Agricultural machinery control methods, devices and equipment

Abstract

This specification discloses an agricultural machinery control method, device, and equipment. The method includes: acquiring the current speed of a navigator agricultural machinery corresponding to a follower agricultural machinery in a target working area, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment; determining the speed increment of the follower agricultural machinery at the current moment according to a preset objective function and preset constraints, wherein the objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment, and the constraints include a preset state equation; determining the target speed of the follower agricultural machinery at the current moment according to the speed increment of the follower agricultural machinery, and controlling the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed.

Description

Technical Field

[0001] This document relates to the field of computer technology, and in particular to agricultural machinery control methods, devices and equipment. Background Technology

[0002] With the rapid development of navigation and vehicle control technologies, agricultural machinery is gradually moving towards automation and precision. In agricultural machinery cluster operations, both follower and leader machinery need to operate in formation.

[0003] In agricultural machinery cluster operations, to ensure that all agricultural machinery operates in a pre-defined formation, following machinery can track a pre-planned static work path. However, in actual agricultural operations, collisions may occur between the machinery in the cluster due to kinematic errors, leading to low efficiency. Therefore, this specification provides a technical solution to improve the efficiency of agricultural machinery cluster operations. Summary of the Invention

[0004] The purpose of the embodiments in this specification is to provide a technical solution to improve the working efficiency of agricultural machinery cluster operations.

[0005] To achieve the above technical solution, the embodiments in this specification are implemented as follows:

[0006] This specification provides an agricultural machinery control method applied to a follower agricultural machinery device. The method includes: acquiring the current speed of a navigator agricultural machinery device corresponding to the follower agricultural machinery device in a target working area, and the longitudinal error between the follower agricultural machinery device and the virtual navigator at the current moment; determining the speed increment of the follower agricultural machinery device at the current moment according to a preset objective function and preset constraints, wherein the objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery device and the virtual navigator at the next moment, and the constraints include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, a preset sampling time, the current speed of the navigator agricultural machinery device, the previous speed of the follower agricultural machinery device, and the speed increment of the follower agricultural machinery device at the current moment; determining the target speed of the follower agricultural machinery device at the current moment based on the speed increment of the follower agricultural machinery device at the current moment, and controlling the follower agricultural machinery device to continue traveling in the target working area at the current moment according to the target speed.

[0007] This specification provides an agricultural machinery control device, comprising: a data acquisition module for acquiring the current speed of a virtual navigator agricultural machinery corresponding to the agricultural machinery control device in the target working area, and the longitudinal error between the agricultural machinery control device and the virtual navigator at the current time; an increment determination module for determining the speed increment of the agricultural machinery control device at the current time according to a preset objective function and preset constraints, wherein the objective function is used to minimize the predicted longitudinal error between the agricultural machinery control device and the virtual navigator at the next time, and the constraints include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next time, the longitudinal error at the current time, a preset sampling time, the current speed of the virtual navigator agricultural machinery, the previous speed of the agricultural machinery control device, and the speed increment of the agricultural machinery control device at the current time; and a speed determination module for determining the target speed of the agricultural machinery control device at the current time based on the speed increment of the agricultural machinery control device at the current time, and controlling the agricultural machinery control device to continue traveling in the target working area at the current time according to the target speed.

[0008] This specification provides an embodiment of an agricultural machinery control device, comprising: a processor; and a memory arranged to store computer-executable instructions, wherein the executable instructions, when executed, cause the processor to: obtain the current speed of the virtual navigator agricultural machinery corresponding to the agricultural machinery control device in the target working area, and the longitudinal error between the agricultural machinery control device and the virtual navigator at the current time; and determine the speed increment of the agricultural machinery control device at the current time according to a preset objective function and preset constraints, wherein the objective function is used to minimize the speed increment of the agricultural machinery control device and the virtual navigator at the next time step. The longitudinal error is measured, and the constraint conditions include a preset state equation. The preset state equation is used to characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the agricultural machinery control equipment at the previous moment, and the speed increment of the agricultural machinery control equipment at the current moment. Based on the speed increment of the agricultural machinery control equipment at the current moment, the target speed of the agricultural machinery control equipment at the current moment is determined, and the agricultural machinery control equipment is controlled to continue driving in the target working area at the current moment according to the target speed.

[0009] This specification also provides a storage medium for storing computer-executable instructions. When executed by a processor, these instructions implement the following process: obtaining the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment; determining the speed increment of the follower agricultural machinery at the current moment according to a preset objective function and preset constraints, wherein the objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment, and the constraints include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, a preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment; determining the target speed of the follower agricultural machinery at the current moment based on the speed increment of the follower agricultural machinery, and controlling the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed.

[0010] This specification also provides a computer program product, including a computer program that, when executed by a processor, implements the following process: obtaining the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment; determining the speed increment of the follower agricultural machinery at the current moment according to a preset objective function and preset constraints, wherein the objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment, and the constraints include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, a preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment; determining the target speed of the follower agricultural machinery at the current moment based on the speed increment of the follower agricultural machinery, and controlling the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is an embodiment of an agricultural machinery control method described in this specification;

[0013] Figure 2 This is a schematic diagram of a multi-aircraft formation operation path planning method as described in this specification;

[0014] Figure 3 This is a schematic diagram of a multi-machine communication topology in this specification;

[0015] Figure 4 This is a schematic diagram of a kinematic model of an agricultural machinery with a following structure, as described in this specification.

[0016] Figure 5 This is yet another embodiment of an agricultural machinery control method described in this specification;

[0017] Figure 6 This is a schematic diagram of a multi-machine platooning path index as described in this specification;

[0018] Figure 7 This is a schematic diagram of an agricultural machinery control system as described in this manual;

[0019] Figure 8 This is a schematic diagram of yet another agricultural machinery control system described in this manual;

[0020] Figure 9 This is a schematic diagram of the steering kinematics model of an agricultural machinery device as described in this manual;

[0021] Figure 10 This is a schematic diagram of a distance-velocity kinematic model as described in this specification;

[0022] Figure 11 This is a schematic diagram of yet another agricultural machinery control system described in this manual;

[0023] Figure 12 This is yet another embodiment of an agricultural machinery control method described in this specification;

[0024] Figure 13 This is a schematic diagram of the location of one type of agricultural machinery as described in this manual;

[0025] Figure 14 This is an embodiment of an agricultural machinery control device described in this specification;

[0026] Figure 15This is an embodiment of an agricultural machinery control device described in this specification. Detailed Implementation

[0027] This specification provides an agricultural machinery control method, device, and equipment through its embodiments.

[0028] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0029] This specification provides a technical solution to improve the working efficiency of agricultural machinery cluster operations. With the rapid development of navigation and vehicle control technologies, agricultural machinery is gradually moving towards automation and precision. In agricultural machinery cluster operations, follower and navigator machinery need to operate in formation. To maintain the machinery in a pre-planned formation, follower machinery can track a pre-planned static work path. However, in actual agricultural operations, collisions may occur due to kinematic errors, leading to low efficiency. Therefore, this specification provides a technical solution to improve the working efficiency of agricultural machinery cluster operations. In this scheme, the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area can be obtained, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, the speed increment of the follower agricultural machinery at the current moment is determined. The objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which can be used to characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined, and the follower agricultural machinery can be controlled to continue traveling in the target working area at the current moment according to the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0030] like Figure 1 As shown in the embodiments of this specification, an agricultural machinery control method is provided. The executing entity of this method can be a server, and this method can be applied to follower agricultural machinery equipment. Follower agricultural machinery equipment can be any agricultural machinery equipment in an agricultural machinery cluster that performs agricultural operations under the guidance of the leader agricultural machinery equipment. Specifically, the method may include the following steps:

[0031] In step S102, the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery is obtained in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment.

[0032] The target work area can be any area where agricultural operations need to be carried out, such as indoor areas like greenhouses. The leader agricultural machinery can be any agricultural machinery capable of carrying out agricultural operations independently, while the follower agricultural machinery can be any agricultural machinery in the agricultural machinery cluster that carries out agricultural operations under the guidance of the leader agricultural machinery.

[0033] In practice, in agricultural machinery cluster operations, there can be multiple follower agricultural machinery devices. For example, taking an agricultural machinery cluster consisting of three agricultural machinery devices as an example, this cluster can have one leader agricultural machinery device and two follower agricultural machinery devices. Suppose these three agricultural machinery devices need to... Figure 2 The rectangular unobstructed farmland shown (i.e., the target work area) is used for coordinated operations. Before coordinating operations, the server can perform full-coverage path planning based on the kinematic characteristics of the agricultural machinery and the working width requirements of the target work area. For example, the server can determine the pre-planned path of the agricultural machinery in the target work area based on the equipment parameters of the agricultural machinery and the area parameters of the target work area using a preset Model Predictive Control (MPC) algorithm. The server can use the AB line offset method to perform full-coverage path planning for the target work area. The multi-machine coordinated operation planning path obtained from the path planning can be as follows: Figure 2 As shown.

[0034] exist Figure 2 In the multiple planned paths shown, each path can consist of straight segments and turning segments. For example, the straight segments in Area 2 can serve as the work segments, where agricultural machinery needs to perform agricultural operations on the plots as required. Conversely, the turning segments in Area 1 can serve as the turning segments, where the machinery only needs to turn around and not perform agricultural operations. To ensure the agricultural machinery fleet can continue operating without stopping at the turning points and avoid collisions, the first agricultural machinery (corresponding to path 3) can be designated as the navigator, and the latter two (corresponding to paths 1 and 2) as follower machinery. The follower and navigator machinery only need to operate on their respective planned paths to maintain lateral distance. Therefore, the server can obtain the current speed of the navigator corresponding to the follower machinery in the target work area, as well as the longitudinal error between the follower machinery and the virtual navigator at the current moment.

[0035] Among them, the navigator agricultural machinery can transmit its current speed within the target working area to each follower agricultural machinery via a wireless communication module. For example, ... Figure 3 As shown, the navigator agricultural machinery can transmit its current speed in the target working area to the follower agricultural machinery 1 and follower agricultural machinery 2 via a wireless communication module. In this way, each follower agricultural machinery can receive the navigator agricultural machinery's current speed in the target working area via the wireless communication module.

[0036] In addition, the navigator agricultural machinery can also send its current location information in the target working area to each follower agricultural machinery through a wireless communication module. In this way, each follower agricultural machinery can determine the location information of the virtual navigator based on the location information of the navigator, and then determine the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment based on the location information of the virtual navigator and the location information of each follower agricultural machinery.

[0037] Among them, the kinematic models of agricultural machinery for both the leader and follower agricultural machinery can be as follows: Figure 4 As shown, the virtual navigator can replace the navigator agricultural machinery as a reference for the follower agricultural machinery. When the follower agricultural machinery reaches the position of the virtual navigator, it means that the formation is in the desired state of motion. At this time, the follower agricultural machinery and the navigator agricultural machinery maintain a predetermined distance and orientation, realizing the formation control of the agricultural machinery cluster.

[0038] In addition, there are many other ways to determine the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Different methods can be selected according to different actual application scenarios. This specification does not limit the specific methods in this way.

[0039] In step S104, the speed increment of the follower agricultural machinery at the current moment is determined according to the preset objective function and preset constraints.

[0040] The objective function can be used to minimize the longitudinal prediction error of the follower agricultural machinery and the virtual navigator at the next moment. The constraints can include a preset state equation, which can be used to characterize the relationship between the longitudinal prediction error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the follower agricultural machinery at the previous moment, and the speed increment of the follower agricultural machinery at the current moment.

[0041] In practice, the server can obtain the speed increment of the follower agricultural machinery at the current moment by solving a quadratic programming (QP) problem constructed by the objective function and constraints. In other words, the server can determine the speed increment of the follower agricultural machinery at the current moment based on the preset objective function and preset constraints.

[0042] In step S106, the target speed of the follower agricultural machinery is determined based on the speed increment of the follower agricultural machinery at the current moment, and the follower agricultural machinery is controlled to continue traveling in the target working area at the current moment according to the target speed.

[0043] In practice, the server can use the MPC algorithm to determine the target speed of the following agricultural machinery at the current moment based on the speed increment of the following agricultural machinery at the current moment. Alternatively, the server can also determine the target speed of the following agricultural machinery at the current moment based on the speed of the following agricultural machinery at the previous moment and the speed increment at the current moment.

[0044] Furthermore, there are many other methods for determining the target speed, and different methods can be selected according to different actual application scenarios. This specification does not specifically limit the methods used in the embodiments.

[0045] This specification provides an agricultural machinery control method that can acquire the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, the method determines the speed increment of the follower agricultural machinery at the current moment. The objective function minimizes the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, the method determines the target speed of the follower agricultural machinery at the current moment and controls the follower agricultural machinery to continue traveling in the target working area at the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0046] In practical applications, there are various ways to process the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment in step S102 above. One optional processing method is provided below, such as... Figure 5 As shown, the specific process may include the following steps S1022 to S1026.

[0047] In step S1022, the first location information of the Navigator agricultural machinery equipment in the target working area at the current moment is obtained.

[0048] In step S1024, the path index point of the virtual navigator at the current moment is determined based on the path index point in the first location information, the expected longitudinal distance of the virtual navigator at the current moment, and the preset distance between two adjacent path index points in the preset planned path of the follower agricultural machinery.

[0049] In implementation, since the MPC algorithm tracks a pre-defined desired trajectory, which can be viewed as a smooth connection of multiple path points, a path index point can be assigned to each trajectory point of the desired trajectory. Each path index point stores its own position coordinates, desired speed, desired heading angle, and other information. The server can obtain the positioning signals of each agricultural machine through satellite navigation or other positioning equipment, and perform signal filtering and other processing to obtain the current pose and status information of the agricultural machine. Based on this information, the server determines the current path index point of the agricultural machine. Thus, the server can determine the location of the agricultural machine within the pre-planned path based on the current path index point.

[0050] Among them, such as Figure 6 As shown, the planned path corresponding to each piece of agricultural machinery in the agricultural machinery cluster contains its own path index points, and the number of path index points contained in the planned path corresponding to each piece of agricultural machinery is the same. The same path index points of each piece of agricultural machinery correspond to the same positions on their respective planned paths. The length between the trajectory points corresponding to two adjacent path index points on the same planned path is d (that is, the preset distance between two adjacent path index points in the preset planned path of the following agricultural machinery is d). Here, d is the length between two adjacent path index points on the same planned path, not the Euclidean distance.

[0051] For a cluster of agricultural machinery consisting of three machines, the expected lateral distance and expected longitudinal distance between the leader machine and the two follower machines can be L1 and L2, respectively. x1 L x2 L y1 L y2 The navigator agricultural machinery and its two followers only need to operate along their pre-planned paths to maintain lateral distance. For longitudinal distance maintenance, the navigator simply follows its own pre-planned path, acquiring its real-time latitude and longitude coordinates (i.e., initial position information) via its satellite navigation module. It then calculates its real-time position index point (i.e., the current path index point) on its pre-planned path using these coordinates. Finally, the navigator uses its wireless communication module to transmit this path index point as follows: Figure 3 The communication topology diagram shown is sent to each follower agricultural machinery device.

[0052] In this way, the follower agricultural machinery can receive the current path index point of the navigator agricultural machinery through the wireless communication module. Then, the follower agricultural machinery can determine the current path index point of the virtual navigator based on the desired longitudinal distance. This virtual navigator path index point is the desired position index point of the follower agricultural machinery. The calculation method for the current path index point of the virtual navigator can be as follows:

[0053]

[0054] Where, index_VL is the path index point of the virtual navigator at the current moment, and index_L is the path index point in the first location information (i.e., the path index point of the navigator agricultural machinery at the current moment), L y Let d be the expected longitudinal distance of the virtual navigator at the current moment, and d be the preset distance between two adjacent path index points in the preset planned path of the follower agricultural machinery. This indicates the rounding up operation.

[0055] In step S1026, the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment is determined based on the current path index point of the follower agricultural machinery and the current path index point of the virtual navigator.

[0056] In implementation, such as Figure 7 As shown, due to the constraint of the planned path, the formation operation task can be decomposed into two sub-tasks: a path tracking sub-task and a formation maintenance sub-task. In the formation system, a separate Model Predictive Control (MPC) controller can be designed for each member. For the navigator agricultural machinery, the MPC controller only needs to complete the path tracking sub-task, while for the follower agricultural machinery, the MPC controller needs to complete both the path tracking and formation maintenance sub-tasks. This approach reduces the computational load on each MPC controller and improves the response speed and real-time performance of the entire formation system.

[0057] In practical applications, the specific processing method for determining the target speed of the follower agricultural machinery in the next moment based on the speed increment of the follower agricultural machinery at the current moment in step S106, and controlling the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed, can be varied. Another optional processing method is provided below, such as... Figure 5 As shown, the specific process may include the following steps S1062 to S1068.

[0058] In step S1062, the first speed of the follower agricultural machinery is determined at the current moment based on the speed increment of the follower agricultural machinery at the current moment using a preset model predictive control algorithm.

[0059] In step S1064, the lateral coordinates and heading angle of the virtual navigator at the current moment are determined based on the first position information, the expected lateral distance and expected heading angle of the virtual navigator at the current moment.

[0060] In step S1066, the lateral error and heading angle error of the follower agricultural machinery and the virtual navigator at the current moment are determined based on the lateral coordinates and heading angle of the follower agricultural machinery at the current moment and the lateral coordinates and heading angle of the virtual navigator at the current moment.

[0061] In implementation, in such Figure 4 In the kinematic model of the navigator and follower agricultural machinery shown, the motion parameters of the navigator, virtual navigator, and follower agricultural machinery in multi-machine cooperative navigation control can include: the global position coordinates and heading angle (X) of the navigator agricultural machinery. L Y L θ L ), global position coordinates and heading angle (X) of the virtual navigator VL ,Y VL θ VL ), and the global position coordinates and heading angle (X) of the following agricultural machinery equipment. F Y F θ F ).

[0062] The global position coordinates and heading angles of the navigator and follower agricultural machinery can be obtained from satellite navigation signals after filtering. The position coordinates of the virtual navigator can be determined from the expected longitudinal distance L between the virtual navigator and the navigator. y With respect to the desired lateral distance L x It is determined that the expected heading angle of the following agricultural machinery can be θ.

[0063] The virtual navigator's global position coordinates and heading angle (X) VL Y VL θ VL The result can be calculated using the following formula:

[0064]

[0065] Lateral deviation between follower agricultural machinery and virtual navigator e x Heading angle deviation e θ It can be represented as:

[0066]

[0067] In step S1068, based on the longitudinal error, longitudinal error and heading angle error at the current moment, and the first speed, the target speed and target turning angle of the follower agricultural machinery at the current moment are determined using a preset model predictive control algorithm, and the follower agricultural machinery is controlled to continue traveling in the target working area at the current moment according to the target speed and target turning angle.

[0068] In implementation, the pose of the virtual navigator, calculated from the pose information of the navigator agricultural machinery, can be used as the desired reference position and pose for the follower agricultural machinery. The follower agricultural machinery needs to meet the lateral deviation e from the virtual navigator. x →0, longitudinal deviation e y →0, heading angle deviation e θ →0, which allows all agricultural machinery to maintain formation while moving. Based on this idea, such as Figure 8 As shown, based on the kinematic characteristics and constraints of each agricultural machinery device, corresponding lower-level MPC path tracking controllers and upper-level MPC speed planners can be designed for each follower agricultural machinery device. The upper-level MPC speed planner for the follower agricultural machinery device can determine the speed increment of the follower agricultural machinery device at the current moment based on a preset objective function and preset constraints, and determine the target speed of the follower agricultural machinery device at the current moment based on the speed increment of the follower agricultural machinery device at the current moment. The lower-level MPC path tracking controller for the follower agricultural machinery device can determine the target speed and target turning angle of the follower agricultural machinery device at the current moment using a preset model predictive control algorithm based on the longitudinal error, longitudinal angle error, and heading angle error at the current moment, as well as the first speed.

[0069] Specifically, the upper-level MPC speed planner of the following agricultural machinery equipment can obtain the optimal control increment input sequence ΔV by solving a quadratic programming (QP) problem. F，k：k+N-1 (i.e., the input sequence consisting of the velocity increments at each time step), and then further based on the optimal control increment input sequence ΔV F，k：k+N-1 The optimal control input sequence V can be obtained. F，k：k+N-1 (That is, the input sequence consisting of the first velocity corresponding to each moment).

[0070] Then, as Figure 8As shown, the optimal control input sequence can be used as the desired speed sequence of the lower-level MPC path tracking controller of the follower agricultural machinery. The lower-level MPC path tracking controller can perform real-time calculations based on the optimal control input sequence, the current longitudinal error, and the heading angle error to obtain the final optimal turning angle control input sequence (i.e., the sequence constructed from the target turning angle at each moment) and the optimal speed control input sequence (i.e., the sequence constructed from the target speed at each moment). The first input of the sequence (i.e., the target speed and target turning angle at the current moment) is then applied to the follower agricultural machinery. By repeating the above process, the current state can be updated in real time, and the follower agricultural machinery can be replanned and controlled.

[0071] In this way, the upper-level MPC speed planner of the following agricultural machinery can adjust the travel speed of the following agricultural machinery in real time to maintain the stability of the formation, reduce distance error and ensure that the following agricultural machinery travels within a safe range, and take into account the actual constraints of the system to smooth speed changes and reduce the burden on the mechanical system.

[0072] In addition, such as Figure 8 As shown, a corresponding MPC path tracking controller can also be designed for Navigator agricultural machinery equipment. This not only fully considers the differences between different agricultural machinery equipment, but also avoids centralized data calculation, reduces the computing burden on the controller, and has strong flexibility.

[0073] As the leader in the convoy, the Navigator agricultural machinery is required to track its pre-planned path. Its MPC path tracking controller needs to be able to precisely control the machinery to follow the preset trajectory. The MPC path tracking controller should consider the Navigator's speed, heading angle, and deviation from the trajectory, adjusting the steering angle and speed in real time to maintain it on the predetermined path.

[0074] First, it is possible to establish, for example Figure 9 The kinematic model of agricultural machinery steering shown is used to describe the motion characteristics of the Navigator agricultural machinery equipment. This model can accurately describe the motion characteristics of the agricultural machinery on a predetermined trajectory. Figure 9 In the figure, φ is the yaw angle of the vehicle body, δ is the front wheel deflection angle (steering angle), v is the vehicle's yaw directional velocity, and l is the vehicle's wheelbase.

[0075] The kinematic model of the vehicle can be obtained as follows:

[0076]

[0077] From the above equation, we can see that the vehicle kinematic model can be regarded as a control system with input u(v, δ) and state variables X(x, y, φ), which can be written in general form as:

[0078]

[0079] For a given reference trajectory of a vehicle, the motion trajectory of the reference vehicle can be described by the vehicle's motion trajectory. Every point on the motion trajectory satisfies the vehicle's kinematic equations, the general form of which is:

[0080]

[0081] Among them, X r =[x r y r φ r ] T u r =[v r δ r ], where r is the reference value. For X in the navigator's reference path... r with u r X is the X-axis corresponding to the work path trajectory point closest to the navigator's current coordinates. r with u r

[0082] To obtain the error between the actual vehicle trajectory and the reference trajectory, the vehicle trajectory in the above formula is expanded using Qinle at the reference trajectory point, and higher-order remainder terms are ignored. For the navigator, this reference trajectory point is the discrete trajectory point on the planned path that is closest to the current position of the agricultural machinery:

[0083]

[0084] Subtracting this type of driving trajectory from the reference trajectory yields the state-space expression for the linearized error model of the unmanned agricultural machinery vehicle:

[0085]

[0086] in,

[0087] After successfully constructing the linear error model of agricultural machinery vehicles, since the prediction part of the model predictive control (MPC) algorithm is based on the discrete linear error model, the forward Euler method can be used to discretize the continuous linear state-space equations.

[0088] Applying the forward Euler method to the above equation, we obtain:

[0089]

[0090] After sorting, we can get:

[0091]

[0092] Therefore, the general form of the discretized linear error model is obtained:

[0093]

[0094] in, Where T is the sampling time.

[0095] When designing a trajectory tracking controller, an objective function of the following form can be used. This objective function consists of three parts: a state deviation term, a control increment input term, and a relaxation factor term. Q and R are weight matrices, and N... p For prediction in the time domain; N c ρ is the control time domain; ε is the weighting coefficient; and ε is the relaxation factor.

[0096]

[0097] The first term quantifies the deviation between the system state and the predetermined trajectory, thus reflecting the system's ability to track the predetermined trajectory. The second term reflects the constraint on changes in the input control quantity. The third term, introducing a relaxation factor, is significant for improving the feasibility of the problem. Faced with unsolvable situations that may arise from strict constraints, the relaxation factor provides flexibility in finding feasible solutions by allowing for moderate violations of the constraints. The advantage of this objective function lies in its ease of transformation into a standard quadratic programming problem, which facilitates the solution.

[0098] The objective function requires calculating the system's output over a future period of time. The following is a prediction model.

[0099] From the above, we know the state-space expression of the discretized linear error model:

[0100]

[0101] To ensure that the final prediction model meets the form required by the objective function, the above equation is transformed as follows, defining a new state variable ξ:

[0102]

[0103] Then we have:

[0104]

[0105] in,

[0106] This leads to the following: using ξ(k | t) as the state variable and the control increment... The new state-space expression, as input, can be used to complete the state-space equation as follows:

[0107]

[0108] In the formula, n is the dimension of the state variables, where n = 3; m is the dimension of the control variables, where m = 2.

[0109] If the prediction time domain of the system is N p The control time domain is N c , where N p ≥N c By recursively analyzing the state variables and system output variables in the prediction time domain and summarizing the patterns, we can obtain the general form of the linear time-varying prediction model output expression of the system derived from the discretized linear error model:

[0110] Y = W k，t ξ(k|t)+Z k，t ΔU

[0111] In this formula:

[0112]

[0113] In the design and analysis of control systems, constraints form the basis of the system's feasible region. They are a set of predefined limitations designed to ensure that the system does not exceed predetermined safety and performance boundaries during operation.

[0114] Constraints can be divided into control variables. Incremental Constraints and Control Quantities constraint.

[0115] The general expression for the constraint can be obtained:

[0116]

[0117] In the formula, i = 0, 1, ..., N c -1, N c To control the time domain.

[0118] After clearly selecting the objective function and establishing the constraints, the entire optimization problem needs to be expressed in matrix form. By transforming the system model, objective function, and constraints into matrix multiplication, the problem can be converted into a quadratic programming problem. This matrix form not only simplifies the solution process but also improves computational efficiency.

[0119] (1) Transformation of the objective function:

[0120] Define the system output quantity Y and the output quantity reference value Y. Ref for:

[0121]

[0122] Let E = Wk，t ξ(k|t) then:

[0123] Substituting into the objective function, ignoring quantities irrelevant to ΔU, the objective function is further simplified by removing the one-dimensional constant term and completing the restorative steps to obtain:

[0124]

[0125] in: That is, X T =[ΔU T ε]; Note:

[0126] (2) Constraint transformation:

[0127] In optimization problems, constraints also need to be transformed into the form of control increments or the product of control increments and transformation matrices. Therefore, it is necessary to transform the constraints and obtain the corresponding transformation matrix, based on the control time domain N. c By recursively applying the formulas, we can obtain the following results.

[0128] U k =U k-1 +A E ΔU

[0129]

[0130] Where E is the identity matrix.

[0131] The following is the transformation matrix form of the constraint conditions:

[0132] U min ≤U=U k-1 +A E ΔU≤U max

[0133] Right now:

[0134]

[0135] The upper and lower limit constraints are as follows:

[0136]

[0137] Therefore, the design of the model predictive controller can be specifically transformed into a quadratic optimization problem:

[0138]

[0139]

[0140] In practical applications, the preset state equation can be e y,k+1 =ey,k +T*(V L,k -(V F,k-1 +ΔV F,k ), where e y,k+1 e represents the longitudinal error of the prediction at the next time step. y,k V represents the longitudinal error at the current moment, T is the preset sampling time, and V is the longitudinal error at the current moment. L,k For the current speed and V of the Navigator agricultural machinery equipment F,k-1 To track the speed of the agricultural machinery at the previous moment, ΔV F,k The speed increment of the following agricultural machinery equipment at the current moment.

[0141] The distance-velocity kinematic model between the navigator agricultural machinery, the virtual navigator, and the follower agricultural machinery can be as follows: Figure 10 As shown in the diagram, a preset state equation can be established and discretized to obtain:

[0142] e y,k+1 =e y,k +T*(V L,k -(V F,k-1 +ΔV F,k )).

[0143] In practical applications, the preset constraints may also include constraints that constrain the longitudinal error of the prediction at the next moment, constraints that constrain the speed increment of the follower agricultural machinery at the current moment, and constraints that constrain the travel speed of the follower agricultural machinery at the current moment.

[0144] The constraint condition for constraining the longitudinal error of the next moment prediction can be a state variable constraint condition, which can be used to ensure that the distance error of the following agricultural machinery is within a safe range. That is, the state variable constraint condition can be:

[0145] e min ≤e y,k ≤e max , where e min and e max It can be a preset value determined based on the equipment parameters of the following agricultural machinery equipment.

[0146] The constraint condition for constraining the speed increment of the following agricultural machinery at the current moment can be an input variable constraint condition, which can be used to control the input variable (i.e., ΔV). F,k To satisfy the actual speed variation limit and take into account the acceleration and deceleration capabilities of the vehicle system, the input variable constraints can be:

[0147] ΔV min ≤ΔV F,k ≤ΔV max , where emin and e max It can be a preset value determined based on the speed limit parameters of the following agricultural machinery.

[0148] The constraint condition for limiting the current speed of the following agricultural machinery can be a speed constraint condition. A speed constraint condition can be used to control the speed of the following agricultural machinery within a feasible range; that is, the speed constraint condition can be:

[0149] V min ≤V F,k ≤V max , where V min and V max It can be a preset value determined based on the equipment parameters of the following agricultural machinery equipment.

[0150] In practical applications, the objective function can be: Where N is the length of the prediction time domain, Q is the first preset weight matrix, R is the second preset weight matrix, and e y,k Let ΔV be the longitudinal error at the current moment. F,k This represents the speed increment of the following agricultural machinery at the current moment. The length of N is no greater than the prediction time domain length N of the lower-level MPC path tracking controller of the following agricultural machinery. p That is, (N≤N) p ).

[0151] The objective function can be used by the upper-level MPC speed planner to minimize the distance error e between the follower and leader agricultural machinery. y At the same time, try to smoothly follow the speed changes of agricultural machinery.

[0152] Based on this distance-velocity discrete state equation (i.e., the preset state equation), and considering the speed control input constraints, input variable constraints, and state variable constraints of the agricultural machinery, an upper-level MPC speed planner can be designed for the following agricultural machinery. The optimal speed control input solution sequence obtained by the upper-level MPC speed planner can be used as the desired speed of the lower-level MPC path tracking controller of the following agricultural machinery. By using the upper-level MPC speed planner to plan the desired speed of the following agricultural machinery in real time, e can be achieved. y The goal is to achieve a target of →0, thereby enabling the formation maintenance function of the aircraft group.

[0153] Thus, as Figure 11 As shown, global path planning for agricultural machinery clusters can be achieved through the planners of each agricultural machinery device.

[0154] In practical applications, the specific processing methods for obtaining the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area at the current moment, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment in step S102 can be varied. Another optional processing method is provided below, such as... Figure 12 As shown, the specific process may include the following steps, S1022.

[0155] In step S1022, if the path index point of the follower agricultural machinery at the current moment is different from the path index point of the virtual navigator at the current moment, the driving speed of the navigator agricultural machinery corresponding to the follower agricultural machinery at the current moment in the target working area, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment are obtained.

[0156] In implementation, the follower agricultural machinery needs to perform two sub-tasks: the first is tracking and controlling the predetermined planned path, and the second is maintaining the formation. When designing the model predictive controller for the follower agricultural machinery, the follower can receive the path index point from the navigator's current position information (i.e., the first position information) sent by the navigator according to the communication topology via a wireless communication module. The follower can then calculate the virtual navigator's path index point (index_VL) using the method described above for calculating the virtual navigator's current path index point.

[0157] If the difference between index_VL and index_F (i.e., the path index point of the follower agricultural machinery) is small, then the discrete trajectory point on the preset path of the follower corresponding to that path index point can be used as the expected reference point for the follower agricultural machinery. The kinematic model of the follower agricultural machinery can be linearized at this point, and the predicted output equation of the machinery can be derived. Based on the predicted output equation, the constraints of the follower machinery, and the objective function, the optimal control sequence can be solved. Except for selecting the desired reference point for linearization, the other steps are consistent with the design steps of the predictive controller for the leader model. That is, when the state error between index_F and the desired index index_VL of the follower agricultural machinery is small, the model's state variables can still converge to the desired trajectory.

[0158] However, when the distance between the current position and the expected position of the follower agricultural machinery at the next moment is large due to certain interferences on the planned path, for example, ... Figure 13As shown, index_VL is located at the turning point, and index_F is located at the straight point, with a large difference between them. Since the expected turning angles at the straight point and the turning point are different, linearizing the follower agricultural machinery located at index_F at index_VL to obtain a linear error model and solving the control law based on this linear error model will cause a large model mismatch, and the prediction model cannot predict the future change trend of the original nonlinear model.

[0159] Therefore, the follower steering kinematics model located at index_F can be linearized at index_F instead of at index_VL. At the same time, the expected speed of the follower agricultural machinery after the planned path index_F can be planned in real time.

[0160] This specification provides an agricultural machinery control method that can acquire the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, the method determines the speed increment of the follower agricultural machinery at the current moment. The objective function minimizes the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, the method determines the target speed of the follower agricultural machinery at the current moment and controls the follower agricultural machinery to continue traveling in the target working area at the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0161] The above are the agricultural machinery control methods provided in the embodiments of this specification. Based on the same idea, the embodiments of this specification also provide an agricultural machinery control device, such as... Figure 14 As shown.

[0162] The agricultural machinery control device includes: a data acquisition module 1401, an increment determination module 1402, and a speed determination module 1403, wherein:

[0163] The data acquisition module 1401 is used to acquire the current speed of the navigator agricultural machinery equipment corresponding to the agricultural machinery control device in the target working area, as well as the longitudinal error between the agricultural machinery control device and the virtual navigator at the current moment.

[0164] The incremental determination module 1402 is used to determine the speed increment of the agricultural machinery control device at the current moment according to a preset objective function and preset constraints. The objective function is used to minimize the predicted longitudinal error of the agricultural machinery control device and the virtual navigator at the next moment. The constraints include a preset state equation, which is used to characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the agricultural machinery control device at the previous moment, and the speed increment of the agricultural machinery control device at the current moment.

[0165] The speed determination module 1403 is used to determine the target speed of the agricultural machinery control device at the current moment based on the speed increment of the agricultural machinery control device at the current moment, and control the agricultural machinery control device to continue to travel in the target working area at the current moment according to the target speed.

[0166] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the agricultural machinery control device embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0167] This specification provides an agricultural machinery control device that can acquire the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, it determines the speed increment of the follower agricultural machinery at the current moment. The objective function minimizes the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, it determines the target speed of the follower agricultural machinery at the current moment and controls the follower agricultural machinery to continue traveling in the target working area at the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0168] The above are the agricultural machinery control devices provided in the embodiments of this specification. Based on the same idea, the embodiments of this specification also provide an agricultural machinery control device, such as... Figure 15 As shown.

[0169] The agricultural machinery control equipment can provide terminal equipment or servers, etc., for the above embodiments.

[0170] Agricultural machinery control devices can vary significantly due to differences in configuration or performance. They may include one or more processors 1501 and memories 1502, with the memory 1502 storing one or more application programs or data. The memory 1502 can be temporary or persistent storage. The application programs stored in the memory 1502 may include one or more modules (not shown), each module including a series of computer-executable instructions for the agricultural machinery control device. Furthermore, the processor 1501 may be configured to communicate with the memory 1502, executing the series of computer-executable instructions stored in the memory 1502 on the agricultural machinery control device. The agricultural machinery control device may also include one or more power supplies 1503, one or more wired or wireless network interfaces 1504, one or more input / output interfaces 1505, and one or more keyboards 1506.

[0171] Specifically, in this embodiment, the agricultural machinery control device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the agricultural machinery control device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0172] The system obtains the current speed of the navigator agricultural machinery corresponding to the agricultural machinery control device in the target working area, as well as the longitudinal error between the agricultural machinery control device and the virtual navigator at the current moment.

[0173] Based on a preset objective function and preset constraints, the speed increment of the agricultural machinery control device at the current moment is determined. The objective function is used to minimize the predicted longitudinal error of the agricultural machinery control device and the virtual navigator at the next moment. The constraints include a preset state equation, which is used to characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the agricultural machinery control device at the previous moment, and the speed increment of the agricultural machinery control device at the current moment.

[0174] Based on the speed increment of the agricultural machinery control equipment at the current moment, the target speed of the agricultural machinery control equipment at the current moment is determined, and the agricultural machinery control equipment is controlled to continue to travel in the target working area at the current moment according to the target speed.

[0175] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. In particular, the embodiments for agricultural machinery control equipment are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0176] This specification provides an agricultural machinery control device that can acquire the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, it determines the speed increment of the follower agricultural machinery at the current moment. The objective function minimizes the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which characterizes the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, it determines the target speed of the follower agricultural machinery at the current moment and controls the follower agricultural machinery to continue traveling in the target working area at the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0177] Furthermore, based on the above Figures 1 to 13 The method shown in this specification, along with one or more embodiments, also provides a storage medium for storing computer-executable instruction information. In one specific embodiment, the storage medium can be a USB flash drive, optical disc, hard disk, etc. When the computer-executable instruction information stored in the storage medium is executed by a processor, it can achieve the following process:

[0178] The following agricultural machinery equipment is obtained at its current speed in the target working area, and the longitudinal error between the following agricultural machinery equipment and the virtual navigator at the current moment.

[0179] Based on a preset objective function and preset constraints, the speed increment of the follower agricultural machinery at the current moment is determined. The objective function is used to minimize the prediction longitudinal error of the follower agricultural machinery and the virtual navigator at the next moment. The constraints include a preset state equation, which is used to characterize the relationship between the prediction longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the follower agricultural machinery at the previous moment, and the speed increment of the follower agricultural machinery at the current moment.

[0180] Based on the speed increment of the follower agricultural machinery at the current moment, the target speed of the follower agricultural machinery at the current moment is determined, and the follower agricultural machinery is controlled to continue traveling in the target working area at the current moment according to the target speed.

[0181] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the above-described storage medium embodiment is basically similar to the method embodiment, so the description is relatively simple; relevant parts can be referred to the description of the method embodiment.

[0182] This specification provides a storage medium that can acquire the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, the speed increment of the follower agricultural machinery at the current moment is determined. The objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which can characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined, and the follower agricultural machinery can be controlled to continue traveling in the target working area at the current moment according to the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0183] Furthermore, based on the above Figures 1 to 13 The method shown in this specification, along with one or more embodiments, also provides a computer program product including a computer program that, when executed by a processor, performs the following process:

[0184] The following agricultural machinery equipment is obtained at its current speed in the target working area, and the longitudinal error between the following agricultural machinery equipment and the virtual navigator at the current moment.

[0185] Based on a preset objective function and preset constraints, the speed increment of the follower agricultural machinery at the current moment is determined. The objective function is used to minimize the prediction longitudinal error of the follower agricultural machinery and the virtual navigator at the next moment. The constraints include a preset state equation, which is used to characterize the relationship between the prediction longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the follower agricultural machinery at the previous moment, and the speed increment of the follower agricultural machinery at the current moment.

[0186] Based on the speed increment of the follower agricultural machinery at the current moment, the target speed of the follower agricultural machinery at the current moment is determined, and the follower agricultural machinery is controlled to continue traveling in the target working area at the current moment according to the target speed.

[0187] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the above-described embodiment of a computer program product is relatively simple in description because it is fundamentally similar to the method embodiment; relevant parts can be referred to the description of the method embodiment.

[0188] This specification provides a computer program product that can acquire the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, as well as the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, it determines the speed increment of the follower agricultural machinery at the current moment. The objective function is used to minimize the predicted longitudinal error between the follower agricultural machinery and the virtual navigator at the next moment. The constraints may include a preset state equation, which can characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the current speed of the navigator agricultural machinery, the previous speed of the follower agricultural machinery, and the speed increment of the follower agricultural machinery at the current moment. Finally, based on the speed increment of the follower agricultural machinery at the current moment, it can determine the target speed of the follower agricultural machinery at the current moment and control the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed. In this way, the current speed of the navigator can be obtained through real-time communication between the follower and navigator agricultural machinery. Then, based on the kinematic characteristics, objective function, and preset conditions of the follower and navigator agricultural machinery, the speed increment of the follower agricultural machinery at the current moment can be determined. Based on the speed increment at the current moment, the target speed of the follower agricultural machinery at the current moment can be determined. This ensures that the follower agricultural machinery can accurately track the predetermined planned path in the agricultural machinery cluster while maintaining the preset formation for operation, reducing the risk of collision and improving the work efficiency of the agricultural machinery cluster operation.

[0189] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0190] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many methodological improvements today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that a methodological improvement cannot be implemented using a hardware physical module. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program a digital system themselves to "integrate" it onto a PLD, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0191] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0192] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0193] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable fraud device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0195] These computer program instructions can also be loaded onto a computer or other programmable device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0196] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0197] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0198] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0199] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0201] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0202] The above description is merely an embodiment of this specification and is not intended to limit this document. Various modifications and variations can be made to this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this specification should be included within the scope of the claims of this specification.

Claims

1. An agricultural machinery control method, the method being applied to follower agricultural machinery equipment, the method comprising: The following agricultural machinery equipment is obtained at its current speed in the target working area, and the longitudinal error between the following agricultural machinery equipment and the virtual navigator at the current moment. Based on a preset objective function and preset constraints, the speed increment of the follower agricultural machinery at the current moment is determined. The objective function is used to minimize the prediction longitudinal error of the follower agricultural machinery and the virtual navigator at the next moment. The constraints include a preset state equation, which is used to characterize the relationship between the prediction longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the follower agricultural machinery at the previous moment, and the speed increment of the follower agricultural machinery at the current moment. Based on the speed increment of the follower agricultural machinery at the current moment, determine the target speed of the follower agricultural machinery at the current moment, and control the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed; The step of obtaining the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment includes: Obtain the first location information of the Navigator agricultural machinery equipment in the target working area at the current moment; Based on the path index point in the first location information, the expected longitudinal distance of the virtual navigator at the current moment, and the preset distance between two adjacent path index points in the preset planned path of the follower agricultural machinery, the path index point of the virtual navigator at the current moment is determined. Based on the current path index point of the follower agricultural machinery and the current path index point of the virtual navigator, determine the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment; The step of determining the target speed of the follower agricultural machinery in the next moment based on the speed increment of the follower agricultural machinery at the current moment, and controlling the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed, includes: Based on the speed increment of the follower agricultural machinery at the current moment, the first speed of the follower agricultural machinery at the current moment is determined using a preset model predictive control algorithm; Based on the first location information, the virtual navigator's expected lateral distance and expected heading angle at the current moment, determine the virtual navigator's lateral coordinates and heading angle at the current moment; Based on the lateral coordinates and heading angle of the follower agricultural machinery at the current moment, and the lateral coordinates and heading angle of the virtual navigator at the current moment, determine the lateral error and heading angle error of the follower agricultural machinery and the virtual navigator at the current moment; Based on the longitudinal error at the current moment, the longitudinal error and the heading angle error, and the first speed, the preset model predictive control algorithm is used to determine the target speed and target turning angle of the follower agricultural machinery at the current moment, and to control the follower agricultural machinery to continue traveling in the target working area at the current moment according to the target speed and the target turning angle.

2. The method according to claim 1, wherein the preset state equation is: ,in, The longitudinal error of the prediction at the next time step. The longitudinal error at the current moment is... The preset sampling time, The speed of the Navigator agricultural machinery at the current moment. The speed of the following agricultural machinery at the previous moment. The speed increment of the follower agricultural machinery at the current moment.

3. The method according to claim 2, wherein the preset constraint conditions further include constraint conditions for constraining the prediction longitudinal error at the next moment, constraint conditions for constraining the speed increment of the follower agricultural machinery at the current moment, and constraint conditions for constraining the travel speed of the follower agricultural machinery at the current moment.

4. The method according to claim 3, wherein the objective function is ,in, N is the length of the prediction time domain. This is the first preset weight matrix. For the second preset weight matrix, The longitudinal error at the current moment is... The speed increment of the follower agricultural machinery at the current moment.

5. The method according to claim 1, wherein obtaining the current speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment, comprises: If the path index point of the follower agricultural machinery at the current moment is different from that of the virtual navigator at the current moment, obtain the current driving speed of the navigator agricultural machinery corresponding to the follower agricultural machinery in the target working area, and the longitudinal error between the follower agricultural machinery and the virtual navigator at the current moment.

6. An agricultural machinery control device, said device being applied to the agricultural machinery control method as described in any one of claims 1 to 5, said device comprising: The data acquisition module is used to acquire the current speed of the navigator agricultural machinery equipment corresponding to the agricultural machinery control device in the target working area, as well as the longitudinal error between the agricultural machinery control device and the virtual navigator at the current moment. The incremental determination module is used to determine the speed increment of the agricultural machinery control device at the current moment according to a preset objective function and preset constraints. The objective function is used to minimize the predicted longitudinal error of the agricultural machinery control device and the virtual navigator at the next moment. The constraints include a preset state equation, which is used to characterize the relationship between the predicted longitudinal error at the next moment, the longitudinal error at the current moment, the preset sampling time, the driving speed of the navigator agricultural machinery at the current moment, the driving speed of the agricultural machinery control device at the previous moment, and the speed increment of the agricultural machinery control device at the current moment. The speed determination module is used to determine the target speed of the agricultural machinery control device at the current moment based on the speed increment of the agricultural machinery control device at the current moment, and to control the agricultural machinery control device to continue to travel in the target working area at the current moment according to the target speed.

7. An agricultural machinery control device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the agricultural machinery control method as described in any one of claims 1 to 5.

8. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the steps of the agricultural machinery control method according to any one of claims 1 to 5.

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