Charging robot control method and device, charging robot, and storage medium

Abstract

The present application relates to the technical field of charging, and discloses a charging robot control method and device, a charging robot and a storage medium, the method comprising: acquiring terrain data of a current driving environment of the charging robot and self attitude data; determining a longitudinal adjustment angle of each wheel of the charging robot based on the terrain data and the self attitude data; driving a wheel assembly of a corresponding wheel to independently rotate around a longitudinal axis according to the longitudinal adjustment angle, so that each tire of the charging robot is in close contact with a current contact road surface; and controlling the charging robot to move to a charging position of a target vehicle based on the close contact state of each tire of the charging robot with the current contact road surface. The present application solves the problem that the existing charging robot has poor tire close contact, poor driving stability and is difficult to drive to the bottom of a vehicle to implement charging when passing through a single-side or double-side inclined road surface.

Description

Technical Field

[0001] This invention relates to the field of charging technology, and more specifically to a charging robot control method, device, charging robot, and storage medium. Background Technology

[0002] With the increasing popularity of new energy vehicles, automated or semi-automated mobile charging robots have attracted widespread attention as a flexible energy replenishment solution. These robots are typically equipped with battery packs and can autonomously move to the parking location of vehicles waiting to be charged, replenishing the vehicles' power through wireless charging or docking with charging ports. In practical applications, the surfaces on which charging robots travel include parking lot ramps, speed bumps, road shoulder edges, gravel roads, and uneven terrain caused by long-term vehicle traffic. The vehicles waiting to be charged may also exhibit different body postures due to the inclination of the parking space ground or differences in load distribution.

[0003] Existing mobile charging robots employ conventional fixed or independent suspension structures, where the longitudinal direction of each wheel is fixed relative to the vehicle body. They rely solely on the passive compression stroke of springs or hydraulic shock absorbers to absorb road undulations. When the charging robot travels on inclined surfaces, the limited adjustment range of the passive suspension and its inability to actively change the longitudinal support angle of individual wheels inevitably result in some tires only contacting the ground at their edges or even being completely suspended in the air. In this situation, the effective contact area between the tires and the road surface is significantly reduced, and the driving force cannot be fully transferred to the ground. When the robot travels uphill or on lateral slopes, it is highly susceptible to slippage, deviation from its intended direction, or even bottoming out and stalling, ultimately making it difficult to stably reach the target vehicle for charging. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a charging robot control method, device, charging robot, and storage medium to solve the problems that existing charging robots are prone to poor tire contact and poor driving stability when passing through inclined roads, making it difficult to drive to the bottom of the vehicle for charging.

[0005] In a first aspect, embodiments of the present invention provide a charging robot control method, the method comprising: Acquire terrain data of the current driving environment and the robot's own posture data; Based on the terrain data and its own posture data, the longitudinal adjustment angle of each wheel in the charging robot is determined; According to the longitudinal adjustment angle, the wheel assemblies of the corresponding wheels are driven to rotate independently around the longitudinal axis so that each tire of the charging robot is in contact with the current road surface; Based on the contact status of each tire of the charging robot with the current contact surface, the charging robot is controlled to move to the charging position of the target vehicle.

[0006] Furthermore, acquiring the terrain data of the current driving environment includes: Obtain the first scene information collected by the charging robot; Acquire second scene information about the surroundings of the charging robot collected by the target vehicle; Receive preset scene information sent from the cloud; By integrating the first scene information, the second scene information, and the preset scene information, terrain data of the current driving environment of the charging robot is generated.

[0007] Furthermore, determining the longitudinal adjustment angle of each wheel in the charging robot based on the terrain data and its own posture data includes: Based on the terrain data, calculate the road surface tilt angle at each wheel contact point; The current horizontal state of the charging robot is determined based on its own posture data; Based on the road surface inclination angle and the current horizontal state, the longitudinal adjustment angle of each wheel in the charging robot is calculated, wherein the longitudinal adjustment angle is the angle required to match the corresponding tire tread with the road surface inclination angle.

[0008] Furthermore, the step of driving the wheel assemblies of the corresponding wheels to rotate independently around the longitudinal axis according to the longitudinal adjustment angle includes: For each wheel, the longitudinal adjustment angle is converted into the rotational drive amount of the corresponding drive motor; The drive motor is controlled to rotate according to the rotational drive amount, so as to drive the wheel assembly to rotate around the longitudinal axis; During rotation, feedback parameters of the wheel are acquired, including motor drive current and / or tire pressure. The feedback parameters are matched with preset benchmark parameters to obtain a matching result; When the matching result is that the feedback parameter matches the preset benchmark parameter, the wheel assembly is determined to be rotated into position.

[0009] Furthermore, after matching the feedback parameters with preset benchmark parameters to obtain a matching result, the method further includes: When the matching result indicates that the feedback parameters do not match the preset benchmark parameters, a re-mapping request is sent to the cloud that is connected to the charging robot, and the charging robot is controlled to re-collect the third scene information. Based on the third scene information, the longitudinal adjustment angle of each wheel is corrected to obtain the corrected longitudinal adjustment angle; The corresponding wheel assembly continues to rotate based on the corrected longitudinal adjustment angle.

[0010] Furthermore, after controlling the charging robot to move to the charging location of the target vehicle, the method further includes: Obtain the relative pose deviation between the charging robot and the target vehicle; The longitudinal angle compensation amount of each wheel in the charging robot is determined based on the relative pose deviation. Drive the corresponding wheel assembly to rotate based on the longitudinal angle compensation amount until the top plane of the charging robot is aligned with the bottom charging plane of the target vehicle, and control the charging robot to perform the charging docking operation.

[0011] Furthermore, controlling the charging robot to move to the charging location of the target vehicle includes: During the movement of the charging robot, resistance feedback data is acquired for each wheel as it travels. Based on the resistance feedback data, identify whether each wheel is in a state of obstruction; When at least one wheel is detected to be in an obstructed state, the longitudinal adjustment angle and power output parameters of that wheel are adjusted according to the resistance feedback data to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters. Based on the adjusted longitudinal adjustment angle and the adjusted power output parameters, the charging robot is controlled to continue moving to the charging position of the target vehicle.

[0012] Furthermore, adjusting the longitudinal adjustment angle and power output parameters of the wheel based on the resistance feedback data to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters includes: The resistance feedback data is matched with the resistance feedback features corresponding to at least one preset working condition type to obtain the target working condition type that matches the resistance feedback data. The longitudinal adjustment angle of the wheel is updated according to the target working condition type to obtain the updated longitudinal adjustment angle. At the same time, the power output mode of the wheel is switched to the target torque output mode corresponding to the target working condition type. The power output parameters of the wheel are adjusted according to the preset power parameters corresponding to the target torque output mode to obtain the adjusted power output parameters.

[0013] Secondly, embodiments of the present invention provide a charging robot control device, the device comprising: The acquisition module is used to acquire terrain data of the current driving environment of the charging robot and its own posture data; The determination module is used to determine the longitudinal adjustment angle of each wheel in the charging robot based on the terrain data and its own posture data; The drive module is used to drive the wheel assemblies of the corresponding wheels to rotate independently around the longitudinal axis according to the longitudinal adjustment angle, so that each tire in the charging robot is in contact with the current road surface; The control module is used to control the charging robot to move to the charging position of the target vehicle based on the contact state between each tire of the charging robot and the current contact surface.

[0014] Thirdly, embodiments of the present invention provide a charging robot, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method described in the first aspect or any corresponding embodiment thereof.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that cause a computer to perform the method described in the first aspect or any of its corresponding embodiments.

[0016] The method provided in this application has the following beneficial effects: The method provided in this application acquires terrain data of the current driving environment and its own posture data of the charging robot, realizing real-time perception and data collection of the geometric features of the driving road surface and the tilt state of the robot body, ensuring the integrity and timeliness of the input information required for subsequent angle calculation; by determining the longitudinal adjustment angle of each wheel in the charging robot based on the terrain data and its own posture data, independent angle control parameters for each wheel are generated, improving the spatial resolution and individual adaptation accuracy of angle allocation; by driving the wheel assembly of the corresponding wheel to rotate independently around the longitudinal axis according to the longitudinal adjustment angle, so that each tire in the charging robot is in contact with the current contact road surface, realizing the active independent adjustment of the support direction of each tire, increasing the actual contact area between each tire and the road surface, and strengthening the longitudinal and lateral stability of the vehicle body on inclined roads; by controlling the charging robot to move to the charging position of the target vehicle based on the contact state of each tire in the charging robot with the current contact road surface, the disturbance of the travel trajectory by local unevenness of the road surface or tire slippage is reduced, and the alignment accuracy between the final stopping position and the vehicle charging port is improved. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart illustrating a charging robot control method according to some embodiments of the present invention; Figure 2 This is a flowchart illustrating another charging robot control method according to some embodiments of the present invention; Figure 3 This is a schematic diagram of the charging robot wheel assembly structure and a comparison of two driving conditions according to some embodiments of the present invention; Figure 4 This is a structural block diagram of a charging robot control device according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] According to embodiments of the present invention, a charging robot control method, apparatus, charging robot, and storage medium are provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0021] This embodiment provides a charging robot control method. Figure 1 This is a flowchart of a charging robot control method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain terrain data of the current driving environment and the robot's own posture data.

[0022] In this embodiment of the application, obtaining terrain data of the current driving environment includes: obtaining first scene information collected by the charging robot; obtaining second scene information around the charging robot collected by the target vehicle; receiving preset scene information sent by the cloud; and fusing the first scene information, the second scene information, and the preset scene information to generate terrain data of the current driving environment of the charging robot.

[0023] Specifically, the first scene information refers to the raw road condition perception data collected in real time by the charging robot's onboard cameras and radar as it scans its current route, including local road slope, unevenness, obstacle distribution, and spatial geometric features. The second scene information refers to the auxiliary observation data about the charging robot's surrounding environment (especially the vehicle's underside and the robot's blind spots) collected by the cameras and radar on the target vehicle (i.e., the vehicle to be charged). The preset scene information refers to the prior terrain data distributed from the cloud based on historical survey data, third-party map data, or pre-stored standard terrain models of the driving area. The terrain data is a comprehensive dataset generated by fusing information from the aforementioned three different sources, used to fully characterize the spatial geometric attributes of the charging robot's current driving environment. The robot's own attitude data refers to the tilt angle values ​​in the pitch and roll directions relative to the absolute horizontal plane, measured in real time by the charging robot's internal level.

[0024] The charging robot first uses its onboard cameras and radar to collect real-time information about the first scene along its current direction of travel, obtaining details of the road conditions ahead, such as local slopes, tilt angles, and potential bumps or depressions. Simultaneously, the charging robot acquires second scene information from the cameras and radar on the target vehicle via wireless communication. This information provides supplementary spatial data about the robot's environment from the perspective of the vehicle's underside and surroundings, especially covering areas that the robot's own sensors cannot directly observe due to limited or obstructed views. Furthermore, the charging robot simultaneously receives preset scene information from the cloud, which provides a macroscopic model of the overall terrain and known road features of the area along its travel path.

[0025] Based on this, the central processing unit inside the charging robot fuses the first scene information, the second scene information, and the preset scene information in a unified spatial coordinate system. Specifically, it uses the real-time high-precision detailed data in the first scene information as the main body to locally correct and complete the accuracy of the macroscopic terrain prior model in the preset scene information. At the same time, it uses the cross-view data in the second scene information to verify and fill the blind spots and uncertainties in the robot's own perception results, eliminating data errors introduced by factors such as limited field of view of a single sensor, ground reflection, or motion occlusion. Finally, through spatiotemporal synchronous superposition and confidence-weighted fusion, it generates terrain data that covers the entire current driving environment and has sufficient spatial resolution. This terrain data includes at least the road surface tilt angle, undulation height change, and relative spatial orientation of the road surface at the point where each wheel is about to contact the road, providing accurate and complete input basis for the subsequent calculation of the independent longitudinal adjustment angle of each wheel.

[0026] By acquiring the first scene information collected by the charging robot itself, real-time self-perception data extraction of local slope, unevenness, and obstacle distribution on the current driving road surface is achieved, ensuring the real-time nature and detail accuracy of terrain perception. By acquiring the second scene information around the charging robot collected by the target vehicle, cross-observation data from the bottom of the vehicle and the blind spot of the robot's view is supplemented, filling the environmental information missing by the charging robot's own sensors due to limited field of view. By receiving preset scene information sent from the cloud, prior data of macroscopic terrain model and known road condition features of the driving path area are introduced, providing a global reference framework for terrain perception. By fusing the first scene information, the second scene information, and the preset scene information to generate terrain data of the charging robot's current driving environment, spatiotemporal synchronization and confidence-weighted fusion of multi-source heterogeneous data in a unified spatial coordinate system are achieved. This removes data errors introduced by single sensors due to limited field of view, ground reflection, or motion occlusion, generating a comprehensive terrain dataset that covers the entire driving environment and has sufficient spatial resolution, providing accurate and complete input basis for subsequent calculation of the independent longitudinal adjustment angle of each wheel.

[0027] Step S102: Based on terrain data and its own posture data, determine the longitudinal adjustment angle of each wheel in the charging robot.

[0028] It should be noted that the longitudinal adjustment angle of each wheel refers to the angular displacement required for each wheel assembly to rotate independently around its longitudinal axis so that each tire tread can match the road surface inclination angle at its respective contact point. The tire tread refers to the outer circumferential surface of the tire that directly contacts the road surface.

[0029] In this embodiment of the application, the longitudinal adjustment angle of each wheel in the charging robot is determined based on terrain data and its own posture data, including: Step A1: Calculate the road surface inclination angle at each wheel contact point based on the terrain data.

[0030] Specifically, each wheel contact point refers to the local area where the tire tread of the charging robot is actually in contact with or about to contact the ground in its current driving state. The road surface tilt angle refers to the degree of deflection of the road surface relative to the horizontal plane at each wheel contact point along the lateral or longitudinal direction of the wheel.

[0031] The current position coordinates of each tire of the charging robot are extracted from the terrain data, and the corresponding road surface height field value is indexed in the terrain data based on these position coordinates. Taking the contact position of each wheel as the center, multiple height sampling points in the surrounding area of ​​that position are extracted. The spatial plane equation of the local area is fitted by these sampling points, and then the angle between the normal vector of the spatial plane and the normal vector of the absolute horizontal plane is calculated. This angle is decomposed into a longitudinal tilt component along the wheel's travel direction and a lateral tilt component perpendicular to the travel direction, thus obtaining the road surface tilt angle at each wheel's contact position. Since each wheel of the charging robot may be in a different spatial position during the driving process, and the terrain undulations and tilt conditions at each position are independent and different, the above extraction and calculation process is performed separately for each wheel to ensure that each wheel obtains a road surface tilt angle uniquely corresponding to its actual contact position.

[0032] Step A2: Determine the current horizontal state of the charging robot based on its own posture data.

[0033] Specifically, the current horizontal state refers to the actual horizontal level of the charging robot body in space, as determined by its own attitude data, including pitch angle and roll angle.

[0034] The system reads the measurement signals output in real time from the level inside the charging robot. This level can sense the gravitational acceleration components of the charging robot in two dimensions: the front-to-back direction (pitch axis) and the left-to-right direction (roll axis), and convert these components into corresponding angle values. These angle values ​​are recorded as the charging robot's current attitude data, and the current level state of the charging robot is determined based on this data, that is, the current front-to-back tilt of the vehicle body in the pitch direction and the left-to-right tilt in the roll direction. Since the charging robot may continuously change its tilt state due to road undulations, acceleration and deceleration, or changes in load distribution during the driving process, the level continuously updates the measurement values ​​at a set sampling frequency, and reads the latest self-attitude data in each control cycle to ensure that the current level state always reflects the charging robot's real-time spatial attitude, providing an accurate vehicle body reference for the subsequent calculation of the longitudinal adjustment angle of each wheel.

[0035] Step A3: Calculate the longitudinal adjustment angle of each wheel in the charging robot based on the road surface inclination angle and the current horizontal state. The longitudinal adjustment angle is the angle required to match the tire tread with the road surface inclination angle.

[0036] Specifically, the tire tread refers to the outer circumferential surface of the tire that directly contacts the road surface. The road surface tilt angle at each wheel contact point is jointly calculated with the current horizontal state of the charging robot. During the calculation, for each wheel, firstly, based on the pitch angle in the current horizontal state, it is determined whether the charging robot has an overall tilt in the longitudinal direction. If so, the influence of this pitch component is subtracted or compensated when calculating the longitudinal adjustment angle of that wheel, so that the adjustment result of that wheel is not affected by the overall pitch of the vehicle body and only responds to the local road surface tilt. Simultaneously, based on the roll angle in the current horizontal state, it is determined whether the charging robot has an overall roll in the lateral direction. If so, this roll angle is added or subtracted when calculating the longitudinal adjustment angle of each wheel. The differential effects of the rolling component at different wheels allow the wheels on the left and right sides of the vehicle to obtain different angles to adapt to the road inclination at their respective positions. Based on this, the road inclination angle at each wheel contact point is combined with the current horizontal state of the charging robot to calculate the final angular displacement that each wheel needs to rotate around the longitudinal axis. This is used as the longitudinal adjustment angle of the wheel, so that when each wheel rotates into place according to its corresponding longitudinal adjustment angle, the tire tread of each wheel can completely fit with the road inclination angle at its respective contact point, achieving maximum contact area and optimal adhesion between the tire and the ground.

[0037] By calculating the road surface tilt angle at each wheel contact point based on terrain data, the system accurately extracts the independent local road surface tilt at each wheel's contact point, ensuring that each wheel obtains road surface tilt angle information uniquely corresponding to its actual position. By determining the charging robot's current horizontal state based on its own attitude data, the system accurately acquires the real-time spatial attitude of the charging robot in the pitch and roll directions, providing a vehicle body reference for subsequent angle calculations. By calculating the longitudinal adjustment angle of each wheel in the charging robot based on the road surface tilt angle and the current horizontal state, and ensuring that this longitudinal adjustment angle matches the corresponding tire tread with the road surface tilt angle, the system achieves a comprehensive joint calculation of the local road surface tilt angle at each wheel contact point and the current vehicle attitude. This allows the longitudinal adjustment angle of each wheel to independently adapt to the varying road surface tilt conditions at its location, while automatically compensating for the impact of the vehicle's overall pitch and roll on tire contact. This ensures that after adjustment, each wheel can achieve complete contact between the corresponding tire tread and the road surface tilt angle at its contact point, thereby maximizing the actual contact area and adhesion coefficient between each tire and the ground.

[0038] Step S103: According to the longitudinal adjustment angle, drive the wheel assembly of the corresponding wheel to rotate independently around the longitudinal axis so that each tire in the charging robot is in contact with the current road surface. In this embodiment of the application, the wheel assemblies of the corresponding wheels are driven to rotate independently around the longitudinal axis according to the longitudinal adjustment angle, including: Step B1: For each wheel, convert the longitudinal adjustment angle into the rotational drive amount of the corresponding drive motor.

[0039] Specifically, the drive motor is a power element installed inside the charging robot, used to drive the wheel assembly to rotate around its longitudinal axis. Rotational drive quantity refers to the quantified value of converting the angular displacement value of the longitudinal adjustment angle into the number of rotations or pulses that the drive motor needs to execute.

[0040] For each wheel in the charging robot, the corresponding longitudinal adjustment angle is obtained and input into the control processor of the drive motor. The control processor converts the longitudinal adjustment angle into the corresponding number of revolutions or pulse steps required for the drive motor to rotate, based on the reduction ratio, gear transmission ratio, and the angle change corresponding to each revolution of the motor. This yields a rotational drive quantity uniquely corresponding to the longitudinal adjustment angle. During the conversion process, the rotation direction of the drive motor is determined based on the sign of the longitudinal adjustment angle. A positive value corresponds to forward rotation of the motor, and a negative value corresponds to reverse rotation of the motor. Thus, the angle magnitude and direction are converted into a rotational drive command that the drive motor can execute.

[0041] Step B2: Control the drive motor to rotate according to the rotational drive amount, so as to drive the wheel assembly to rotate around the longitudinal axis.

[0042] Specifically, the wheel assembly refers to an integral structure composed of the wheel, the lower support connecting rod of the wheel, the upper support connecting steering rod of the wheel, and the rotating support gear of the wheel assembly. The wheel assembly as a whole can rotate around the longitudinal axis. The longitudinal axis refers to the rotation center axis of the rotating support gear of the wheel assembly, which extends along the front-rear direction of the charging robot.

[0043] The rotational drive quantity is sent to the control processor of the drive motor. The control processor outputs the corresponding motor drive signal according to the received rotational drive quantity, and controls the drive motor to start rotating in the specified direction and number of revolutions. The output shaft of the drive motor drives the external rotating gear of the motor to rotate. This external rotating gear of the motor meshes with the rotating support gear of the wheel assembly. When the external rotating gear of the motor rotates, it drives the rotating support gear of the wheel assembly to rotate around the longitudinal axis through gear meshing transmission. The rotating support gear of the wheel assembly is fixedly connected to the upper support connecting rod and the lower support connecting rod of the wheel. When this rotating gear rotates, it drives the entire wheel assembly (including the wheel, support connecting rod and tire) to rotate around the longitudinal axis, thereby changing the longitudinal support angle of the wheel, so that the tire tread can gradually approach the posture that matches the current inclination angle of the road surface.

[0044] Step B3: During the rotation process, obtain the feedback parameters of the wheel, including the motor drive current and / or tire pressure.

[0045] Specifically, feedback parameters refer to the characteristic data characterizing the tire's contact state with the road surface, returned in real time by sensors on the drive motor and tires during the rotation of the wheel assembly around its longitudinal axis. Motor drive current refers to the real-time operating current value of the drive motor while rotating the wheel assembly; changes in this current value reflect the magnitude of the load resistance borne by the motor. Tire pressure refers to the real-time monitored value of the air pressure inside the tire; changes in the tire tread-road contact area lead to changes in tire pressure, causing corresponding fluctuations in the tire pressure value.

[0046] Throughout the process of the drive motor rotating the wheel assembly around its longitudinal axis, the instantaneous operating current value of the drive motor is collected in real time by the current detection module, while the current tire pressure is obtained in real time by the tire pressure monitoring module installed inside the tire. The motor drive current and tire pressure are continuously recorded as feedback parameters at a set sampling frequency. The real-time motor drive current and tire pressure are compared with the pre-stored reference values ​​of motor drive current and tire pressure when the tire tread is in complete contact with the road surface. This serves as a quantitative basis for determining whether the tire is in correct contact with the road surface. The feedback parameters are also used to monitor for any unexpected situations such as abnormal resistance or tire deformation during rotation.

[0047] Step B4: Match the feedback parameters with the preset benchmark parameters to obtain the matching results.

[0048] Specifically, the preset benchmark parameters refer to the standard values ​​of feedback parameters that should be present when the tire tread is in complete contact with the road surface, stored in the charging robot's local or cloud database. These preset benchmark parameters include the benchmark current value that the drive motor should present when in full contact with the road surface and the benchmark pressure value that the tire pressure should reach. The matching result is a conclusion on the consistency between the real-time collected feedback parameters and the preset benchmark parameters, used to determine the current degree of tire contact with the road surface. The matching result includes two cases: the feedback parameters and the preset benchmark parameters are consistent (matching) and the feedback parameters and the preset benchmark parameters are inconsistent (mismatching).

[0049] The real-time collected motor drive current and tire pressure values ​​are compared item by item with the reference current and reference pressure values ​​stored in the preset reference parameters. If the deviation between the motor drive current value and the reference current value is within the preset allowable error range, and the deviation between the tire pressure value and the reference pressure value is also within the preset allowable error range, the central processing unit determines that the feedback parameter matches the preset reference parameter, and the matching result is consistent. If the deviation between the motor drive current value and the reference current value exceeds the preset allowable error range, or the deviation between the tire pressure value and the reference pressure value exceeds the preset allowable error range, the central processing unit determines that the feedback parameter does not match the preset reference parameter, and the matching result is inconsistent. This matching result is used to trigger different subsequent control branches: when matching, the rotation is confirmed to be in place; when not matching, the re-surveying and angle correction process is triggered.

[0050] Step B5: When the matching result shows that the feedback parameters match the preset benchmark parameters, the wheel assembly is determined to be rotated into position.

[0051] Specifically, "rotation into position" refers to the final state where the wheel assembly has rotated around its longitudinal axis to the correct angle, ensuring the tire tread is fully in contact with the road surface at the correct angle, maximizing the contact area. When the matching result shows that the feedback parameters match the preset reference parameters, it is determined that the current wheel assembly has rotated to the correct longitudinal adjustment angle. At this point, a stop rotation command is sent to the drive motor's control processor, controlling the drive motor to stop rotating and maintaining the current wheel assembly's angular position through a locking mechanism. Simultaneously, the rotation status of the wheel is marked as complete, the actual rotation angle of the current wheel assembly is recorded, and this angle is fed back to the charging robot's state management module for attitude control during subsequent driving and charging processes. For each wheel in the charging robot, the rotation drive and position confirmation process is executed independently, ensuring that all wheels complete their own independent longitudinal angle adjustment and achieve a state of complete contact with the road surface.

[0052] By converting the longitudinal adjustment angle of each wheel into the corresponding rotational drive of the drive motor, precise conversion of angle control commands into motor-executable rotation commands is achieved, ensuring the quantitative accuracy of angle adjustment. By controlling the drive motor to rotate according to the rotational drive, the wheel assembly rotates around its longitudinal axis, enabling active and independent adjustment of the longitudinal angle of each wheel. This allows each wheel to rotate to its target support posture according to its required adjustment angle. Real-time acquisition and dynamic monitoring of tire-road contact state characteristic data are achieved by acquiring feedback parameters of the wheel's motor drive current and / or tire pressure during rotation, providing a quantitative basis for determining whether the contact is adequate. Matching the feedback parameters with preset reference parameters yields a matching result, allowing for a quantitative determination of the consistency and accuracy of whether the tire tread is fully in contact with the road surface after rotation. When the matching result shows that the feedback parameters match the preset reference parameters, the wheel assembly is determined to be in position, reliably confirming the completion of longitudinal adjustment for each wheel. This ensures that each wheel reaches a fully contact posture with the road surface before subsequent driving, avoiding poor tire contact and loss of driving force transmission due to insufficient angle.

[0053] In this embodiment of the application, after matching the feedback parameters with the preset benchmark parameters to obtain the matching result, the method further includes: when the matching result is that the feedback parameters do not match the preset benchmark parameters, sending a re-mapping request to the cloud connected to the charging robot and controlling the charging robot to re-collect the third scene information; correcting the longitudinal adjustment angle of each wheel based on the third scene information to obtain the corrected longitudinal adjustment angle; and continuing to drive the corresponding wheel assembly to rotate according to the corrected longitudinal adjustment angle.

[0054] Specifically, the cloud refers to a remote server connected to the charging robot via a wireless network. This server stores preset scene information and preset benchmark parameters and can receive data requests sent by the charging robot. The re-mapping request is an instruction sent by the charging robot to the cloud when it determines that the current feedback parameters do not match the preset benchmark parameters, requesting the reacquisition of terrain data for the driving environment. The third scene information refers to the original road condition perception data re-collected by the charging robot after receiving the re-mapping instruction, by scanning the current driving environment again using its onboard cameras and radar. This data is used to update and correct the previously fused terrain data. The corrected longitudinal adjustment angle refers to the updated angle value used to drive the wheel assembly to continue rotating, obtained by recalculating and adjusting the original longitudinal adjustment angle based on the third scene information.

[0055] After determining that the feedback parameters do not match the preset benchmark parameters, the system first determines that the rotation angle of the current wheel assembly failed to achieve complete contact between the tire tread and the road surface. This may be due to sudden changes in the road surface after the initial terrain data collection, such as obstacles, depressions, or local deformations not included in the original terrain data. The system then sends a re-mapping request to the cloud via the charging robot's wireless communication module. This request includes the current wheel's identification information and position coordinates. Simultaneously, the charging robot's camera and radar pause their movement and perform a second scan of the current driving environment, re-collecting third-scene information. The collection range of this third-scene information is centered on the wheel contact position that triggered the mismatch and expands outwards to the surrounding area. After collection, the third-scene information is compared with previously... The first scene information is fused to generate updated terrain data. Based on this updated terrain data, the road surface tilt angle at the wheel contact point is re-extracted. Combined with the current posture data of the charging robot, the required longitudinal adjustment angle of the wheel is recalculated as the corrected longitudinal adjustment angle. This corrected longitudinal adjustment angle is then re-inputted to the control processor of the drive motor. The control processor executes the angle conversion and motor drive process again based on the corrected longitudinal adjustment angle, driving the corresponding wheel assembly to continue rotating to the new target angle. The rotation is then verified again by matching the feedback parameters with the preset reference parameters. If the match is successful, the wheel assembly is confirmed to have rotated to the correct position. If the match is still not successful, the above re-surveying and angle correction process is repeated until the feedback parameters match the preset reference parameters.

[0056] When the matching result indicates that the feedback parameters do not match the preset benchmark parameters, a re-mapping request is sent to the cloud and the charging robot is controlled to re-collect third-scene information. This enables timely perception and response to changes in the road surface, such as newly added obstacles, depressions, or local deformations, after the initial terrain data collection, providing updated real-time road condition information for angle correction. By correcting the longitudinal adjustment angle of each wheel based on the third-scene information, the corrected longitudinal adjustment angle is obtained, realizing the recalculation and error correction of the initial longitudinal adjustment angle and eliminating the angle calculation deviation caused by sudden changes in the road surface. By continuing to drive the corresponding wheel assembly to rotate according to the corrected longitudinal adjustment angle, a second precise adjustment of the wheels that have not rotated to the correct position is achieved. Combined with the feedback parameter matching verification again, a closed-loop correction is formed until the wheel assembly finally rotates to the correct position. This effectively copes with the interference of real-time road condition changes on the wheel angle adaptability and improves the adaptability and robustness of the charging robot in dynamically changing environments.

[0057] Step S104: Based on the contact state between each tire of the charging robot and the current contact surface, control the charging robot to move to the charging position of the target vehicle.

[0058] In this embodiment of the application, controlling the charging robot to move to the charging location of the target vehicle includes: Step C1: During the movement of the charging robot, acquire resistance feedback data for each wheel as it travels.

[0059] Specifically, resistance feedback data refers to the load resistance characteristics information fed back to the drive motor when each wheel contacts the road surface during the charging robot's movement. This data is specifically reflected in the fluctuation of the real-time operating current of the drive motor while maintaining wheel rotation. Wheel movement refers to the process in which the charging robot moves towards the target vehicle along the planned path using its own power after completing the longitudinal angle adjustment of each wheel, during which each wheel is in a driven rolling state.

[0060] During the charging robot's movement towards the target vehicle's charging location, the current detection module of the drive motor continuously monitors the real-time operating current value of the drive motor corresponding to each wheel. The difference between the real-time collected motor operating current value and the reference current value of the drive motor under no-load conditions is calculated to obtain the current increment corresponding to the additional load resistance experienced by each wheel during driving. At the same time, combined with the tire pressure fluctuation data fed back in real time by the tire pressure monitoring module inside the tire during driving, the current increment and tire pressure fluctuation data are used together as the resistance feedback data of each wheel during driving. For each wheel in the charging robot, its own resistance feedback data is independently collected and recorded. This data is updated in real time as the wheel passes through different road surface areas, reflecting the actual interaction force between each wheel and the road surface at the current contact position.

[0061] Step C2: Based on the resistance feedback data, identify whether each wheel is in a state of obstruction.

[0062] Specifically, the obstructed state refers to an abnormal driving condition in which the wheel's rolling resistance is abnormally increased, the tire spins freely, or it is unable to maintain normal driving force transmission due to uneven features such as local bumps, depressions, or soft sinking on the road surface it is currently in contact with.

[0063] The resistance feedback data of each wheel is compared with the resistance benchmark range under normal driving conditions pre-stored in the charging robot. The comparison method is to determine whether the real-time operating current value of the drive motor exceeds the upper limit threshold of the current under normal driving conditions, or whether the tire pressure drops or rises sharply beyond the normal fluctuation range, or whether the real-time operating current value of the drive motor fluctuates violently. If any indicator in the resistance feedback data of a wheel exceeds the corresponding normal range, it is initially determined that the wheel is currently in an obstructed state. To further confirm and eliminate misjudgments caused by sensor noise or slight road roughness, the camera image data at the corresponding position of the wheel is retrieved, and the road condition in the contact area of ​​the wheel in the image is visually verified. If obvious obstacles, depressions, or soft road surface features are confirmed in the image, the wheel is confirmed to be in an obstructed state. If no abnormal road condition features are confirmed in the image, but the resistance feedback data is still abnormal, it is determined that the wheel may be in an abnormal force state caused by longitudinal adjustment angle deviation, and it is also identified as an obstructed state. The above identification process is performed independently for each wheel, and the wheels in the obstructed state and the wheels in the normal driving state are marked respectively.

[0064] Step C3: When at least one wheel is detected to be in an obstructed state, adjust the longitudinal adjustment angle and power output parameters of the wheel according to the resistance feedback data to obtain the adjusted longitudinal adjustment angle and the adjusted power output parameters.

[0065] Specifically, based on the resistance feedback data, the longitudinal adjustment angle and power output parameters of the wheel are adjusted to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters, including: Step C301: Match the resistance feedback data with the resistance feedback features corresponding to at least one preset working condition type to obtain the target working condition type that matches the resistance feedback data.

[0066] Specifically, preset working condition types refer to typical working condition classifications stored in the charging robot's local or cloud database to characterize different road obstruction scenarios. Each preset working condition type corresponds to a specific set of resistance feedback characteristics, which include, but are not limited to, the fluctuation amplitude, fluctuation frequency, fluctuation duration of the drive motor current, and the rate and magnitude of change in tire pressure. The target working condition type refers to the preset working condition type that best matches the current obstruction state after comparing and matching the resistance feedback data of the current wheel with the resistance feedback characteristics corresponding to all preset working condition types.

[0067] The system receives resistance feedback data corresponding to wheels identified as being in an obstructed state. This resistance feedback data includes at least the fluctuation sequence of the real-time operating current of the drive motor and the change sequence of tire pressure. The real-time collected resistance feedback data is processed using both time-domain and frequency-domain feature extraction methods to extract feature parameters such as the peak amplitude, fluctuation period, fluctuation duration of the current fluctuation, and the slope and extreme values ​​of the tire pressure change. These extracted feature parameters are then compared sequentially with the resistance feedback features corresponding to each preset working condition type stored in the database. The similarity calculation uses a multi-dimensional Euclidean distance or cosine similarity algorithm, assigning different weight coefficients to the current fluctuation feature and the tire pressure change feature. The preset working condition type with the highest similarity exceeding a preset matching threshold is selected from all preset working condition types and designated as the target working condition type. If multiple preset working condition types have similarities exceeding the preset matching threshold, the one with the highest similarity is selected as the target working condition type, and the matching result is recorded in the charging robot's status log.

[0068] Step C302: Update the longitudinal adjustment angle of the wheel according to the target working condition type to obtain the updated longitudinal adjustment angle. At the same time, switch the power output mode of the wheel to the target torque output mode corresponding to the target working condition type. Adjust the power output parameters of the wheel according to the preset power parameters corresponding to the target torque output mode to obtain the adjusted power output parameters.

[0069] Specifically, the power output mode refers to the control strategy followed by the drive motor when delivering driving force to the wheels. Different power output modes correspond to different torque-speed output characteristics. The target torque output mode refers to a pre-set torque output strategy that corresponds to a target operating condition. This mode is configured to provide the most suitable power response characteristics under specific obstructed conditions. For example, a low-speed, high-torque output mode is used under convex obstruction conditions, while a progressive torque ramp-up mode is used under soft, sunken conditions. The updated longitudinal adjustment angle refers to the angle value obtained after correcting the current wheel's longitudinal adjustment angle according to the angle compensation rule corresponding to the target operating condition type, after identifying the target operating condition type. The adjusted power output parameters refer to the specific set of values ​​used to control the actual output of the drive motor, obtained by adjusting the drive motor's current limit, torque setpoint, and speed limit according to the preset power parameters corresponding to the target torque output mode.

[0070] Based on the target operating condition type, the corresponding angle compensation rules and power output control parameters are retrieved from the database. Regarding angle updates, according to the angle compensation rules corresponding to the target operating condition type—for example, increasing the longitudinal adjustment angle of the wheel to raise the tire and allow it to climb over obstacles in a raised condition, and decreasing the longitudinal adjustment angle to allow the tire to better fit the concave surface in a depressed condition—the updated longitudinal adjustment angle is calculated by superimposing the corresponding compensation angular displacement on the current longitudinal adjustment angle of the wheel. Regarding power adjustment, a pre-configured target torque output mode is retrieved from the database based on the target operating condition type. This mode includes a power output characteristic curve that matches the current obstructed operating condition. For example, when encountering obstacles, the drive motor's output characteristics are switched to a low-speed, high-torque mode; when encountering loose sand and gravel, the drive motor's output characteristics are switched to a progressive torque ramp-up mode with anti-slip control. The preset power parameters (including current limit, torque setpoint, speed limit, and acceleration limit) corresponding to the target torque output mode are sent to the drive motor's control processor. The control processor reconfigures the actual output of the drive motor according to these preset power parameters, enabling the wheel to move forward with power characteristics adapted to the current obstructed conditions during subsequent driving. At the same time, the updated longitudinal adjustment angle and the adjusted power output parameters are bundled and recorded as a new control command set for the wheel.

[0071] By matching the resistance feedback data with the resistance feedback features corresponding to at least one preset working condition type, a target working condition type matching the resistance feedback data is obtained. This enables accurate classification and identification of the specific working conditions of the current wheel obstruction road surface (including bulge obstruction, depression and suspension, soft sand and gravel, etc.), allowing for the selection of targeted control strategies adapted to the current obstruction scenario for subsequent angle updates and power adjustments. The updated longitudinal adjustment angle of the wheel is obtained by updating it according to the target working condition type, while simultaneously switching the wheel's power output mode to the target torque output mode corresponding to the target working condition type and adjusting it according to preset power parameters. The adjusted power output parameters are obtained by adjusting the power output parameters of the pairs of pairs. This enables synchronous and differentiated adjustment of the longitudinal support angle of the wheel and the output characteristics of the drive motor according to the working condition classification results: in the case of convex obstruction, the longitudinal angle is increased and the output is switched to low speed and high torque to climb over the obstacle; in the case of depression, the longitudinal angle is decreased and the output is switched to progressive torque increase to fit the depression surface; in the case of soft sand and gravel, the anti-slip control mode is adopted to avoid idling; through the coordinated matching of angle and power, the wheel can pass through the current obstructed road section with the most suitable posture and driving force characteristics, which improves the charging robot's ability to pass through and driving stability under various complex road conditions.

[0072] Step C4: Based on the adjusted longitudinal adjustment angle and the adjusted power output parameters, control the charging robot to continue moving to the charging position of the target vehicle.

[0073] Specifically, the charging location of the target vehicle refers to the charging receiving area located on the bottom of the vehicle to be charged, which docks with the charging plane on the top of the charging robot. Simultaneously, the updated longitudinal adjustment angle and adjusted power output parameters are obtained for each wheel in an obstructed state. For wheels not in an obstructed state, their current longitudinal adjustment angle and conventional power output parameters are retained. The updated longitudinal adjustment angle is sent to the drive motor control processor of the corresponding wheel. The control processor drives the wheel assembly to rotate around the longitudinal axis to the updated angle position. At the same time, the adjusted power output parameters are sent to the drive motor of the corresponding wheel. The drive motor outputs driving force to the wheel according to the current limit, torque setpoint, and speed limit specified by the adjusted power output parameters. All wheels work together according to their independent longitudinal adjustment angles and power output parameters, continuously controlling the charging robot to move towards the charging position of the target vehicle according to the preset travel path. During the continued movement, the resistance feedback data of each wheel is obtained in real time, and it is used to dynamically identify whether there are still wheels in an obstructed state. If there are still wheels in an obstructed state, the angle update and power parameter adjustment are performed again, forming a closed-loop control of moving, identifying, and adjusting simultaneously, until the charging robot moves to the charging position of the target vehicle along the planned path and triggers the arrival signal.

[0074] By acquiring resistance feedback data for each wheel during the robot's movement, real-time monitoring and data collection of the interaction force between each wheel and the road surface are achieved, providing a quantitative basis for identifying the obstructed state of each wheel. By identifying whether each wheel is obstructed based on the resistance feedback data, accurate identification of abnormal conditions such as abnormal rolling resistance, freewheeling, or inability to maintain normal driving force transmission caused by local bumps, depressions, or soft road surfaces is achieved, providing a prerequisite for subsequent targeted adjustments. When at least one wheel is identified as obstructed, the longitudinal adjustment angle and power output parameters of that wheel are adjusted based on the resistance feedback data to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters. The system outputs parameters to achieve synchronous and coordinated adjustment of the longitudinal support angle and driving force of the obstructed wheel, enabling the tire to climb over or conform to the obstructed road surface at an angle more adapted to the current obstacle. At the same time, it matches the torque output characteristics of the drive motor to adapt to the current working conditions, avoiding the problem of insufficient response caused by adjusting the angle or power alone. By controlling the charging robot to continue moving to the charging position of the target vehicle based on the adjusted longitudinal adjustment angle and the adjusted power output parameters, it achieves closed-loop dynamic control of moving, identifying and adjusting at the same time. This allows the charging robot to traverse complex road surfaces with continuous and stable driving force and tire ground contact and accurately reach the target charging position, avoiding the overall trajectory deviation or power loss caused by the obstruction of a single wheel.

[0075] In this embodiment of the application, after controlling the charging robot to move to the charging location of the target vehicle, as follows: Figure 2 As shown, the method also includes: Step S201: Obtain the relative pose deviation between the charging robot and the target vehicle.

[0076] In this embodiment, relative pose deviation refers to the spatial positional and angular offset between the top plane of the charging robot and the bottom charging plane of the target vehicle after the charging robot moves to the charging position of the target vehicle. Specifically, this deviation includes forward / backward and left / right positional offsets in the horizontal direction, as well as angular offsets in the pitch and roll directions. The bottom charging plane of the target vehicle refers to the energy transmission interface located under the chassis of the target vehicle, used for docking with the top charging plane of the charging robot. This plane tilts synchronously with the vehicle when it tilts.

[0077] After the charging robot moves to the charging position of the target vehicle according to the control process and triggers the arrival signal, the posture calibration process before charging docking is initiated. First, multiple distance sensors or laser rangefinders installed on the top of the charging robot emit distance measurement signals to multiple feature points on the bottom charging plane of the target vehicle to obtain distance measurement values ​​at multiple locations between the top plane of the charging robot and the bottom charging plane of the vehicle. At the same time, the current posture data measured by the level instrument inside the charging robot is obtained, and the current pitch angle and roll angle data of the target vehicle are received by wireless communication. The multiple distance measurement values ​​are subjected to spatial geometric calculation, and combined with the posture data of the charging robot and the target vehicle, the spatial position offset of the top plane of the charging robot relative to the bottom charging plane of the target vehicle in the front-back, left-right, and up-down directions, as well as the angular offset in the pitch and roll axes, are calculated. These offsets are combined as the relative posture deviation between the charging robot and the target vehicle.

[0078] Step S202: Determine the longitudinal angle compensation amount of each wheel in the charging robot based on the relative pose deviation.

[0079] In this embodiment, the longitudinal angle compensation amount refers to the amount of angular displacement required for each wheel assembly in the charging robot to rotate around the longitudinal axis in order to eliminate the relative pose deviation between the charging robot and the target vehicle and to achieve complete alignment between the top plane of the charging robot and the bottom charging plane of the target vehicle. This compensation amount is an incremental angle added on the basis of the currently adjusted longitudinal adjustment angle.

[0080] The relative pose deviation is input into a pre-constructed pose-angle mapping model, which stores the kinematic correspondence between the longitudinal adjustment angle changes of each wheel of the charging robot and the pose changes in the top plane space of the charging robot. Based on this kinematic correspondence, the pitch angle deviation in the relative pose deviation is decomposed into the differential longitudinal angle adjustment amount required by the front axle wheel and the rear axle wheel, the roll angle deviation is decomposed into the differential longitudinal angle adjustment amount required by the left wheel and the right wheel, and the front-to-back position deviation and the left-to-right position deviation are decomposed into the comprehensive longitudinal angle fine-tuning amount required by each wheel. The above decomposed amounts are superimposed on each wheel to obtain the additional longitudinal angle compensation amount required for that wheel. For each wheel in the charging robot, the above decomposition and superposition calculation is performed independently to ensure that each wheel obtains its own unique longitudinal angle compensation amount to adapt to the differentiated contribution of different wheels to the body pose adjustment at different positions.

[0081] Step S203: Drive the corresponding wheel assembly to rotate based on the longitudinal angle compensation amount until the top plane of the charging robot is aligned with the bottom charging plane of the target vehicle, and control the charging robot to perform the charging docking operation.

[0082] In this embodiment, the top plane of the charging robot refers to the energy emission plane set on the upper surface of the charging robot for contacting or closely aligning with the bottom charging plane of the target vehicle. The spatial orientation of this plane is determined by the longitudinal adjustment angle of each wheel. The charging docking operation refers to the process by which the charging robot begins to transfer electrical energy to the target vehicle through wireless charging or contact charging after the top plane is aligned with the bottom charging plane of the vehicle.

[0083] The longitudinal angle compensation value of each wheel is sent to the corresponding wheel's drive motor control processor. The control processor adds this longitudinal angle compensation value to the existing longitudinal adjustment angle of the current wheel to form the final target rotation angle. Following the drive and feedback verification process, it drives each wheel assembly to independently rotate around the longitudinal axis to this final target angle. During rotation, multiple distance and angle values ​​between the top plane of the charging robot and the bottom charging plane of the target vehicle are monitored in real time by a distance sensor mounted on the top, and the real-time pose deviation between the two is continuously calculated. As the longitudinal angle of each wheel is gradually adjusted to the correct position, the real-time pose deviation gradually decreases until it approaches zero. When the top plane of the charging robot is determined to be aligned with the target vehicle's bottom charging plane, the real-time pose deviation is calculated. When all positional and angular deviations between the charging planes at the bottom of the target vehicle fall within the preset allowable docking error range, the alignment of the two planes is confirmed. A docking command is then sent to the charging control module, which controls the charging transmitter on top of the charging robot to establish an energy transfer connection with the charging receiver at the bottom of the target vehicle, and begins the charging docking operation. During charging, the positional relationship between the two planes continues to be monitored using distance sensors and a level. If the relative positional deviation exceeds the allowable range due to ground subsidence, vehicle load changes, or external disturbances, the calibration process is retried, dynamically adjusting the longitudinal angles of each wheel to maintain continuous alignment between the two planes.

[0084] By acquiring the relative pose deviation between the charging robot and the target vehicle, the precise quantification of the spatial position and angular offset between the top plane of the charging robot and the bottom charging plane of the target vehicle was achieved, providing a basis for attitude calibration before docking. By determining the longitudinal angle compensation amount of each wheel in the charging robot based on the relative pose deviation, the pose deviation in three-dimensional space was decomposed into independent angle increments required by each wheel according to kinematic relationships. This allows each wheel to obtain different compensation angles based on its own positional differences, adapting to the differentiated contributions of different wheels to the vehicle's pose adjustment. By driving the corresponding wheel assembly to rotate based on the longitudinal angle compensation amount until the top plane of the charging robot is aligned with the bottom charging plane of the target vehicle and controlling the charging robot to perform the charging docking operation, precise fine-tuning of the angles of each wheel was achieved so that the top plane of the vehicle gradually approaches and eventually aligns completely with the bottom charging plane of the vehicle. This ensures a tight fit between the charging transmitter and receiver, avoiding the decrease in charging efficiency or interruption of safety protection caused by angular deviations between the two due to ground deformation or changes in vehicle load, thus improving the success rate of charging docking and the stability of energy transmission.

[0085] Figure 3 This is a schematic diagram showing the structure of the charging robot wheel assembly and a comparison of two driving conditions in an embodiment of this application. Figure 3As shown, the charging robot (01) has multiple wheels (02, 15) at its bottom. Each wheel is connected to the robot body via a wheel under-support connecting rod (03), a hydraulic shock-absorbing connecting support arm (04), and a wheel upper support connecting directional rod (05). The wheel assembly rotation support rotating gear (07) is fixed at the end of the wheel upper support connecting directional rod (05). This gear meshes with the motor external rotating gear (08). The motor external rotating gear (08) is driven by the internal rotating motor assembly (09) of the charging robot. The rotating motor processor (10) controls the forward and reverse rotation, the number of rotations, and the stop position of the motor. When the motor drives the motor external rotating gear (08) to rotate, the gear meshing drives the wheel assembly rotation support rotating gear (07) to rotate around the longitudinal axis, thereby driving the entire wheel assembly (including the wheel, support connecting rod, and tire) to rotate synchronously around the longitudinal axis, causing the longitudinal support angle of the wheel to change. In flat terrain, the wheel (02) maintains a normal angle of contact with the road surface. In sloping terrain, after the longitudinal angle of the wheel (15) changes, its tire tread is fully in contact with the slope (13) at the tilt angle. The wheel (02) on flat ground does not adjust its longitudinal angle or maintains an angle of contact with the flat ground (14). This allows each wheel in the charging robot to independently adapt to the tilt angle of the road surface at its current contact position. This structure enables the charging robot to independently drive the wheel assembly to rotate around the longitudinal axis to the corresponding longitudinal adjustment angle for each wheel based on terrain data and its own posture data, ensuring that each tire achieves maximum contact area and optimal adhesion with its current contact surface.

[0086] This embodiment also provides a charging robot control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0087] This embodiment provides a charging robot control device, such as... Figure 4 As shown, it includes: The acquisition module 41 is used to acquire terrain data of the current driving environment of the charging robot and its own posture data; The determination module 42 is used to determine the longitudinal adjustment angle of each wheel in the charging robot based on terrain data and its own posture data. Drive module 43 is used to drive the wheel assemblies of the corresponding wheels to rotate independently around the longitudinal axis according to the longitudinal adjustment angle, so that each tire in the charging robot is in contact with the current contact surface. The control module 44 is used to control the charging robot to move to the charging position of the target vehicle based on the contact state between each tire of the charging robot and the current contact surface.

[0088] In this embodiment of the application, the acquisition module 41 is specifically used to acquire first scene information collected by the charging robot; acquire second scene information around the charging robot collected by the target vehicle; receive preset scene information sent by the cloud; and fuse the first scene information, the second scene information and the preset scene information to generate terrain data of the current driving environment of the charging robot.

[0089] In this embodiment of the application, the determining module 42 is specifically used to calculate the road surface tilt angle at each wheel contact position based on terrain data; determine the current horizontal state of the charging robot based on its own posture data; and calculate the longitudinal adjustment angle of each wheel in the charging robot based on the road surface tilt angle and the current horizontal state, wherein the longitudinal adjustment angle is the angle required to match the corresponding tire tread with the road surface tilt angle.

[0090] In this embodiment, the drive module 43 is specifically used to convert the longitudinal adjustment angle into a rotational drive amount of the corresponding drive motor for each wheel; control the drive motor to rotate according to the rotational drive amount, so as to drive the wheel assembly to rotate around the longitudinal axis; during the rotation, acquire the feedback parameters of the wheel, wherein the feedback parameters include the motor drive current and / or tire pressure; match the feedback parameters with preset reference parameters to obtain a matching result; when the matching result is that the feedback parameters match the preset reference parameters, determine that the wheel assembly has rotated into position.

[0091] In this embodiment of the application, the device further includes: a correction module, used to send a re-mapping request to the cloud connected to the charging robot when the matching result is that the feedback parameter does not match the preset benchmark parameter, and control the charging robot to re-collect the third scene information; correct the longitudinal adjustment angle of each wheel based on the third scene information to obtain the corrected longitudinal adjustment angle; and continue to drive the corresponding wheel assembly to rotate according to the corrected longitudinal adjustment angle.

[0092] In this embodiment of the application, the device further includes: an execution module, used to acquire the relative pose deviation between the charging robot and the target vehicle; determine the longitudinal angle compensation amount of each wheel in the charging robot based on the relative pose deviation; drive the corresponding wheel assembly to rotate based on the longitudinal angle compensation amount until the top plane of the charging robot is aligned with the bottom charging plane of the target vehicle, and control the charging robot to perform a charging docking operation.

[0093] In this embodiment, the control module 44 is specifically used to acquire resistance feedback data of each wheel during the movement of the charging robot; identify whether each wheel is in an obstructed state based on the resistance feedback data; when at least one wheel is identified as being in an obstructed state, adjust the longitudinal adjustment angle and power output parameters of that wheel based on the resistance feedback data to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters; and control the charging robot to continue moving to the charging position of the target vehicle based on the adjusted longitudinal adjustment angle and adjusted power output parameters.

[0094] In this embodiment, the control module 44 is specifically used to match the resistance feedback data with the resistance feedback features corresponding to at least one preset working condition type to obtain a target working condition type that matches the resistance feedback data; update the longitudinal adjustment angle of the wheel according to the target working condition type to obtain the updated longitudinal adjustment angle; and simultaneously switch the power output mode of the wheel to the target torque output mode corresponding to the target working condition type, and adjust the power output parameters of the wheel according to the preset power parameters corresponding to the target torque output mode to obtain the adjusted power output parameters.

[0095] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a charging robot provided in an optional embodiment of the present invention, such as... Figure 5 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system).

[0096] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0097] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0098] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device as shown by a landing page for an app. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, which can be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0099] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0100] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0101] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0102] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A control method for a charging robot, characterized in that, The method includes: Acquire terrain data of the current driving environment and the robot's own posture data; Based on the terrain data and its own posture data, the longitudinal adjustment angle of each wheel in the charging robot is determined; According to the longitudinal adjustment angle, the wheel assemblies of the corresponding wheels are driven to rotate independently around the longitudinal axis so that each tire of the charging robot is in contact with the current road surface; Based on the contact status of each tire of the charging robot with the current contact surface, the charging robot is controlled to move to the charging position of the target vehicle.

2. The method according to claim 1, characterized in that, The acquisition of terrain data for the current driving environment includes: Obtain the first scene information collected by the charging robot; Acquire second scene information about the surroundings of the charging robot collected by the target vehicle; Receive preset scene information sent from the cloud; By integrating the first scene information, the second scene information, and the preset scene information, terrain data of the current driving environment of the charging robot is generated.

3. The method according to claim 1, characterized in that, The step of determining the longitudinal adjustment angle of each wheel in the charging robot based on the terrain data and its own posture data includes: Based on the terrain data, calculate the road surface tilt angle at each wheel contact point; The current horizontal state of the charging robot is determined based on its own posture data; Based on the road surface inclination angle and the current horizontal state, the longitudinal adjustment angle of each wheel in the charging robot is calculated, wherein the longitudinal adjustment angle is the angle required to match the corresponding tire tread with the road surface inclination angle.

4. The method according to claim 1, characterized in that, The step of driving the wheel assemblies of the corresponding wheels to rotate independently around the longitudinal axis according to the longitudinal adjustment angle includes: For each wheel, the longitudinal adjustment angle is converted into the rotational drive amount of the corresponding drive motor; The drive motor is controlled to rotate according to the rotational drive amount, so as to drive the wheel assembly to rotate around the longitudinal axis; During rotation, feedback parameters of the wheel are acquired, including motor drive current and / or tire pressure. The feedback parameters are matched with preset benchmark parameters to obtain a matching result; When the matching result is that the feedback parameter matches the preset benchmark parameter, the wheel assembly is determined to be rotated into position.

5. The method according to claim 4, characterized in that, After matching the feedback parameters with preset benchmark parameters to obtain a matching result, the method further includes: When the matching result indicates that the feedback parameters do not match the preset benchmark parameters, a re-mapping request is sent to the cloud that is connected to the charging robot, and the charging robot is controlled to re-collect the third scene information. Based on the third scene information, the longitudinal adjustment angle of each wheel is corrected to obtain the corrected longitudinal adjustment angle; The corresponding wheel assembly continues to rotate based on the corrected longitudinal adjustment angle.

6. The method according to claim 1, characterized in that, After controlling the charging robot to move to the charging location of the target vehicle, the method further includes: Obtain the relative pose deviation between the charging robot and the target vehicle; The longitudinal angle compensation amount of each wheel in the charging robot is determined based on the relative pose deviation. Drive the corresponding wheel assembly to rotate based on the longitudinal angle compensation amount until the top plane of the charging robot is aligned with the bottom charging plane of the target vehicle, and control the charging robot to perform the charging docking operation.

7. The method according to claim 1, characterized in that, The control of the charging robot to move to the charging location of the target vehicle includes: During the movement of the charging robot, resistance feedback data is acquired for each wheel as it travels. Based on the resistance feedback data, identify whether each wheel is in a state of obstruction; When at least one wheel is detected to be in an obstructed state, the longitudinal adjustment angle and power output parameters of that wheel are adjusted according to the resistance feedback data to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters. Based on the adjusted longitudinal adjustment angle and the adjusted power output parameters, the charging robot is controlled to continue moving to the charging position of the target vehicle.

8. The method according to claim 7, characterized in that, The step of adjusting the longitudinal adjustment angle and power output parameters of the wheel based on the resistance feedback data to obtain the adjusted longitudinal adjustment angle and adjusted power output parameters includes: The resistance feedback data is matched with the resistance feedback features corresponding to at least one preset working condition type to obtain the target working condition type that matches the resistance feedback data. The longitudinal adjustment angle of the wheel is updated according to the target working condition type to obtain the updated longitudinal adjustment angle. At the same time, the power output mode of the wheel is switched to the target torque output mode corresponding to the target working condition type. The power output parameters of the wheel are adjusted according to the preset power parameters corresponding to the target torque output mode to obtain the adjusted power output parameters.

9. A charging robot control device, characterized in that, The device includes: The acquisition module is used to acquire terrain data of the current driving environment of the charging robot and its own posture data; The determination module is used to determine the longitudinal adjustment angle of each wheel in the charging robot based on the terrain data and its own posture data; The drive module is used to drive the wheel assemblies of the corresponding wheels to rotate independently around the longitudinal axis according to the longitudinal adjustment angle, so that each tire in the charging robot is in contact with the current road surface; The control module is used to control the charging robot to move to the charging position of the target vehicle based on the contact state between each tire of the charging robot and the current contact surface.

10. A charging robot, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 8.

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