Control method for operation equipment and related device

Abstract

The invention discloses a control method for operation equipment and a related device, and relates to the technical field of operation equipment. The control method comprises the following steps: determining a driving path and a target end point based on environment information collected by operation equipment; determining a first distance between the current position of the operation equipment and the target end point; based on a comparison result between the first distance and a preset threshold value, adopting a first control strategy or a second control strategy to drive the operation equipment to move, wherein under the condition that the first distance is greater than the preset threshold value, path tracking is carried out along the driving path by adopting the first control strategy; and under the condition that the first distance is smaller than or equal to the preset threshold value, switching to the second control strategy to enable the operation equipment to move to the target end point. According to the invention, the operation precision of the unmanned forklift in a complex outdoor scene can be improved.

Description

Technical Field

[0001] This application relates to the field of work equipment technology, and more specifically to a control method and related apparatus for work equipment. Background Technology

[0002] In the industrial logistics sector, forklifts, as cargo handling equipment, are widely used in various scenarios such as warehousing, ports, and freight yards. With the development trend of intelligent and unmanned logistics, their application scenarios are gradually expanding from highly structured indoor environments to complex outdoor environments. In outdoor scenarios, factors such as undulating terrain, changing lighting, and random distribution of obstacles place higher demands on the trajectory control and positioning accuracy of forklifts. Unmanned forklifts, with their automated operation capabilities, have become a key direction for solving the problems of efficiency and safety in outdoor handling.

[0003] Currently, most mainstream automation control methods rely on the global application of a single algorithm. For example, pure tracking and other geometric path tracking algorithms have simple structures, good real-time performance, and stable performance during the path following phase. However, these algorithms have poor convergence near the endpoint, making it difficult to achieve high-precision pose positioning control. Therefore, a single control algorithm cannot simultaneously ensure the stability of long-distance trajectory tracking and the accuracy of endpoint positioning, resulting in lower operational accuracy of unmanned forklifts in complex outdoor scenarios. Summary of the Invention

[0004] The purpose of this application is to provide a control method and related apparatus for operating equipment, in order to solve the problem of low operating accuracy of existing unmanned forklifts in complex outdoor scenarios.

[0005] To achieve the above objectives, the first aspect of this application provides a control method for operating equipment, the control method comprising: Based on the environmental information collected by the operating equipment, the driving route and target destination are determined; Determine a first distance between the current position of the working equipment and the target endpoint; Based on the comparison result between the first distance and the preset threshold, the first control strategy or the second control strategy is adopted to drive the working equipment to move. Specifically, when the first distance is greater than the preset threshold, the first control strategy is used to track the path along the driving path; when the first distance is less than or equal to the preset threshold, the second control strategy is switched to enable the working equipment to move to the target endpoint.

[0006] A second aspect of this application provides a control device for operating equipment, the control device comprising: The first determining module is used to determine the driving route and target destination based on the environmental information collected by the operating equipment; The second determining module is used to determine a first distance between the current position of the working equipment and the target endpoint; The drive module is used to drive the working equipment to move based on the comparison result between the first distance and the preset threshold, using a first control strategy or a second control strategy. Specifically, when the first distance is greater than the preset threshold, the first control strategy is used to track the path along the driving path; when the first distance is less than or equal to the preset threshold, the second control strategy is switched to enable the working equipment to move to the target endpoint.

[0007] A third aspect of this application provides an operating device, including: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the control method as described in any one of the first aspects.

[0008] A fourth aspect of this application provides a machine-readable storage medium storing instructions that cause a machine to perform the control method according to the first aspect.

[0009] The fifth aspect of this application provides a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform the control method for an operating device as described above.

[0010] In this application, a travel path and target endpoint are first planned. By continuously calculating the distance between the current position and the endpoint and comparing it with a preset threshold, the system autonomously makes decisions and dynamically switches control strategies. When the distance is long, a first control strategy focused on path tracking is adopted to ensure that the equipment can travel along the planned path efficiently and stably. When approaching the endpoint, a second control strategy focused on precise pose adjustment is seamlessly switched to ensure that the equipment can accurately reach and stop at the target location. The mechanism of this application enables the equipment to automatically call the most suitable control mode at different stages of operation. Without relying on manual intervention, it effectively unifies and improves the tracking stability and positioning accuracy of long-distance travel, achieving fully automated and high-precision control from the starting point to the endpoint.

[0011] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0012] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The illustration shows a schematic flowchart of a control method for working equipment according to an embodiment of this application; Figure 2 This illustration schematically shows a structural diagram of a control device for working equipment according to an embodiment of this application; Figure 3 The illustration shows a general flow diagram of a control method for working equipment according to an embodiment of this application; Figure 4 A schematic diagram of the hardware structure of the operating device according to an embodiment of this application is shown. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application all comply with relevant laws and regulations. In the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solution of this application, and do not imply that the applicant has already used or necessarily used such solutions.

[0015] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0016] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0017] It should be noted that the control method for working equipment provided in the subsequent embodiments of this application can be applied to any working equipment. For the purpose of clearly illustrating the technical solution, the application of the control method to working equipment is used as an example to illustrate the embodiments.

[0018] The operating equipment of this application is described below with reference to the accompanying drawings.

[0019] Figure 1 The illustration schematically shows a flow chart of a control method for operating equipment according to an embodiment of this application. For example... Figure 1 As shown in the figure, this application provides a control method for operating equipment, which may include the following steps.

[0020] Step 101: Based on the environmental information collected by the operating equipment, determine the driving route and target destination; In this embodiment, the operating equipment includes, but is not limited to, autonomous forklifts, mobile robots, automated guided vehicles, and other devices with autonomous mobility. The "environmental information" can be acquired through the sensing system onboard the equipment, including data collected by one or more of the following: lidar, vision cameras, millimeter-wave radar, and ultrasonic sensors. For example, point cloud data of the surrounding environment can be acquired through lidar scanning, or image information can be acquired through a vision camera.

[0021] In practice, this step can be further subdivided into sub-steps such as environmental perception, localization, map building, and path planning. In one embodiment, simultaneous localization and map building technology can be used to generate or update an environmental map in real time based on LiDAR point cloud data, and the target destination can be determined by combining task instructions or by identifying specific markers. The target destination includes at least one spatial coordinate, and preferably also includes a desired terminal heading angle, constituting the target destination pose. Subsequently, based on the current position, the target destination position, and obstacle information in the environmental map, a collision-free driving path that conforms to kinematic constraints is calculated using a path planning algorithm.

[0022] Step 102: Determine the first distance between the current position of the working equipment and the target endpoint; In this embodiment, the current position refers to the real-time position of the operating equipment in the global coordinate system or the path coordinate system, which is usually represented by the position components in its real-time pose, including the horizontal and vertical coordinates and the heading angle. Methods for obtaining real-time pose include, but are not limited to, positioning technology based on multi-sensor fusion.

[0023] The calculation of the first distance preferably uses the Euclidean distance formula. Let the coordinates of the target endpoint be... The current position coordinates of the working equipment are Then the first distance It can be calculated as: ; Step 103: Based on the comparison result between the first distance and the preset threshold, the first control strategy or the second control strategy is adopted to drive the working equipment to move; Specifically, when the first distance is greater than the preset threshold, the first control strategy is used to track the path along the driving path; when the first distance is less than or equal to the preset threshold, the second control strategy is switched to enable the working equipment to move to the target endpoint.

[0024] In this embodiment, a preset threshold is used. A radius of the switching area centered on the target endpoint is defined. The setting of this threshold must consider control precision: if set too high, the equipment may enter precise adjustment mode prematurely, potentially reducing overall operating efficiency; if set too low, there may be insufficient margin for precise adjustment, affecting the final docking accuracy. Its value can be determined comprehensively based on the physical dimensions of the operating equipment, its motion characteristics (such as braking distance), and the required docking accuracy, or through experimental calibration.

[0025] The first control strategy is applicable to... > The main control objective is to enable the operating equipment to accurately and smoothly track the planned travel path, ensuring stability and efficiency on the way to the destination. This strategy typically does not impose strict constraints on terminal accuracy.

[0026] The second control strategy is applicable to ≤ In this situation, the control objective shifts from tracking the path to precisely arriving at and docking at the target endpoint. This strategy aims to minimize the positional and heading angle errors of the equipment at the endpoint, achieving high-precision fixed-point docking. As an example, movement stops when the positional error between the working equipment and the target endpoint is less than 0.01m and the heading angle error is less than 1°.

[0027] In this embodiment, a travel path and target endpoint are first planned. By continuously calculating the distance between the current position and the endpoint and comparing it with a preset threshold, the system autonomously makes decisions and dynamically switches control strategies. When the distance is long, a first control strategy focused on path tracking is adopted to ensure that the device can travel along the planned path efficiently and stably. When approaching the endpoint, it seamlessly switches to a second control strategy focused on precise pose adjustment, thereby ensuring that the device can accurately reach the target location and stop. This mechanism enables the device to automatically call the most suitable control mode at different stages of operation, effectively unifying and improving the tracking stability and terminal positioning accuracy over long distances without relying on manual intervention, achieving fully automated and high-precision control from start to finish.

[0028] In one embodiment of this application, the step of using the first control strategy to perform path tracking along the driving path when the first distance is greater than the preset threshold includes: The aiming distance is determined based on the real-time speed of the operating equipment; Based on the pre-aiming distance, determine the pre-aiming point on the driving path; Obtain the second distance between the center point of the rear wheel of the working equipment and the nearest point on the travel path; The front wheel angle of the working equipment is calculated based on the pre-aiming point, the driving path, and the second distance. The movement of the work equipment is controlled based on the front wheel steering angle so that the work equipment can track the path along the travel path.

[0029] In this embodiment, real-time speed typically refers to the longitudinal linear velocity of the working equipment at the current moment, which can be estimated in real time using wheel speed sensors, inertial measurement units, or multi-sensor fusion algorithms. Pre-aiming distance refers to the path length or straight-line distance between a target point (i.e., a pre-aiming point) selected by the algorithm on the planned driving path and the vehicle's current position. Its determination method is dynamic and positively correlated with real-time speed: at higher vehicle speeds, a larger pre-aiming distance needs to be set to ensure smooth and stable control, avoiding oscillations due to response lag; at lower vehicle speeds, a smaller pre-aiming distance can be used to improve tracking accuracy. In a preferred embodiment, the pre-aiming distance... The determination also takes into account the vehicle's acceleration coefficient, speed coefficient, and minimum turning radius. Isokinetic constraints, through formula Calculations are performed, in which, For aiming distance, For the preset acceleration coefficient, For the preset speed coefficient, For real-time speed, This is the minimum turning radius.

[0030] After determining the aiming distance, draw a circle with the current position of the working equipment as the center and the aiming distance as the radius. Calculate the first intersection point between this circle and the travel path; this intersection point is the aiming point. Alternatively, you can start from the beginning of the travel path or the previous aiming point and accumulate the arc length or straight-line distance forward along the path until the accumulated distance first reaches or exceeds the aiming distance. If so, the path point corresponding to that position is determined as the aiming point for the current cycle. This aiming point represents the short-term target position that the operating equipment expects to reach or approach in the next control cycle.

[0031] Calculate the perpendicular distances from the center point of the vehicle's rear wheel to all line segments along the driving path, and take the point where the perpendicular is perpendicular to the shortest distance; this is the closest point. The straight-line distance between this point and the center point of the rear wheel is the second distance.

[0032] The calculation of the front wheel steering angle is based on the geometric principles of a pure tracking algorithm. This involves treating the center point of the rear wheels and the aiming point of the equipment as points on the same circle. The required rotation angle of the front wheels is derived through geometric relationships to ensure the equipment travels along the trajectory of this circle to the aiming point. Specifically, it is calculated using the first formula:

[0033] in, For the front wheel steering angle, For aiming distance, For the wheelbase of the working equipment, This is the second distance.

[0034] The calculated front wheel angle is converted into a control command for the steering actuator of the work equipment, driving the steering mechanism to rotate to the corresponding angle; at the same time, combined with the real-time speed of the work equipment, the power system is controlled to maintain a stable driving state.

[0035] Additionally, it should be noted that during the equipment's operation, the above steps are repeated in a loop: the pre-aiming distance, pre-aiming point, and second distance are updated in real time, and the front wheel angle is recalculated to continuously adjust the equipment's driving direction, thereby achieving accurate tracking along the planned path.

[0036] In this embodiment, the pre-aiming distance is determined by dynamically adapting to the real-time speed, and the front wheel angle is calculated and adjusted cyclically by combining geometric logic. This not only adapts to the characteristics of the equipment to avoid tracking failure, but also improves the tracking accuracy of long-distance paths and enhances the driving stability of the operating equipment in complex outdoor scenarios.

[0037] In one embodiment of this application, the step of switching to the second control strategy when the first distance is less than or equal to the preset threshold, so as to move the working equipment to the target endpoint, includes: The pose error is determined based on the deviation between the real-time pose of the working equipment and the final pose of the target endpoint. The pose error includes lateral displacement error and heading angle error. A first control component is generated based on the pose error, and the first control component is used to adjust the front wheel angle of the working equipment. Based on the longitudinal displacement error between the real-time position of the working equipment and the target endpoint, a second control component is generated, which is used to adjust the speed of the working equipment. Based on the first control component and the second control component, the working equipment is driven to move toward and stop at the target endpoint.

[0038] In this embodiment, real-time pose refers to the current spatial state of the working equipment, including real-time position coordinates. With real-time heading angle The real-time position coordinates and real-time heading angle are obtained by the multi-sensor fusion module through a Kalman filter algorithm, fusing the longitudinal acceleration data from the IMU and the wheel speed data from the wheel speed sensor; the endpoint pose refers to the preset state of the target endpoint, including the endpoint position coordinates. With the final heading angle .

[0039] Lateral displacement error refers to the distance from the reference point of the working equipment (such as the rear axle center) to a straight line passing through the target endpoint and perpendicular to the target heading. It characterizes the lateral deviation of the vehicle in the direction perpendicular to the desired parking direction. Heading angle error is calculated as the difference between the current heading angle and the target heading angle.

[0040] The first control component is the front wheel steering angle correction for lateral displacement error and heading angle error, including the lateral displacement error correction. and heading angle error correction The incremental PID algorithm can be used to calculate this.

[0041] Specifically, in one embodiment, generating the first control component based on the pose error includes: At least one first historical error value corresponding to each of the lateral displacement error and the heading angle error is obtained respectively; The first control component is obtained by incremental calculation based on the lateral displacement error, the heading angle error, and the first historical error value.

[0042] In this embodiment, historical error data is first acquired, that is, the previous sampling period value of the "lateral displacement error" is collected respectively. The value of the previous sampling period and the previous sampling period value of "heading angle error". The value of the previous sampling period .

[0043] Then, calculate the lateral displacement error correction amount respectively. The heading angle error correction amount corresponding to the heading angle error Specifically, the calculation is performed using the third and fourth formulas: ; ; in, The proportionality coefficient of the lateral error, The integral coefficient of the transverse error, The differential coefficients of the lateral error, The proportionality coefficient for the heading angle error, The integral coefficient of the heading angle error, The differential coefficients of the heading angle error, The sampling period.

[0044] Correction amount for lateral displacement error and heading angle error correction By superimposing these values, the final front wheel steering angle correction, i.e., the first control component, is obtained. .

[0045]

[0046] The second control component is an acceleration control quantity targeting the longitudinal displacement error. By adjusting the acceleration, the equipment speed is controlled, ultimately achieving a smooth stop at the destination. It is also calculated using an incremental PID algorithm. Specifically, in one embodiment, generating the second control component based on the longitudinal displacement error between the real-time position of the working equipment and the target destination includes: Obtain at least one second historical error value corresponding to the longitudinal displacement error; The second control component is obtained by incremental calculation based on the longitudinal displacement error and the second historical error value.

[0047] In this embodiment, the longitudinal displacement error is first determined. The deviation between the current longitudinal position of the working equipment and the longitudinal position of the endpoint is... = - Then, the longitudinal displacement error value from the previous sampling period is collected. Finally, the second control component is calculated using the fifth formula. : ; in, The proportionality coefficient of longitudinal error, The integral coefficient of the longitudinal error, is the differential coefficient of the longitudinal error.

[0048] Finally, based on the first control component and the second control component, the working equipment is driven to move towards and stop at the target endpoint, specifically: The first and second control components are converted into control commands for the actuators, which then work together to drive the work equipment to complete the destination stop. For the first control component, it is superimposed on the basic front wheel angle output by the pure tracking algorithm to obtain the final front wheel angle command, which is sent to the steering actuator of the working equipment to drive the servo motor to rotate to the corresponding angle and correct the lateral position and heading angle of the equipment. For the second control component, the vehicle speed is adjusted according to the target acceleration, specifically through the formula... Update the current vehicle speed and send the updated speed command to the power / brake actuator to control the equipment to decelerate or fine-tune the speed; The real-time position and velocity of the equipment are continuously collected, and the above calculation and control steps are repeated until the lateral displacement error and longitudinal displacement error of the equipment are both less than 0.01m and the heading angle error is less than 1°. At this time, the equipment completes precise docking and the control process ends.

[0049] In this embodiment, by extracting the pose error and longitudinal displacement error in different dimensions, control components for the front wheel angle and vehicle speed are generated in a targeted manner, which solves the problem that traditional single strategies cannot take into account both the endpoint position and directional accuracy. At the same time, the application of incremental PID avoids abrupt changes in control commands and improves the smoothness of docking. The coordinated adjustment of pose and speed achieves high-precision docking.

[0050] In one embodiment of this application, the real-time pose of the working equipment is determined through the following steps: Obtain the longitudinal acceleration information and wheel speed information of the operating equipment; Based on the longitudinal acceleration information and wheel speed information, the real-time pose is obtained by fusing them using a state estimation algorithm.

[0051] In this embodiment, the real-time longitudinal acceleration information of the equipment can be collected through the IMU (Inertial Measurement Unit) mounted on the working equipment. This information directly reflects the longitudinal motion acceleration state of the equipment. At the same time, the real-time rotational speed information of the tires is collected through wheel speed sensors, which represents the motion speed-related data of the equipment. The acquisition cycles of the two types of sensor data are kept synchronized to ensure timing consistency.

[0052] The rotational speed information collected by the wheel speed sensor is preprocessed using the formula. Convert to linear velocity, where Inch is the tire radius, 25.4 is the conversion factor between inches and millimeters, and divide by 1000 to convert millimeters to meters, obtaining a measurement value directly related to vehicle speed.

[0053] Kalman filtering is employed as the state estimation algorithm to fuse preprocessed linear velocity information with longitudinal acceleration information acquired by the IMU. Through the Kalman filtering's "prediction-update" iterative process, the predicted values ​​from the system model and the sensor measurements are balanced, effectively suppressing noise interference such as wheel speed sensor slippage and IMU acceleration drift, outputting accurate real-time vehicle speed and longitudinal acceleration. Furthermore, by integrating the real-time vehicle speed over time, the real-time position coordinates of the working equipment are obtained. With real-time heading angle That is, the complete real-time pose.

[0054] The Kalman filter algorithm will be explained in detail below: Longitudinal acceleration in IMU sensor Vehicle speed and wheel speed sensor As the observation vector of the filtering algorithm, Acceleration and vehicle speed are chosen as the state vectors of the filter. According to kinematic relationships, the current vehicle speed equals the vehicle speed at the previous moment plus the speed provided by acceleration during the previous sampling period. Therefore, the state transition matrix is: ,in Given the sampling period, and selecting the observed acceleration and vehicle speed, the observation matrix is... .

[0055] In Kalman filtering, after initializing the state variables, the calculation process mainly consists of five steps, including: 1. Predict the state estimate by using the best estimate from the previous time step to predict the current state; ; This step utilizes the existing optimal state estimate from the previous time step. By combining the state transition matrix F, the estimated state value at the current time is predicted. .

[0056] 2. Predict the covariance matrix and update the uncertainty of the predicted state; ; This step is used to determine the covariance matrix at the previous time step. After mapping by the state transition matrix, process noise is superimposed. The uncertainty of the current predicted state is obtained. .

[0057] 3. Calculate the Kalman gain to balance the weights of prediction and observation; the larger the value, the more trust the observation data has. ; The core function of Kalman gain is to "determine whether to trust the predicted state or the observed data more": If observation noise Smaller (higher sensor accuracy) results in a smaller denominator. The larger the dataset, the more the algorithm trusts the observational data. If process noise Smaller (more reliable model) Get smaller The smaller the value, the more the algorithm trusts the predicted state.

[0058] 4. Update the state estimate by correcting the predicted value with sensor measurements to obtain the current optimal estimate; ; The "observation residual," which is the difference between the actual observed value and the predicted value, is expressed through Kalman gain. After weighting, the predicted state After making corrections, the optimal state estimate for the current moment is finally obtained. .

[0059] 5. Update the covariance matrix and update the uncertainty of the current optimal state; ; in and These are the covariance matrices for process noise and observation noise, respectively. By controlling the sizes of these two matrices, we can adjust whether the filter chooses to place more weight on the measurement results or the observation results. It is Kalman gain. It is the covariance matrix.

[0060] After each round of filtering iteration, the optimal state vector at the current time is output. ,in For vehicle speed, This is the longitudinal acceleration.

[0061] In this embodiment, by fusing longitudinal acceleration and wheel speed information, the state estimation algorithm effectively suppresses errors such as wheel slippage and IMU drift, outputting accurate real-time pose, providing reliable data support for subsequent pose error calculation and control strategy execution, and improving the terminal docking accuracy of the working equipment.

[0062] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0063] Figure 2 A schematic flowchart of a control method for a working device according to an embodiment of the present invention is shown.

[0064] like Figure 2As shown, the planned path and destination information output by the path planning module are used as input, and the control strategy is dynamically switched by judging distance: First, the judgment of "current distance to destination > set distance" is executed: if the result is "yes", the pure tracking algorithm strategy is activated, and the steps of "calculating the aiming distance", "finding the aiming point" and "front wheel steering angle control" are completed in sequence. Then, the loop returns to the distance judgment stage and continues to execute pure tracking control. If the distance judgment result is "no", then switch to PID control strategy, adjust the equipment state through "braking and front wheel angle control" to finally complete path tracking and destination arrival.

[0065] Figure 3 A schematic diagram of the structure of a control device provided in another embodiment of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0066] Reference Figure 3 The control device 300 may include: The first determining module 301 is used to determine the driving route and the target destination based on the environmental information collected by the operating equipment; The second determining module 302 is used to determine a first distance between the current position of the working equipment and the target endpoint; The drive module 303 is used to drive the working equipment to move based on the comparison result between the first distance and the preset threshold, using a first control strategy or a second control strategy. Specifically, when the first distance is greater than the preset threshold, the first control strategy is used to track the path along the driving path; when the first distance is less than or equal to the preset threshold, the second control strategy is switched to enable the working equipment to move to the target endpoint.

[0067] Optionally, the drive module 303 includes: The first determining submodule is used to determine the pre-aiming distance based on the real-time speed of the operating equipment; The second determining submodule is used to determine the pre-aiming point on the driving path based on the pre-aiming distance; The first acquisition submodule is used to acquire the second distance between the center point of the rear wheel of the working equipment and the nearest point on the driving path; The first calculation submodule is used to calculate the front wheel angle of the working equipment based on the pre-aiming point, the driving path and the second distance; The control submodule is used to control the movement of the working equipment based on the front wheel steering angle, so that the working equipment can track the path along the travel path.

[0068] Optionally, the first calculation submodule is specifically used for: The front wheel angle of the working equipment is calculated using the first formula, which is:

[0069] in, For the front wheel steering angle, For aiming distance, For the wheelbase of the working equipment, This is the second distance.

[0070] Optionally, the drive module 303 also includes: The third determining submodule is used to determine the pose error based on the deviation between the real-time pose of the working equipment and the endpoint pose of the target endpoint. The pose error includes lateral displacement error and heading angle error. The first generation submodule is used to generate a first control component based on the pose error, and the first control component is used to adjust the front wheel angle of the working equipment. The second generation submodule is used to generate a second control component based on the longitudinal displacement error between the real-time position of the working equipment and the target endpoint. The second control component is used to adjust the speed of the working equipment. The drive submodule is used to drive the working equipment to move toward and stop at the target endpoint based on the first control component and the second control component.

[0071] Optionally, the first generation submodule includes: The first acquisition unit is used to acquire at least one first historical error value corresponding to the lateral displacement error and the heading angle error, respectively. The first calculation unit is used to perform incremental calculations based on the lateral displacement error, the heading angle error, and the first historical error value to obtain the first control component.

[0072] Optionally, the second generation submodule includes: The second acquisition unit is used to acquire at least one second historical error value corresponding to the longitudinal displacement error; The second calculation unit is used to perform incremental calculations based on the longitudinal displacement error and the second historical error value to obtain the second control component.

[0073] Optionally, the control device 300 is also specifically used for: Obtain the longitudinal acceleration information and wheel speed information of the operating equipment; Based on the longitudinal acceleration information and wheel speed information, the real-time pose is obtained by fusing them using a state estimation algorithm.

[0074] Figure 4 A schematic diagram of the hardware structure of the operating device provided in an embodiment of this application is shown.

[0075] The operating device may include a processor 401 and a memory 402 storing program instructions.

[0076] When processor 401 executes the program, it implements the steps in any of the above method embodiments.

[0077] For example, the program can be divided into one or more modules / units, one or more of which are stored in memory 402 and executed by processor 401 to complete this application. The one or more modules / units can be a series of program instruction segments capable of performing a specific function, which describe the execution process of the program in the device.

[0078] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0079] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 402 is non-volatile solid-state memory.

[0080] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.

[0081] The processor 401 implements any of the methods described above by reading and executing program instructions stored in the memory 402.

[0082] In one example, the operating device may also include a communication interface 403 and a bus 410. The processor 401, memory 402, and communication interface 403 are connected via the bus 410 and communicate with each other.

[0083] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0084] Bus 410 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0085] Furthermore, in conjunction with the methods in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores program instructions; when these program instructions are executed by a processor, they implement any of the methods in the above embodiments.

[0086] This application also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0087] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0088] This application provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above method embodiments and achieve the same technical effects. To avoid repetition, it will not be described again here.

[0089] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0090] The functional modules shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on machine-readable media or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable media" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer grids such as the Internet, intranets, etc.

[0091] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0092] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to create a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0093] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A control method for operating equipment, characterized in that, The control method includes: Based on the environmental information collected by the operating equipment, the driving route and target destination are determined; Determine a first distance between the current position of the working equipment and the target endpoint; Based on the comparison result between the first distance and the preset threshold, the first control strategy or the second control strategy is adopted to drive the working equipment to move. Specifically, when the first distance is greater than the preset threshold, the first control strategy is used to track the path along the driving path; when the first distance is less than or equal to the preset threshold, the second control strategy is switched to enable the working equipment to move to the target endpoint.

2. The control method as described in claim 1, characterized in that, When the first distance is greater than the preset threshold, the first control strategy is used to perform path tracking along the driving path, including: The aiming distance is determined based on the real-time speed of the operating equipment; Based on the pre-aiming distance, determine the pre-aiming point on the driving path; Obtain the second distance between the center point of the rear wheel of the working equipment and the nearest point on the travel path; The front wheel angle of the working equipment is calculated based on the pre-aiming point, the driving path, and the second distance. The movement of the work equipment is controlled based on the front wheel steering angle so that the work equipment can track the path along the travel path.

3. The control method as described in claim 2, characterized in that, The calculation of the front wheel angle of the working equipment based on the pre-aiming point, the driving path, and the second distance includes: The front wheel angle of the working equipment is calculated using the first formula, which is: in, For the front wheel steering angle, For aiming distance, For the wheelbase of the working equipment, This is the second distance.

4. The control method as described in claim 1, characterized in that, The step of switching to the second control strategy when the first distance is less than or equal to the preset threshold, so as to move the working equipment to the target endpoint, includes: The pose error is determined based on the deviation between the real-time pose of the working equipment and the final pose of the target endpoint. The pose error includes lateral displacement error and heading angle error. A first control component is generated based on the pose error, and the first control component is used to adjust the front wheel angle of the working equipment. Based on the longitudinal displacement error between the real-time position of the working equipment and the target endpoint, a second control component is generated, which is used to adjust the speed of the working equipment. Based on the first control component and the second control component, the working equipment is driven to move toward and stop at the target endpoint.

5. The control method as described in claim 4, characterized in that, The step of generating a first control component based on the pose error includes: At least one first historical error value corresponding to each of the lateral displacement error and the heading angle error is obtained respectively; The first control component is obtained by incremental calculation based on the lateral displacement error, the heading angle error, and the first historical error value.

6. The control method as described in claim 4, characterized in that, The step of generating a second control component based on the longitudinal displacement error between the real-time position of the working equipment and the target endpoint includes: Obtain at least one second historical error value corresponding to the longitudinal displacement error; The second control component is obtained by incremental calculation based on the longitudinal displacement error and the second historical error value.

7. The control method according to any one of claims 4 to 6, characterized in that, The real-time pose of the working equipment is determined through the following steps: Obtain the longitudinal acceleration information and wheel speed information of the operating equipment; Based on the longitudinal acceleration information and wheel speed information, the real-time pose is obtained by fusing them using a state estimation algorithm.

8. A control device for operating equipment, characterized in that, The control device includes: The first determining module is used to determine the driving route and target destination based on the environmental information collected by the operating equipment; The second determining module is used to determine a first distance between the current position of the working equipment and the target endpoint; The drive module is used to drive the working equipment to move based on the comparison result between the first distance and the preset threshold, using a first control strategy or a second control strategy. Specifically, when the first distance is greater than the preset threshold, the first control strategy is used to track the path along the driving path; when the first distance is less than or equal to the preset threshold, the second control strategy is switched to enable the working equipment to move to the target endpoint.

9. A working device, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it implements the control method as described in any one of claims 1-7.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the control method according to any one of claims 1 to 7.

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