A real-time sensing method and device for complex terrain of an agricultural vehicle based on a contact type probe wheel

Abstract

The present application relates to the field of agricultural machinery environment perception and measurement technology, and particularly relates to a real-time perception method and device for complex terrain of agricultural vehicles based on a contact type detection wheel, comprising the following steps: establishing a two-dimensional motion model, setting a coordinate origin O and a horizontal reference plane, and obtaining a rotation angle theta through an angle sensor; geometric backstepping height solving, solving a noisy original vertical height variation; high-fidelity terrain state reconstruction, outputting a corrected road surface terrain true value with spatial phase shift and amplitude attenuation; and performing a pre-look space-time mapping solution, solving a pre-look action delay time. The device is applied to agricultural vehicles, and comprises a support arm, a connecting rod assembly, a detection wheel, an angle sensor, a controller and a protective baffle. The connecting rod assembly comprises a connecting rod, a hinge seat and a shock absorber assembly, and the connecting rod swings relative to the support arm through the hinge seat. The present application realizes real-time perception of complex terrain of agricultural vehicles, and provides feedforward data support for realizing active leveling of a chassis.

Description

Technical Field

[0001] This invention relates to the field of agricultural machinery environmental sensing and measurement technology, and in particular to a method and device for real-time sensing of complex terrain by agricultural vehicles based on contact-type detection wheels. Background Technology

[0002] In mechanized agricultural operations, the stability of tractors and self-propelled boom sprayers is a key indicator for ensuring operational quality. To achieve high-precision active leveling control of the chassis, it is essential to acquire high-fidelity elevation contours of the complex terrain ahead of the vehicle in advance, providing accurate pre-aiming data for the rear suspension system. Currently, commonly used front-end terrain sensing methods for agricultural vehicles are mainly divided into two categories: non-contact and contact sensing. However, existing front-end terrain sensing technologies and supporting data processing methods have significant limitations in complex, unstructured farmland terrain.

[0003] Conventional contact-type mechanical contour-following structures suffer from hysteresis in dynamic response and lateral slippage. To overcome interference from vegetation and dust, some existing technologies employ mechanical structures that directly contact the ground. However, these structures are designed to compact soil or force agricultural implements to maintain their depth, resulting in a large overall weight and extremely high moment of inertia. When traveling at high speeds on bumpy roads, the high-inertia mechanism cannot achieve high-frequency ground-hugging following, easily leading to delayed bouncing and jumping. Existing non-contact sensing typically uses filtering algorithms, which are prone to terrain feature distortion and spatial phase shift, inevitably introducing severe signal delay. To smooth out violent bouncing, the amplitude of real deep ditches or high embankments is often severely weakened, failing to achieve accurate pre-leveling and instead easily outputting compensation actions at incorrect times, worsening the vehicle's posture.

[0004] Therefore, there is a need for a method and device for terrain perception of agricultural vehicles based on contact-type detection wheels that can perform real-time perception in complex terrain. Summary of the Invention

[0005] The main objective of this invention is to provide a method and device for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels, aiming to solve the problems of contact detection being easily affected by environmental interference and non-contact detection being prone to measurement inaccuracies in existing technologies.

[0006] To achieve the above objectives, this invention proposes a real-time terrain sensing device for agricultural vehicles based on contact-type detection wheels, applicable to agricultural vehicles, comprising:

[0007] The outrigger is rigidly connected to the agricultural vehicle and is a hollow tubular structure.

[0008] A linkage assembly, comprising a link, a hinge seat, and a shock absorber assembly, wherein one end of the hinge seat is hinged to the end of the support arm away from the agricultural vehicle, and the other end of the hinge seat is hinged to the link, and the end of the link swings relative to the support arm via the hinge seat.

[0009] A detection wheel, the detection wheel being rotatably connected to one end of the connecting rod away from the hinge seat;

[0010] An angle sensor is disposed on one end of the connecting rod near the hinge seat, and the angle sensor is used to measure the rotation angle of the connecting rod relative to the horizontal reference plane;

[0011] A controller is disposed on one side of the hinge seat. The controller is connected to the angle sensor and the agricultural vehicle via signal connection. The controller is used to receive noisy angle signals.

[0012] A protective baffle is detachably mounted on the connecting rod at one end near the detection wheel.

[0013] The present invention also discloses a real-time perception method for complex terrain of agricultural vehicles based on contact detection wheels, which is applied to the real-time perception device for complex terrain of agricultural vehicles described in the above technical solution, including the following steps: establishing a two-dimensional motion model of the real-time perception device for complex terrain of agricultural vehicles, setting the center of the hinge seat as the coordinate origin O, setting a plane parallel to the horizontal ground passing through the coordinate origin O as the horizontal reference plane, and obtaining the rotation angle θ between the connecting rod and the horizontal reference plane through the angle sensor.

[0014] Geometric back-calculation of height involves establishing a geometric kinematic model, inputting the rotation angle θ into the controller of the agricultural vehicle, and calculating in real time the change in the original vertical height of the detection wheel contact point relative to the vehicle body reference surface, which is noisy.

[0015] High-fidelity terrain state reconstruction uses the noisy original vertical height change as an observation variable input to a Kalman filter to construct a discrete state space model. By dynamically adjusting the gain through two-step iteration of prediction and update, the true value of road surface terrain Δy, which is corrected for spatial phase shift and amplitude attenuation, is output while filtering out high-frequency mechanical vibration noise.

[0016] Perform pre-aiming spatiotemporal mapping calculation, collect the current motion state of agricultural vehicles, combine it with the terrain true value, calculate the pre-aiming action delay time, and realize real-time perception of agricultural vehicles in complex terrain.

[0017] Furthermore, the geometric back-calculation of height, establishing a geometric kinematic model, inputting the rotation angle θ into the agricultural vehicle's controller, and calculating the noisy original vertical height change of the detection wheel contact point relative to the vehicle's reference surface in real time based on the geometric kinematic model, also includes:

[0018] Obtain the vertical drop distance h between the center of the probe wheel and the center of the hinge seat's rotation axis. drop The total vertical distance h between the lowest contact point of the probe wheel and the center of the rotating shaft is calculated.

[0019] Furthermore, the high-fidelity terrain state reconstruction, which uses the noisy original vertical height change as an observation variable input to a Kalman filter to construct a discrete state-space model, and dynamically adjusts the gain through a two-step iterative process of prediction and update, outputs the true road surface terrain value Δy that corrects for spatial phase shift and amplitude attenuation while filtering out high-frequency mechanical vibration noise, also includes:

[0020] Obtain the rate of change of road surface height to form a device state variable vector, observe the device state variable vector, and obtain a dataset of noisy raw heights;

[0021] The noisy raw height dataset is processed by Kalman filtering, and the posterior estimate is output. The posterior estimate is used as the final reconstructed ground truth of the terrain.

[0022] Furthermore, the step of processing the noisy raw height dataset through Kalman filtering and outputting a posterior estimate, which is then used as the final reconstructed terrain ground truth, further includes:

[0023] During the operation of agricultural vehicles, angle data is collected at a fixed frequency, and a two-step iteration of prediction and update is performed using Kalman filtering. Based on the optimal terrain state at the previous moment, combined with the vehicle speed and state transition matrix, the terrain height at the current moment is predicted.

[0024] The Kalman gain is dynamically calculated to balance the reliability of the Kalman filter prediction with the current angle sensor observation and to suppress burst noise.

[0025] Furthermore, the step of performing pre-aiming spatiotemporal mapping calculation, collecting the current motion state of the agricultural vehicle, combining it with the terrain ground truth, and calculating the pre-aiming action delay time to achieve real-time perception of complex terrain for the agricultural vehicle also includes:

[0026] Obtain the current forward speed of the agricultural vehicle;

[0027] Based on the preset static calibration parameters, the longitudinal physical wheelbase between the contact point of the probe wheel and the axle to be leveled on the vehicle chassis or the hydraulic cylinder to be executed is obtained;

[0028] Calculate the delay time of the aiming action.

[0029] This invention also discloses a real-time perception system for complex terrain of agricultural vehicles based on contact-type detection wheels, applied to the real-time perception device for complex terrain of agricultural vehicles described in the above technical solution, comprising:

[0030] A detection module is installed at the front end of the agricultural vehicle chassis to physically sense the terrain undulations ahead.

[0031] The controller module, which integrates a geometry backpropagation module, a Kalman filter algorithm module, and a pre-aiming spatiotemporal matching module, is used to receive sensor data and generate terrain truth values ​​and pre-aiming delay times without phase hysteresis.

[0032] This invention introduces Kalman filtering to construct motion state and predict and update the noisy data collected by the angle sensor. It can effectively filter out the high-frequency mechanical vibrations generated by contact detection. By acquiring terrain elevation data through the contact detection wheel and combining it with the current motion state of the vehicle, it performs pre-aiming spatiotemporal mapping matching, calculates the delay trigger time and compensation travel reference of the rear suspension system of the agricultural vehicle, and provides feedforward data support for achieving active leveling of the chassis with zero phase error. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the processes shown in these drawings without creative effort.

[0034] Figure 1 A schematic diagram of the structure of a real-time sensing device for complex terrain of agricultural vehicles provided in an embodiment of the present invention;

[0035] Figure 2 This is a schematic diagram of the structure of an agricultural vehicle and a complex terrain real-time sensing device for agricultural vehicles according to an embodiment of the present invention.

[0036] Figure 3 A flowchart illustrating a real-time sensing device for complex terrain of agricultural vehicles provided in an embodiment of the present invention;

[0037] Figure 4 A schematic diagram of a two-dimensional kinematic model of an agricultural vehicle real-time terrain sensing device provided in an embodiment of the present invention;

[0038] Figure 5 An equivalent stress cloud map of an agricultural vehicle complex terrain real-time sensing device provided in an embodiment of the present invention;

[0039] Figure 6 This is a displacement cloud map of an agricultural vehicle complex terrain real-time sensing device provided in an embodiment of the present invention;

[0040] Figure 7 A safety factor distribution diagram of a real-time sensing device for complex terrain of agricultural vehicles provided in an embodiment of the present invention;

[0041] Figure 8 A terrain reconstruction and error comparison diagram of a real-time perception device for complex terrain of agricultural vehicles provided in an embodiment of the present invention;

[0042] Figure 9 This is a schematic diagram of the module structure of a real-time perception system for complex terrain of agricultural vehicles provided in an embodiment of the present invention.

[0043] Explanation of icon numbers:

[0044] Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention 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 indication will also change accordingly.

[0047] Furthermore, the use of terms such as "first" and "second" in this invention is 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, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. When the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed by this invention.

[0048] In existing technologies, non-contact sensing technologies suffer from poor environmental adaptability and are prone to vegetation misjudgment. For example, existing technologies employ non-contact sensors such as lidar, ultrasonic sensors, or binocular vision to scan and preview the road surface ahead. However, in harsh agricultural environments, there is often significant dust, mud splashes, and strong light interference. The detection surfaces of conventional optical or acoustic sensors are easily covered by mud or affected by dust, resulting in false signals. More seriously, in areas with crop cover or overgrown weeds, non-contact sensors often misjudge the crop canopy as ground level, failing to penetrate the vegetation to detect the actual hard soil layer where tires are touching the ground, leading to severely distorted terrain reconstruction data.

[0049] Conventional contact sensing devices in existing technologies are usually limited to a non-steerable structure. The entire sensing device is rigidly connected to the agricultural vehicle it operates on (such as tractors and other agricultural machinery), and does not have the mechanical freedom to turn left or right or sense lateral deviations. When the agricultural vehicle turns or turns around, the non-steerable sensing device inevitably produces severe lateral slippage and lateral dry friction on the ground. This not only introduces abnormal mechanical interference noise from non-terrain undulations, but also generates huge lateral bending moments on the linkage structure, which greatly reduces the service life of the sensing device and the accuracy of the collected data.

[0050] Understandably, existing conventional filtering algorithms cause terrain feature distortion and spatial phase shift. To filter out high-frequency mechanical noise from contact sensors, existing data processing systems typically employ conventional algorithms such as first-order low-pass filtering and moving average filtering. While these algorithms can smooth data curves to some extent, they inevitably introduce severe signal delay. This signal delay, during the dynamic movement of agricultural vehicles, directly translates into an uncontrollable shift in the spatial phase of the reconstructed terrain waveform. Furthermore, conventional filtering algorithms, in their attempt to smooth out violent fluctuations, often severely weaken the amplitude of actual deep ditches or high ridges. When the agricultural vehicle's control system receives data distorted in both spatial position and amplitude, it not only fails to achieve accurate pre-leveling but also tends to output compensation actions at incorrect times, worsening the vehicle's posture. The Kalman filter involved in this invention is an algorithm that uses the state equations of a linear system to optimally estimate the system state through system input and output observation data. The Kalman filter in this invention can effectively filter out noise while maintaining the undistorted phase of the reconstructed terrain curve.

[0051] Based on this, this application provides a real-time terrain sensing device 1000 for agricultural vehicles based on contact-type detection wheels, applied to agricultural vehicles 2000, including:

[0052] Support arm 100, rigidly connected to agricultural vehicle 2000, is a hollow tubular structure; Linkage assembly 200, including link 210, hinge seat 220, and shock absorber assembly 230, one end of hinge seat 220 is hinged to the end of support arm 100 away from agricultural vehicle 2000, and the other end of hinge seat 220 is hinged to link 210, the end of link 210 swings relative to support arm 100 via hinge seat 220; Detector wheel 300, rotatably connected to link 210 away from hinge seat 220. One end of the connecting rod 210; an angle sensor 400, which is located on one end of the connecting rod 210 near the hinge seat 220, is used to measure the rotation angle of the connecting rod 210 relative to the horizontal reference plane; a controller 600, which is located on one side of the hinge seat 220, and is connected to the angle sensor 400 and the agricultural vehicle 2000 for signal reception of noisy angle signals; and a protective baffle 500, which is detachably located on one end of the connecting rod 210 near the detection wheel 300.

[0053] refer to Figure 1 and Figure 2 In detail, the agricultural vehicle complex terrain real-time sensing device 1000 and the agricultural vehicle 2000 can be fixedly connected by means including but not limited to welding, bolting, and riveting. In this embodiment, the agricultural vehicle complex terrain real-time sensing device 1000 also includes a shock absorber assembly 230 and a controller 600. The shock absorber assembly 230 is connected between the support arm 100 and the connecting rod 210. The shock absorber assembly 230 can be a spring and / or a damping element, forcing the connecting rod 210 to generate a continuous downward clamping torque around the hinge seat 220, ensuring that the detection wheel 300 always keeps close to the ground surface during high-speed operation. At the same time, the shock absorber assembly 230 can also effectively absorb high-frequency vibration energy, and physically suppress the throwing and bouncing phenomenon under severe bumpy conditions through mechanical structure. In this invention, the support arm 100 is made of hollow metal tubing (including but not limited to stainless steel, aluminum alloy, etc.), which can effectively reduce the weight of the front overhang while ensuring the overall installation strength, achieving lightweighting of the curb weight, which is beneficial for field operations. The hinge seat 220 is hinged at both ends to the support arm 100 and the connecting rod 210, respectively. The hinge seat 220 allows the connecting rod 210 to swing up and down following the contours of the terrain, and also automatically adjusts the deflection direction to eliminate lateral slippage when the agricultural vehicle 2000 turns. The connecting rod 210, through the hinge seat 220, can swing according to the terrain undulations. In this embodiment, the connecting rod 210 is also made of hollow profile, enabling high-frequency dynamic response. The detection wheel 300 directly contacts the ground, converting the terrain undulations in front of the agricultural vehicle 2000 into the swing of the connecting rod 210.

[0054] More specifically, the present invention may also include a protective baffle 500, which is an arc-shaped structure that covers the detection wheel 300 and is rigidly fixed to one end of the connecting rod 210. The protective baffle 500 is used to block splashed mud, gravel, and crop residues in unstructured farmland environments, preventing contamination of the angle sensor 400 and the hinge seat 220. Especially in working scenarios with common tall crops planted in fields (such as corn, sugarcane, cotton, etc.), if the protective baffle 500 is not set, the detection wheel 300 may be entangled by the stalks and fallen leaves of the crops. That is, the present invention can also effectively block or displace fallen crops in the field, preventing them from entangled or stuck on the detection wheel 300 and causing abnormal interference to the sensing data, thus ensuring the reliability and data purity of the present invention during long-term operation.

[0055] It is understood that the controller 600 can be installed in any safe location (i.e., a location that will not collide with the external environment during the sensing process) within the agricultural vehicle complex terrain real-time sensing device 1000. In this embodiment, the controller 600 is located on one side of the hinge seat 220. The controller 600 is connected to the angle sensor 400 and the chassis suspension actuator of the agricultural vehicle 2000 via signal connection. The controller 600 is used to receive noisy angle signals, reconstruct the true terrain value, and output a pre-aiming delayed trigger command to the chassis suspension actuator of the agricultural vehicle 2000. In this invention, the agricultural vehicle 2000 can be a tractor, harvester, or other agricultural machinery. It is understood that existing agricultural machinery has the ability to receive external control signals via wired connection and adjust its own actuators according to the external control signals. The agricultural vehicle 2000 also provides an installation carrier and propulsion power for the agricultural vehicle complex terrain real-time sensing device 1000.

[0056] like Figure 3 As shown, this invention also discloses a real-time perception method for complex terrain of agricultural vehicles based on contact-type detection wheels, applied to the real-time perception device 1000 for complex terrain of agricultural vehicles in the above technical solution, including the following steps:

[0057] Step S10: Establish a two-dimensional motion model of the agricultural vehicle complex terrain real-time perception device 1000, take the center of the hinge seat 220 as the coordinate origin O, set a plane parallel to the horizontal ground passing through the coordinate origin O as the horizontal reference plane, and obtain the rotation angle θ between the connecting rod 210 and the horizontal reference plane through the angle sensor 400.

[0058] Step S20: Geometric back-calculation of height. Establish a geometric kinematic model, input the rotation angle θ into the controller 600 of the agricultural vehicle 2000, and calculate the noise-infused original vertical height change of the contact point of the detection wheel 300 relative to the vehicle body reference surface in real time based on the geometric kinematic model.

[0059] Step S30: High-fidelity terrain state reconstruction. The noisy original vertical height change is used as the observation variable and input into the Kalman filter to construct a discrete state space model. The gain is dynamically adjusted through two-step iteration of prediction and update. While filtering out high-frequency mechanical vibration noise, the true value of road surface terrain Δy, which corrects the spatial phase shift and amplitude attenuation, is output.

[0060] Step S40: Perform pre-aiming spatiotemporal mapping calculation, collect the current motion state of agricultural vehicle 2000, combine it with the terrain true value, calculate the pre-aiming action delay time, and realize real-time perception of agricultural vehicle 2000 in complex terrain.

[0061] Further, step S20, geometric back-calculation of height, involves establishing a geometric kinematic model, inputting the rotation angle θ into the controller 600 of the agricultural vehicle 2000, and calculating in real time the noisy original vertical height change of the contact point of the detection wheel 300 relative to the vehicle body reference surface based on the geometric kinematic model. This step also includes:

[0062] Step S21: Obtain the vertical drop distance h between the center of the probe wheel 300 and the center of the pivot of the hinge seat 220. drop The total vertical distance h between the lowest contact point of the probe wheel 300 and the center of the rotating shaft is calculated.

[0063] Specifically, such as Figure 4 As shown, according to trigonometric geometry, the vertical drop h between the axis center of the probe wheel 300 and the rotation axis center of the hinge seat 220 is... drop for:

[0064] ;

[0065] in, This is a fixed length from the center of the rotating shaft to the center of the shaft of the probe wheel 300. The angle between the connecting rod 210 and the horizontal reference plane is measured by the angle sensor 400, and the positive direction is defined when the connecting rod 210 is below the horizontal reference plane.

[0066] At this point, ignoring the tangential offset of the wheel-to-ground contact point caused by the steep local slope of the terrain, it can be approximately assumed that the wheel-to-ground contact point is always located directly below the center of the detection wheel 300. The total vertical distance h of the lowest contact point of the detection wheel 300 relative to the center of the shaft is then:

[0067] ;

[0068] Furthermore, the specific formula for calculating the true value Δy of the road surface terrain in step S30 is as follows:

[0069] ;

[0070] in, This represents the real-time terrain undulation. The calibrated installation height is set when the pivot center of hinge seat 220 is on a horizontal road surface. The radius of the probe wheel 300.

[0071] The above formula can be used to convert the electrical signal of the angle sensor 400. This is converted into the change in terrain height Δy in physical space, providing the original observation data for subsequent Kalman filtering.

[0072] In detail, this invention constructs a terrain state observer based on the Kalman filter algorithm, which achieves distortion-free reconstruction of the terrain contour while filtering out high-frequency mechanical noise. The discrete state-space model for the motion of the probe wheel 300 is defined as follows:

[0073] (1) State variables:

[0074] Let the rate of change of road surface height (i.e., the vertical velocity caused by terrain slope) be set as State variable vector for:

[0075] ;

[0076] (2) Observed variables:

[0077] The observed variable is the noisy original height calculated by the geometric inverse model, denoted as a vector. :

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] in, Let A be the sampling time, and A be the state transition matrix, based on the sampling time. Construct kinematic relationships; H is the observation matrix. In this invention, only the height can be directly observed, so H=[1,0] in this invention; The process noise is represented by its covariance matrix Q, where Q represents the roughness and unpredictability of the farmland terrain itself. To measure the noise, its covariance matrix is ​​R, where R represents the electrical noise of the angle sensor 400 and the high-frequency mechanical vibration caused by the bouncing of the probe wheel 300.

[0083] To verify the posterior estimate (y) output by this invention after Kalman filtering...k To assess the reliability of the final reconstructed terrain truth, this invention also conducts simulation experiments, as follows:

[0084] The structural parameters are set as follows:

[0085] The calibrated mounting height of the pivot center of hinge seat 220 =700mm, connecting rod 210 length L=590mm, detection wheel 300 radius R=200mm.

[0086] The operating conditions for Agricultural Vehicle 2000 are set as follows: the simulated field operation speed of Agricultural Vehicle 2000 is set to a constant speed of v=1.0m / s (i.e., 3.6km / h). Figure 8 The horizontal axis represents the total simulation duration (10s). Under this time-space mapping relationship, this time axis position corresponds to an actual continuous driving road surface with a total length of 10m in front of the vehicle.

[0087] The input conditions for complex terrain are set as follows: the terrain undulation data combines low-frequency macro undulation (simulating field mounds with a maximum amplitude of approximately ±40 mm), high-frequency undulation (simulating gravel clods with a maximum amplitude of approximately ±8 mm), and random surface roughness white noise.

[0088] Noise Interference and Algorithm Parameter Settings: Considering the high-frequency mechanical fluctuations and electrical noise caused by severe turbulence, a mixed random noise ranging from approximately ±0.5° is superimposed at the ideal sensor angle, and the system sampling frequency is set to 10Hz (sampling period Δt=0.1s). The traditional low-pass filter coefficient is set to α=0.1; the process noise covariance and measurement noise variance of the Kalman filter in this invention are optimized and calibrated based on the aforementioned physical noise range.

[0089] The verification results are as follows Figure 8 As shown in the system error comparison chart below, the algorithm curve proposed in this invention (blue solid line) highly coincides with the actual terrain curve (black solid line) at the peak and trough time points, with almost no phase lag. This provides extremely accurate "lead time" data for subsequent active suspension. Traditional algorithms (red dashed line) experience a sharp increase in error when the terrain changes drastically; while the Kalman filter algorithm of this invention (blue solid line) keeps the reconstruction error within a very small range, and the error curve fluctuates uniformly around the 0 axis, demonstrating that the algorithm has extremely high robustness and fidelity in complex farmland terrain.

[0090] Further, step S40, which involves performing pre-aiming spatiotemporal mapping calculation, collecting the current motion state of the agricultural vehicle 2000, and combining it with the terrain ground truth to calculate the pre-aiming action delay time, thus achieving real-time perception of the agricultural vehicle 2000 in complex terrain, also includes:

[0091] Step S41: Obtain the current forward speed of agricultural vehicle 2000;

[0092] Step S42: Based on the preset static calibration parameters, obtain the longitudinal physical wheelbase between the contact point of the probe wheel 300 and the axle to be leveled on the vehicle chassis or the hydraulic cylinder to be executed.

[0093] Step S43: Calculate the aiming delay time.

[0094] In detail, step S40 of this invention involves using the perceived terrain truth value for chassis suspension control by performing a pre-aiming spatiotemporal mapping calculation. Specifically, this involves obtaining the forward speed v of the current agricultural vehicle 2000 and calculating the pre-aiming action delay time according to the preset mechanical structure static calibration parameters of this device (i.e., the longitudinal physical wheelbase D between the contact point of the detection wheel 300 and the axle to be leveled or the hydraulic cylinder of the agricultural vehicle 2000 chassis). The specific calculation formula is as follows:

[0095] ;

[0096] The controller 600 in this invention pushes the current terrain truth value into the data buffer queue, and after the aforementioned pre-aiming delay time... Subsequently, the suspension actuator of the agricultural vehicle 2000 is triggered to achieve real-time perception and motion compensation. This invention effectively overcomes the shortcomings of traditional feedback control, which only passively compensates after encountering terrain disturbances, through a spatiotemporal mapping mechanism. It achieves seamless connection between early perception and precise action, providing feedforward data support for achieving active leveling of the chassis with zero phase error.

[0097] This invention also discloses structural strength and stiffness verification. The verification method employs finite element analysis (FEA) technology to analyze the strength and stiffness of the sensing device in this invention, verifying the device's resistance to fracture and deformation under extreme impact conditions. The verification results are as follows: Figures 5-7 As shown, the details are as follows:

[0098] The connecting rod is made of 6061-T6 aluminum alloy, and its key material parameters are: elastic modulus 69 GPa, Poisson's ratio 0.33, and material yield strength 275MPa. In terms of geometric dimensions, the key force-bearing cross-section of the connecting rod is 60×40 mm, the tube wall thickness is 4 mm, and the effective force transmission arm length is 590 mm.

[0099] (1) Strength verification (with stress as the test target)

[0100] To verify the linkage as the main force transmission structural component, the connection of the hinge seat was rigidified and equivalently constrained. Considering the soft and muddy characteristics of farmland soil and the dynamic bumps generated by agricultural vehicles during operation, a vertical impact load of 1500N and a horizontal traction resistance of 800N were set. At the same time, the shock absorber assembly was equivalent to a virtual two-force bar with hinge holes installed at the top and bottom to improve calculation efficiency.

[0101] In one embodiment, such as Figure 5 As shown in the equivalent stress cloud diagram, under simulated maximum field impact load conditions, the maximum stress of the real-time sensing device of this invention is concentrated at the hinge connection of the connecting rod, reaching a peak value of 132 MPa. This stress peak value is still significantly lower than the yield strength of the selected material, combined with... Figure 7 As shown in the safety factor distribution cloud map, the minimum safety factor of the real-time sensing device of this invention is 1.9. This proves that the core structural components can be free from plastic deformation and fatigue fracture under harsh working conditions, fully meeting the strength requirements for practical use.

[0102] (2) Stiffness verification (using displacement as the test target)

[0103] like Figure 6 The displacement cloud diagram shown indicates that, under extreme stress conditions, the maximum elastic deformation at the end of the probe wheel is 11.50 mm.

[0104] Considering the 590mm long cantilever structure and the complexity of the undulating farmland, the mechanical deformation is within a reasonable range allowed by engineering.

[0105] This proves that the link has sufficient bending stiffness, and the angle change read by the angle sensor can truly and effectively reflect the terrain undulations rather than the structural bending under stress. From a physical perspective, this ensures that the boundary conditions of the geometric back-calculation mathematical model of this invention always hold.

[0106] like Figure 9 As shown, the present invention also discloses a real-time perception system for complex terrain of agricultural vehicles based on contact-type detection wheels, the real-time perception system for complex terrain of agricultural vehicles comprising:

[0107] Detection module 10, which is located at the front end of the agricultural vehicle chassis, is used to physically sense the undulations of the terrain ahead;

[0108] The controller module 20 has a built-in geometric backpropagation module, a Kalman filter algorithm module, and a pre-aiming spatiotemporal matching module, which are used to receive sensor data and generate terrain true values ​​and pre-aiming delay time without phase hysteresis.

[0109] The present invention also proposes a storage medium, which is a computer-readable storage medium, and stores a computer program on the storage medium. When the computer program is executed by a processor, it follows the steps of the method for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels in any of the above technical solutions.

[0110] The present invention also proposes a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the real-time perception method for complex terrain of agricultural vehicles based on contact-type detection wheels in the above embodiments.

[0111] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0112] This invention introduces Kalman filtering to construct motion state and predict and update the noisy data collected by the angle sensor. It can effectively filter out the high-frequency mechanical vibrations generated by contact detection. By acquiring terrain elevation data through the contact detection wheel and combining it with the current motion state of the vehicle, it performs pre-aiming spatiotemporal mapping matching, calculates the delay trigger time and compensation travel reference of the rear suspension system of the agricultural vehicle, and provides feedforward data support for achieving active leveling of the chassis with zero phase error.

[0113] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A real-time terrain sensing device for agricultural vehicles based on contact-type detection wheels, characterized in that, Applications in agricultural vehicles, including: The outrigger is rigidly connected to the agricultural vehicle and is a hollow tubular structure. A linkage assembly, comprising a link, a hinge seat, and a shock absorber assembly, wherein one end of the hinge seat is hinged to the end of the support arm away from the agricultural vehicle, and the other end of the hinge seat is hinged to the link, and the end of the link swings relative to the support arm via the hinge seat. A detection wheel, the detection wheel being rotatably connected to one end of the connecting rod away from the hinge seat; An angle sensor is disposed on one end of the connecting rod near the hinge seat, and the angle sensor is used to measure the rotation angle of the connecting rod relative to the horizontal reference plane; A controller is disposed on one side of the hinge seat. The controller is connected to the angle sensor and the agricultural vehicle via signal connection. The controller is used to receive noisy angle signals. A protective baffle is detachably mounted on the connecting rod at one end near the detection wheel.

2. A method for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels, characterized in that, The application of the agricultural vehicle complex terrain real-time sensing device as described in claim 1 includes the following steps: A two-dimensional motion model of the agricultural vehicle complex terrain real-time perception device is established. The center of the hinge seat rotation axis is set as the coordinate origin O. A plane parallel to the horizontal ground passing through the coordinate origin O is set as the horizontal reference plane. The rotation angle θ between the connecting rod and the horizontal reference plane is obtained through the angle sensor. Geometric back-calculation of height involves establishing a geometric kinematic model, inputting the rotation angle θ into the controller of the agricultural vehicle, and calculating in real time the change in the original vertical height of the detection wheel contact point relative to the vehicle body reference surface, which is noisy. High-fidelity terrain state reconstruction uses the noisy original vertical height change as an observation variable input to a Kalman filter to construct a discrete state space model. By dynamically adjusting the gain through two-step iteration of prediction and update, the true value of road surface terrain Δy, which is corrected for spatial phase shift and amplitude attenuation, is output while filtering out high-frequency mechanical vibration noise. Perform pre-aiming spatiotemporal mapping calculation, collect the current motion state of agricultural vehicles, combine it with the terrain true value, calculate the pre-aiming action delay time, and realize real-time perception of agricultural vehicles in complex terrain.

3. The method for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels as described in claim 2, characterized in that, The geometric back-calculation of height, which involves establishing a geometric kinematic model, inputting the rotation angle θ into the agricultural vehicle's controller, and calculating the noisy original vertical height change of the detection wheel contact point relative to the vehicle's reference surface in real time based on the geometric kinematic model, also includes: Obtain the vertical drop distance h between the center of the probe wheel and the center of the hinge seat's rotation axis. drop The total vertical distance h between the lowest contact point of the probe wheel and the center of the rotating shaft is calculated.

4. The method for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels as described in claim 2, characterized in that, The high-fidelity terrain state reconstruction, which uses the noisy original vertical height change as an observation variable input to a Kalman filter to construct a discrete state-space model, and dynamically adjusts the gain through a two-step iterative process of prediction and update, outputs the true road surface terrain value Δy that corrects for spatial phase shift and amplitude attenuation while filtering out high-frequency mechanical vibration noise, also includes: Obtain the rate of change of road surface height to form a device state variable vector, observe the device state variable vector, and obtain a dataset of noisy raw heights; The noisy raw height dataset is processed by Kalman filtering, and the posterior estimate is output. The posterior estimate is used as the final reconstructed ground truth of the terrain.

5. The method for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels as described in claim 4, characterized in that, The step of processing the noisy raw height dataset through Kalman filtering and outputting a posterior estimate, which is then used as the final reconstructed ground truth terrain value, further includes: During the operation of agricultural vehicles, angle data is collected at a fixed frequency, and a two-step iteration of prediction and update is performed using Kalman filtering. Based on the optimal terrain state at the previous moment, combined with the vehicle speed and state transition matrix, the terrain height at the current moment is predicted. The Kalman gain is dynamically calculated to balance the reliability of the Kalman filter prediction with the current angle sensor observation and to suppress burst noise.

6. The method for real-time perception of complex terrain by agricultural vehicles based on contact-type detection wheels as described in claim 2, characterized in that, The steps of performing pre-aiming spatiotemporal mapping calculation, collecting the current motion state of agricultural vehicles, combining it with terrain ground truth, calculating the pre-aiming action delay time, and realizing real-time perception of complex terrain for agricultural vehicles, also include: Obtain the current forward speed of the agricultural vehicle; Based on the preset static calibration parameters, the longitudinal physical wheelbase between the contact point of the probe wheel and the axle to be leveled on the vehicle chassis or the hydraulic cylinder to be executed is obtained; Calculate the delay time of the aiming action.

7. A real-time terrain sensing system for agricultural vehicles based on contact-type detection wheels, characterized in that, The agricultural vehicle complex terrain real-time sensing device as described in claim 1 includes: A detection module is installed at the front end of the agricultural vehicle chassis to physically sense the terrain undulations ahead. The controller module, which integrates a geometry backpropagation module, a Kalman filter algorithm module, and a pre-aiming spatiotemporal matching module, is used to receive sensor data and generate terrain true values ​​and pre-aiming delay times without phase hysteresis.

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