Intelligent equipment control method and system, electronic equipment and computer storage medium

Abstract

The invention provides an intelligent device control method and system, an electronic device and a computer storage medium. The method comprises the steps of obtaining current information of an intelligent device; determining the position of a next target point through a target guidance algorithm according to the planned path and the current information; controlling the intelligent equipment to move along the next target point; the target guidance algorithm is a guidance algorithm in combination with a Serret-Frenet coordinate system. Due to the fact that the Serret-Frenet coordinate system is introduced into the target guidance algorithm, a local coordinate system can be defined on a curve path, and the algorithm can process complex paths of any shape. Therefore, the target point of the intelligent device is determined in real time through the target guidance algorithm, then path tracking of the intelligent device is achieved, the intelligent device can be accurately controlled to move under the irregular path, and application scenes are increased.

Description

Technical Field

[0001] The present application relates to the field of automatic control, and in particular to an intelligent device control method, system, electronic device and computer storage medium. Background Art

[0002] In the current wave of technological development, the application fields of intelligent devices are becoming increasingly extensive, from intelligent transportation in industrial automation production, to agricultural machinery equipment in agricultural automation production, to unmanned reconnaissance aircraft and mine-sweeping robots in military activities, and sweeping robots in smart home devices. The motion control of intelligent devices has become one of the key technologies to realize their functions.

[0003] At present, the control of smart devices is mainly based on straight-line paths. This control method uses algorithms to calculate the shortest or optimal straight-line path from the starting point to the end point of the device through preset path points, and guides the movement of the device accordingly. However, in actual scenarios, the scenarios in which smart devices are located are usually more complex, such as farmland, minefields, or home environments, which are often irregular paths. Traditional straight-line path control methods have difficulty dealing with sudden changes and uncertainties in the path, and it is difficult to achieve precise control of smart devices on irregular paths. Summary of the invention

[0004] In view of this, the purpose of the embodiments of the present application is to provide a smart device control method, system, electronic device and computer storage medium, which can accurately control the movement of the smart device even under irregular paths and increase application scenarios.

[0005] In a first aspect, an embodiment of the present application provides a method for controlling an intelligent device, comprising: obtaining current information of the intelligent device; determining the position of a next target point according to a planned path and the current information through a target guidance algorithm; and controlling the intelligent device to move along the next target point; wherein the target guidance algorithm is a guidance algorithm combined with a Serret-Frenet coordinate system.

[0006] In the above implementation process, since the target guidance algorithm introduces the Serret-Frenet coordinate system, a local coordinate system can be defined on the curved path, so that the algorithm can handle complex paths of arbitrary shapes. Therefore, by using the target guidance algorithm to determine the target point of the smart device in real time, and then realizing the path tracking of the smart device, it is possible to accurately control the movement of the smart device even on irregular paths, increasing the application scenarios.

[0007] In one embodiment, wherein the current information includes the current position; determining the position of the next target point according to the planned path and the current information through the target guidance algorithm includes: establishing the Serret-Frenet coordinate system at the reference point of the planned path; wherein the first coordinate axis of the Serret-Frenet coordinate system is along the tangent direction of the reference point, and the second coordinate axis of the Serret-Frenet coordinate system is perpendicular to the tangent direction and points to the outside of the planned path; converting the current position to the Serret-Frenet coordinate system, and obtaining the position error of the current position in the Serret-Frenet coordinate system; and determining the position of the next target point according to the position error.

[0008] In the above implementation process, since the parameters of the path tracking point are dynamically updated in the target guidance algorithm, the algorithm will adjust the position of the target point according to the real-time position, path curvature and current speed of the smart device, so that the smart device can quickly respond to changes in the path during path tracking, and improve the flexibility of the movement of the smart device. In addition, the target guidance algorithm controls the tangential and normal errors so that the smart device can not only align with the path direction, but also accurately stay on the path by gradually reducing the normal error, which increases the robustness of the algorithm and can reduce the lateral error caused by path deviation. Furthermore, the target guidance algorithm can dynamically adjust the position of the control point, so that the smart device can effectively apply curved paths and complex paths, increasing the application scenarios of smart device control.

[0009] In one embodiment, the obtaining of the current information of the smart device includes: obtaining the corrected positioning information of the mobile station; wherein the mobile station is configured to receive the position error sent by the base station and correct the position error; the base station is configured to obtain the GNSS signal fed back by the GNSS antenna and calculate the position error; obtaining the GNSS signal fed back by multiple GNSS antennas on the smart device; wherein the positional relationship between the multiple GNSS antennas is known; determining the time difference between the multiple GNSS signals; and determining the current position and heading angle of the smart device according to the time difference.

[0010] In the above implementation process, by adopting multi-antenna RTK technology to obtain the current information of the smart device, the speed and heading of the agricultural machinery can be controlled, accurate unmanned navigation can be achieved, and the accuracy of the current position determination of the smart device can be improved.

[0011] In one embodiment, before controlling the smart device to move along the next target point, the method further includes: determining the motion information of the smart device through an active disturbance rejection control algorithm; wherein the active disturbance rejection control algorithm is configured to compensate for environmental disturbances.

[0012] In the above implementation process, since the self-disturbance rejection control algorithm can adaptively offset external disturbances (such as terrain undulations, soil quality differences, air flow fields, etc.), the intelligent device can maintain a stable operating speed and heading control under various terrain conditions. By using the self-disturbance rejection control algorithm to determine the motion information of the intelligent device, the accuracy of the motion information of the intelligent device can be improved. In addition, the self-disturbance rejection control algorithm can automatically adjust the operating parameters, and only one parameter is required to achieve precise debugging, which can significantly reduce the system debugging workload and time cost caused by terrain undulations. This high adaptability and convenience enables the self-disturbance rejection control algorithm to operate stably under variable terrain conditions, ensure operation consistency, effectively reduce the requirements for the operating environment, and improve the reliability and operation effect of intelligent device control in complex terrain.

[0013] In one embodiment, determining the motion information of the smart device through an anti-disturbance control algorithm includes: decoupling the target motion information in the kinematic model of the smart device to obtain the original system; wherein the target motion information is the motion information that needs to be controlled; constructing an extended state observer based on the original system; wherein the extended state observer is configured to estimate the state and total disturbance of the system in real time; constructing a PD controller based on the original system; and substituting the PD controller into the kinematic model to determine the target motion information.

[0014] In the above implementation process, since the ADRC algorithm is known for its portability in engineering applications, it has the advantage of only requiring one parameter to complete the system debugging. Therefore, by determining the target motion information through the ADRC algorithm, the complexity of the control system can be simplified and the debugging difficulty can be reduced. Not only can the reliability and stability of smart device navigation be improved, but also the debugging and maintenance costs of the smart device control system can be reduced, and the economy and efficiency of the smart device control method in practical applications can be improved.

[0015] In one embodiment, before determining the position of the next target point according to the planned path and the current information of the smart device through the target guidance algorithm, the method also includes: constructing a motion coordinate system at the center of mass of the smart device; and constructing a kinematic model of the smart device under the motion coordinate system.

[0016] In the above implementation process, by constructing a motion coordinate system at the center of mass of the smart device and constructing a kinematic model of the smart device under the motion coordinate system, precise control of the motion behavior of the smart device can be achieved, thereby improving the accuracy of smart device control.

[0017] In one embodiment, before determining the position of the next target point according to the planned path and the current information of the smart device through the target guidance algorithm, the method also includes: constructing a chassis motion model according to the distance between the first motion element and the second motion element in the smart device, the first speed of the first motion element, the second speed of the second motion element and the turning radius; the first motion element and the second motion element are configured to be respectively arranged on both sides of the smart device along the motion direction; wherein the chassis motion model is configured to determine the magnitude and direction relationship between the first speed and the second speed; the magnitude and direction relationship between the first speed and the second speed is configured to determine the steering type.

[0018] In the above implementation process, before adjusting the next target point of the smart device, a chassis motion model is first constructed according to the actual parameters of the smart device. The chassis motion model can be used to control the complex movements of the smart device, thereby improving the accuracy and flexibility of the smart device control.

[0019] In a second aspect, an embodiment of the present application also provides an intelligent device control system, comprising: an electronic device and an intelligent device; wherein the electronic device is configured to execute the steps of the method in the above-mentioned first aspect, or any possible implementation of the first aspect; the electronic device is connected to the intelligent device; wherein the electronic device is configured to control the movement of the intelligent device.

[0020] In a third aspect, an embodiment of the present application also provides an intelligent device control device, including: an acquisition module for acquiring current information of the intelligent device; an adjustment module for adjusting the position of the next target point according to the planned path and the current information through the SFLOS guidance algorithm; a control module for controlling the intelligent device to move along the next target point; wherein the SFLOS guidance algorithm is a guidance algorithm combined with the Serret-Frenet coordinate system.

[0021] In a fourth aspect, an embodiment of the present application further provides an electronic device, comprising: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the machine-readable instructions are executed by the processor to perform the steps of the method in the above-mentioned first aspect, or any possible implementation of the first aspect.

[0022] In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the intelligent device control method in the above-mentioned first aspect, or any possible implementation of the first aspect are executed.

[0023] In order to make the above-mentioned objects, features and advantages of the present application more obvious and understandable, embodiments are given below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A schematic diagram of the interaction between a smart device and an electronic device in a smart device control system provided in an embodiment of the present application;

[0026] Figure 2 A block diagram of an electronic device provided in an embodiment of the present application;

[0027] Figure 3 A flowchart of a smart device control method provided in an embodiment of the present application;

[0028] Figure 4 A schematic diagram of the Serret-Frenet coordinate system provided in an embodiment of the present application;

[0029] Figure 5 A schematic diagram of a device for the RTK differential positioning technology provided in an embodiment of the present application;

[0030] Figure 6 A schematic diagram of a motion coordinate system provided in an embodiment of the present application;

[0031] Figure 7 A schematic diagram of a chassis corresponding to the chassis motion model provided in an embodiment of the present application;

[0032] Figure 8 A schematic diagram of the functional modules of the intelligent device control device provided in an embodiment of the present application.

[0033] Description of the drawings: 100 - electronic device, 111 - memory, 113 - processor, 200 - smart device, 301 - acquisition module, 302 - adjustment module, 303 - control module, 400 - base station, 500 - mobile station, 600 - GNSS satellite. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application.

[0035] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0036] In the control of intelligent equipment, intelligent equipment usually needs to face complex terrain. Take agricultural production as an example. In agricultural production, the promotion of mechanization has significantly improved operating efficiency and crop yields. However, traditional agricultural machinery relies on manual operation, and has shortcomings such as high operating difficulty, limited efficiency, and high requirements for operators. Especially in large-scale agriculture and areas with complex geographical environments, traditional agricultural machinery faces many challenges. The development of unmanned agricultural machinery provides a new solution to these problems: through high-precision positioning and navigation technology, unmanned agricultural machinery can autonomously complete agricultural tasks such as plowing, sowing, and spraying, reduce human intervention, improve operating efficiency, and reduce labor costs. At the same time, automated agricultural machinery can achieve more precise operations, reduce resource waste and environmental pollution, and meet the requirements of green agriculture. Therefore, unmanned agricultural machinery based on modern technology has full application value and inevitable development needs in future agriculture.

[0037] However, the inventor of the present application has found through long-term research that in agricultural production, the terrain of farmland is usually more complex. For example, hilly and mountainous areas are mostly small and scattered, forming numerous small fragmented fields. Due to the scattered small fragmented fields in hilly and mountainous areas and the large undulating terrain, the traditional straight path control method is difficult to achieve flexible and accurate control of agricultural machinery, resulting in low operation accuracy and low efficiency.

[0038] In view of this, the present application proposes a method for controlling an intelligent device, which can realize accurate unmanned navigation by controlling the speed and heading of the intelligent device, can intuitively display the implementation process of high-precision navigation, can improve the accuracy of intelligent device navigation, and is particularly suitable for scenes such as farmland operations and military operations that require precise positioning. In addition, the target guidance algorithm is introduced in the agricultural machinery path tracking, and the efficiency and accuracy of the target guidance algorithm in curve tracking are fully utilized, so that the intelligent device can better perform automatic navigation on the predetermined path, breaking through the limitations of the traditional intelligent device guidance algorithm. Furthermore, by introducing the self-disturbance rejection control algorithm, it is used for dual-loop control of speed and heading. The self-disturbance rejection control algorithm is known for its portability in engineering applications. It has the advantage of only requiring one parameter to complete the debugging of the system, which simplifies the complexity of the intelligent device control system and reduces the difficulty of debugging. It can not only improve the reliability and stability of the intelligent device navigation, but also reduce the debugging and maintenance costs of the intelligent device control system.

[0039] To facilitate understanding of this embodiment, a smart device control system that executes a smart device control method disclosed in an embodiment of the present application is first introduced in detail.

[0040] like Figure 1 , which is a schematic diagram of the interaction between smart devices and electronic devices in the smart device control system provided by the embodiment of the present application. The electronic device 100 is connected to one or more smart devices 200 through a network for data communication or interaction.

[0041] The above-mentioned intelligent device 200 can be an intelligent agricultural machine, a mine-clearing robot, an unmanned reconnaissance aircraft and other equipment, and the intelligent device 200 can be selected according to actual conditions.

[0042] In one embodiment, the smart device 200 includes at least two moving parts, and the at least two moving parts are arranged on both sides of the moving direction of the smart device 200.

[0043] For example, the smart device 200 is a smart agricultural machine. If it is in a flat farmland operation environment, the moving part of the smart agricultural machine can be a wheeled chassis. The smart device 200 includes four wheels, which are respectively arranged on both sides along the moving direction of the smart agricultural machine, with two wheels on each side.

[0044] If the intelligent agricultural machine is operated in a complex farmland environment such as hilly mountainous areas, furrowed fields, etc., the moving part of the intelligent agricultural machine can be a crawler chassis. The intelligent device 200 includes two crawlers, which are respectively arranged on both sides of the moving direction of the intelligent agricultural machine.

[0045] The above-mentioned structure of the smart device 200 is only exemplary, and the specific structure of the smart device 200 can be adjusted according to the actual application scenario of the smart device 200.

[0046] It should be understood that the electronic device 100 is used to determine the location of the next target point according to the current information of the smart device 200, and control the smart device 200 to move toward the target point. The smart device 200 is configured to move along the path planned by the electronic device 100 and perform corresponding tasks.

[0047] Optionally, the electronic device 100 may be a device independent of the smart device 200. For example, the electronic device 100 may be a network server, a database server, a personal computer (PC), a tablet computer, a smart phone, a personal digital assistant (PDA), etc.

[0048] The electronic device 100 may also be an integrated device with the smart device 200, and the electronic device 100 is a unit or component in the smart device 200. For example, a single chip microcomputer, a programmable controller, etc. The electronic device 100 may be selected according to actual conditions.

[0049] To facilitate understanding of this embodiment, the electronic device that executes the smart device control method disclosed in the embodiment of the present application is introduced in detail below.

[0050] like Figure 2 , which is a block diagram of an electronic device. The electronic device 100 may include a memory 111 and a processor 113. A person skilled in the art may understand that Figure 2 The structure shown is only for illustration and does not limit the structure of the electronic device 100. For example, the electronic device 100 may further include Figure 2 More or fewer components as shown, or with Figure 2 Different configurations shown.

[0051] The memory 111 and the processor 113 are directly or indirectly electrically connected to each other to achieve data transmission or interaction. For example, these elements can be electrically connected to each other via one or more communication buses or signal lines. The processor 113 is used to execute the executable module stored in the memory.

[0052] The memory 111 may be, but not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), etc. The memory 111 is used to store programs, and the processor 113 executes the program after receiving the execution instruction. The method executed by the electronic device 100 defined by the process disclosed in any embodiment of the present application can be applied to the processor 113, or implemented by the processor 113.

[0053] The processor 113 may be an integrated circuit chip with signal processing capability. The processor 113 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0054] The electronic device 100 in this embodiment can be used to execute each step in each method provided in the embodiments of the present application. The implementation process of the intelligent device control method is described in detail below through several embodiments.

[0055] See also Figure 3 , is a flow chart of the intelligent device control method provided by the embodiment of the present application. Figure 3 The specific process shown is described in detail.

[0056] Step S201, obtaining current information of the smart device.

[0057] The smart devices here refer to devices that need to automatically perform corresponding tasks according to the set path, such as smart agricultural machinery, mine-sweeping robots, unmanned reconnaissance aircraft, sweeping robots, etc.

[0058] In one embodiment, a positioning device is provided on the smart device, and the positioning device is used to locate the real-time position of the smart device.

[0059] The positioning device can also obtain the heading angle, posture and other motion information of the smart device. The function of the positioning device can be adjusted according to actual conditions.

[0060] The above-mentioned current information may include one or more information such as current position (eg, current longitude and latitude), current heading angle, current attitude, current speed, current angular velocity, etc. The current information may be selected according to actual conditions.

[0061] Step S202, determining the position of the next target point according to the planned path and current information through a target guidance algorithm.

[0062] Among them, the target guidance algorithm is a guidance algorithm combined with the Serret-Frenet coordinate system.

[0063] The principle of the target guidance algorithm here can be: path tracking is achieved by establishing a rectangular coordinate system and a local coordinate system of a curve on a two-dimensional plane. The algorithm calculates the geometric relationship between the position of the smart device and the planned path, dynamically adjusts the target point and controls the smart device to move toward the target point, thereby effectively achieving path tracking.

[0064] Among them, the target guidance algorithm is suitable for guidance of paths of arbitrary shapes.

[0065] The above-mentioned planned path refers to the ideal driving path of the smart device planned in advance.

[0066] Step S203: Control the smart device to move along the next target point.

[0067] It should be understood that after the position of the next target point is determined, corresponding control information can be generated to control the smart device to move along the next target point.

[0068] In the above implementation process, since the target guidance algorithm introduces the Serret-Frenet coordinate system, a local coordinate system can be defined on the curved path, so that the algorithm can handle complex paths of arbitrary shapes. Therefore, by using the target guidance algorithm to determine the target point of the smart device in real time, and then realizing the path tracking of the smart device, it is possible to accurately control the movement of the smart device even on irregular paths, increasing the application scenarios.

[0069] In a possible implementation, step S202 includes: establishing a Serret-Frenet coordinate system at a reference point of the planned path; converting the current position to the Serret-Frenet coordinate system, and obtaining a position error of the current position in the Serret-Frenet coordinate system; and determining the position of the next target point based on the position error.

[0070] The first coordinate axis of the Serret-Frenet coordinate system is along the tangent direction of the reference point, and the second coordinate axis of the Serret-Frenet coordinate system is perpendicular to the tangent direction and points to the outside of the planned path.

[0071] The reference point here can be any location point on the planned path.

[0072] In one embodiment, the planned path may be a path consisting of a plurality of continuous parameterized points.

[0073] For example, Figure 4 As shown, the creation of the Serret-Frenet coordinate system can be as follows:

[0074] Define a line consisting of multiple continuous parameterized points P i (x i ,y i ), i=1,2,…,n constitutes the path P d (μ) = [x d (μ),y d (μ)] T ; where μ is the path parameter variable, [x d (μ), y d (μ)] are the abscissa and ordinate points in the Serret-Frenet coordinate system.

[0075] For every point P on the path d (μ), the tangent direction angle θ can be determined based on the coordinate derivatives of these points d (μ):

[0076]

[0077] Reference point P on the planned path d (μ), the Serret-Frenet coordinate system is established so that X d Axis along the tangent direction, y d The axis points outward from the path perpendicular to the tangent direction.

[0078] Among them, X d The y axis is the first coordinate axis, d Axis is the second coordinate.

[0079] The rotation angle of the Serret-Frenet coordinate system relative to the global coordinate system is θ d (μ).

[0080] In one embodiment, the position error of the current position in the Serret-Frenet coordinate system can be obtained in the following manner:

[0081] Determine the position error of the current position in the Serret-Frenet coordinate system;

[0082]

[0083] Among them, e x and e y Respectively represent the position error along the tangent direction and the normal direction, R(θ d (μ)) is the rotation matrix, (x r ,y r ) is the coordinate of the next target point.

[0084] The rotation matrix can be determined as follows:

[0085]

[0086] It can be understood that the goal of the target guidance algorithm is to guide the smart device to the virtual target point at the foresight distance along the tangent direction of the path. Among them, the foresight distance is a key adaptive parameter, which is dynamically adjusted based on the position error in the normal direction.

[0087] The calculation formula for the foresight distance here can be:

[0088]

[0089] Among them, Δ is the foresight distance, Δ min is the minimum foresight distance, Δ max is the maximum foresight distance, e y is the position error along the normal direction, is the threshold parameter.

[0090] The threshold parameter here is configured to adjust the foresight distance.

[0091] The position of the next target point mentioned above can be achieved by the following formula:

[0092]

[0093] Among them, (x los ,y los ) is the position coordinate of the next target point, θ d (μ) is the tangent direction angle.

[0094] In one embodiment, the target heading angle of the smart device can also be calculated according to the position of the next target point. The specific expression formula is as follows:

[0095]

[0096] Among them, ψ d′ is the target heading angle, (x los ,y los ) is the position coordinate of the next target point, (x r ,y r ) is the current position, Δ is the foresight distance, v y is the approach speed to the next target point.

[0097] It should be understood that since the lateral distance between the current position point and the next target point is very small, it hardly affects the speed of the smart device. Therefore, the speed at which the smart device moves from the current position point to the next target point is basically determined by the v y Sure.

[0098] In the above implementation process, since the parameters of the path tracking point are dynamically updated in the target guidance algorithm, the algorithm will adjust the position of the target point according to the real-time position, path curvature and current speed of the smart device, so that the smart device can quickly respond to changes in the path during path tracking, and improve the flexibility of the movement of the smart device. In addition, the target guidance algorithm controls the tangential and normal errors so that the smart device can not only align with the path direction, but also accurately stay on the path by gradually reducing the normal error, which increases the robustness of the algorithm and can reduce the lateral error caused by path deviation. Furthermore, the target guidance algorithm can dynamically adjust the position of the control point, so that the smart device can effectively apply curved paths and complex paths, increasing the application scenarios of smart device control.

[0099] In a possible implementation, step S201 includes: obtaining positioning information corrected by the mobile station; obtaining GNSS signals fed back by multiple GNSS antennas on the smart device; determining the time difference between the multiple GNSS signals; and determining the current position and heading angle of the smart device based on the time difference.

[0100] The mobile station is configured to receive the position error sent by the base station and correct the position error; the base station is configured to obtain the GNSS signal fed back by the GNSS antenna and calculate the position error.

[0101] The positional relationship between the multiple GNSS antennas here is known.

[0102] In one embodiment, the current information acquisition in the intelligent device control method in the embodiment of the present application adopts multi-antenna RTK technology, for example, dual-antenna RTK technology.

[0103] Dual-antenna RTK technology is a positioning technology that uses two GNSS receivers and corresponding antennas. The two GNSS receivers are usually installed at a predetermined fixed distance to form a "baseline".

[0104] RTK differential positioning technology is a high-precision positioning technology based on GNSS. Figure 5 As shown, it works through two components: a stationary base station 400 and one or more mobile mobile stations 500 ( Figure 5 2 are shown in the figure). The base station 400 is located at a point with known coordinates, and is used to receive signals from the GNSS satellite 600 and calculate its position error, and send these error information to the mobile station in real time via radio waves. The mobile station receives these differential signals and corrects the satellite signals it receives in real time, thereby obtaining centimeter-level positioning accuracy.

[0105] Dual-antenna direction finding technology is used to determine the direction or attitude of the carrier through the GNSS signals obtained by two antennas. Since the positional relationship between the two antennas is known, there will be a small time difference in the signals they receive from the same group of satellites. By calculating this time difference, the system can very accurately determine the angle relationship between the two antennas, thereby inferring the attitude of the carrier, including the heading angle. Compared with magnetometer direction finding, it has the advantages of higher accuracy, better real-time performance, and is not affected by metal or magnetic materials.

[0106] A receiver and two GNSS antennas can be installed on the smart device here to obtain precise latitude and longitude information and the heading angle of the smart device.

[0107] After receiving the current information such as longitude and latitude and heading angle, the electronic device uses the guidance algorithm to guide the smart device to move in the specified direction according to the planned path.

[0108] In one embodiment, determining the current position and heading angle of the smart device according to the time difference may be achieved by the following steps:

[0109] The carrier phase signals received by the two receivers are differentially processed; the baseline vector between the two receivers is solved through the carrier phase observation value after differential processing; the baseline vector is converted to the northeast celestial coordinate system to obtain the coordinates of the baseline vector in the coordinate system; the heading angle and roll angle of the carrier can be calculated through the coordinate value of the baseline vector.

[0110] In the above implementation process, by adopting multi-antenna RTK technology to obtain the current information of the smart device, the speed and heading of the agricultural machinery can be controlled, accurate unmanned navigation can be achieved, and the accuracy of the current position determination of the smart device can be improved.

[0111] In a possible implementation, before step S203, the method further includes: determining motion information of the smart device by using an anti-disturbance control algorithm.

[0112] Among them, the active disturbance rejection control algorithm is configured to compensate for environmental disturbances.

[0113] Optionally, the environmental disturbance includes one or more of terrain undulations, soil properties, air flow fields, soil quality differences, etc.

[0114] The ADRC algorithm here is a control method for dealing with system uncertainty and external disturbances. It estimates the state and disturbance of the system in real time through an extended state observer and compensates for it in the control law, thereby improving the robustness and control performance of the system.

[0115] The above-mentioned motion information refers to the motion parameters of the smart device during driving, for example, speed, acceleration, etc.

[0116] In one embodiment, the ADRC controller may be designed according to the speed and lateral motion error of the smart device.

[0117] In the above implementation process, since the self-disturbance rejection control algorithm can adaptively offset external disturbances (such as terrain undulations, soil quality differences, air flow fields, etc.), the intelligent device can maintain a stable operating speed and heading control under various terrain conditions. By using the self-disturbance rejection control algorithm to determine the motion information of the intelligent device, the accuracy of the motion information of the intelligent device can be improved. In addition, the self-disturbance rejection control algorithm can automatically adjust the operating parameters, and only one parameter is required to achieve precise debugging, which can significantly reduce the system debugging workload and time cost caused by terrain undulations. This high adaptability and convenience enables the self-disturbance rejection control algorithm to operate stably under variable terrain conditions, ensure operation consistency, effectively reduce the requirements for the operating environment, and improve the reliability and operation effect of intelligent device control in complex terrain.

[0118] In a possible implementation, the motion information of the smart device is determined by an anti-disturbance control algorithm, including: decoupling the target motion information in the kinematic model of the smart device to obtain the original system; constructing an extended state observer based on the original system; constructing a PD controller based on the original system; substituting the PD controller into the kinematic model to determine the target motion information.

[0119] The target motion information is the motion information that needs to be controlled.

[0120] The extended state observer here is configured to estimate the state and total disturbance of the system in real time.

[0121] Taking the target motion information as the heading angle as an example, the original system obtained by decoupling the lateral motion and the heading angle can be expressed in the following form:

[0122]

[0123] Where b is the input parameter, ω is the speed difference between the left and right moving parts (where ω = ω r -ω l ), is the total disturbance of the system (including internal uncertainty and external disturbance), is the heading acceleration.

[0124] If you Converting the above equation into a state space equation can be expressed as:

[0125]

[0126] Where x1 is the heading angle estimated by the extended state observer, and x2 is the heading angular rate estimated by the extended state observer.

[0127] If we introduce the extended state into the state space equation Then the original system can be expressed as:

[0128]

[0129] Among them, x3 is the total disturbance estimate of heading angle control.

[0130] Furthermore, the expression of the constructed extended state observer can be as follows:

[0131]

[0132] in, are the estimated values ​​of states x1, x1, x3 respectively, and β1, β2, β3 are the gain parameters of the extended state observer.

[0133] Here, the expression for estimating the state of the system through the extended state observer can be:

[0134]

[0135] Building a PD controller based on the original system can be achieved by following the steps below:

[0136] Assume that the total disturbance of the system is bounded, then the original system can be simplified to a double integrator model, namely:

[0137]

[0138] Furthermore, a simple PD controller can be designed without considering the accumulation of errors:

[0139]

[0140] Among them, θ d is the target heading angle determined by the target guidance algorithm, k p ,k d is the gain parameter of the PD controller.

[0141] Substituting the above PD controller into the kinematic model, the trajectory tracking control law of the intelligent device based on the active disturbance rejection control algorithm is obtained:

[0142]

[0143] Among them, ω l is the speed of the left moving part of the smart device, ω ris the speed of the right moving part of the smart device, r is the turning radius of the moving part, v c,ref is the average speed of the left and right moving parts, d LR is the width of the smart device.

[0144] The intelligent device trajectory tracking control law here is configured to determine the target motion information.

[0145] In the above implementation process, since the ADRC algorithm is known for its portability in engineering applications, it has the advantage of only requiring one parameter to complete the system debugging. Therefore, by determining the target motion information through the ADRC algorithm, the complexity of the control system can be simplified and the debugging difficulty can be reduced. Not only can the reliability and stability of smart device navigation be improved, but also the debugging and maintenance costs of the smart device control system can be reduced, and the economy and efficiency of the smart device control method in practical applications can be improved.

[0146] In a possible implementation, before step S202, the method further includes: constructing a motion coordinate system at the center of mass of the smart device; and constructing a kinematic model of the smart device in the motion coordinate system.

[0147] Among them, the centroid refers to the mass center of the smart device.

[0148] For example, Figure 6 As shown in the figure, taking the moving part of the smart device as a crawler as an example, the established motion coordinate system can be as follows Figure 6 {X c ,Y c}as shown.

[0149] The kinematic model here can be a distance and heading angle kinematic model.

[0150] In one embodiment, constructing a kinematic model of an intelligent device in a motion coordinate system may be achieved in the following manner:

[0151] The average linear velocity of the left and right moving parts of the smart device can be expressed as:

[0152]

[0153] Among them, v c is the average linear velocity of the left and right moving parts (also the linear velocity at the center of mass), v l is the linear velocity of the left moving part, v r is the linear velocity of the right moving part.

[0154] The steering angular velocity of the smart device is calculated by the linear velocity difference of the left and right moving parts, and its expression can be:

[0155]

[0156] Among them, ω c is the steering angular velocity, and B is the distance between the left moving part and the right moving part.

[0157] The above expression is written in matrix form, and the relationship between the speed of the smart device and the linear speed of the left and right moving parts is obtained as follows:

[0158]

[0159] According to the kinematic relationship of the driving wheel, the relationship between the linear velocity of the left and right moving parts and the speed of the driving wheel can be:

[0160]

[0161] Where r is the turning radius of the moving part.

[0162] According to the above two formulas, the positive kinematic equation of the speed of the smart device and the speed of the driving wheel can be obtained as follows:

[0163]

[0164] Furthermore, the inverse kinematics equation of the driving wheel speed and the smart device speed can be determined as:

[0165]

[0166] In the global coordinate system {X,Y}, the speed v at the center of mass of the smart device c The X and Y direction components are expressed as:

[0167]

[0168] Where θ is the heading angle of the smart device (i.e., X c The deflection angle of the axis relative to the global coordinate system {X,Y}).

[0169] The rate of change of the heading angle here is the steering angular velocity:

[0170]

[0171] in, is the rate of change of heading angle, ω c is the steering angular velocity.

[0172] Furthermore, the state equation between the speed state of the smart device and the speed of the driving wheel can be obtained:

[0173]

[0174] in, is the speed v at the center of mass of the smart devicec The X-direction component of is the velocity v at the center of mass of the smart device c Y-direction component.

[0175] Here the velocity state x=[X,Y,θ] T , driving wheel speed u=[ω l ,ω r ] T .

[0176] In the above implementation process, by constructing a motion coordinate system at the center of mass of the smart device and constructing a kinematic model of the smart device under the motion coordinate system, precise control of the motion behavior of the smart device can be achieved, thereby improving the accuracy of smart device control.

[0177] In a possible implementation, before step S202, the method further includes: constructing a chassis motion model according to a distance between a first motion element and a second motion element in the smart device, a first speed of the first motion element, a second speed of the second motion element, and a turning radius.

[0178] The first moving element and the second moving element are configured to be respectively arranged on two sides of the smart device along the moving direction.

[0179] The chassis motion model here is configured to determine the magnitude and direction relationship between the first speed and the second speed; the magnitude and direction relationship between the first speed and the second speed is configured to determine the steering type.

[0180] Optionally, the chassis of the smart device may include multiple motion modes, such as outer differential steering, unilateral braking steering, inner differential steering, and in-situ steering. The motion mode of the chassis of the smart device may be selected according to actual conditions.

[0181] In one embodiment, the turning radius of the moving component can be further expressed as:

[0182]

[0183] Where B is the distance between the left and right moving parts, v l is the velocity of the left moving part, v r is the speed of the right moving part.

[0184] It can be understood that the chassis motion model of the smart device can be determined according to the speeds of the left and right moving components.

[0185] For example, Figure 7 As shown, the chassis motion model can be:

[0186] If v r >vl >0, the smart device is in outside differential steering. Outside differential steering means the steering center is outside the chassis.

[0187] It should be understood that when v r >v l >0 means that the speed directions of the left and right moving parts are the same, but the speeds are different. At this time, the smart device performs differential motion, and the turning radius is greater than half of the center distance of the moving parts.

[0188] If v r >v l =0, the intelligent device is in unilateral braking and steering. Among them, unilateral braking and steering refers to the steering of the intelligent device around a stationary moving part.

[0189] It should be understood that when v r >v l = 0 means that the moving part on one side keeps the same speed while the moving part on the other side stops. At this time, the turning radius is half of the center distance of the moving part.

[0190] If v r >0>v l , the smart device is an inner differential steering. The inner differential steering means that the steering center is on the inner side of the chassis.

[0191] It should be understood that when v r >0>v l It means that the speeds of the left and right moving parts are in opposite directions and at different speeds. At this time, the turning radius is less than half of the center distance of the moving parts.

[0192] If v r =v l >0, the smart device is turning on the spot.

[0193] It should be understood that when v r =v l >0 means that the speeds of the left and right moving parts are opposite and the speeds are the same. At this time, a zero radius turn can be achieved.

[0194] In the above implementation process, before adjusting the next target point of the smart device, a chassis motion model is first constructed according to the actual parameters of the smart device. The chassis motion model can be used to control the complex movements of the smart device, thereby improving the accuracy and flexibility of the smart device control.

[0195] Based on the same application concept, an intelligent device control device corresponding to the intelligent device control method is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to that in the aforementioned intelligent device control method embodiment, the implementation of the device in this embodiment can refer to the description in the embodiment of the above method, and the repeated parts will not be repeated.

[0196] See also Figure 8 , is a functional module diagram of the intelligent device control device provided in the embodiment of the present application. Each module in the intelligent device control device in this embodiment is used to execute each step in the above method embodiment. The intelligent device control device includes an acquisition module 301, an adjustment module 302, and a control module 303; wherein,

[0197] The acquisition module 301 is used to acquire the current information of the smart device.

[0198] The adjustment module 302 is used to adjust the position of the next target point according to the planned path and the current information through the SFLOS guidance algorithm.

[0199] The control module 303 is used to control the intelligent device to move along the next target point; wherein the SFLOS guidance algorithm is a guidance algorithm combined with the Serret-Frenet coordinate system.

[0200] In a possible implementation, the adjustment module 302 is further used to: establish the Serret-Frenet coordinate system at the reference point of the planned path; wherein the first coordinate axis of the Serret-Frenet coordinate system is along the tangent direction of the reference point, and the second coordinate axis of the Serret-Frenet coordinate system is perpendicular to the tangent direction and points to the outside of the planned path; convert the current position to the Serret-Frenet coordinate system, and obtain the position error of the current position in the Serret-Frenet coordinate system; and adjust the position of the next target point according to the position error.

[0201] In a possible implementation, the acquisition module 301 is further used to: obtain the corrected positioning information of the mobile station; wherein the mobile station is configured to receive the position error sent by the base station and correct the position error; the base station is configured to obtain the GNSS signal fed back by the GNSS antenna and calculate the position error; obtain the GNSS signal fed back by multiple GNSS antennas on the smart device; wherein the positional relationship between the multiple GNSS antennas is known; determine the time difference between the multiple GNSS signals; and determine the current position and heading angle of the smart device based on the time difference.

[0202] In a possible implementation manner, the intelligent device control apparatus is further provided with a determination module, which is used to: determine the motion information of the intelligent device by an active disturbance rejection control algorithm; wherein the active disturbance rejection control algorithm is configured to compensate for environmental disturbances.

[0203] In one possible implementation, a determination module is specifically used to: decouple the target motion information in the kinematic model of the smart device to obtain the original system; the target motion information is the motion information that needs to be controlled; construct an extended state observer based on the original system; wherein the extended state observer is configured to estimate the state and total disturbance of the system in real time; construct a PD controller based on the original system; and substitute the PD controller into the kinematic model to determine the target motion information.

[0204] In a possible implementation manner, the intelligent device control apparatus is further provided with a construction module, which is used to: construct a motion coordinate system at the center of mass of the intelligent device; and construct a kinematic model of the intelligent device in the motion coordinate system.

[0205] In addition, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the smart device control method described in the above method embodiment are executed.

[0206] The computer program product of the smart device control method provided in the embodiment of the present application includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the steps of the smart device control method described in the above method embodiment. For details, please refer to the above method embodiment, which will not be repeated here.

[0207] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0208] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0209] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk. It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprises" or any other variation thereof are intended to cover non-exclusive inclusion, so that a process, method, article or apparatus that includes a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or apparatus. In the absence of more restrictions, the elements defined by the sentence "includes..." do not exclude the presence of other identical elements in the process, method, article or apparatus that includes the elements.

[0210] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0211] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for controlling an intelligent device, characterized in that: include: Get the current information of smart devices; Determine the position of the next target point according to the planned path and the current information through a target guidance algorithm; Controlling the smart device to move along the next target point; Among them, the target guidance algorithm is a guidance algorithm combined with the Serret-Frenet coordinate system.

2. The method according to claim 1, characterized in that in, The current information includes the current location; The step of determining the position of the next target point according to the planned path and the current information by using the target guidance algorithm includes: At the reference point of the planned path, the Serret-Frenet coordinate system is established; wherein the first coordinate axis of the Serret-Frenet coordinate system is along the tangent direction of the reference point, and the second coordinate axis of the Serret-Frenet coordinate system is perpendicular to the tangent direction and points to the outside of the planned path; Converting the current position to the Serret-Frenet coordinate system, and obtaining a position error of the current position in the Serret-Frenet coordinate system; The position of the next target point is determined according to the position error.

3. The method according to claim 1, characterized in that The obtaining of current information of the smart device includes: Obtaining the corrected positioning information of the mobile station; wherein the mobile station is configured to receive the position error sent by the base station and correct the position error; the base station is configured to obtain the GNSS signal fed back by the GNSS antenna and calculate the position error; Acquire GNSS signals fed back by multiple GNSS antennas on the smart device; wherein the positional relationship between the multiple GNSS antennas is known; determining a time difference between a plurality of said GNSS signals; The current position and heading angle of the smart device are determined according to the time difference.

4. The method according to claim 1, characterized in that: Before controlling the smart device to move along the next target point, the method further includes: Determining motion information of the smart device through an automatic disturbance rejection control algorithm; Wherein, the active disturbance rejection control algorithm is configured to compensate for environmental disturbances.

5. The method according to claim 4, characterized in that The determining the motion information of the smart device by using an auto-disturbance rejection control algorithm includes: Decoupling the target motion information in the kinematic model of the smart device to obtain an original system; wherein the target motion information is the motion information that needs to be controlled; Constructing an extended state observer based on the original system; wherein the extended state observer is configured to estimate the state and total disturbance of the system in real time; Building a PD controller based on the original system; Substitute the PD controller into the kinematic model to determine the target motion information.

6. The method according to any one of claims 1 to 5, characterized in that: Before determining the position of the next target point according to the planned path and the current information of the smart device by the target guidance algorithm, the method further includes: Constructing a motion coordinate system at the center of mass of the smart device; A kinematic model of the smart device is constructed in the motion coordinate system.

7. The method according to any one of claims 1 to 5, characterized in that: Before determining the position of the next target point according to the planned path and the current information of the smart device by the target guidance algorithm, the method further includes: A chassis motion model is constructed according to a distance between a first motion element and a second motion element in the smart device, a first speed of the first motion element, a second speed of the second motion element, and a turning radius; the first motion element and the second motion element are configured to be respectively arranged on both sides of the smart device along a motion direction; The chassis motion model is configured to determine the magnitude and direction relationship between the first speed and the second speed; and the magnitude and direction relationship between the first speed and the second speed is configured to determine a steering type.

8. An intelligent device control system, characterized in that: include: An electronic device and a smart device; wherein the electronic device is configured to perform the steps of the method described in any one of claims 1 to 7 above; The electronic device is connected to the smart device; Wherein, the electronic device is configured to control the movement of the smart device.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the machine-readable instructions are executed by the processor to perform the steps of any method described in claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are executed.

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