An intelligent control system and method for a handling robot

Abstract

The present application relates to the technical field of robot intelligent control, in particular to an intelligent control system and method for a carrying robot, in the present application, by acquiring the first magnetic field signal and the second magnetic field signal of the carrying robot relative to the two sides in the target passing area, calculating the offset, and generating the offset feature accordingly, and then forming the trajectory regression control; then, the basic distribution relationship is determined according to the offset degree, and the control distribution strategy is formed according to the offset change trend, the rudder wheel deflection correction amount and the driving wheel speed difference correction amount are cooperatively distributed, so that the carrying robot is returned to the target passing center; at the same time, the first magnetic field signal and the second magnetic field signal are continuously updated and cyclically adjusted during the running process, which helps to improve the control coordination and continuous passing stability for different offset states.

Description

Technical Field

[0001] This invention relates to the field of intelligent robot control technology, specifically to an intelligent control system and method for a handling robot. Background Technology

[0002] In warehousing and logistics, production and distribution, and site transfer applications, material handling robots typically need to continuously travel along predetermined routes to complete tasks such as material handling, path switching, and site docking. To ensure good path-following ability during movement, relevant solutions often incorporate magnetic guidance structures within the travel area, with magnetic field detection components on the robot body outputting corresponding detection signals. The control system then performs path corrections based on the detection results. Existing technologies can generally identify the robot's deviation from the travel center during this control process. However, in actual continuous operation, the robot's deviation is not static but changes continuously throughout the operation, and the control requirements differ at different stages. One approach focuses on return control through steering mechanisms, while another focuses on attitude correction through speed adjustment on the drive side. Although both can achieve deviation adjustment to some extent, when the robot is at different degrees and stages of deviation development, a single control method often struggles to balance the timeliness of the return action with the coordination of the movement process.

[0003] In addition, the existing control logic still uses detection results in a relatively scattered manner, and often lacks a processing mechanism that can establish a continuous control relationship around changes in the operating state. This makes the coordination between steering control and drive control not close enough, which in turn affects the robot's return process and stable passage effect in the target passage area. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent control system and method for a transport robot, to solve the problems mentioned in the background art. Specific technical problems include how to generate mutually coordinating steering wheel deflection control and drive wheel speed difference control based on the magnetic field detection results of the transport robot within the target passage area, and to dynamically coordinate the two to solve the problem of insufficient control coordination for different offset states during continuous passage of the transport robot.

[0005] To achieve the above objectives, one objective of this invention is to provide an intelligent control method for a handling robot, comprising the following method steps: After the transport robot enters the target passage area, it first acquires the first and second magnetic field signals on its opposite sides, and calculates the offset based on these signals. Then, by combining the sign of the offset, the absolute value of the offset, and the changes in the offset within continuous control cycles, an offset feature is formed to characterize the current operating state.

[0006] After obtaining the offset characteristics, two interrelated adjustment methods are formed. On the one hand, the steering wheel deflection direction is determined based on the offset direction, and the steering wheel deflection correction amount is determined based on the offset degree to form a corresponding steering correction. On the other hand, the drive wheel speed difference correction amount is determined based on the offset degree, and the drive wheel speed difference correction amount is distributed to the left and right drive wheels in combination with the offset direction to form a corresponding drive correction. The above two adjustment methods are not executed independently, but are coordinated around the robot's current offset state.

[0007] Furthermore, the basic allocation relationship between the steering wheel deflection correction and the drive wheel speed difference correction is determined based on the degree of deviation. This basic allocation relationship is then adjusted according to the deviation trend to obtain the control allocation strategy. Subsequently, based on the control allocation strategy, the steering wheel deflection correction and the drive wheel speed difference correction are coordinated, ensuring that steering adjustment and drive adjustment work together within the same control cycle, thereby guiding the transport robot back to the target passage center.

[0008] Furthermore, during robot operation, the first and second magnetic field signals are continuously updated, and the processes of offset state determination, trajectory regression control formation, allocation relationship adjustment, and cooperative allocation are executed cyclically, enabling the control output to adjust synchronously with changes in the robot's offset state. Through this method, the present invention establishes a linkage control mechanism between steering correction and drive correction within the target passage area, which helps improve the path regression coordination of the handling robot during continuous passage.

[0009] The specific steps are as follows: S1. Acquire the first and second magnetic field signals of the transport robot on opposite sides of the target passage area, specifically including: Two magnetic guide markers are set parallel to each other along the predetermined passage direction within the target passage area, corresponding to the detection paths on opposite sides of the handling robot; A first Hall sensor array and a second Hall sensor array are respectively installed on opposite sides of the chassis of the handling robot; When the transport robot moves along the target passage area, the first Hall sensor array passes over one of the magnetic guide signs and outputs a first magnetic field signal, and the second Hall sensor array passes over another magnetic guide sign and outputs a second magnetic field signal. Once the transport robot enters the target passage area, it periodically and synchronously acquires the first magnetic field signal and the second magnetic field signal. When multiple magnetic field detection devices are set on each side, the multiple detection values ​​on the same side are first summed and averaged to form the magnetic field signal of the corresponding side. The first and second magnetic field signals are preprocessed.

[0010] Step S1 acquires the first and second magnetic field signals of the transport robot on opposite sides of the target passage area, providing a basic input for subsequent determination of the position of the transport robot relative to the target passage center. The use of corresponding magnetic field detection on both sides helps to form a detection basis reflecting the difference in lateral position. By combining periodic synchronous acquisition, summing and averaging of multiple detection values ​​on the same side, and preprocessing the first and second magnetic field signals, the impact of local detection fluctuations on subsequent control judgments is reduced during continuous passage, thereby providing a relatively stable data basis for the subsequent generation of mutually cooperating steering wheel deflection control and drive wheel speed difference control.

[0011] S2. Calculate the offset based on the first magnetic field signal and the second magnetic field signal, specifically including: After acquiring the first magnetic field signal and the second magnetic field signal, the offset is calculated based on the difference between the first magnetic field signal and the second magnetic field signal; The offset is calculated using a normalized difference method. The difference between the first magnetic field signal and the second magnetic field signal is used to characterize the difference between the magnetic field signals of the handling robot relative to both sides, and the difference is normalized by the sum of the first magnetic field signal and the second magnetic field signal. The offset is used to characterize the deviation of the handling robot from the target passage center.

[0012] The offset direction is determined by the sign of the offset, the degree of offset is determined by the absolute value of the offset, and the offset trend is determined by the change of the offset within a continuous control cycle, in order to generate offset characteristics, which specifically include: The offset direction is determined based on the sign of the offset, and the zero-point tolerance range is set. The degree of offset is determined based on the absolute value of the offset, and the degree of offset is divided into small offset, medium offset, and large offset states. The offset change trend is determined based on the change in offset within a continuous control cycle. The offset change trend includes decreasing, increasing, and stabilizing. The offset direction, offset degree, and offset change trend together constitute the offset characteristics.

[0013] Step S2 calculates the offset based on the first and second magnetic field signals, and further determines the offset direction, offset degree, and offset change trend based on the offset to generate offset features. This transforms the magnetic field detection results into control criteria that characterize the current operating state of the handling robot. The offset direction reflects which side of the target passage center the handling robot is located on, the offset degree reflects the level of deviation of the current position from the target passage center, and the offset change trend reflects the development of the deviation state within a continuous control cycle. Through the above processing, the magnetic field detection results are no longer used only for single position judgment, but provide a unified state characterization basis for the subsequent generation of steering wheel deflection control and drive wheel speed difference control, as well as the dynamic coordination between the two.

[0014] S3. Determine the steering wheel deflection direction based on the offset direction, determine the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of offset, and form trajectory regression control based on the steering wheel deflection direction, steering wheel deflection correction, and drive wheel speed difference correction, specifically including: Determine the steering wheel deflection direction based on the offset direction; The steering wheel deflection correction amount is determined based on the degree of deviation; The correction amount for the speed difference of the drive wheels is determined based on the degree of offset; Trajectory regression control is formed based on steering wheel deflection direction, steering wheel deflection correction amount, and drive wheel speed difference correction amount; Specifically, when the offset direction indicates that the transport robot is located to the right of the target passage center, the steering wheel deflection direction is determined to be the left deflection direction; when the offset direction indicates that the transport robot is located to the left of the target passage center, the steering wheel deflection direction is determined to be the right deflection direction.

[0015] Step S3 determines the steering wheel deflection direction based on the offset direction, determines the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and forms trajectory regression control based on the steering wheel deflection direction, steering wheel deflection correction amount, and drive wheel speed difference correction amount. This enables the handling robot to simultaneously provide two types of control outputs—steering correction and drive correction—after detecting a deviation from the target travel center. Among them, the steering wheel deflection control focuses on adjusting the regression direction, while the drive wheel speed difference control focuses on coordinating with the adjustment of the vehicle's travel posture. The two work together around the current offset state in the trajectory regression process, thus providing a control execution basis for solving the problem of insufficient control coordination for different offset states during continuous travel of the handling robot.

[0016] S4. Determine the basic distribution relationship between the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of offset, specifically including: The basic allocation relationship is represented by the first allocation factor and the second allocation factor; The steering wheel deflection correction amount corresponds to the first allocation factor, the drive wheel speed difference correction amount corresponds to the second allocation factor, and the sum of the first allocation factor and the second allocation factor is one. The basic allocation relationship is determined based on the degree of offset.

[0017] The basic allocation relationship is corrected based on the trend of offset change, and the corrected basic allocation relationship is used as the control allocation strategy, specifically including: The basic allocation relationship is corrected based on the trend of offset change; When the offset trend is decreasing, increase the first allocation factor and decrease the second allocation factor based on the current base value; When the offset trend is increasing, decrease the first allocation factor and increase the second allocation factor based on the current base value; When the offset trend is stable and the current offset is in a medium or large offset state, decrease the first allocation factor and increase the second allocation factor based on the current base value. When the offset trend is stable and the current offset is in a state of slight offset or small offset, the basic allocation relationship remains unchanged; Set boundary constraints for the modified first and second allocation factors; The revised basic allocation relationship will be used as the control allocation strategy.

[0018] Step S4 determines the basic allocation relationship between the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of offset, and corrects the basic allocation relationship according to the offset change trend. The corrected basic allocation relationship is then used as the control allocation strategy to establish a coordinated relationship between the two types of control outputs that adjusts with changes in the operating state. This not only reflects the difference in emphasis between the two types of control outputs under different degrees of offset, but also allows for dynamic adjustment of the current allocation method based on the offset change trend. This makes the coordination between steering wheel deflection control and drive wheel speed difference control more targeted, thereby improving the linkage between the two control methods during continuous passage.

[0019] S5. Based on the control allocation strategy, the steering wheel deflection correction and the drive wheel speed difference correction in trajectory regression control are coordinated and allocated to control the transport robot to regress towards the target passage center. Specifically, this includes: Based on the control allocation strategy, the steering wheel deflection correction amount corresponding to the degree of deviation is multiplied by the corrected first allocation factor to obtain the final steering wheel deflection correction amount; Multiply the drive wheel speed difference correction amount corresponding to the degree of offset by the corrected second allocation factor to obtain the final drive wheel speed difference correction amount; The final steering wheel deflection correction is applied to the corresponding steering wheel deflection direction based on the offset direction, and the final drive wheel speed difference correction is distributed to the left and right drive wheels.

[0020] Step S5, based on the control allocation strategy, coordinates the steering wheel deflection correction and the drive wheel speed difference correction in trajectory regression control, controlling the transport robot to return to the target passage center. This implements the aforementioned allocation relationship into specific control outputs, ensuring that steering wheel deflection control and drive wheel speed difference control no longer act independently but cooperate under the same control logic. By appropriately allocating the steering wheel deflection correction and drive wheel speed difference correction, and combining this with the offset direction to apply control, the transport robot can obtain combined control outputs that better match its current operating state under different offset states, thereby improving control coordination during continuous passage.

[0021] S6. During operation, continuously update the first and second magnetic field signals, and repeatedly execute offset feature determination, trajectory regression control formation, control allocation strategy determination, and cooperative allocation until stable passage conditions are met, specifically including: Within each control cycle, the following steps are executed sequentially: acquiring the first magnetic field signal and the second magnetic field signal; calculating the offset based on the first magnetic field signal and the second magnetic field signal; determining the offset characteristics based on the offset; forming trajectory regression control based on the offset characteristics; determining the basic allocation relationship based on the degree of offset and forming a control allocation strategy based on the offset change trend; performing collaborative allocation based on the control allocation strategy; and outputting control commands. Once the stable passage conditions are met, stop outputting the steering wheel deflection correction and drive wheel speed difference correction, and control the handling robot to enter the normal straight-line state; When the absolute value of the offset is subsequently detected to be greater than the preset condition, the offset feature determination, trajectory regression control formation, control allocation strategy determination, and collaborative allocation are re-executed.

[0022] Step S6 continuously updates the first and second magnetic field signals during operation and repeatedly executes offset feature determination, trajectory regression control formation, control allocation strategy determination, and collaborative allocation until stable passage conditions are met. This enables the handling robot to have continuous adjustment capabilities in the control process within the target passage area. Since the offset state of the handling robot changes continuously with its position during movement, updating the magnetic field detection results in each control cycle and regenerating the corresponding control output helps ensure that the steering wheel deflection control and drive wheel speed difference control always maintain a cooperative relationship adapted to the current offset state, thereby alleviating the problem of insufficient control coordination for different offset states during continuous passage.

[0023] The second objective of this invention is to provide a system for an intelligent control method for a handling robot, comprising a magnetic field signal acquisition module, an offset feature analysis module, a trajectory regression control generation module, a control allocation strategy determination module, a cooperative allocation control module, and a dynamic update and closed-loop iteration module, wherein: The magnetic field signal acquisition module acquires the first and second magnetic field signals of the transport robot on opposite sides of the target passage area; The offset feature analysis module calculates the offset based on the first magnetic field signal and the second magnetic field signal; determines the offset direction based on the sign of the offset, determines the offset degree based on the absolute value of the offset, and determines the offset change trend based on the change of the offset within the continuous control cycle, so as to generate offset features. The trajectory regression control generation module determines the steering wheel deflection direction based on the offset direction, determines the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and forms trajectory regression control based on the steering wheel deflection direction, steering wheel deflection correction amount and drive wheel speed difference correction amount; The control allocation strategy determination module determines the basic allocation relationship between the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of offset, and corrects the basic allocation relationship based on the offset change trend, and uses the corrected basic allocation relationship as the control allocation strategy. The collaborative allocation control module, based on the control allocation strategy, collaboratively allocates the steering wheel deflection correction and the drive wheel speed difference correction in the trajectory regression control, controlling the transport robot to return to the target passage center. The dynamic update and closed-loop iteration module continuously updates the first magnetic field signal and the second magnetic field signal during operation, and repeatedly executes offset feature determination, trajectory regression control formation, control allocation strategy determination and collaborative allocation until the stable passage conditions are met.

[0024] Compared with the prior art, the beneficial effects of the present invention are: The intelligent control method for a transport robot provided by this invention acquires first and second magnetic field signals of the transport robot on opposite sides of a target passage area, calculates the offset based on the first and second magnetic field signals, and further determines the offset direction, offset degree, and offset change trend to generate offset characteristics. Based on this, steering wheel deflection control and drive wheel speed difference control are respectively formed, and a basic allocation relationship is determined according to the offset degree. A control allocation strategy is formed according to the offset change trend, and the steering wheel deflection correction and drive wheel speed difference correction are collaboratively allocated, so that the steering wheel deflection control and drive wheel speed difference control can cooperate around the current offset state of the transport robot. By continuously updating the first and second magnetic field signals and repeatedly executing the relevant control processes during operation, it helps to improve the control coordination of the transport robot for different offset states within the target passage area, the continuity of the trajectory regression process, and the operational stability during continuous passage. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall method steps of the present invention; Figure 2 This is a schematic diagram of the core process for generating offset features in this invention; Figure 3 This is a schematic diagram of the core process of applying offset features in this invention; Figure 4 This is a schematic diagram of the overall cyclic control process of the present invention; Figure 5 This is a comparison diagram of the effects of the present invention; Figure 6 This is a schematic diagram of the overall module flow of the present invention.

[0026] In the diagram: 100, Magnetic field signal acquisition module; 200, Offset feature analysis module; 300, Trajectory regression control generation module; 400, Control allocation strategy determination module; 500, Cooperative allocation control module; 600, Dynamic update and closed-loop iteration module. Detailed Implementation

[0027] The technical solutions in 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, and 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.

[0028] Next, please refer to Figure 1 One of the objectives of this embodiment is to provide an intelligent control method for a handling robot, comprising the following steps: S1. Acquire the first magnetic field signal and the second magnetic field signal of the transport robot on opposite sides of the target passage area; S2. Calculate the offset based on the first magnetic field signal and the second magnetic field signal; determine the offset direction based on the sign of the offset, determine the offset degree based on the absolute value of the offset, and determine the offset change trend based on the change of the offset within the continuous control cycle to generate offset characteristics. S3. Determine the steering wheel deflection direction based on the offset direction, determine the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and form trajectory regression control based on the steering wheel deflection direction, steering wheel deflection correction amount and drive wheel speed difference correction amount; S4. Determine the basic distribution relationship between the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of offset, and correct the basic distribution relationship according to the offset change trend, and use the corrected basic distribution relationship as the control distribution strategy. S5. Based on the control allocation strategy, the steering wheel deflection correction and the drive wheel speed difference correction in the trajectory regression control are coordinated and allocated to control the handling robot to return to the target passage center. S6. During operation, continuously update the first magnetic field signal and the second magnetic field signal, and repeatedly execute the offset feature determination, trajectory regression control formation, control allocation strategy determination and cooperative allocation until the stable passage conditions are met.

[0029] This intelligent control method for handling robots is applicable to the passage control of handling robots in target passage areas, especially suitable for scenarios with high passage accuracy requirements such as elevator door areas, elevator car interiors, narrow corridors, temporary passages on construction floors, and loading and unloading docking areas; the following uses the scenario of handling robots entering and exiting elevators as an example for illustration.

[0030] In this embodiment, the target passage area corresponds to a target passage center, which is determined according to the center line of the preset passage path, or according to the positional relationship of the elevator door center, the guide center line inside the car, or the magnetic guide mark on the ground. The target passage center serves as a reference benchmark for the return control of the handling robot and is used to measure the deviation of the handling robot from the predetermined passage path.

[0031] The chassis of the transport robot is equipped with magnetic field detection devices on both sides to detect changes in the magnetic field within the target passage area. These magnetic field detection devices include Hall sensors, Hall sensor arrays, magnetoresistive sensors, or other devices suitable for detecting changes in the magnetic field. Magnetic guidance markers are set on the ground within the target passage area. These magnetic guidance markers are magnetic strips, magnetic nail arrays, magnetic rulers, or other magnetic media capable of forming a stable magnetic field distribution.

[0032] The specific steps are as follows: S1. Acquire the first and second magnetic field signals of the transport robot on opposite sides of the target passage area, specifically including: To achieve high-precision sensing of the lateral position deviation of the transport robot, this embodiment sets two magnetic guide markers parallel to each other along the predetermined travel direction within the target passage area, corresponding to the detection paths on the left and right sides of the transport robot, respectively. Each magnetic guide marker consists of multiple N-pole magnets and S-pole magnets arranged sequentially, with a center-to-center distance of 18mm to 22mm, preferably 20mm. A first Hall sensor array and a second Hall sensor array are respectively set on opposite sides of the transport robot chassis, each array containing 3 to 5 Hall sensors, linearly arranged along the travel direction. When the transport robot travels along the target passage area, the first Hall sensor array continuously outputs a first magnetic field signal as it passes over the first magnetic guide marker; the second Hall sensor array continuously outputs a second magnetic field signal as it passes over the second magnetic guide marker. Simultaneous acquisition of signals from both sides forms the basis for real-time sensing of lateral magnetic field differences.

[0033] When the transport robot enters the target passage area, it periodically acquires the first magnetic field signal and the second magnetic field signal on the opposite sides of the transport robot. The first side and the second side are the two sides of the transport robot chassis that are laterally opposite, such as the left side and the right side. If multiple magnetic field detection devices are set on each side, the multiple detection values ​​on the same side are first summed and averaged to form the magnetic field signal of the corresponding side.

[0034] To reduce the impact of noise and transient jitter, the first and second magnetic field signals are preprocessed. The preprocessing methods include moving average filtering, median filtering, low-pass filtering, or a combination thereof. For example, the sampled values ​​within 3 to 10 consecutive control cycles are averaged to obtain smoothed first and second magnetic field signals. The control cycle is set to 10 to 50 milliseconds, preferably 20 milliseconds, depending on the control accuracy requirements and hardware response capability.

[0035] Please see Figure 2 S2. Calculate the offset based on the first and second magnetic field signals; determine the offset direction based on the sign of the offset, determine the offset degree based on the absolute value of the offset, and determine the offset trend based on the change of the offset within the continuous control cycle to generate offset characteristics, specifically including: After acquiring the first and second magnetic field signals, the offset is calculated based on these signals. The offset characterizes the deviation of the transport robot from the target passage center. The offset is calculated using a normalized difference method, i.e.:

[0036] D = K·(M1-M2) / (M1+M2), where D represents the offset, K represents the standard coefficient, M1 represents the first magnetic field signal, and M2 represents the second magnetic field signal. The standard coefficient K is used to convert the difference in magnetic field signals into the actual offset distance, and its unit is millimeters. The specific value is obtained through experimental calibration based on factors such as the type of magnetic field detection device, installation location, and the layout of magnetic guidance marks in the target passage area. In the above formula, the numerator M1-M2 is used to characterize the difference in magnetic field signals between the two sides of the handling robot, and the denominator M1+M2 is used to normalize the difference, thereby reducing the influence of the overall magnetic field strength fluctuation on the offset calculation result. Therefore, the offset D obtained by this formula can characterize the direction and distance of the handling robot's deviation from the target passage center.

[0037] During calibration, the corresponding first and second magnetic field signals can be obtained at multiple known lateral offset positions, and the standard coefficient K is determined based on the correspondence between the known lateral offset positions and the normalized difference. When the detection characteristics or installation structure of the magnetic field detection device exhibit nonlinear changes in different offset intervals, a segmented calibration method is used to determine the corresponding standard coefficient K in different offset intervals. To avoid abnormal offset calculations when the first and second magnetic field signals are too weak, a valid threshold for the sum is pre-set. The valid threshold for the sum is determined through experimental calibration based on the sensitivity of the magnetic field detection device, noise level, magnetic field strength of the magnetic guide marker in the target passage area, and control accuracy requirements. When the sum of the first and second magnetic field signals is less than the valid threshold for the sum, it is determined that the denominator used to calculate the offset is too small or the reliability of the magnetic field detection in the current control cycle is insufficient. The offset calculation result of the previous control cycle is maintained, or the update of the offset in the current control cycle is paused until the sum of the first and second magnetic field signals is not less than the valid threshold for the sum.

[0038] Using the above calculation method, the offset can directly reflect the direction and distance of the transport robot's deviation from the target passage center. For example, when the offset is positive 6 mm, it means that the transport robot is located to the right of the target passage center and deviated by 6 mm; when the offset is negative 6 mm, it means that the transport robot is located to the left of the target passage center and deviated by 6 mm. The larger the absolute value of the offset, the greater the distance of the transport robot from the target passage center.

[0039] The offset direction is determined based on the sign of the offset. When the offset is greater than zero, the transport robot is determined to be located to the right of the target passage center; when the offset is less than zero, the transport robot is determined to be located to the left of the target passage center. To avoid frequent switching of the offset direction due to small signal fluctuations, a zero-point tolerance range ε ​​is set, which is 0.5 mm to 1 mm, preferably 1 mm. When the absolute value of the offset is not greater than ε, the transport robot is determined to be located near the target passage center, or the offset direction judgment result of the previous control cycle remains unchanged.

[0040] The degree of offset is determined based on the absolute value of the offset. To ensure clarity in the determination of the degree of offset, a first offset threshold, a second offset threshold, and a third offset threshold are preset, with the third offset threshold being greater than the second offset threshold, and the second offset threshold being greater than the first offset threshold. In this embodiment, the first offset threshold is 2 mm, the second offset threshold is 5 mm, and the third offset threshold is 10 mm. When the absolute value of the offset is not greater than 2 mm, the handling robot is determined to be in a small offset state; when the absolute value of the offset is greater than 2 mm but not greater than 5 mm, the handling robot is determined to be in a small offset state; when the absolute value of the offset is greater than 5 mm but not greater than 10 mm, the handling robot is determined to be in a medium offset state; and when the absolute value of the offset is greater than 10 mm, the handling robot is determined to be in a large offset state.

[0041] The above offset thresholds are only example values. In practical applications, the first, second, and third offset thresholds are determined through experimental calibration based on the robot's body width, wheelbase, steering wheel structure, target passage area width, allowable safety margin, operating speed, and control cycle. For example, in scenarios where the elevator door is narrow and the robot's body width is close to the door width, the three offset thresholds are adjusted to 1.5 mm, 4 mm, and 8 mm, respectively; in scenarios with a large passageway margin, the three offset thresholds are adjusted to 3 mm, 6 mm, and 12 mm, respectively.

[0042] The offset trend is determined based on the change in offset within a continuous control cycle. The absolute value of the offset in the current control cycle is compared with the absolute value of the offset in the previous control cycle, and a change judgment threshold is set. The change judgment threshold is between 0.3 mm and 0.8 mm, preferably 0.5 mm. When the absolute value of the offset in the current control cycle decreases by more than 0.5 mm compared to the previous control cycle, the offset trend is determined to be decreasing. When the increase is greater than 0.5 mm, the offset trend is determined to be increasing. When the change is between -0.5 mm and +0.5 mm, the offset trend is determined to be stable. Thus, the offset direction, offset degree, and offset trend together generate the offset feature.

[0043] Please see Figure 3S3. Determine the steering wheel deflection direction based on the offset direction, determine the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of offset, and form trajectory regression control based on the steering wheel deflection direction, steering wheel deflection correction, and drive wheel speed difference correction, specifically including: After determining the offset characteristics, the steering wheel deflection direction is determined based on the offset direction; when the offset direction indicates that the handling robot is located to the right of the target passage center, the steering wheel deflection direction is determined to be the left deflection direction; when the offset direction indicates that the handling robot is located to the left of the target passage center, the steering wheel deflection direction is determined to be the right deflection direction.

[0044] The steering wheel deflection correction amount is determined based on the degree of offset and corresponds one-to-one with the classification results of the absolute value of the offset. In this embodiment, when the absolute value of the offset is no greater than 2 mm, the steering wheel deflection correction amount is 1° to 2°; when the absolute value of the offset is greater than 2 mm but no greater than 5 mm, the steering wheel deflection correction amount is 2° to 4°; when the absolute value of the offset is greater than 5 mm but no greater than 10 mm, the steering wheel deflection correction amount is 4° to 6°; and when the absolute value of the offset is greater than 10 mm, the steering wheel deflection correction amount is 6° to 9°. By limiting the steering wheel deflection correction amount within the above range, over-adjustment can be suppressed when the handling robot approaches the target passage center, and the return capability can be improved when the deviation distance increases.

[0045] In a set of preferred values, when the absolute value of the offset is no greater than 2 mm, the steering wheel deflection correction is 1.5°; when the absolute value of the offset is greater than 2 mm but no greater than 5 mm, the steering wheel deflection correction is 3°; when the absolute value of the offset is greater than 5 mm but no greater than 10 mm, the steering wheel deflection correction is 5°; when the absolute value of the offset is greater than 10 mm, the steering wheel deflection correction is 8°. The above values ​​correspond to the additional deflection of the steering wheel in the current basic driving direction. If the maximum allowable deflection angle of the steering wheel mechanism is ±15° or ±20°, then the above additional deflection amounts are all within the executable range.

[0046] The speed difference correction amount for the drive wheels is also determined based on the degree of offset and corresponds one-to-one with the classification results of the absolute value of the offset. In this embodiment, when the absolute value of the offset is no greater than 2 mm, the speed difference correction amount between the left and right drive wheels is controlled within the range of 0.02 m / s to 0.04 m / s; when the absolute value of the offset is greater than 2 mm but not greater than 5 mm, the speed difference correction amount is controlled within the range of 0.04 m / s to 0.08 m / s; when the absolute value of the offset is greater than 5 mm but not greater than 10 mm, the speed difference correction amount is controlled within the range of 0.08 m / s to 0.12 m / s; and when the absolute value of the offset is greater than 10 mm, the speed difference correction amount is controlled within the range of 0.12 m / s to 0.18 m / s. By setting the speed difference correction amount in intervals, the continuity of trajectory adjustment can be maintained under small offset conditions, and the path regression capability can be enhanced under large offset conditions.

[0047] In a set of preferred values, when the absolute value of the offset is no greater than 2 mm, the drive wheel speed difference correction is 0.03 m / s; when the absolute value of the offset is greater than 2 mm but no greater than 5 mm, the drive wheel speed difference correction is 0.06 m / s; when the absolute value of the offset is greater than 5 mm but no greater than 10 mm, the drive wheel speed difference correction is 0.10 m / s; and when the absolute value of the offset is greater than 10 mm, the drive wheel speed difference correction is 0.16 m / s.

[0048] In practical execution, if the basic forward speed is denoted as V0 and the speed difference correction of the drive wheels is denoted as B, then when the steering wheel deflects to the left, the target speed of the left drive wheel is V0-B / 2 and the target speed of the right drive wheel is V0+B / 2; when the steering wheel deflects to the right, the target speed of the left drive wheel is V0+B / 2 and the target speed of the right drive wheel is V0-B / 2. Through the joint control of steering wheel deflection and drive wheel speed difference, trajectory regression control can simultaneously act on the robot's driving direction and steering posture.

[0049] Therefore, trajectory regression control consists of the steering wheel deflection direction, steering wheel deflection correction amount, and drive wheel speed difference correction amount. When the handling robot is located to the right of the target passage center and the absolute value of the offset is greater than 10 mm, trajectory regression control causes the steering wheel to deflect to the left by 6° to 9° and creates a speed difference of 0.12 m / s to 0.18 m / s between the left and right drive wheels. When the absolute value of the offset is no greater than 2 mm, trajectory regression control causes the steering wheel to deflect to the target passage center by 1° to 2° and creates a speed difference of 0.02 m / s to 0.04 m / s between the left and right drive wheels. This balances regression capability and attitude stability.

[0050] S4. After establishing trajectory regression control, further determine the basic distribution relationship between the steering wheel deflection correction and the drive wheel speed difference correction based on the degree of deviation, and correct the basic distribution relationship according to the deviation change trend. Use the corrected basic distribution relationship as the control distribution strategy, specifically including: For ease of execution, the basic allocation relationship is represented by two allocation factors, where the steering wheel deflection correction corresponds to the first allocation factor α, and the drive wheel speed difference correction corresponds to the second allocation factor β, and the sum of α and β is 1. In actual output, the steering wheel deflection correction corresponding to the current offset degree is multiplied by α, and the drive wheel speed difference correction corresponding to the current offset degree is multiplied by β, so as to obtain the final executed steering wheel deflection correction and the final executed drive wheel speed difference correction.

[0051] The basic allocation relationship is determined based on the degree of offset, and satisfies the following conditions: the smaller the absolute value of the offset, the larger the first allocation factor; the larger the absolute value of the offset, the larger the second allocation factor. In this embodiment, when the absolute value of the offset is not greater than 2 mm, the first allocation factor α is 0.75 and the second allocation factor β is 0.25; when the absolute value of the offset is greater than 2 mm but not greater than 5 mm, the first allocation factor α is 0.60 and the second allocation factor β is 0.40; when the absolute value of the offset is greater than 5 mm but not greater than 10 mm, the first allocation factor α is 0.45 and the second allocation factor β is 0.55; when the absolute value of the offset is greater than 10 mm, the first allocation factor α is 0.30 and the second allocation factor β is 0.70.

[0052] The above basic allocation relationship means that: under small offset conditions, the steering wheel deflection correction is dominant, suitable for fine-tuning the attitude; under small offset conditions, the steering wheel deflection correction is still higher than the drive wheel speed difference correction, but the difference between the two is narrowing; under medium offset conditions, the drive wheel speed difference correction is slightly higher than the steering wheel deflection correction; under large offset conditions, the drive wheel speed difference correction is dominant to enhance lateral return capability; if an interval representation method is used, when the absolute value of the offset is no greater than 5 mm, the first allocation factor is 0.60 to 0.75, and the second allocation factor is 0.25 to 0.40; when the absolute value of the offset is greater than 5 mm but no greater than 10 mm, the first allocation factor is 0.45 to 0.50, and the second allocation factor is 0.50 to 0.55; when the absolute value of the offset is greater than 10 mm, the first allocation factor is 0.30 to 0.40, and the second allocation factor is 0.60 to 0.70.

[0053] After obtaining the basic allocation relationship, it is modified according to the offset change trend. To make the modification rule clear, an allocation modification step size G is set, which is between 0.03 and 0.10, preferably 0.05. When the offset change trend is decreasing, the first allocation factor is increased by G on the current basic value, and the second allocation factor is decreased by G on the current basic value. When the offset change trend is increasing, the first allocation factor is decreased by G on the current basic value, and the second allocation factor is increased by G on the current basic value. When the offset change trend is stable and the current offset degree is in a medium or large offset state, the first allocation factor is decreased by G / 2 on the current basic value, and the second allocation factor is increased by G / 2 on the current basic value. When the offset change trend is stable and the current offset degree is in a small or minor offset state, the basic allocation relationship remains unchanged.

[0054] For example, when the handling robot is in a neutral offset state, the basic allocation relationship is α=0.45 and β=0.55. If the offset trend is decreasing and G is 0.05, the corrected allocation factors are α=0.50 and β=0.50; if the offset trend is increasing, the corrected allocation factors are α=0.40 and β=0.60; if the offset trend is stable, the corrected allocation factors are α=0.425 and β=0.575. It can be seen that even if the degree of offset is at the same level, different offset trends will lead to different final output ratios for the two types of corrections.

[0055] To prevent the allocation factors from exceeding the allowable range after correction, boundary constraints are set for the first and second allocation factors. In this embodiment, the first allocation factor α is limited to between 0.20 and 0.80, and the second allocation factor β is limited to between 0.20 and 0.80. When a corrected allocation factor exceeds the boundary, it is truncated according to the boundary value, and the other allocation factor is adjusted synchronously according to the constraint that the sum of α and β is 1. For example, when the basic allocation relationship is α=0.75, β=0.25, and the offset trend is decreasing, and G is 0.05, if α=0.80 and β=0.20 are calculated according to the rules, the result is still within the allowable range. If the correction in the same direction continues, α=0.80 and β=0.20 will remain, and will not continue to increase or decrease.

[0056] S5. After obtaining the control allocation strategy, based on the control allocation strategy, the steering wheel deflection correction and the drive wheel speed difference correction in the trajectory regression control are coordinated and allocated to control the transport robot to regress towards the target passage center. Specifically, this includes: Multiply the steering wheel deflection correction amount corresponding to the degree of offset by the corrected first allocation factor to obtain the final steering wheel deflection correction amount; multiply the drive wheel speed difference correction amount corresponding to the degree of offset by the corrected second allocation factor to obtain the final drive wheel speed difference correction amount; For example, when the absolute value of the current offset is between 5 mm and 10 mm, the handling robot is determined to be in a medium offset state. At this time, the steering wheel deflection correction is 5°, and the drive wheel speed difference correction is 0.10 m / s. If the basic distribution relationship in this state is α=0.45 and β=0.55, and the offset trend is decreasing, then after correction, α=0.50 and β=0.50, the corresponding final steering wheel deflection correction is 2.5°, and the final drive wheel speed difference correction is 0.05 m / s. If the offset trend is increasing, then after correction, α=0.40 and β=0.60, the corresponding final steering wheel deflection correction is 2.0°, and the final drive wheel speed difference correction is 0.06 m / s. If the offset trend is stable, then after correction, α=0.425 and β=0.575, the corresponding final steering wheel deflection correction is 2.125°, and the final drive wheel speed difference correction is 0.0575 m / s.

[0057] In practice, the final steering wheel deflection correction is applied to the corresponding deflection direction based on the offset direction, and the final drive wheel speed difference correction is distributed to the left and right drive wheels based on the offset direction. For example, when the offset direction is right, the steering wheel deflects to the left, and the target speed of the right drive wheel is higher than that of the left drive wheel; when the offset direction is left, the steering wheel deflects to the right, and the target speed of the left drive wheel is higher than that of the right drive wheel. Thus, the driving direction and lateral return capability can be adjusted simultaneously within the same control cycle.

[0058] Please see Figure 4 S6. During operation, the first magnetic field signal and the second magnetic field signal are continuously updated, and the offset feature determination, trajectory regression control formation, control allocation strategy determination, and cooperative allocation are repeatedly executed, specifically including: During operation, the first and second magnetic field signals are continuously updated, and the processes of offset feature determination, trajectory regression control formation, control allocation strategy determination, and collaborative allocation are repeatedly executed. The first and second magnetic field signals serve as real-time feedback inputs, and the offset features, trajectory regression control, and control allocation strategy are continuously updated as the state of the handling robot changes. Within each control cycle, the following processes are executed sequentially: Acquire the first magnetic field signal and the second magnetic field signal; calculate the offset based on the first magnetic field signal and the second magnetic field signal; determine the offset direction, offset degree and offset change trend based on the offset; form trajectory regression control based on the offset direction and offset degree; determine the basic allocation relationship based on the offset degree, and correct the basic allocation relationship based on the offset change trend to form a control allocation strategy; coordinately allocate the steering wheel deflection correction amount and the drive wheel speed difference correction amount in the trajectory regression control according to the control allocation strategy; output control commands to the steering wheel actuator and the drive wheel actuator.

[0059] Stable passage conditions are set according to actual control requirements. In this embodiment, stable passage conditions are: the absolute value of the offset is no more than 2 mm for five consecutive control cycles, and the angle between the robot's body orientation and the target passage direction is no more than 3° for five consecutive control cycles. When stable passage conditions are met, it is determined that the robot has stabilized near the target passage center. The angle between the robot's body orientation and the target passage direction is obtained through vehicle attitude detection devices, inertial measurement devices, encoder calculations, or visual detection results.

[0060] Once the stable passage conditions are met, the output of steering wheel deflection correction and drive wheel speed difference correction is stopped, and the handling robot is controlled to enter the normal straight-line state. When the absolute value of the subsequent offset is detected to be greater than 2 mm, S2 to S6 are re-executed. Through the above processing method, it is possible to avoid applying large additional control quantities when the robot is already close to the target passage center.

[0061] In addition to the above implementation methods, the steering wheel deflection correction and the drive wheel speed difference correction can also be determined using continuous functions. For example, the steering wheel deflection correction can be expressed as: A = m|D| + n; the drive wheel speed difference correction can be expressed as: B = p|D| + q; where A represents the steering wheel deflection correction, B represents the drive wheel speed difference correction, D represents the offset, and m, n, p, and q are all calibration parameters. Parameters m and p are used to characterize the degree of influence of the offset change on the steering wheel deflection correction and the drive wheel speed difference correction, respectively, and parameters n and q are used to characterize the base values ​​of the corresponding corrections in the initial correction state. Through the above relationships, the steering wheel deflection correction and the drive wheel speed difference correction increase with the increase of the absolute value of the offset |D|, thereby matching the control output with the deviation of the handling robot. The calibration parameters are determined by experimental fitting or table lookup based on the regression effect, attitude change, and stability requirements under different offsets.

[0062] To ensure the control values ​​remain within acceptable limits, upper and lower limits for the output can be set separately. For example, the upper limit for the steering wheel deflection correction could be 9°, and the lower limit 1°; the upper limit for the drive wheel speed difference correction could be 0.18 m / s, and the lower limit 0.02 m / s. The basic allocation relationship can also be represented as a continuous function, where the first allocation factor decreases as the absolute value of the offset increases, and the second allocation factor increases as the absolute value of the offset increases. However, from the perspective of clarity and ease of implementation, the aforementioned threshold grading combined with correction rules is easier to implement.

[0063] In one alternative implementation, visual information can be introduced to assist in determining the target passage center, target passage direction, or stable passage conditions. For example, before the transport robot enters the elevator, the camera identifies the elevator door edge, door center position, and entrance direction. After the transport robot enters the car, the camera acquires the transport robot's movement trajectory, and the guide bar image information is used to assist in correcting the offset judgment, offset change trend judgment, or stable passage condition judgment. Visual information serves as a supplementary information source and does not change the core technical concept of this application, which is to determine the offset characteristics, form trajectory regression control, and control allocation strategy based on the first magnetic field signal and the second magnetic field signal.

[0064] In this embodiment, each parameter was obtained through experimental calibration. The standard coefficient K was obtained by collecting the first and second magnetic field signals at a known offset distance and establishing a mapping relationship. The first, second, and third offset thresholds were determined by combining the wheelbase, body width, target passage area width, allowable safety margin, control cycle, and running speed of the transport robot. The range of steering wheel deflection correction was determined by testing the regression time, attitude fluctuation, and overshoot number under different offset states. The range of drive wheel speed difference correction was determined by testing the regression capability, tire slippage, and passage stability under different speed differences. The basic allocation relationship and allocation correction step size G were determined by comparing the regression efficiency, oscillation number, and maximum attitude deviation under different parameter combinations.

[0065] As can be seen from this embodiment, the deviation of the transport robot in the target passage area is no longer handled by a single differential speed control. Instead, the offset is first calculated based on the first magnetic field signal and the second magnetic field signal. Then, the offset is used to further determine the offset direction, offset degree and offset change trend to generate offset characteristics. Subsequently, trajectory regression control is formed based on the offset characteristics, and a control allocation strategy is further established to coordinate the allocation of the steering wheel deflection correction and the drive wheel speed difference correction in the trajectory regression control, so that the transport robot returns to the target passage center.

[0066] To further verify the effectiveness of the intelligent control method for a handling robot in this embodiment, under the same test conditions, the method of this embodiment was compared with a trajectory regression control method that only uses steering wheel deflection correction and a trajectory regression control method that only uses drive wheel speed difference correction. Test scenarios included elevator door entry, passage inside the elevator car, and straight-line regression in a narrow passage. The absolute values ​​of the initial offsets for each sample group in the test set ranged from 8 mm to 12 mm, the initial angle between the vehicle's orientation and the target passage direction ranged from 2° to 5°, the base forward speed remained consistent, and the control cycle was set to 20 milliseconds.

[0067] Under the same test set, using the average number of control cycles required to meet stable passage conditions as a comparison index, the method in this embodiment achieves 18 control cycles; the trajectory regression control method using only steering wheel deflection correction achieves 29 control cycles; and the trajectory regression control method using only drive wheel speed difference correction achieves 34 control cycles.

[0068] Furthermore, using the peak absolute value of the offset during the test as a comparison indicator, the peak absolute value of the offset corresponding to the method in this embodiment is 10.8 mm; the peak absolute value of the offset corresponding to the trajectory regression control method that only uses the steering wheel deflection correction is 13.6 mm; and the peak absolute value of the offset corresponding to the trajectory regression control method that only uses the drive wheel speed difference correction is 14.9 mm.

[0069] Meanwhile, using the achievement rate of meeting stable passage conditions within the test set as a comparison indicator, the method in this embodiment is 97.5%; the trajectory regression control method using only steering wheel deflection correction is 90.0%; and the trajectory regression control method using only drive wheel speed difference correction is 86.7%.

[0070] As can be seen from the above comparison results, the method of this embodiment is superior to the trajectory regression control method that only uses steering wheel deflection correction and the trajectory regression control method that only uses drive wheel speed difference correction in terms of offset convergence speed, peak suppression capability of absolute offset value, continuity of trajectory regression control, and stability in meeting stable passage conditions. The reason is that this embodiment does not rely solely on a single control quantity for correction, but generates offset characteristics based on the first magnetic field signal and the second magnetic field signal, and further coordinates the steering wheel deflection correction and drive wheel speed difference correction in trajectory regression control, so that the handling robot can return to the target passage center more quickly in the target passage area and maintain better continuous passage stability during the return process.

[0071] Please see Figure 5 This figure shows a comparison of the absolute values ​​of the offset as a function of the control cycle for different control methods under the same test set; Figure 5 It can be seen that the absolute value of the offset decreases faster in the method of this embodiment, the control cycle required to enter the stable range is shorter, and the subsequent fluctuation amplitude is smaller. In contrast, the trajectory regression control method that only uses the steering wheel deflection correction amount and the trajectory regression control method that only uses the drive wheel speed difference correction amount both show a longer convergence process and more obvious fluctuations. This shows that the method of this embodiment has better control coordination and continuous passage stability in the target passage center regression process of the handling robot.

[0072] As can be seen from the above description, the intelligent control method for a handling robot provided in this embodiment has the following technical effects, specifically including:

[0073] Based on the offset characteristics calculated from the first and second magnetic field signals, corresponding steering wheel deflection corrections and drive wheel speed difference corrections are generated synchronously. Trajectory regression control is then formed based on the offset direction and degree, causing the handling robot to perform a return motion towards the target passage center. Furthermore, a control allocation strategy is formed based on the offset degree and trend, dynamically coordinating the steering wheel deflection corrections and drive wheel speed difference corrections in the trajectory regression control. This helps improve the control coordination, continuity of the trajectory regression process, and sustained passage stability of the handling robot in elevator doorways and narrow passages.

[0074] Please see Figure 6 The second objective of this embodiment is to provide a system for an intelligent control method for a handling robot, including a magnetic field signal acquisition module 100, an offset feature analysis module 200, a trajectory regression control generation module 300, a control allocation strategy determination module 400, a cooperative allocation control module 500, and a dynamic update and closed-loop iteration module 600, wherein:

[0075] The magnetic field signal acquisition module 100 acquires the first magnetic field signal and the second magnetic field signal of the transport robot on opposite sides of the target passage area;

[0076] The offset feature analysis module 200 calculates the offset based on the first magnetic field signal and the second magnetic field signal; determines the offset direction based on the sign of the offset, determines the offset degree based on the absolute value of the offset, and determines the offset change trend based on the change of the offset within the continuous control cycle, so as to generate offset features.

[0077] The trajectory regression control generation module 300 determines the steering wheel deflection direction based on the offset direction, determines the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and forms trajectory regression control based on the steering wheel deflection direction, steering wheel deflection correction amount and drive wheel speed difference correction amount;

[0078] The control allocation strategy determination module 400 determines the basic allocation relationship between the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and corrects the basic allocation relationship based on the offset change trend, and uses the corrected basic allocation relationship as the control allocation strategy.

[0079] The collaborative allocation control module 500, based on the control allocation strategy, collaboratively allocates the steering wheel deflection correction amount and the drive wheel speed difference correction amount in the trajectory regression control, and controls the handling robot to return to the target passage center.

[0080] The dynamic update and closed-loop iteration module 600 continuously updates the first magnetic field signal and the second magnetic field signal during operation, and repeatedly performs offset feature determination, trajectory regression control formation, control allocation strategy determination and collaborative allocation until the stable passage conditions are met.

[0081] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control method for a handling robot, characterized in that, The methods and steps include the following: S1. Acquire the first magnetic field signal and the second magnetic field signal of the transport robot on opposite sides of the target passage area; S2. Calculate the offset based on the first magnetic field signal and the second magnetic field signal; determine the offset direction based on the sign of the offset, determine the offset degree based on the absolute value of the offset, and determine the offset change trend based on the change of the offset within the continuous control cycle, so as to generate offset features; S3. Determine the steering wheel deflection direction based on the offset direction, determine the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the offset degree, and form trajectory regression control based on the steering wheel deflection direction, the steering wheel deflection correction amount and the drive wheel speed difference correction amount; S4. Determine the basic allocation relationship between the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and correct the basic allocation relationship based on the offset change trend, and use the corrected basic allocation relationship as the control allocation strategy. S5. Based on the control allocation strategy, the steering wheel deflection correction amount and the drive wheel speed difference correction amount in the trajectory regression control are coordinated and allocated to control the transport robot to return to the target passage center. S6. During operation, continuously update the first magnetic field signal and the second magnetic field signal, and repeatedly perform offset feature determination, trajectory regression control formation, control allocation strategy determination and collaborative allocation until stable passage conditions are met.

2. The intelligent control method for a handling robot according to claim 1, characterized in that, The acquisition process of the first magnetic field signal and the second magnetic field signal specifically includes: Two magnetic guide markers are set parallel to each other along the predetermined passage direction within the target passage area, corresponding to the detection paths on opposite sides of the handling robot; A first Hall sensor array and a second Hall sensor array are respectively installed on opposite sides of the chassis of the handling robot; When the transport robot travels along the target passage area, the first Hall sensor array passes over one of the magnetic guide signs and outputs the first magnetic field signal, and the second Hall sensor array passes over the other magnetic guide sign and outputs the second magnetic field signal; When the transport robot enters the target passage area, it periodically and synchronously acquires the first magnetic field signal and the second magnetic field signal. When multiple magnetic field detection devices are set on each side, the multiple detection values ​​on the same side are first summed and averaged to form the magnetic field signal of the corresponding side. The first magnetic field signal and the second magnetic field signal are preprocessed.

3. The intelligent control method for a handling robot according to claim 1, characterized in that, The calculation process for the offset specifically includes: After acquiring the first magnetic field signal and the second magnetic field signal, the offset is calculated based on the difference between the first magnetic field signal and the second magnetic field signal; The offset is calculated using a normalized difference method, where the difference between the first magnetic field signal and the second magnetic field signal represents the difference between the magnetic field signals of the transport robot relative to both sides, and the difference is normalized using the sum of the first magnetic field signal and the second magnetic field signal. The offset is used to characterize the deviation of the transport robot from the target passage center.

4. The intelligent control method for a handling robot according to claim 1, characterized in that, The process of generating the offset feature specifically includes: The offset direction is determined based on the sign of the offset, and a zero-point tolerance range is set. The degree of offset is determined based on the absolute value of the offset, and the degree of offset is divided into small offset state, medium offset state, and large offset state. The offset change trend is determined based on the change in the offset within a continuous control cycle, and the offset change trend includes decreasing, increasing, and stabilizing. The offset direction, the offset degree, and the offset change trend together constitute the offset feature.

5. The intelligent control method for a handling robot according to claim 1, characterized in that, The formation process of the trajectory regression control specifically includes: The steering wheel deflection direction is determined based on the offset direction; The steering wheel deflection correction amount is determined based on the degree of offset; The speed difference correction amount of the drive wheels is determined based on the degree of offset; The trajectory regression control is formed based on the steering wheel deflection direction, the steering wheel deflection correction amount, and the drive wheel speed difference correction amount; Specifically, when the offset direction indicates that the transport robot is located to the right of the target passage center, the steering wheel deflection direction is determined to be a left deflection direction; when the offset direction indicates that the transport robot is located to the left of the target passage center, the steering wheel deflection direction is determined to be a right deflection direction.

6. The intelligent control method for a handling robot according to claim 1, characterized in that, The process of determining the basic allocation relationship specifically includes: The basic allocation relationship is represented by a first allocation factor and a second allocation factor; The steering wheel deflection correction amount corresponds to the first allocation factor, the drive wheel speed difference correction amount corresponds to the second allocation factor, and the sum of the first allocation factor and the second allocation factor is one; The basic allocation relationship is determined based on the degree of offset.

7. The intelligent control method for a handling robot according to claim 6, characterized in that, The process of determining the control allocation strategy specifically includes: The basic allocation relationship is corrected based on the trend of the offset change; When the offset trend is decreasing, the first allocation factor is increased and the second allocation factor is decreased based on the current base value; When the offset trend is increasing, decrease the first allocation factor and increase the second allocation factor based on the current base value; When the offset change trend is stable and the current offset degree is in a medium offset state or a large offset state, the first allocation factor is decreased and the second allocation factor is increased based on the current base value; When the offset change trend is stable and the current offset degree is in a small offset state or a minor offset state, the basic allocation relationship remains unchanged; Set boundary constraints for the modified first allocation factor and second allocation factor; The revised basic allocation relationship is used as the control allocation strategy.

8. The intelligent control method for a handling robot according to claim 7, characterized in that, The collaborative allocation process specifically includes: Based on the control allocation strategy, the steering wheel deflection correction amount corresponding to the degree of offset is multiplied by the corrected first allocation factor to obtain the final steering wheel deflection correction amount; Multiply the drive wheel speed difference correction amount corresponding to the degree of offset by the corrected second allocation factor to obtain the final drive wheel speed difference correction amount; The final steering wheel deflection correction is applied to the corresponding steering wheel deflection direction according to the offset direction, and the final drive wheel speed difference correction is distributed to the left and right drive wheels.

9. The intelligent control method for a handling robot according to claim 1, characterized in that, The continuous updating process specifically includes: Within each control cycle, the following steps are executed sequentially: acquiring the first magnetic field signal and the second magnetic field signal; calculating the offset based on the first magnetic field signal and the second magnetic field signal; determining the offset feature based on the offset; forming the trajectory regression control based on the offset feature; determining the basic allocation relationship based on the offset degree and forming the control allocation strategy based on the offset change trend; performing the collaborative allocation based on the control allocation strategy; and outputting control commands. Once the stable passage conditions are met, the output of the steering wheel deflection correction and the drive wheel speed difference correction is stopped, and the handling robot is controlled to enter the normal straight-line state. When the absolute value of the offset is subsequently detected to be greater than the preset condition, the offset feature determination, trajectory regression control formation, control allocation strategy determination, and collaborative allocation are re-executed.

10. A system using the intelligent control method for a handling robot according to any one of claims 1-9, characterized in that, It includes a magnetic field signal acquisition module, a offset feature analysis module, a trajectory regression control generation module, a control allocation strategy determination module, a cooperative allocation control module, and a dynamic update and closed-loop iteration module, wherein: The magnetic field signal acquisition module acquires the first and second magnetic field signals of the transport robot on opposite sides of the target passage area; The offset feature analysis module calculates the offset based on the first magnetic field signal and the second magnetic field signal; determines the offset direction based on the sign of the offset, determines the offset degree based on the absolute value of the offset, and determines the offset change trend based on the change of the offset within a continuous control cycle, so as to generate offset features; The trajectory regression control generation module determines the steering wheel deflection direction based on the offset direction, determines the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the offset degree, and forms trajectory regression control based on the steering wheel deflection direction, the steering wheel deflection correction amount and the drive wheel speed difference correction amount; The control allocation strategy determination module determines the basic allocation relationship between the steering wheel deflection correction amount and the drive wheel speed difference correction amount based on the degree of offset, and corrects the basic allocation relationship based on the offset change trend, and uses the corrected basic allocation relationship as the control allocation strategy. The collaborative allocation control module, based on the control allocation strategy, collaboratively allocates the steering wheel deflection correction and the drive wheel speed difference correction in the trajectory regression control, and controls the transport robot to return to the target passage center. The dynamic update and closed-loop iteration module continuously updates the first magnetic field signal and the second magnetic field signal during operation, and repeatedly performs offset feature determination, trajectory regression control formation, control allocation strategy determination and collaborative allocation until the stable passage conditions are met.

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