback control can be introduced on top of the open-loop control to collect vehicle displacement in real time and dynamically adjust the control variables.

[0072] In some embodiments, step S62 may include the following steps: calculating the displacement deviation and the corresponding deviation change rate based on the running speed, acceleration, and acceleration / deceleration time. The above steps may specifically include:

[0073] Calculate the corresponding target displacement of the vehicle based on the running speed, acceleration, and acceleration / deceleration time;

[0074] Calculate the displacement deviation between the vehicle displacement and the corresponding target displacement in the operating parameters;

[0075] According to the displacement deviation, the corresponding deviation change rate is calculated.

[0076] In some embodiments, after controlling the vehicle, a target displacement of the vehicle can be calculated based on the vehicle's initial position, operating speed, acceleration, and acceleration / deceleration duration. For example, the acceleration / deceleration duration can be 20 seconds, and a time node can be set every 1 second. For example, the first time node can be 1 second after the vehicle accelerates / decelerates, and the second time node can be 2 seconds after the vehicle accelerates / decelerates. Of course, the specific time interval length is not limited. The target displacement of the vehicle equivalent to the initial position can be calculated at each preset time node.

[0077] In some embodiments, after controlling the vehicle, the displacement of the vehicle relative to the initial position can be calculated using the vehicle's real-time position and the vehicle's initial position. Subsequently, the displacement deviation between the displacement and the corresponding target displacement can be calculated at the same time point.

[0078] In some embodiments, after the displacement deviation is calculated, the displacement deviation and time can be fitted to obtain a corresponding fitting function. Subsequently, the first-order derivative of the displacement deviation can be solved to calculate the corresponding deviation change rate.

[0079] In some embodiments, step S62 may further include the following steps: calculating overshoot risk data using a pre-trained risk assessment model based on the displacement deviation and the corresponding deviation change rate. The risk assessment model may be a DNN deep neural network model. ff The calculation formula of (t) can be expressed as: X(t)=[e(t),Δe(t),yref(t)], Among them, yref(t) can be expressed as displacement, e(t) can be expressed as displacement deviation, Δe(t) can be expressed as deviation change rate, X(t) can be expressed as input vector, ω j It can be expressed as the weight value of the j-th output layer, It can be expressed as the j-th Gaussian activation function of the hidden layer, C j It can be expressed as ω j The center of b j It can be expressed as the basis width, and m can be expressed as the number of output layer weight values.

[0080] In some embodiments, step S62 may further include the following steps: controlling the vehicle according to the comparison result of the overshoot risk data with the corresponding risk threshold. The above steps may specifically include:

[0081] Determine overshoot risk data and corresponding risk thresholds:

[0082] When the overshoot risk data is less than the risk threshold, the vehicle is controlled according to the target speed and the acceleration and deceleration time;

[0083] Otherwise, the feedback value is calculated based on the acceleration through the pre-trained dynamic compensation model;

[0084] Adjust the target speed according to the feedback value;

[0085] Driving is controlled according to the adjusted target speed and acceleration / deceleration time.

[0086] In some embodiments, the specific size of the risk threshold may not be limited, for example, it may be 0.4 / 0.5 / 0.6, etc.

[0087] In some embodiments, when the overshoot risk data is less than the risk threshold, it can be considered that the driving is accelerated or decelerated according to the target speed and acceleration or deceleration time. At this time, there is no need to adjust the target speed, and the driving can continue to be controlled according to the target speed and acceleration or deceleration time.

[0088] In some embodiments, when the overshoot risk data is greater than or equal to the risk threshold, it can be considered that the vehicle is not accelerating or decelerating according to the target speed and acceleration / deceleration time. At this time, the target speed needs to be adjusted. At this time, the feedback value can be calculated by a pre-trained dynamic compensation model. The dynamic compensation model can be a DNN deep neural network model. Feedback value The calculation formula can be expressed as: Among them, V dnn It can be expressed as the weight matrix of the hidden layer of the DNN neural network, W dnn It is represented as the weight matrix of the output layer of the DNN neural network. It can be expressed as the activation function of the DNN neural network, x hist (t) can be expressed as a time series vector. hist (t) = [u(t-1), x(t-1), ..., u(tn), x(tn)], where u represents the acceleration of the vehicle, t represents the duration of acceleration and deceleration, and tn represents the corresponding time point.

[0089] In some embodiments, after the feedback value is calculated, the feedback value may be added to the acceleration to obtain an adjusted acceleration, and then the target speed may be readjusted according to the adjusted acceleration to obtain an adjusted target speed.

[0090] In some embodiments, when controlling driving, the adjusted target speed and acceleration / deceleration time can be sent to the inverter in the motor drive system of the trolley or car, and the inverter outputs current and voltage to the motor system, which generates a driving torque to drive the trolley or car to run, and ultimately make the load run quickly and stably to the predetermined position.

[0091] See also Figure 3 The present invention also discloses an anti-sway control device for an unmanned vehicle. The anti-sway control method described above can be applied to the anti-sway control device. The anti-sway control device can include: a data acquisition module 100, a function fitting module 200, a function construction module 300, a period calculation module 400, a duration calculation module 500, and a speed control module 600.

[0092] In some embodiments, the data acquisition module 100 may be used to acquire operating parameters of the crane in real time, the operating parameters including the rope length between the crane and the load.

[0093] In some embodiments, the function fitting module 200 may be configured to fit a rope length function according to the rope length of the operating parameter.

[0094] In some embodiments, the function construction module 300 may be used to construct a swing period function based on a rope length function.

[0095] In some embodiments, the period calculation module 400 may be used to solve the swing period function to calculate the swing period of the load.

[0096] In some embodiments, the duration calculation module 500 may be used to calculate the acceleration and deceleration duration of the vehicle according to the swing cycle.

[0097] In some embodiments, the speed control module 600 can be used to control the driving of the vehicle according to operating parameters and acceleration and deceleration durations.

[0098] The specific definition of the anti-sway control device can be found in the definition of the anti-sway control method above and will not be repeated here. Each module in the anti-sway control device described above may be implemented in whole or in part via software, hardware, or a combination thereof. Each of the modules described above may be embedded in or independent of the memory of the electronic device in hardware form, or may be stored in the memory of the electronic device in software form, so that the memory can access and execute the corresponding operations of each module.

[0099] See also Figure 4 In one embodiment, the electronic device 10 may include a memory 11, a processor 12, and a bus. It may also include a computer program stored in the memory 11 and executable on the processor 12, such as an anti-sway control program for an unmanned vehicle.

[0100] In one embodiment, the memory 11 includes at least one type of readable storage medium, including flash memory, mobile hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 10, such as a mobile hard disk of the electronic device 10. In other embodiments, the memory 11 can also be an external storage device of the electronic device 10, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 10. Furthermore, the memory 11 can also include both an internal storage unit of the electronic device 10 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 10, such as the anti-sway control code of the unmanned vehicle, but can also be used to temporarily store data that has been output or is about to be output.

[0101] In one embodiment, the processor 12 may be comprised of an integrated circuit, such as a single packaged integrated circuit or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 12 is the control core (Control Unit) of the electronic device 10, connecting the various components of the electronic device 10 using various interfaces and circuits. It executes programs or modules stored in the memory 11 (e.g., a program for anti-sway control of an unmanned vehicle) and accesses data stored in the memory 11 to perform various functions of the electronic device 10 and process data.

[0102] In one embodiment, the processor 12 executes the operating system and various installed application programs of the electronic device 10. The processor 12 executes the application programs to implement the steps in the above-mentioned anti-sway control method for the unmanned vehicle.

[0103] In one embodiment, the computer program may be divided into one or more modules, one or more of which are stored in the memory 11 and executed by the processor 12 to complete the present application. One or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device 10. For example, the computer program may be divided into a data acquisition module 100, a function fitting module 200, a function construction module 300, a period calculation module 400, a duration calculation module 500, a speed control module 600, etc.

[0104] The embodiments of the present invention disclosed above are intended only to illustrate the present invention. They do not describe all details in detail, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for controlling the anti-sway of an unmanned vehicle, characterized in that: include: Acquiring the operating parameters of the crane in real time, including the rope length between the crane and the load; fitting a rope length function according to the rope length of the operating parameters; constructing a swing period function according to the rope length function; Solving the swing period function to calculate the swing period of the load; Calculating the acceleration and deceleration time of the vehicle according to the swing period; Driving is controlled according to the operating parameters and the acceleration / deceleration time.

2. The anti-sway control method for an unmanned vehicle according to claim 1, characterized in that: The swing period function is expressed as: The rope length function is expressed as: Where a is the acceleration of the rope length change, l0 is the initial rope length at time t0, n is the number of swing cycles, g is the acceleration due to gravity, and t is time.

3. The anti-sway control method for an unmanned vehicle according to claim 1, characterized in that: The step of calculating the acceleration and deceleration duration of the vehicle according to the swing period includes: Based on a preset swing model, the acceleration and deceleration duration is calculated according to the swing period; the swing model represents the functional relationship between the acceleration and deceleration duration and the swing period.

4. The anti-sway control method for an unmanned vehicle according to claim 1, characterized in that: The step of controlling the driving according to the operating parameters and the acceleration / deceleration duration includes: Calculating a target speed of the vehicle based on the acceleration and running speed of the vehicle in the operating parameters, and the acceleration and deceleration time; Driving is controlled according to the target speed and the acceleration / deceleration time.

5. The anti-sway control method for an unmanned vehicle according to claim 4, characterized in that: The step of controlling the driving according to the target speed and the acceleration / deceleration time includes: Calculating the displacement deviation and the corresponding deviation change rate according to the running speed, the acceleration, and the acceleration / deceleration time; Calculating overshoot risk data based on the displacement deviation and the corresponding deviation change rate using a pre-trained risk assessment model; the risk assessment model is a deep neural network model; The driving is controlled according to a comparison result of the overshoot risk data and a corresponding risk threshold.

6. The anti-sway control method for an unmanned vehicle according to claim 5, characterized in that: The step of calculating the displacement deviation and the corresponding deviation change rate based on the running speed, the acceleration, and the acceleration / deceleration time includes: Calculating the corresponding target displacement of the vehicle according to the running speed, the acceleration, and the acceleration / deceleration time; Calculating a displacement deviation between a displacement of the vehicle and a corresponding target displacement in the operating parameters; According to the displacement deviation, a corresponding deviation change rate is calculated.

7. The anti-sway control method for an unmanned vehicle according to claim 5, characterized in that: The step of controlling the driving of the vehicle according to the comparison result of the overshoot risk data and the corresponding risk threshold comprises: Determine the overshoot risk data and the corresponding risk threshold: When the overshoot risk data is less than a risk threshold, controlling the driving of the vehicle according to the target speed and the acceleration / deceleration duration; Otherwise, a feedback value is calculated according to the acceleration by a pre-trained dynamic compensation model; the dynamic compensation model is a deep neural network model; adjusting the target speed according to the feedback value; The driving is controlled according to the adjusted target speed and the acceleration / deceleration time.

8. An anti-sway control device for an unmanned vehicle, characterized in that: include: A data acquisition module, for acquiring operating parameters of the crane in real time, the operating parameters including the rope length between the crane and the load; A function fitting module, used for fitting a rope length function according to the rope length of the operating parameter; A function construction module, used for constructing a swing period function according to the rope length function; A period calculation module, used to solve the swing period function and calculate the swing period of the load; A duration calculation module, used to calculate the acceleration and deceleration duration of driving according to the swing period; The speed control module is used to control the driving of the vehicle according to the operating parameters and the acceleration and deceleration time.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the anti-sway control method for an unmanned vehicle according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the anti-sway control method for an unmanned vehicle according to any one of claims 1 to 7 are implemented.

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