Turning self-adaptive control method and device of electric carrier

Abstract

The invention discloses a turning adaptive control method and device for an electric carrier, and the method comprises the steps: carrying out the environment perception when the distance between a to-be-turned position in the advancing direction of the electric carrier and the electric carrier is smaller than or equal to a preset threshold value, and obtaining various types of environment perception information; when it is judged that a dynamic target object exists based on the multiple types of environment perception information, determining motion trend information of the target object; or when it is judged that the static object exists based on the multiple types of environment perception information, contour parameters of the static object are obtained; predicting an optimal turning radius according to the motion trend information of the target object; or predicting the optimal turning radius according to the contour parameters of the static object; and according to the size of the optimal turning radius, the current rotating angle and the current rotating speed of the wheels of the electric carrier are adjusted in a self-adaptive mode. By the adoption of the method and device, when the electric carrier faces a complex scene, the operation parameters of the electric carrier can be rapidly and accurately adjusted in a self-adaptive mode, and therefore the stability and safety of the electric carrier are improved.

Description

Technical Field

[0001] This application relates to the technical field of vehicle control, and particularly to a turning adaptive control method and device for an electric forklift truck. Background Art

[0002] In logistics and manufacturing, electric forklift trucks are widely used in material handling scenarios. For example, in large warehouses, vehicles need to frequently shuttle in narrow aisles to transport large amounts of goods to designated locations. Such application scenarios pose extremely high requirements for the turning flexibility and stability of the vehicles.

[0003] In the related art, in terms of turning technology, existing heavy-duty electric forklift trucks mainly improve turning performance by optimizing wheel layout and driving methods. For example, the TMR flat heavy-duty AGV adopts a double-steering-wheel group structure and has an omnidirectional driving ability, enabling flexible turning in a narrow space.

[0004] However, currently, relying on fixed wheel layouts and driving methods, lacking the ability to perceive environmental information in real time and make intelligent decisions, and failing to fully consider the dynamic changes in complex environments, such as the sudden appearance of pedestrians and obstacles, makes it difficult for electric forklift trucks to make rapid and accurate adaptive adjustments when facing complex scenarios, thus affecting the stability and safety of electric forklift trucks. Summary of the Invention

[0005] Embodiments of this application provide a turning adaptive control method and device for an electric forklift truck. To provide a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary part is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments. Its sole purpose is to present some concepts in a simple form as a prelude to the subsequent detailed description.

[0006] In a first aspect, embodiments of this application provide a turning adaptive control method for an electric forklift truck. A lidar, a camera, and a millimeter-wave radar are installed on the electric forklift truck. The method includes: During the driving process of the electric forklift truck, when the distance between the turning position to be turned in the forward direction of the electric forklift truck on the preset driving route and the electric forklift truck is less than or equal to a preset distance threshold, environmental perception is performed through the lidar, the camera, and the millimeter-wave radar to obtain various types of environmental perception information; In the case where it is determined based on various types of environmental perception information that there is a dynamic target object in a preset area in the forward direction, determine the motion trend information of the target object; or in the case where it is determined based on various types of environmental perception information that there is a static object in a preset area in the forward direction, obtain the contour parameters of the static object; Predict the optimal turning radius of the electric forklift during the turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift; or predict the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object; Adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift according to the size of the optimal turning radius.

[0007] In a second aspect, an embodiment of the present application provides a turning adaptive control device for an electric forklift, the device includes: An environment perception module, configured to, during the driving process of the electric forklift, when the distance between the turning position to be turned in the forward direction of the electric forklift in the preset driving route and the electric forklift is less than or equal to a preset distance threshold, perform environment perception through a lidar, a camera, and a millimeter wave radar to obtain various types of environment perception information; A determination module, configured to determine the motion trend information of the target object when it is determined that there is a dynamic target object in the preset area in the forward direction based on various types of environment perception information; or obtain the contour parameters of the static object when it is determined that there is a static object in the preset area in the forward direction based on various types of environment perception information; A prediction module, configured to predict the optimal turning radius of the electric forklift during the turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift; or predict the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object; An adaptive adjustment module, configured to adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift according to the size of the optimal turning radius.

[0008] The technical solution provided by the embodiment of the present application may include the following beneficial effects: In the embodiment of the present application, on the one hand, when the vehicle approaches the turning position to be turned in the preset driving route, various types of environment perception information can be obtained through a lidar, a camera, and a millimeter wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in the complex environment. Based on the optimal turning radius, the electric forklift can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric forklift. On the other hand, the present application provides a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius. Combining the query mechanism of the MAP coordinate curve graph, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric forklift maintains a stable motion state during the turning process and reduces the risk of rollover caused by sharp turns or sudden decelerations.

[0009] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and should not limit this application. Brief Description of the Drawings

[0010] The drawings herein are incorporated into the specification and form a part of this specification, showing embodiments consistent with this application, and are used together with the specification to explain the principles of this application.

[0011] Figure 1 It is a schematic flowchart of a method for adaptive turning control of an electric forklift provided by an embodiment of this application; Figure 2 It is a schematic diagram of the sensor distribution of an electric forklift provided by an embodiment of this application; Figure 3 It is a MAP coordinate curve graph between a pre-fitted potential collision risk value and a turning radius provided by an embodiment of this application; Figure 4 It is a schematic flowchart of a method for generating a MAP coordinate curve graph provided by an embodiment of this application; Figure 5 It is a schematic structural diagram of an adaptive turning control device for an electric forklift provided by an embodiment of this application; Figure 6 It is a schematic structural diagram of an electronic device provided by an embodiment of this application. Detailed Embodiments

[0012] The following description and the drawings fully illustrate the specific embodiments of this application, enabling those skilled in the art to practice them.

[0013] It should be clear that the described embodiments are only a part of the embodiments of this application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application without creative efforts belong to the scope of protection of this application.

[0014] When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. On the contrary, they are merely examples of devices and methods consistent with some aspects of this application as detailed in the appended claims.

[0015] In the description of this application, it should be understood that the terms "first", "second", etc. are only for descriptive purposes and cannot be construed as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, in the description of this application, unless otherwise specified, "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0016] Currently, in terms of turning technology, existing heavy-duty electric forklifts mainly improve turning performance by optimizing wheel layout and driving methods. For example, the TMR flat heavy-duty AGV adopts a double-steering-wheel group structure and has omnidirectional driving ability, enabling flexible turning in narrow spaces.

[0017] The inventor realizes that currently, relying on fixed wheel layouts and driving methods, lacking the ability to perceive environmental information in real time and make intelligent decisions, and failing to fully consider dynamic changes in complex environments, such as the sudden appearance of pedestrians and obstacles, makes it difficult for electric forklifts to make rapid and accurate adaptive adjustments when facing complex scenarios, thus affecting the stability and safety of electric forklifts.

[0018] To solve the above problems, this application provides a turning adaptive control method and device for an electric forklift to solve the problems existing in the above related technical problems. In the embodiments of this application, on the one hand, when the vehicle approaches the turning position in the preset driving route, various types of environmental perception information can be obtained through lidar, cameras, and millimeter-wave radars. This information enables the predicted optimal turning radius to fully consider dynamic changes in complex environments. Based on the optimal turning radius, the electric forklift can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric forklift. On the other hand, this application provides a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius. Combining the query mechanism of the MAP coordinate curve graph, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric forklift maintains a stable motion state during turning and reduces the risk of rollover caused by sharp turns or sudden deceleration. The following will be described in detail with exemplary embodiments.

[0019] The following will be combined with the attached Figure 1 - attached Figure 4, a detailed introduction to the turning adaptive control method for the electric forklift provided in the embodiments of the present application is given. This method can be implemented relying on a computer program and can run on the turning adaptive control device of the electric forklift based on the von Neumann architecture. This computer program can be integrated into an application or run as an independent tool-type application.

[0020] Please refer to Figure 1 , which is a schematic flowchart of a turning adaptive control method for an electric forklift provided in the embodiments of the present application. A lidar, a camera, and a millimeter-wave radar are installed on the electric forklift. As Figure 1 shown, the method of the embodiments of the present application may include the following steps: S101, during the driving process of the electric forklift, when the distance between the turning position to be turned in the forward direction of the electric forklift in the preset driving route and the electric forklift is less than or equal to a preset distance threshold, perform environmental perception through the lidar, the camera, and the millimeter-wave radar to obtain multiple types of environmental perception information; Among them, the electric forklift is an automated vehicle for material handling, usually used in logistics, warehousing, or manufacturing, and can travel on a preset path and complete the handling task. For example Figure 2 shown, a lidar, a camera, and a millimeter-wave radar are installed on the electric forklift. The preset driving route is a pre-planned vehicle driving path generated by a navigation system or a path planning algorithm, and is used to guide the driving direction and target position of the electric forklift. The forward direction is the straight-ahead direction in which the electric forklift is currently driving. The turning position to be turned is a specific position where the vehicle needs to turn in the driving path, and is a key node on the path, such as the entrance of an intersection, a curve, or a narrow passage. The preset distance threshold is a pre-set distance value used to determine whether the vehicle is approaching the turning position to be turned. When the distance between the vehicle and the turning position to be turned is less than or equal to this threshold, the environmental perception operation is triggered. The camera is a sensor for capturing visual images, which can provide visual information of the vehicle's surrounding environment for identifying objects, pedestrians, traffic signs, etc. The lidar is a sensor that uses lasers for distance measurement and environmental scanning, which can generate high-precision three-dimensional point cloud data of the vehicle's surrounding environment for detecting obstacles. The millimeter-wave radar is a radar sensor that uses the millimeter-wave frequency band, which can detect the speed and distance between the electric forklift and surrounding objects. The multiple types of environmental perception information refer to different types of environmental information obtained through multiple sensors, and are used to comprehensively evaluate the environmental state around the vehicle.

[0021] Among them, the lidar is installed on the top of the electric forklift for 360° environmental scanning. The camera is installed at the front of the vehicle for capturing visual information ahead. The millimeter-wave radar is installed at the front and both sides of the vehicle for detecting the speed and distance of dynamic targets. The preset distance threshold can be set to 3 meters, indicating that when the electric forklift is within 3 meters of the turning point, the environmental perception process is triggered.

[0022] In some embodiments of the present application, there is an electric forklift traveling on a preset driving route in a logistics warehouse, and the task is to transport goods from one end of the warehouse to the other end. The driving route of the vehicle includes multiple turning points, and there may be dynamic target objects (such as pedestrians, forklifts, or other forklifts) in the warehouse. The vehicle travels according to the preset route, and the navigation system monitors the vehicle position in real time. When the distance between the vehicle and the next turning point is less than or equal to 3 meters, the navigation system triggers the environmental perception process. The lidar starts to scan the environment around the vehicle to generate a three-dimensional point cloud map of the environment around the electric forklift. The camera captures the visual image in front of the vehicle, and the image recognition algorithm detects the moving target object in front and identifies the moving direction of the target object. The millimeter-wave radar detects the dynamic targets in front of and on both sides of the vehicle for measuring their speed and distance. The control system of the electric forklift preprocesses the data of the lidar, camera, and millimeter-wave radar to generate various types of environmental perception information about the environment around the vehicle, including: the position and shape of the target object (lidar data), shape and distance, moving direction and speed, the visual image of the target object (camera data), the speed and moving direction of the target object (millimeter-wave radar data).

[0023] S102, in the case that a dynamic target object exists in the preset area in the forward direction based on various types of environmental perception information, determining the motion trend information of the target object; or in the case that a static object exists in the preset area in the forward direction based on the various types of environmental perception information, obtaining the contour parameters of the static object; the contour parameters of the static object include the static object position, static object size, and static object shape; Among them, the preset area is the turning area that the electric forklift is about to enter, and this area is used to define the range of target objects to be detected. The motion trend information is the information describing the motion state of the target object, and the motion trend information of the target object includes the first direction and the first acceleration.

[0024] In some embodiments of the present application, the vehicle control system fuses the data of lidar, cameras, and millimeter-wave radars, determines that there is a dynamic target object in a preset area in the vehicle's forward direction. Through the data of the millimeter-wave radar and cameras, the system determines that the target object is moving leftward. By combining the positions of the target object at different times, the acceleration of the target object can be determined. Or it is determined that there is a static object in a preset area in the vehicle's forward direction, and through the data of the millimeter-wave radar and cameras, the position, size, and shape of the static object are obtained.

[0025] S103. Predict the optimal turning radius of the electric forklift during a turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift; or predict the optimal turning radius of the electric forklift during a turning process according to the contour parameters of the static object; Wherein, the current operating parameters of the electric forklift refer to the real-time parameters during the driving process of the electric forklift, including the current speed, driving direction, and steering angle. The optimal turning radius is the predicted radius that is most suitable for the electric forklift to turn.

[0026] In an embodiment of the present application, the specific process of predicting the optimal turning radius of the electric forklift during a turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift includes: establishing a vehicle coordinate system with the current position of the electric forklift as the origin, where the forward direction of the vehicle in the vehicle coordinate system is the positive x-axis direction, and the left side of the vehicle is the positive y-axis direction; determining whether the target object meets the electric forklift in a preset area at the position to be turned according to the first direction and the first acceleration; in the case where the target object meets the electric forklift in a preset area at the position to be turned, calculating the first relative position and the first relative speed of the target object and the electric forklift in the preset area according to the first direction, the first acceleration, and the current operating parameters; in the vehicle coordinate system, determining the potential collision risk value corresponding to the target object and the electric forklift according to the first relative position and the first relative speed; querying the optimal turning radius of the electric forklift during a turning process from a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius according to the potential collision risk value; wherein, the MAP coordinate curve graph is used to represent the optimal turning radius adopted by the electric forklift under different potential collision risks.

[0027] Wherein, the potential collision risk value is a quantified collision risk value calculated according to the relative position and relative speed of the target object. The higher the risk value, the greater the possibility of collision. The MAP coordinate curve graph is a pre-fitted curve graph used to represent the relationship between the potential collision risk value and the optimal turning radius. By querying this curve graph, the optimal turning radius corresponding to the risk value can be obtained.

[0028] In the embodiments of the present application, the MAP coordinate curve graph fits the relationship between the potential collision risk and the turning radius in a graphical manner. Through the MAP curve graph, the electric forklift can dynamically adjust the turning radius according to the real-time perceived potential collision risk value. This dynamic adjustment ability can ensure that the vehicle always selects the optimal turning strategy in a complex environment, improving the driving efficiency and safety.

[0029] For example, taking the current position of the electric forklift as the origin, the forward direction of the vehicle as the positive x-axis direction, and the left side of the vehicle as the positive y-axis direction. It is detected that there is a pedestrian in front, and the moving direction (the first direction) is moving to the left, and the acceleration (the first acceleration) is 0.2 m / s². According to the motion trend information of the target object, it is judged whether it will meet the electric forklift within the preset area at the turning position. Assume that the pedestrian is moving to the left and is about to enter the turning area of the vehicle. The relative position between the pedestrian and the vehicle is =(3 m, 1 m). The relative velocity between the pedestrian and the vehicle is =(-1 m / s, 0 m / s), indicating that the relative velocity between the pedestrian and the vehicle in the x direction is 1 m / s. According to the relative position and relative velocity, the potential collision risk value is calculated through the risk assessment model. For example, the risk value is 0.95 (the range is from 0 to 1, and 1 represents the highest risk). According to the potential collision risk value of 0.65, the MAP coordinate curve graph is queried to obtain the optimal turning radius of 2 meters. When the risk is 0, the system can turn according to the default turning radius of 0.5 meters.

[0030] Specifically, the specific process of determining whether the target object meets the electric forklift within the preset area at the turning position includes: obtaining the current speed and current position of the electric forklift; according to the current speed and current position, calculating the first duration required for the electric forklift to enter the preset range of the turning position; predicting the first position of the target object after moving for the first duration according to the first direction and the first acceleration; when the first position is within the preset range, determining that the target object meets the electric forklift within the preset area at the turning position; or, when the first position is not within the preset range, determining that the target object does not meet the electric forklift within the preset area at the turning position.

[0031] For example, assume that the current speed of the electric forklift is =2 m / s. The current position of the electric forklift is =(0, 0). The distance from the turning position to the current position of the electric forklift is =10 m. According to the vehicle speed, calculate the required time:

[0032] For example, the initial position of the target object (pedestrian) is =(8, 2). The moving direction (the first direction) of the target object is to move left, and the speed is = 1 m / s. According to the speed and direction of the target object, predict its position after = 5 s:

[0033]

[0034] Therefore, the first position of the target object is (3, 2).

[0035] The preset range is a circular area with a radius of R = 2 m centered on the position to turn. The position to turn is = (10, 0) Calculate the distance between the first position of the target object and the position to turn:

[0036] Since is greater than a circular area with a radius of R = 2 m centered on the position to turn, it can be known that the first position of the target object is not within the preset range. That is, the target object and the electric forklift do not meet within the preset area at the position to turn.

[0037] Among them, the current operating parameters include the current speed, the current acceleration, and the current position.

[0038] Specifically, the specific process of calculating the first relative position and the first relative velocity of the target object and the electric forklift in the preset area according to the first direction, the first acceleration, and the current operating parameters is as follows: Obtain the current position of the target object; in the vehicle coordinate system, calculate the first absolute position and the first absolute velocity of the target object at the encounter moment according to the current position, the first direction, and the first acceleration of the target object; the first absolute position includes the first position of the target object in the horizontal direction and the second position in the vertical direction in the vehicle coordinate system; the first absolute velocity includes the first velocity of the target object in the horizontal direction and the second velocity in the vertical direction in the vehicle coordinate system; in the vehicle coordinate system, calculate the second absolute position and the second absolute velocity of the electric forklift at the encounter moment according to the current position, the current velocity, the current acceleration, and the direction of the electric forklift; the second absolute position includes the third position of the electric forklift in the horizontal direction and the fourth position in the vertical direction in the vehicle coordinate system; the first absolute velocity includes the third velocity of the electric forklift in the horizontal direction and the fourth velocity in the vertical direction in the vehicle coordinate system; calculate the position difference between the first position and the third position to obtain the first position difference of the target object relative to the electric forklift in the horizontal direction in the vehicle coordinate system; calculate the position difference between the second position and the fourth position to obtain the second position difference of the target object relative to the electric forklift in the vertical direction in the vehicle coordinate system; combine the first position difference and the second position difference to obtain the first relative position of the target object and the electric forklift in the preset area; calculate the velocity difference between the first velocity and the third velocity to obtain the first velocity difference of the target object relative to the electric forklift in the horizontal direction in the vehicle coordinate system; calculate the velocity difference between the second velocity and the fourth velocity to obtain the second velocity difference of the target object relative to the electric forklift in the vertical direction in the vehicle coordinate system; combine the first velocity difference and the second velocity difference to obtain the first relative velocity of the target object and the electric forklift in the preset area.

[0039] For example, to calculate the first absolute position and the first absolute velocity of the target object at the encounter moment, for example, the initial position of the target object (pedestrian) is =(8, 2). In the vehicle coordinate system, assume that the movement direction (the first direction) of the target object is to move left, and the speed is = 1 m / s, and the acceleration (the first acceleration) of the target object is = 0.2 m / . After t = 5 s, the position of the target object is: ;

[0040] The velocity of the target object is:

[0041] Calculate the second absolute position and the second absolute velocity of the electric forklift at the encounter moment: For example, the current position of the electric forklift is =(0, 0), and the current velocity of the electric forklift is = 2 m / s, and the acceleration of the vehicle is = 0.1 m / , after t = 5 s, the position of the electric forklift is: ;

[0042] The velocity of the electric forklift is:

[0043] Calculate the relative position and the relative velocity: Relative position:

[0044]

[0045] Relative velocity: ; = 0 - 0 = 0 m / s.

[0046] Therefore, the first relative position of the target object and the electric forklift within the preset area is (-7, 2). The first relative velocity of the target object and the electric forklift within the preset area is (-2.5, 0).

[0047] Among them, the first relative position includes the first position difference of the target object relative to the electric forklift in the horizontal direction and the second position difference in the vertical direction in the vehicle coordinate system; the first relative velocity includes the first velocity difference of the target object relative to the electric forklift in the horizontal direction and the second velocity difference in the vertical direction in the vehicle coordinate system.

[0048] In some embodiments of the present application, the specific process of determining the potential collision risk value corresponding to the target object and the electric forklift according to the first relative position and the first relative speed includes: taking the square root of the sum of the squares of the first position difference and the second position difference to obtain the actual distance between the target object and the electric forklift; taking the square root of the sum of the squares of the first speed difference and the second speed difference to obtain the magnitude of the relative movement speed between the target object and the electric forklift; calculating the ratio between the actual distance and the absolute value of the relative movement speed magnitude to obtain the collision time between the target object and the electric forklift; comparing the collision time with a preset interval threshold to determine the target interval corresponding to the collision time; and obtaining the target collision risk value corresponding to the target interval from the mapping relationship between the interval and the collision risk value established in advance as the potential collision risk value corresponding to the target object and the electric forklift.

[0049] In some embodiments of the present application, the specific process of generating a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius includes: simulating the running state of the electric forklift at different preset turning radii at a preset turning position point through simulation software, where the different preset turning radii include at least one moving object within a preset area; recording multiple simulation running parameters of the electric forklift and multiple simulation movement trends of at least one moving object at each preset turning radius to obtain multiple simulation running parameters and multiple simulation movement trends corresponding to each preset turning radius; arranging and combining the multiple simulation running parameters and multiple simulation movement trends corresponding to each preset turning radius to obtain multiple groups of simulation data corresponding to each preset turning radius; determining the potential collision risk value corresponding to each group of simulation data as the multiple potential collision risk values corresponding to each preset turning radius according to the second direction, the second acceleration, and each simulation running parameter included in each simulation movement trend in each group of simulation data corresponding to each preset turning radius; using each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable to fit the relationship between the input variable and the output variable; and drawing a curve graph between each preset turning radius and each potential collision risk value according to the fitted relationship as the pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius.

[0050] Among them, the specific process of determining the potential collision risk value corresponding to each set of simulation data according to the second direction, second acceleration, and each simulation operation parameter included in each simulation motion trend in each set of simulation data corresponding to each preset turning radius is as follows: According to the second direction, second acceleration, and each simulation operation parameter included in each simulation motion trend in each set of simulation data corresponding to each preset turning radius, calculate the second relative position and second relative velocity of the moving object and the electric forklift within the preset area; According to the second relative position and second relative velocity, determine the potential collision risk value of the moving object and the electric forklift; Use the potential collision risk value of the moving object and the electric forklift as the potential collision risk value corresponding to each set of simulation data.

[0051] It should be noted that the logic of determining the potential collision risk value of the moving object and the electric forklift according to the second relative position and second relative velocity is the same as that of determining the potential collision risk value of the target object and the electric forklift according to the first relative position and first relative velocity, which will not be elaborated here.

[0052] It should be noted that the MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius is as follows Figure 3 As shown, when the potential collision risk is high, the electric forklift needs to select a larger turning radius to avoid the instability or collision risk caused by a sharp turn. When the potential collision risk is low, the vehicle can select a smaller turning radius to complete the turn more compactly. A larger turning radius can reduce the lateral acceleration of the vehicle during the turning process and reduce the risk of the vehicle losing control or colliding.

[0053] In some other embodiments of the present application, the specific process of predicting the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object includes: According to the static object size and static object shape, determine the minimum safety distance between the electric forklift and the static object, and the minimum safety distance is used to indicate that the electric forklift will not collide with the static object during the turn; According to the static object position and the minimum safety distance, calculate the optimal turning radius of the electric forklift during the turning process.

[0054] Among them, the calculation formula for the optimal turning radius of the electric forklift during the turning process is:

[0055] Among them, is the abscissa position of the static object, is the ordinate position of the static object, is the abscissa position of the electric forklift, is the ordinate position of the electric forklift.

[0056] For example, the position of the static object is (5 meters, 2 meters), the coordinates of the electric forklift in the vehicle coordinate system established with the electric forklift as the origin are (0, 0), the minimum safety distance is 0.5 meters, and the width of the vehicle is 1 meter.

[0057] At this time,

[0058] Specifically, when determining the minimum safety distance between the electric forklift and the static object according to the size and shape of the static object, a preset safety margin set for the electric forklift is obtained. The preset safety margin is to ensure that the vehicle can still safely avoid the static object when there are errors in perception and control; the width of the static object is quantified according to the size and shape of the static object; according to the width of the static object, the width of the electric forklift, and the preset safety margin, the minimum safety distance between the electric forklift and the static object is calculated.

[0059] Minimum safety distance = ; In the embodiments of the present application, the electric forklift can predict the optimal turning radius according to the contour parameters of the static object and adjust the driving strategy to avoid static obstacles, ensuring safety and flexibility in a complex environment.

[0060] S104. According to the size of the optimal turning radius, adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift.

[0061] In some embodiments of the present application, the specific process of adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric forklift according to the size of the optimal turning radius includes: obtaining the current speed of the wheels of the electric forklift and the vehicle wheelbase; calculating the target steering angle of the electric forklift according to the size of the vehicle wheelbase and the optimal turning radius; calculating the target linear speed of the wheels of the electric forklift according to the current speed, the vehicle wheelbase, and the size of the optimal turning radius; and adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric forklift according to the target steering angle and the target linear speed.

[0062] Among them, the calculation formula for the target steering angle is:

[0063] Among them, is the target steering angle, is the vehicle wheelbase, is the optimal turning radius, is the arctangent function; Among them, the calculation formula for the target linear speed is:

[0064] Wherein, and are respectively the target linear velocities of the left and right wheels of the electric forklift, is the current speed; Specifically, the specific process of adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric forklift according to the target steering angle and target linear velocity includes: controlling the current steering angle of the electric forklift to smoothly transition to the target steering angle through the PID controller of the electric forklift to adaptively adjust the current rotation angle of the wheels of the electric forklift; controlling the current speed of the electric forklift to smoothly transition to the target linear velocity through the speed controller of the electric forklift to adaptively adjust the current rotation speed of the wheels of the electric forklift.

[0065] In the embodiment of the present application, on the one hand, when the vehicle approaches the turning position in the preset driving route, multi-class environmental perception information can be obtained through lidar, camera and millimeter wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in the complex environment. Based on the optimal turning radius, the electric forklift can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric forklift. On the other hand, the present application provides a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius. Combining the query mechanism of the MAP coordinate curve graph, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric forklift maintains a stable motion state during the turning process and reduces the risk of rollover caused by sharp turns or sudden decelerations.

[0066] Please refer to Figure 4 , which is a schematic flowchart of a method for generating a MAP coordinate curve graph provided by an embodiment of the present application. As Figure 4 shown, the method of the embodiment of the present application may include the following steps: S201, Simulate the running state of the electric forklift at different preset turning radii at the preset turning position point through simulation software, and different preset turning radii include at least one moving object within the preset area; S202, Record multiple simulation running parameters of the electric forklift and multiple simulation motion trends of at least one moving object at each preset turning radius to obtain multiple simulation running parameters and multiple simulation motion trends corresponding to each preset turning radius; S203, Arrange and combine the multiple simulation running parameters and multiple simulation motion trends corresponding to each preset turning radius to obtain multiple groups of simulation data corresponding to each preset turning radius; S204. Determine the potential collision risk value corresponding to each set of simulation data as the multiple potential collision risk values corresponding to each preset turning radius according to the second direction, second acceleration, and each simulation operation parameter included in each simulation movement trend in each set of simulation data corresponding to each preset turning radius. S205. Use each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable to fit the relationship between the input variable and the output variable. S206. According to the fitted relationship, draw a curve graph between each preset turning radius and each potential collision risk value as the MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius.

[0067] In some embodiments of the present application, a simulation software is used to simulate the operating states of an electric forklift at different preset turning radii at a preset turning position point. The preset turning radii are 3 meters, 4 meters, 5 meters, and 6 meters respectively. For each preset turning radius, multiple simulation operation parameters (such as speed, acceleration, steering angle) of the electric forklift and multiple simulation movement trends (such as speed, direction, acceleration) of at least one moving object (such as a pedestrian) are recorded. For example, for a turning radius of 3 meters, the vehicle speed is recorded as 2 m / s, the pedestrian speed is 1 m / s, and the pedestrian movement direction is to the left. Arrange and combine the multiple simulation operation parameters and multiple simulation movement trends corresponding to each preset turning radius to obtain multiple sets of simulation data corresponding to each preset turning radius. For example, for a turning radius of 3 meters, the following sets of simulation data are obtained: Data set 1: vehicle speed 2 m / s, pedestrian speed 1 m / s, pedestrian direction to the left. Data set 2: vehicle speed 2 m / s, pedestrian speed 1.5 m / s, pedestrian direction to the right. Determine the potential collision risk value corresponding to each set of simulation data according to the second direction, second acceleration, and each simulation operation parameter included in each simulation movement trend in each set of simulation data corresponding to each preset turning radius. For example, for data set 1 (turning radius of 3 meters, vehicle speed 2 m / s, pedestrian speed 1 m / s, pedestrian direction to the left), the potential collision risk value calculated through the collision risk assessment model is 0.6. Use each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable to fit the relationship between the input variable and the output variable. Use the regression analysis method to fit the relationship curve between the potential collision risk value and the turning radius. For example, draw a curve graph between the potential collision risk values (0.6, 0.4, 0.2, and 0.1) corresponding to the turning radii of 3 meters, 4 meters, 5 meters, and 6 meters.

[0068] In the embodiments of the present application, the present application provides a MAP coordinate curve graph between pre-fitted potential collision risk values and turning radii. Combining with the query mechanism of the MAP coordinate curve graph, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric forklift maintains a stable motion state during the turning process and reduces the risk of rollover caused by sharp turns or sudden decelerations.

[0069] The following is an embodiment of the device of the present application, which can be used to execute the method embodiment of the present application. For the details not disclosed in the embodiment of the device of the present application, please refer to the method embodiment of the present application.

[0070] Please refer to Figure 5 , which shows a schematic structural diagram of a turning adaptive control device for an electric forklift provided by an exemplary embodiment of the present application. The turning adaptive control device of the electric forklift can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The device 1 includes an environment perception module 10, a determination module 20, a prediction module 30, and an adaptive adjustment module 40.

[0071] The environment perception module 10 is configured to, during the driving process of the electric forklift, when the distance between the turning position to be turned in the forward direction of the electric forklift in the preset driving route and the electric forklift is less than or equal to a preset distance threshold, perform environment perception through a lidar, a camera, and a millimeter wave radar to obtain various types of environment perception information; The determination module 20 is configured to determine the motion trend information of the target object when it is determined that there is a dynamic target object in a preset area in the forward direction based on various types of environment perception information; or obtain the contour parameters of the static object when it is determined that there is a static object in a preset area in the forward direction based on various types of environment perception information; The prediction module 30 is configured to predict the optimal turning radius of the electric forklift during the turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift; or predict the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object; The adaptive adjustment module 40 is configured to adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift according to the size of the optimal turning radius.

[0072] It should be noted that when the turning self - adaptive control device of the electric forklift provided in the above - mentioned embodiments executes the turning self - adaptive control method of the electric forklift, only the division of the above - mentioned functional modules is used for illustration. In practical applications, the above - mentioned functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the turning self - adaptive control device of the electric forklift provided in the above - mentioned embodiments and the embodiments of the turning self - adaptive control method of the electric forklift belong to the same concept. The implementation process is detailed in the method embodiments and will not be elaborated here.

[0073] The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0074] In the embodiments of the present application, on the one hand, when the vehicle approaches the turning position in the preset driving route, multiple types of environmental perception information can be obtained through lidar, camera, and millimeter - wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in complex environments. Based on the optimal turning radius, the electric forklift can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric forklift. On the other hand, the present application provides a MAP coordinate curve graph between the pre - fitted potential collision risk value and the turning radius. Combining with the query mechanism of the MAP coordinate curve graph, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric forklift maintains a stable motion state during turning and reduces the risk of rollover caused by sharp turns or sudden deceleration.

[0075] The present application also provides a computer - readable medium, on which program instructions are stored. When the program instructions are executed by a processor, the turning self - adaptive control method of the electric forklift provided in each of the above - mentioned method embodiments is implemented.

[0076] The present application also provides a computer program product containing instructions. When it runs on a computer, it enables the computer to execute the turning self - adaptive control method of the electric forklift provided in each of the above - mentioned method embodiments.

[0077] Please refer to Figure 6 , which is a schematic structural diagram of an electronic device provided in an embodiment of the present application. As Figure 6 shown, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, and at least one communication bus 1002.

[0078] Among them, the communication bus 1002 is used to realize the connection and communication between these components.

[0079] Among them, the user interface 1003 may include a display screen and a camera. Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface.

[0080] Among them, the network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0081] Among them, the processor 1001 may include one or more processing cores. The processor 1001 is connected to various parts within the entire electronic device 1000 through various interfaces and circuits. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and by calling data stored in the memory 1005, the processor 1001 performs various functions of the electronic device 1000 and processes data. Optionally, the processor 1001 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 1001 may integrate a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, the user interface, and application programs, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001 and may be implemented separately through a single chip.

[0082] Among them, the memory 1005 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store the data involved in the above-mentioned various method embodiments. The memory 1005 may optionally also be at least one storage system located far from the aforementioned processor 1001. As Figure 6 shown, the memory 1005, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a turning adaptive control application program for an electric forklift.

[0083] In Figure 6 the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user to obtain the data input by the user; while the processor 1001 can be used to call the turning adaptive control application program for the electric forklift stored in the memory 1005 and specifically perform the following operations: During the driving process of the electric forklift, when the distance between the turning position to be turned in the forward direction of the electric forklift in the preset driving route and the electric forklift is less than or equal to the preset distance threshold, perform environmental perception through a lidar, a camera, and a millimeter wave radar to obtain various types of environmental perception information; In the case of determining that there is a dynamic target object in the preset area in the forward direction based on various types of environmental perception information, determine the motion trend information of the target object; or in the case of determining that there is a static object in the preset area in the forward direction based on various types of environmental perception information, obtain the contour parameters of the static object; According to the motion trend information of the target object and the current operating parameters of the electric forklift, predict the optimal turning radius of the electric forklift during the turning process; or according to the contour parameters of the static object, predict the optimal turning radius of the electric forklift during the turning process; According to the size of the optimal turning radius, adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift.

[0084] In one embodiment, when the processor 1001 predicts the optimal turning radius of the electric forklift during a turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift, the following operations are specifically performed: Establish a vehicle coordinate system with the current position of the electric forklift as the origin. In the vehicle coordinate system, the forward direction of the vehicle is the positive x-axis direction, and the left side of the vehicle is the positive y-axis direction; Determine whether the target object meets the electric forklift within a preset area at the turning position to be turned according to the first direction and the first acceleration; In the case where the target object meets the electric forklift within a preset area at the turning position to be turned, calculate the first relative position and the first relative speed between the target object and the electric forklift within the preset area according to the first direction, the first acceleration, and the current operating parameters; In the vehicle coordinate system, determine the potential collision risk value corresponding to the target object and the electric forklift according to the first relative position and the first relative speed; According to the potential collision risk value, query the optimal turning radius of the electric forklift during the turning process from a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius; wherein, the MAP coordinate curve graph is used to represent the optimal turning radius adopted by the electric forklift under different potential collision risks.

[0085] In one embodiment, when the processor 1001 executes to generate a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius, the following operations are specifically performed: Simulate the operating state of the electric forklift at different preset turning radii at a preset turning position point through simulation software, and different preset turning radii include at least one moving object within the preset area; Record multiple simulation operating parameters of the electric forklift and multiple simulation motion trends of at least one moving object at each preset turning radius, and obtain multiple simulation operating parameters and multiple simulation motion trends corresponding to each preset turning radius; Arrange and combine the multiple simulation operating parameters and multiple simulation motion trends corresponding to each preset turning radius to obtain multiple sets of simulation data corresponding to each preset turning radius; According to the second direction and the second acceleration included in each simulation motion trend and each simulation operating parameter in each set of simulation data corresponding to each preset turning radius, determine the potential collision risk value corresponding to each set of simulation data as multiple potential collision risk values corresponding to each preset turning radius; Use each potential collision risk value corresponding to each preset turning radius as an input variable, and use each preset turning radius as an output variable to fit the relationship between the input variable and the output variable; According to the fitted relationship, a curve graph between each preset turning radius and each potential collision risk value is plotted, as the MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius.

[0086] In one embodiment, when the processor 1001 executes to determine the potential collision risk value corresponding to each set of simulation data according to the second direction, second acceleration, and each simulation operation parameter included in each simulation motion trend in each set of simulation data corresponding to each preset turning radius, the following specific operations are performed: According to the second direction, second acceleration, and each simulation operation parameter included in each simulation motion trend in each set of simulation data corresponding to each preset turning radius, calculate the second relative position and second relative velocity of the moving object and the electric forklift in the preset area; According to the second relative position and second relative velocity, determine the potential collision risk value of the moving object and the electric forklift; Take the potential collision risk value of the moving object and the electric forklift as the potential collision risk value corresponding to each set of simulation data.

[0087] In one embodiment, when the processor 1001 executes to determine whether the target object meets the electric forklift in the preset area at the turning position to be turned, the following specific operations are performed: Obtain the current speed and current position of the electric forklift; According to the current speed and current position, calculate the first duration required for the electric forklift to enter the preset range of the turning position to be turned; According to the first direction and first acceleration, predict the first position of the target object after moving for the first duration; When the first position is within the preset range, determine that the target object meets the electric forklift in the preset area at the turning position to be turned; or, When the first position is not within the preset range, determine that the target object does not meet the electric forklift in the preset area at the turning position to be turned.

[0088] In one embodiment, when the processor 1001 executes to calculate the first relative position and first relative velocity of the target object and the electric forklift in the preset area according to the first direction, first acceleration, and current operation parameters, the following specific operations are performed: Obtain the current position of the target object; In the vehicle coordinate system, according to the current position, the first direction, and the first acceleration of the target object, calculate the first absolute position and the first absolute velocity of the target object at the encounter moment; the first absolute position includes the first position of the target object in the horizontal direction and the second position in the vertical direction in the vehicle coordinate system; the first absolute velocity includes the first velocity of the target object in the horizontal direction and the second velocity in the vertical direction in the vehicle coordinate system; In the vehicle coordinate system, according to the current position, the current velocity, the current acceleration, and the direction of the electric forklift, calculate the second absolute position and the second absolute velocity of the electric forklift at the encounter moment; the second absolute position includes the third position of the electric forklift in the horizontal direction and the fourth position in the vertical direction in the vehicle coordinate system; the first absolute velocity includes the third velocity of the electric forklift in the horizontal direction and the fourth velocity in the vertical direction in the vehicle coordinate system; Calculate the position difference between the first position and the third position to obtain the first position difference of the target object relative to the electric forklift in the horizontal direction in the vehicle coordinate system; Calculate the position difference between the second position and the fourth position to obtain the second position difference of the target object relative to the electric forklift in the vertical direction in the vehicle coordinate system; Combine the first position difference and the second position difference to obtain the first relative position of the target object and the electric forklift within the preset area; Calculate the velocity difference between the first velocity and the third velocity to obtain the first velocity difference of the target object relative to the electric forklift in the horizontal direction in the vehicle coordinate system; Calculate the velocity difference between the second velocity and the fourth velocity to obtain the second velocity difference of the target object relative to the electric forklift in the vertical direction in the vehicle coordinate system; Combine the first velocity difference and the second velocity difference to obtain the first relative velocity of the target object and the electric forklift within the preset area.

[0089] In one embodiment, when the processor 1001 executes the process of determining the potential collision risk value corresponding to the target object and the electric forklift according to the first relative position and the first relative velocity, the following operations are specifically performed: Take the square root of the sum of the squares of the first position difference and the second position difference to obtain the actual distance between the target object and the electric forklift; Take the square root of the sum of the squares of the first velocity difference and the second velocity difference to obtain the magnitude of the relative motion velocity between the target object and the electric forklift; Calculate the ratio between the actual distance and the absolute value of the magnitude of the relative motion velocity to obtain the collision time between the target object and the electric forklift; Compare the collision time with the preset interval threshold to determine the target interval corresponding to the collision time; Obtain the target collision risk value corresponding to the target interval from the mapping relationship between the pre-established intervals and the collision risk values, and use it as the potential collision risk value between the target object and the electric forklift.

[0090] In one embodiment, when the processor 1001 executes the operation of adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric forklift according to the size of the optimal turning radius, it specifically performs the following operations: Obtain the current speed of the wheels of the electric forklift and the wheelbase of the vehicle; Calculate the target steering angle of the electric forklift according to the wheelbase of the vehicle and the size of the optimal turning radius; Calculate the target linear speed of the wheels of the electric forklift according to the current speed, the wheelbase of the vehicle, and the size of the optimal turning radius; Adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift according to the target steering angle and the target linear speed.

[0091] In one embodiment, when the processor 1001 executes the operation of adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric forklift according to the target steering angle and the target linear speed, it specifically performs the following operations: Control the current steering angle of the electric forklift to smoothly transition to the target steering angle through the PID controller of the electric forklift, so as to adaptively adjust the current rotation angle of the wheels of the electric forklift; Control the current speed of the electric forklift to smoothly transition to the target linear speed through the speed controller of the electric forklift, so as to adaptively adjust the current rotation speed of the wheels of the electric forklift.

[0092] In one embodiment, when the processor 1001 executes the operation of predicting the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object, it specifically performs the following operations: Determine the minimum safe distance between the electric forklift and the static object according to the static object size and the static object shape, and the minimum safe distance is used to indicate that the electric forklift will not collide with the static object during turning; Calculate the optimal turning radius of the electric forklift during the turning process according to the static object position and the minimum safe distance.

[0093] In an embodiment of the present application, on the one hand, when the vehicle approaches a turning position in a preset driving route, multiple types of environmental perception information can be obtained through lidar, cameras, and millimeter-wave radars. This information enables the predicted optimal turning radius to fully consider the dynamic changes in a complex environment. Based on the optimal turning radius, the electric forklift can quickly and accurately adjust the wheel angle and speed adaptively to adapt to different turning scenarios, thereby improving the stability and safety of the electric forklift. On the other hand, the present application provides a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius. Combining with the query mechanism of the MAP coordinate curve graph, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric forklift maintains a stable motion state during turning and reduces the risk of rollover caused by sharp turns or sudden deceleration.

[0094] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in the above embodiments can be completed by instructing relevant hardware through a computer program. The program for the turning adaptive control of the electric forklift can be stored in a computer-readable storage medium. When this program is executed, it can include the processes of the embodiments of the above various methods. Among them, the storage medium of the program for the turning adaptive control of the electric forklift can be a magnetic disk, an optical disc, a read-only memory, or a random access memory, etc.

[0095] The above-disclosed are only the preferred embodiments of the present application. Of course, the scope of the rights of the present application cannot be limited thereby. Therefore, equivalent changes made according to the claims of the present application still fall within the scope covered by the present application.

Claims

1. A turning adaptive control method for an electric forklift, characterized in that A lidar, a camera, and a millimeter-wave radar are installed on the electric forklift. The method includes: During the driving process of the electric forklift, when the distance between the electric forklift and the turning position to be turned in the forward direction of the electric forklift in the preset driving route is less than or equal to a preset distance threshold, environmental perception is performed through the lidar, the camera, and the millimeter-wave radar to obtain multiple types of environmental perception information; When it is determined that there is a dynamic target object in the preset area in the forward direction based on the multiple types of environmental perception information, the movement trend information of the target object is determined; or when it is determined that there is a static object in the preset area in the forward direction based on the multiple types of environmental perception information, the contour parameters of the static object are obtained; According to the movement trend information of the target object and the current operating parameters of the electric forklift, the optimal turning radius of the electric forklift during the turning process is predicted; or according to the contour parameters of the static object, the optimal turning radius of the electric forklift during the turning process is predicted; According to the magnitude of the optimal turning radius, the current rotation angle and the current rotation speed of the wheels of the electric forklift are adaptively adjusted.

2. The method according to claim 1, wherein The movement trend information of the target object includes a first direction and a first acceleration; The predicting the optimal turning radius of the electric forklift during the turning process according to the movement trend information of the target object and the current operating parameters of the electric forklift includes: A vehicle coordinate system is established with the current position of the electric forklift as the origin. In the vehicle coordinate system, the forward direction of the vehicle is the positive direction of the x-axis, and the left side of the vehicle is the positive direction of the y-axis; According to the first direction and the first acceleration, it is determined whether the target object meets the electric forklift in the preset area at the turning position; When the target object meets the electric forklift in the preset area at the turning position, according to the first direction, the first acceleration, and the current operating parameters, the first relative position and the first relative speed of the target object and the electric forklift in the preset area are calculated; In the vehicle coordinate system, according to the first relative position and the first relative speed, the potential collision risk value corresponding to the target object and the electric forklift is determined; According to the potential collision risk value, the optimal turning radius of the electric forklift during the turning process is queried from a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius; wherein, the MAP coordinate curve graph is used to represent the optimal turning radius adopted by the electric forklift under different potential collision risks.

3. The method according to claim 2, characterized in that Generating a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius according to the following steps includes: Simulating the operating state of the electric forklift at different preset turning radii at the preset turning position point through simulation software, and the different preset turning radii include at least one moving object in the preset area; Recording a plurality of simulated operating parameters of the electric transport vehicle and a plurality of simulated motion trends of the at least one moving object at each preset turning radius, and obtaining a plurality of simulated operating parameters and a plurality of simulated motion trends corresponding to each preset turning radius; Arrange and combine the multiple simulation operation parameters and the multiple simulation motion trends corresponding to each preset turning radius to obtain multiple sets of simulation data corresponding to each preset turning radius; Determining, based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operation parameter, a potential collision risk value corresponding to each set of simulation data as a plurality of potential collision risk values corresponding to each preset turning radius; Using each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable, fitting the relationship between the input variable and the output variable; According to the fitted relationship, a curve graph between each preset turning radius and each potential collision risk value is drawn as a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius.

4. The method according to claim 3, characterized in that, The determining, based on the second direction and the second acceleration included in each simulated motion trend and each simulation operation parameter in each set of simulation data corresponding to each preset turning radius, a potential collision risk value corresponding to each set of simulation data includes: Calculating a second relative position and a second relative speed between the moving object and the electric transport vehicle within a preset area based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operation parameter; determining a potential collision risk value corresponding to the moving object and the electric transport according to the second relative position and the second relative speed; The potential collision risk value corresponding to the moving object and the electric transport is used as the potential collision risk value corresponding to each set of simulation data.

5. The method according to claim 2, wherein The determining whether the target object meets the electric transport vehicle within a preset area of the position to be turned includes: Obtaining the current speed and current position of the electric transport vehicle; Calculating a first time required for the electric transport vehicle to enter a preset range of the position to be turned according to the current speed and the current position; Predicting, based on the first direction and the first acceleration, a first position of the target object after it moves for the first duration; When the first position is within the preset range, it is determined that the target object and the electric transport vehicle meet within the preset area of the position to be turned; or When the first position is not within the preset range, it is determined that the target object and the electric transport vehicle do not meet within the preset area of the position to be turned.

6. The method according to claim 2, wherein The current operating parameters include current speed, current acceleration and current position; The calculating, based on the first direction and the first acceleration and the current operating parameters, a first relative position and a first relative speed between the target object and the electric transport vehicle within a preset area includes: Get the current position of the target object; In the vehicle coordinate system, calculate the first absolute position and the first absolute velocity of the target object at the encounter moment according to the current position of the target object, the first direction, and the first acceleration; the first absolute position includes the first position of the target object in the horizontal direction and the second position in the vertical direction in the vehicle coordinate system; the first absolute velocity includes the first velocity of the target object in the horizontal direction and the second velocity in the vertical direction in the vehicle coordinate system. In the vehicle coordinate system, calculate the second absolute position and the second absolute velocity of the electric forklift at the encounter moment according to the current position, the current velocity, the current acceleration, and the direction of the electric forklift; the second absolute position includes the third position of the electric forklift in the horizontal direction and the fourth position in the vertical direction in the vehicle coordinate system; the first absolute velocity includes the third velocity of the electric forklift in the horizontal direction and the fourth velocity in the vertical direction in the vehicle coordinate system. Calculate the position difference between the first position and the third position to obtain the first position difference of the target object relative to the electric forklift in the horizontal direction in the vehicle coordinate system. Calculate the position difference between the second position and the fourth position to obtain the second position difference of the target object relative to the electric forklift in the vertical direction in the vehicle coordinate system. Combine the first position difference and the second position difference to obtain the first relative position of the target object and the electric forklift within the preset area. Calculate the velocity difference between the first velocity and the third velocity to obtain the first velocity difference of the target object relative to the electric forklift in the horizontal direction in the vehicle coordinate system. Calculate the velocity difference between the second velocity and the fourth velocity to obtain the second velocity difference of the target object relative to the electric forklift in the vertical direction in the vehicle coordinate system. Combine the first velocity difference and the second velocity difference to obtain the first relative velocity of the target object and the electric forklift within the preset area.

7. The method according to claim 2, wherein The first relative position includes the first position difference of the target object relative to the electric forklift in the horizontal direction and the second position difference in the vertical direction in the vehicle coordinate system; the first relative velocity includes the first velocity difference of the target object relative to the electric forklift in the horizontal direction and the second velocity difference in the vertical direction in the vehicle coordinate system. Determining the potential collision risk value corresponding to the target object and the electric forklift according to the first relative position and the first relative velocity includes: Sum the squares of the first position difference and the second position difference and then take the root value to obtain the actual distance between the target object and the electric forklift. Sum the squares of the first velocity difference and the second velocity difference and then take the root value to obtain the magnitude of the relative motion velocity between the target object and the electric forklift. Calculate the ratio between the actual distance and the absolute value of the magnitude of the relative motion velocity to obtain the collision time between the target object and the electric forklift. Compare the collision time with the preset interval threshold to determine the target interval corresponding to the collision time. Obtain the target collision risk value corresponding to the target interval from the mapping relationship between the pre-established intervals and the collision risk values, as the potential collision risk value corresponding to the target object and the electric forklift.

8. The method according to claim 1, characterized in that: According to the magnitude of the optimal turning radius, adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift, including: Obtain the current speed of the wheels of the electric forklift and the vehicle wheelbase; Calculate the target steering angle of the electric forklift according to the vehicle wheelbase and the magnitude of the optimal turning radius; Calculate the target linear speed of the wheels of the electric forklift according to the current speed, the vehicle wheelbase and the magnitude of the optimal turning radius; Through the PID controller of the electric forklift, control the current steering angle of the electric forklift to smoothly transition to the target steering angle, so as to adaptively adjust the current rotation angle of the wheels of the electric forklift; Through the speed controller of the electric forklift, control the current speed of the electric forklift to smoothly transition to the target linear speed, so as to adaptively adjust the current rotation speed of the wheels of the electric forklift.

9. The method according to claim 1, characterized in that: The contour parameters of the static object include the static object position, the static object size and the static object shape; The predicting the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object includes: Determine the minimum safe distance between the electric forklift and the static object according to the static object size and the static object shape, and the minimum safe distance is used to indicate that the electric forklift will not collide with the static object during turning; Calculate the optimal turning radius of the electric forklift during the turning process according to the static object position and the minimum safe distance.

10. An adaptive turning control device for an electric forklift, characterized in that, The device includes: An environment perception module, configured to, during the driving process of the electric forklift, when the distance between the turning position to be turned in the forward direction of the electric forklift on the preset driving route and the electric forklift is less than or equal to a preset distance threshold, perform environment perception through a lidar, a camera and a millimeter wave radar to obtain various types of environment perception information; A determination module, configured to determine the motion trend information of the target object when it is determined based on the various types of environment perception information that there is a dynamic target object in the preset area in the forward direction; or to obtain the contour parameters of the static object when it is determined based on the various types of environment perception information that there is a static object in the preset area in the forward direction; A prediction module, configured to predict the optimal turning radius of the electric forklift during the turning process according to the motion trend information of the target object and the current operating parameters of the electric forklift; or to predict the optimal turning radius of the electric forklift during the turning process according to the contour parameters of the static object; An adaptive adjustment module, configured to adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric forklift according to the magnitude of the optimal turning radius.

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