A control method of a robot for a dense stacking job scenario

Abstract

This invention relates to a control method for a robot in a densely stacked work scenario, with the following specific steps: including picking up and unloading goods; during picking up goods: controlling the vehicle to travel to the designated picking point; after reaching the identification point, a multi-line lidar identifies the pose of the carrier end face in the corresponding storage location, and adjusts it within the established adjustment limits; using a wire encoder to determine and adjust the fork height so that the forks are within the safe threshold for entering the carrier's openings; then, based on the identified relative longitudinal distance, the vehicle reverses and stops after a certain distance from the identification result; the forks are extended to pick up the goods; after the forks are extended to the correct position, the forks are raised and tilted; after picking up the goods, the vehicle moves forward and the forks are reset; this invention achieves high-precision alignment and operation of the carrier in a densely stacked, narrow space, significantly improving the accuracy and reliability of the operation.

Description

Technical Field

[0001] This invention relates to the field of mobile robot technology, and more specifically to a control method for a robot in a densely stacked work scenario. Background Technology

[0002] With the rapid development of intelligent manufacturing and smart logistics, automated guided vehicles (AGVs) and various mobile robots are increasingly widely used in warehousing, production lines, and other scenarios. Among them, forklift AGVs (or unmanned forklifts) with autonomous picking and placing functions have become one of the core equipment for realizing logistics automation and reducing labor costs because they can replace manual labor in handling pallets, bins, and other containers. In certain specific industrial scenarios, such as high-density automated warehouses and material buffer areas next to production lines, there exists a "dense stacking operation scenario." In such scenarios, containers (such as pallets and racks) are usually tightly and multi-layered, with narrow operating aisles and extremely compact storage space. This places far more stringent requirements on the robot's operating accuracy, posture perception capabilities, adaptive adjustment capabilities, and operational safety than in ordinary scenarios.

[0003] Currently, AGV forklifts used in such scenarios typically employ technologies such as laser navigation (e.g., reflector navigation, SLAM navigation) or visual navigation (e.g., QR code navigation) for global path planning and positioning. When approaching the target storage location, they often rely on preset fixed stopping points or simple proximity sensors for alignment. However, existing technologies reveal several significant shortcomings when dealing with densely stacked operations:

[0004] Insufficient positioning and perception accuracy: In densely stacked environments, due to the small spacing between carriers, potential obstructions from shelves or stacks, and the inevitable small cumulative deviations in carrier posture after multiple operations, relying solely on the vehicle's own navigation system (such as top-mounted LiDAR) is insufficient to accurately perceive the real-time, high-precision posture (including longitudinal and lateral distances and heading angle deviations) of the target carrier relative to the robot's fork end. While QR code navigation can provide discrete, precise location points, it cannot directly perceive the carrier's own posture. Once the carrier is moved or placed with deviation, the robot will be unable to align effectively.

[0005] Weak adaptive adjustment capability: Most existing solutions assume that the vehicle's posture is standard or within a very small tolerance range. When a posture deviation exceeding a preset threshold is detected, they can usually only report an error or request manual intervention, lacking a control strategy that automatically and intelligently performs multi-dimensional (vehicle position, heading, fork lateral movement / height / pitch) coordinated adjustments within safety thresholds. For example, they cannot intelligently determine whether to prioritize fine-tuning the vehicle body or combining fork lateral movement based on lateral deviation, and cannot make a safety decision when alignment cannot be achieved after multiple adjustment attempts.

[0006] Safety safeguards during the loading and unloading process are inadequate: In densely stacked scenarios, the insertion and removal of the forks from the carrier's openings pose extremely high risks. Existing technologies may rely solely on limit switches or simple position detection, lacking real-time, sensitive detection of the contact state between the forks and the carrier. During fork movements (such as extension, descent, and retraction), accidental interference or collisions, if not stopped immediately, can easily damage goods, carriers, or even the robot itself. Furthermore, the safety confirmation mechanism for the height of the forks during lifting and lowering is also insufficient.

[0007] The identification strategies for vehicles at different heights are limited: densely stacked vehicles often involve different heights. Existing sensor deployments (such as fixed 2D LiDAR or single-point ranging) may not be able to simultaneously meet the identification needs of both low-level and high-level vehicles. For high-level vehicles, their key feature points (such as structures above the support legs) may differ from those of low-level vehicles, requiring different point cloud data processing algorithms and identification logic. Existing solutions often lack this adaptive, layered identification capability. Summary of the Invention

[0008] To address the aforementioned technical problems, the present invention aims to provide a control method for robots in densely stacked work scenarios. This method should integrate high-precision navigation with real-time vehicle pose recognition and possess intelligent and adaptive pose adjustment capabilities under complex constraints.

[0009] To achieve the objectives of the invention described above, the present invention adopts the following technical solution:

[0010] A control method for a robot in a densely stacked operation scenario, the robot comprising a vehicle body, a gantry mechanism, and forks, wherein the gantry mechanism is slidably mounted on the vehicle body, and the forks are slidably mounted on the gantry mechanism, and the forks are capable of lateral movement, vertical movement, and horizontal / vertical tilting relative to the gantry mechanism. The vehicle body is equipped with a main controller, a navigation LiDAR, a front blind-spot LiDAR, a QR code recognition camera, and a controller unit. The gantry mechanism is equipped with three sets of wire encoders and two micro-motion detection mechanisms for fork horizontal and vertical tilting, used to detect the lateral movement distance, lifting distance, front-to-back distance, and horizontal / vertical tilting states of the forks, respectively. The gantry mechanism is also equipped with a multi-line LiDAR. The specific steps are as follows:

[0011] During pickup: The vehicle's main controller receives the task from the dispatch system, then resets the forks, and controls the vehicle to travel to the designated pickup point according to the route and speed specified by the dispatch system. At the designated action point, the forks are raised to the recognition height of the pickup point in advance. Upon reaching the recognition point, the multi-line LiDAR installed under the mast mechanism performs pose recognition of the carrier's end face in the corresponding storage location. Based on the recognition results, the pose is judged and adjusted within the specified adjustment limits. Within the safe adjustment threshold range, the pose is adjusted and released. Level the forks, wait 1 second to ensure the forks are horizontal, use a cable encoder to adjust the fork height so that the forks are within the safe threshold for entering the carrier's opening, then reverse according to the identified relative longitudinal distance, and stop after a certain distance from the identification result. Extend the forks to pick up the goods, and during the fork extension to pick up the goods, constantly check whether the micro-motion detection mechanism of the fork tip is triggered. If triggered, stop picking up the goods immediately. After the forks are extended to the correct position, raise the forks and tilt them up. After picking up the goods, the vehicle moves forward, the forks return to their original position, and the height is checked at the fork height check point to ensure it is safe.

[0012] During unloading: The vehicle's main controller receives the task from the dispatch system, then resets the forks, and controls the vehicle to travel to the designated unloading point according to the route and speed specified by the dispatch system. At the designated action point, the forks are raised to the identification height of the unloading point in advance. After reaching the identification point, the multi-line LiDAR installed under the mast mechanism performs pose recognition on the front face of the corresponding cargo below. Based on the recognition results, the pose is judged and adjusted within the specified adjustment limits. Within the safe adjustment threshold range, the pose is adjusted. After the pose is adjusted to the correct position, the vehicle reverses from the target point based on the relative distance of the recognition, and then moves to a certain distance from the target point. After stopping at the target location, determine the fork extension distance based on the relative position of the vehicle's parking point and the target point. Once the forks are fully extended, adjust their left and right positions based on the relative lateral distance between the vehicle's current position and the target point. Next, lower the forks to the unloading height and level them. Wait 1 second to ensure the forks are horizontal. Use a pull-wire encoder to adjust the fork height so that the forks are within the safe threshold for safe removal. Remove the forks, and during the removal process, continuously check whether the micro-motion detection mechanism at the fork tip is triggered. If triggered, immediately stop the removal. After unloading, the vehicle moves forward, the forks reset, and the height is checked at the fork height check point to ensure it is safe.

[0013] As a preferred option, during the pickup and unloading steps, the vehicle is navigated to its destination using a fusion positioning system that combines navigation LiDAR, front blind spot LiDAR, and QR code recognition camera.

[0014] As a preferred option, the specific process of pose recognition and decision-making in the pickup step is as follows:

[0015] Once the forks are raised to the recognition height, the vehicle detection signal is triggered, activating the multi-line LiDAR. The multi-line LiDAR scans the front face of the vehicle, and the point cloud is processed by algorithms to extract the center points of the two support legs. The longitudinal deviation x, lateral deviation y, and yaw angle deviation θ of the vehicle relative to the vehicle body are calculated. It is then determined whether the deviation is within the threshold range. If it exceeds the threshold, it is judged as "abnormal posture", a warning log is reported, the dispatch system intervenes, and an instruction is issued to return the vehicle to the standby point; if it is within the threshold, the automatic adjustment process is initiated.

[0016] As a preferred solution, the automatic position adjustment and picking process is as follows: Position adjustment: The controller unit calculates the amount of adjustment required based on the identified lateral deviation y, and adjusts the position of the vehicle body to align the target fork with the carrier socket; if it exceeds the fork lateral movement adjustment range, the vehicle enters the forward adjustment mode, moves forward a certain distance and then reverses to the identification point, and continues to identify until the lateral deviation is within the fork lateral movement range, so that the target fork is aligned with the carrier socket;

[0017] Fine-tuning of heading: Controlling the vehicle to make a small-angle spin to correct the heading angle deviation θ.

[0018] Height Confirmation: Confirm the fork carriage height is within the range where it can be safely inserted into the carrier hole using a cable encoder;

[0019] Reversing to retrieve goods: Reverse according to the longitudinal deviation x, and stop at a certain distance from the goods to prepare for retrieval;

[0020] Lateral shift retrieval: The controller unit controls the target gantry to move forward based on the identified required lateral shift distance, and the collision plate trigger serves as an auxiliary judgment for the retrieval to be in place;

[0021] Lifting and Resetting: After successful pickup, the forks are raised to a safe height, then the mast is moved back to the zero position, the forks are lowered to a safe operating height, and the vehicle drives away from the warehouse.

[0022] As a preferred option, the pickup step also includes a task receiving step: when the task is received, first check whether the LiDAR data is normal, mainly including the navigation LiDAR on the roof and front of the vehicle, the front blind spot LiDAR, and the multi-line LiDAR on the mast mechanism; check whether the forks, main controller and controller unit are faulty, check whether the positioning data jumps, if there is a fault, it is necessary to broadcast a fault voice and stop the execution of the task.

[0023] As a preferred solution: In the picking process, if the relative pose is not adjusted to the safe picking threshold within the set adjustment limits and number of times, a picking failure will be announced via voice and reported to the scheduling system.

[0024] As a preferred solution: In the picking step, if the height of the forks is not adjusted to the threshold range in a single adjustment, the forks are raised to a certain height and then lowered again, repeating this process three times until the height reaches the safe threshold range.

[0025] As a preferred embodiment: When the multi-line lidar is working, when the forks are in the lowest position for identification, the multi-line lidar can identify the left and right support corners below the front face of the vehicle on the ground, and adjust the vehicle's posture according to the identification results; when the multi-line lidar is working, when the forks are in the highest position for identification, the multi-line lidar's field of view can identify the uprights and crossbeams at the upper left and right support corners above the front face of the lower vehicle, and adjust the vehicle's posture according to the identification results.

[0026] As a preferred embodiment: the mast mechanism is located at the rear of the vehicle body, and the lateral movement, vertical movement, forward and backward movement, and tilting and flattening mechanism of the forks are located on the mast mechanism. The vehicle body navigates outside the densely stacked area using a top navigation lidar, and uses a QR code recognition camera to recognize QR codes for navigation within the densely stacked area.

[0027] As a preferred embodiment: the multi-line lidar moves laterally and vertically along with the forks; the lower part of the vehicle body is also equipped with a forward safety mechanism, which includes a safety contact edge, an obstacle avoidance lidar, and a blind spot lidar, used to ensure the safety of the forklift during automatic forward operation; the multi-line lidar on the forks is also used for reverse obstacle avoidance to detect the safety of the surrounding environment when the vehicle is reversing; the forks are also equipped with a collision plate A and a collision plate B for confirming that the goods have been picked up; the fork tips are equipped with microswitches and photoelectric sensors for detecting collisions.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0029] The method of this invention constructs a fully closed-loop automated process from receiving tasks, autonomous navigation, pose recognition, adaptive adjustment to the final completion of picking up / unloading. By executing a series of actions based on feedback from precise sensors such as multi-line LiDAR and wire encoders, high-precision alignment and operation of the vehicle are achieved in densely stacked and narrow spaces, significantly improving the accuracy and reliability of operations.

[0030] The method of this invention directly scans the front face of the carrier using a multi-line lidar on the gantry, acquiring rich 3D point cloud information to accurately calculate the longitudinal, lateral, and yaw angle deviations of the carrier relative to the robot forks. This solves the problem of traditional solutions relying on preset positions and being unable to perceive the actual carrier pose, and forms the basis for adaptive adjustment.

[0031] The method of this invention also has multiple layers of safety protection: 1. Process safety: During the fork extension for picking up goods and withdrawal for unloading, "the micro-motion detection mechanism of the fork tip is constantly detected, and if it is triggered, it stops immediately," which provides contact-type hard safety protection and can effectively prevent collision damage caused by positional errors or unexpected obstacles; 2. Height safety: "Fork height check point is set to check whether the height is safe," and the fork is adjusted to "within the safe threshold" using a wire encoder before insertion / withdrawal, preventing interference between the fork and the carrier or the ground; 3. Action stability: "Leveling the fork and waiting 1 second to ensure the fork is horizontal" and other operations ensure a stable state after the action is in place, avoiding errors caused by mechanism shaking or inertia.

[0032] The method of the present invention includes logical judgments based on the recognition results, such as "reversing according to the relative longitudinal distance of the recognition, and stopping after a certain distance from the recognition result", so that the robot's movement and fork actions are no longer fixed programs, but dynamically generated according to the real-time environmental perception results, thereby enhancing environmental adaptability. Attached Figure Description

[0033] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute a limitation thereof.

[0034] Figure 1 This is a schematic diagram of the structure of the robot of the present invention;

[0035] Figure 2 for Figure 1 A partially enlarged structural diagram;

[0036] Figure 3 This is a structural schematic diagram of the robot of the present invention from another angle;

[0037] Figure 4 This is a schematic diagram of the robot's pickup task process according to the present invention;

[0038] Figure 5 This is a schematic diagram of the unloading task process of the robot of the present invention.

[0039] The labels in the attached diagram are: 1. Vehicle body; 2. Mast mechanism; 3. Forks; 4. Navigation LiDAR; 5. Front blind spot LiDAR; 6. QR code recognition camera; 7. Multi-line LiDAR; 8. Collision plate A; 9. Collision plate B. Detailed Implementation

[0040] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0041] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0042] Furthermore, in the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0043] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, unless explicitly defined otherwise.

[0044] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0045] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0047] like Figures 1 to 5 As shown, a control method for a robot in a densely stacked operation scenario is disclosed. The robot includes a vehicle body 1, a gantry mechanism 2, and forks 3. The gantry mechanism 2 is slidably mounted on the vehicle body 1, and the forks 3 are slidably mounted on the gantry mechanism 2. The forks 3 are capable of lateral movement, vertical movement, and horizontal / vertical tilting relative to the gantry mechanism 2. The vehicle body 1 is equipped with a main controller, a navigation LiDAR 4, a front blind spot LiDAR 5, a QR code recognition camera 6, and a controller unit. The gantry mechanism is equipped with three sets of wire encoders and two micro-motion detection mechanisms for horizontal and vertical tilting of the forks, which are used to detect the lateral movement distance, lifting distance, front-to-back distance, and horizontal / vertical tilting states of the forks 3, respectively. The gantry mechanism is also equipped with a multi-line LiDAR 7. The specific steps are as follows:

[0048] Upon receiving a task, first check if the LiDAR data is normal, mainly including the navigation LiDAR 4 on the roof and front of the vehicle, the front blind spot LiDAR 5, and the multi-line LiDAR 7 on the mast mechanism 2; check if the forks 3, the main controller and the controller unit are faulty, and check if the positioning data jumps. If there is a fault, a fault voice should be broadcast and the task should be stopped.

[0049] The aforementioned technical solution shifts fault diagnosis from "handling during / after occurrence" to "prevention before the task begins." By performing self-checks, major safety hazards such as sensor failure, mechanical jamming, and positioning jumps (which could lead to collisions) can be detected in advance. Simultaneously, it prevents robots from operating with defects, avoiding task failures, cargo drops, or equipment damage due to sudden malfunctions during operation, thus improving the overall stability and availability of the system.

[0050] During pickup: The main controller of vehicle body 1 receives the task from the dispatch system and then resets the forks 3, mainly including up / down, left / right, and forward / backward movements. Then, it controls vehicle body 1 to travel to the designated pickup point according to the route and speed specified by the dispatch system. At the designated action point, the forks 3 are raised to the identification height of the pickup point in advance. Upon reaching the identification point, the multi-line lidar 7 installed under the mast mechanism 2 is used to identify the position of the carrier's end face in the corresponding storage location. Based on the identification results, the position is judged, and adjustments are made within the prescribed adjustment limits to avoid collisions with goods during the adjustment process. If the relative position is not adjusted to within the safe pickup threshold within the prescribed adjustment limits and number of attempts, a pickup failure is announced via voice and reported to the dispatch system.

[0051] The above process sets clear exit conditions for the automatic adjustment process, preventing the robot from making unlimited or excessive attempts in abnormal scenarios that cannot be corrected (such as severely tilted vehicles or goods falling and blocking holes), saving time and preventing potential secondary risks. Furthermore, the combination of "voice broadcast" and "reporting system" instantly notifies on-site personnel and central dispatch, facilitating rapid response and handling of abnormal operating conditions.

[0052] Within the safe adjustment threshold range, positional adjustment is performed, and forks 3 are leveled. A 1-second wait is taken to ensure forks 3 are horizontal. A wire encoder is used to determine and adjust the height of forks 3 so that they are within the safe threshold range for entering the carrier hole. If the fork height adjustment does not reach the threshold range in a single attempt, it is raised again to a certain height and lowered again, repeating this process three times until the height reaches the safe threshold. During fork descent, uneven ground, carrier deformation, or momentary sensor errors may cause inaccurate height judgment. This retry mechanism, through the "raise-lower" action, eliminates mechanical backlash, overcomes slight jamming, and improves the reliability of the final height positioning through multiple measurements, ensuring the safety of insertion / removal.

[0053] The vehicle reverses based on the identified relative longitudinal distance and stops after a certain distance from the identification result. The forks 3 extend forward to pick up the goods. During the process of the forks 3 extending forward to pick up the goods, the micro-motion detection mechanism of the fork tips is constantly monitored to see if it is triggered. If it is triggered, the picking up of goods is stopped immediately. After the forks 3 extend forward to the position, the forks 3 are raised and tilted. After the goods are picked up, the vehicle moves forward, the forks 3 are reset, and the height of the forks 3 is checked at the height check point to ensure that the height is safe in order to avoid collision with the equipment during the journey.

[0054] During unloading: The main controller of vehicle body 1 receives the task from the dispatch system, then resets the forks, and controls the vehicle to travel to the designated unloading point according to the route and speed specified by the dispatch system. At the designated action point, the forks 3 are raised to the identification height of the unloading point in advance. After reaching the identification point, the multi-line LiDAR 7 installed under the mast mechanism performs pose recognition on the front face of the corresponding cargo below. Based on the recognition results, pose judgment is made, mainly judging the deviation of the current vehicle body's front-back, left-right, and angle from the end face of the cargo below, and making adjustments within the specified adjustment limits to avoid collision with the cargo during the adjustment process. For ground unloading, no warehouse location recognition is performed; the main comparison is between the current pose of the vehicle body and the coordinate pose of the warehouse location.

[0055] Within the safe adjustment threshold range, the vehicle's posture is adjusted. If the relative posture is not adjusted to within the safe unloading threshold within the specified adjustment limits and number of attempts, a voice announcement of pickup failure is made, and the information is reported to the dispatch system. After the posture is adjusted to the correct position, the vehicle reverses based on the recognized relative distance as the target point and stops after a certain distance from the target point. The fork extension distance is determined based on the relative position between the vehicle's stopping point and the target point. After the fork extension is complete, the left and right positions of the fork 3 are adjusted based on the relative lateral distance between the vehicle's current position and the target point. Next, the fork 3 is lowered to the unloading height and leveled. After waiting for 1 second to ensure the fork is horizontal, the height of the fork 3 is adjusted using a pull-line encoder to ensure that the fork 3 is within the safe threshold for safe removal. The fork 3 is then removed, and during the removal process, the micro-motion detection mechanism of the fork tip is constantly monitored to see if it is triggered. If triggered, the removal is stopped immediately. After unloading is completed, the vehicle moves forward, the fork 3 is reset, and the height of the fork 3 is checked at the height check point to ensure it is safe and to avoid collisions with the equipment during travel.

[0056] When the multi-line lidar 7 is working, when the forks 3 are in the lowest position for identification, the multi-line lidar 7 can identify the left and right support corners below the front face of the vehicle on the ground, and adjust the vehicle's posture according to the identification results; when the multi-line lidar 7 is working, when the forks 3 are in the highest position for identification, the multi-line lidar 7's field of view can identify the uprights and crossbeams at the left and right support corners above the front face of the lower vehicle, and adjust the vehicle's posture according to the identification results.

[0057] To address the issue that vehicles at different heights in densely stacked environments may exhibit different visual characteristics, this solution defines specific targets for radar identification at different fork heights. This ensures that the system can find stable and reliable identification feature points for pose calculation, whether operating vehicles at the bottom or top levels, greatly enhancing the versatility of the solution in complex 3D stacking scenarios.

[0058] During the picking and unloading processes, vehicle 1 navigates to its destination using a fusion positioning system comprised of navigation LiDAR 4, front blind-spot LiDAR 5, and QR code recognition camera 6. Optimal positioning methods are employed in different areas of densely stacked scenarios. In wide passageways or areas, LiDAR SLAM or reflectors can be used for navigation. In narrow passageways with sparse features, such as inside stacks, or where absolute positioning is required, QR code visual positioning is switched to. The front blind-spot LiDAR compensates for navigation blind spots. This multi-sensor fusion positioning avoids the risk of system failure due to the malfunction of a single navigation method (such as blocked LiDAR or damaged QR codes), ensuring the robot can stably and accurately travel to the starting point of the operation, laying the foundation for subsequent precision operations.

[0059] The specific process of pose recognition and decision-making in the pickup step is as follows:

[0060] After the forks 3 are raised to the recognition height, the vehicle detection signal is triggered, and the multi-line LiDAR 7 is activated. The multi-line LiDAR 7 scans the front face of the vehicle, and the point cloud is processed by the algorithm to extract the center points of the two support legs. The longitudinal deviation x, lateral deviation y, and yaw angle deviation θ of the vehicle relative to the vehicle body 1 are calculated. It is determined whether the deviation is within the threshold range. If it exceeds the threshold, it is determined as "abnormal posture", a warning log is reported, the dispatch system intervenes, and an instruction is issued to make the vehicle return to the standby point; if it is within the threshold, the automatic adjustment process is initiated.

[0061] The identification, calculation, and judgment processes are standardized and automated. A clearly defined "threshold range" enables automatic assessment of operational feasibility. When deviations exceed the automatically adjustable threshold (i.e., "abnormal posture"), instead of blindly attempting or stopping, a warning log is reported, and the dispatch system intervenes. This allows the upper-level management system to be aware of the abnormal situation on-site and flexibly dispatch vehicles to perform other tasks or wait for further processing, achieving task-level safe and efficient management.

[0062] The automatic posture adjustment and retrieval process is as follows:

[0063] Position adjustment: The controller unit calculates the amount of adjustment required based on the identified lateral deviation y, and fine-tunes the position of the vehicle body to align the target fork 7 with the carrier socket; if it exceeds the lateral movement adjustment range of the fork 3, the vehicle enters the forward adjustment mode, moves forward a certain distance and then reverses to the identification point, and continues to identify until the lateral deviation is within the lateral movement range of the fork 3, so that the target fork 3 is aligned with the carrier socket.

[0064] Fine-tuning of heading: Controlling the vehicle to make a small-angle spin to correct the heading angle deviation θ.

[0065] Height Confirmation: Confirm the height of fork carriage 3 is within the range where it can be safely inserted into the carrier hole using a cable encoder;

[0066] Reversing to retrieve goods: Reverse according to the longitudinal deviation x, and stop at a certain distance from the goods to prepare for retrieval;

[0067] Lateral shift retrieval: The controller unit controls the target gantry to move forward based on the identified required lateral shift distance, and the collision plate trigger serves as an auxiliary judgment for the retrieval to be in place;

[0068] Lifting and Resetting: After successful pickup, forks 3 are raised to a safe height, then the mast is moved back to the zero position, forks 3 are lowered to a safe operating height, and the vehicle drives away from the warehouse.

[0069] The aforementioned technical features disclose how to comprehensively utilize vehicle body movement (forward / backward / spinning) and the fork's own degrees of freedom (lateral movement) to collaboratively compensate for pose deviations. In particular, the strategy of "if the fork lateral movement adjustment range is exceeded, the vehicle enters forward adjustment mode" significantly expands the range of deviations that can be corrected in a single recognition-adjustment cycle and improves the fault tolerance for larger initial pose deviations.

[0070] Meanwhile, the pickup accuracy not only relies on the forward reach distance set by the program, but also incorporates physical "collision trigger as an auxiliary judgment for pickup accuracy," forming a dual guarantee of "travel control + contact feedback," which greatly improves the success rate and reliability of pickup operations.

[0071] The mast mechanism 2 is located at the rear of the vehicle body 1. The lateral movement, vertical movement, forward and backward movement, and tilting / flattening mechanisms of the forks 3 are mounted on the mast mechanism 2. The vehicle body 1 navigates outside the densely stacked area using a top-mounted navigation lidar 4, and within the densely stacked area, it uses a QR code recognition camera 6 to recognize QR codes for navigation. A rear-mounted mast is a common layout for forklift AGVs, which is beneficial for visibility and weight distribution. The explicit zoned navigation strategy (laser + QR code) further refines claim 2, ensuring that the navigation method precisely matches the scene characteristics (area openness, requirement for absolute positioning accuracy), optimizing system cost and computational complexity while maintaining accuracy.

[0072] The multi-line lidar 7 moves laterally and vertically along with the forks 3; the lower part of the vehicle body 1 is also equipped with a forward safety mechanism, which consists of a safety contact edge, an obstacle avoidance lidar, and a blind spot lidar, used to ensure the safety of the forklift during automatic forward operation; the multi-line lidar on the forks 3 is also used for reverse obstacle avoidance to detect the safety of the surrounding environment when the vehicle is reversing; the forks 3 are also equipped with a collision plate A8 and a collision plate B9 for confirming that the goods have been picked up; the fork tips of the forks 3 are equipped with microswitches and photoelectric sensors for detecting collisions.

[0073] The multi-line radar moves with the forks, ensuring its field of view is always aligned with the fork operating surface for more direct identification. The multi-line radar on the forks also functions as an obstacle avoidance sensor when reversing, achieving hardware function reuse, saving costs and simplifying the structure. The forward safety mechanism (edge ​​contact, laser) combined with the reversing obstacle avoidance function provides omnidirectional safety protection for the robot.

[0074] Building upon the impact plate, the fork tips are further equipped with microswitches and photoelectric sensors. This forms a triple positioning / collision prevention detection system: programmed distance judgment (basic) + physical impact plate triggering (mechanical assistance) + fork tip micro-motion / photoelectric sensor (direct contact / close-range detection), elevating the safety of the loading and unloading process to the highest level.

[0075] The robot body of this invention adopts a counterweight design and belongs to the mast-mounted forklift robot category. The forks possess four degrees of freedom of motion: lifting motion: achieved by a lifting motor mounted on the mast driving a hydraulic push rod, with a maximum lifting height of not less than 3.6 meters; lateral translation: driven by a lateral translation motor, with a maximum total left and right travel of not less than 0.2 meters; mast forward movement: achieved by a forward movement motor mounted on the mast driving a hydraulic push rod, with a maximum forward movement distance of not less than 0.55 meters; tilting and leveling motion: achieved by a motor mounted on the mast driving a hydraulic push rod. Except for the tilting and leveling motion, all other movements are equipped with a pull-cord encoder to provide real-time feedback on the absolute position of the forks (height, lateral movement, forward / backward position), with an accuracy of ±1mm. The tilting and leveling motion is detected by two microswitches.

[0076] The robot perception system of this invention is deployed as follows: Navigation and positioning sensor group: As shown in Figure 1, a 16-line LiDAR is installed on the top as the main sensor for SLAM mapping and global localization. A blind spot LiDAR is installed at the front to detect low obstacles and obstacles in the blind spot of the 16-line LiDAR. A high-precision IMU is integrated inside the vehicle body to measure the vehicle's pitch, roll, and angular velocity. Pose recognition sensor: A multi-line LiDAR is centrally installed below the mast beam, between the two forks. Its installation height is precisely calibrated to ensure that the radar beam is not blocked by the forks when the forks are at the lowest recognition height, and can completely scan the front face and support legs of the ground vehicle. Safety sensors: Fork tip photoelectric switches are installed at the fork tips to detect whether a collision has occurred. Photoelectric sensors are installed at the fork tips to detect obstacles behind the forks; mechanical anti-collision strips and emergency stop switches are arranged around the vehicle body.

[0077] The computing and communication unit of the robot of this invention is as follows: The main controller uses an industrial control computer, running the Ubuntu 18.04 operating system and the ROS Melodic framework, integrating all core software modules such as perception, localization, control, and communication. It is equipped with an industrial-grade wireless router, maintaining communication with the scheduling system (RCS / WMS) via Wi-Fi, following a custom communication protocol, receiving tasks, and reporting status.

[0078] This invention relates to a stacking robot control method and system suitable for dense stacking (minimum gap between cargo rows is 170mm), small stacking carrier tolerance (vertical tolerance of 15mm between carrier fork guide slot and fork, horizontal tolerance of 25mm), and high stacking height (maximum stacking height is 3190mm). It achieves functions such as high-precision control, intelligent obstacle avoidance, safe stacking, and high operating efficiency, thereby improving the automation level of existing dense stacking and reducing the dangers of manual operation.

[0079] This invention is also a robot control method specifically designed for densely stacked operation scenarios. This method can integrate high-precision navigation and real-time vehicle pose recognition, and has the ability to intelligently and adaptively adjust pose under complex constraints. It embeds multi-layered safety detection and protection mechanisms and improves the robustness and intelligence level of the entire operation system, so as to achieve efficient, reliable and safe unmanned dense storage and retrieval operations.

[0080] This invention enables the vehicle to operate manually or via a host computer in an automated robotic mode by setting up a central controller. Furthermore, the original counterbalance forklift chassis, after modification, still meets strength and stability requirements to ensure safety. A QR code camera is added to the existing reach truck mechanism, maintaining navigation stability even when switching between multi-line LiDAR navigation on the top of the chassis and QR code scanning navigation using a camera on the bottom of the chassis in stacked areas. When operating in densely stacked areas, the control system and method maintain stable movement at speeds of 1.0 m / s in both forward and reverse directions. Multiple LiDARs with different functions are installed on the vehicle. In addition to ensuring basic automatic navigation and obstacle avoidance functions, the LiDARs are used for vehicle pose recognition and automatic stacking, allowing the forklift to perform basic operations in densely stacked aisles unmanned while ensuring safety.

[0081] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0082] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principles and spirit of the present invention. Any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention shall still fall within the scope of the technical solution of the present invention.

Claims

1. A control method for a robot in a densely stacked work scenario, characterized in that: The robot includes a vehicle body (1), a gantry mechanism (2), and forks (3). The gantry mechanism (2) is slidably mounted on the vehicle body (1), and the forks (3) are slidably mounted on the gantry mechanism (2). The forks (3) can move laterally, vertically, and horizontally relative to the gantry mechanism (2). The vehicle body (1) is equipped with a main controller, a navigation laser radar (4), a front blind spot laser radar (5), a QR code recognition camera (6), and a controller unit. The gantry mechanism is equipped with three sets of wire encoders and two micro-motion detection mechanisms for the horizontal and vertical positioning of the forks, which are used to detect the lateral movement distance, lifting distance, front-to-back distance, and horizontal and vertical positioning status of the forks (3), respectively. The gantry mechanism is also equipped with a multi-line laser radar (7). The specific steps are as follows: When picking up goods: The main controller of the vehicle body (1) receives the task issued by the dispatch system, then resets the forks (3), and then controls the vehicle body (1) to travel to the designated picking point according to the route and speed issued by the dispatch system; at the specified action point, the forks (3) are raised in advance to the identification height of the picking point. After reaching the identification point, the multi-line laser radar (7) installed under the mast mechanism (2) is used to identify the position of the vehicle end face in the corresponding storage location. The position is judged according to the identification result, and adjustments are made within the specified adjustment limits. The position is adjusted within the safe adjustment threshold range, and the forks (3) are leveled. Wait 1 second to ensure the forks (3) are horizontal. Use the wire encoder to determine and adjust the height of the forks (3) so that the forks (3) are within the safe threshold for entering the vehicle hole. Then reverse according to the identified relative longitudinal distance and stop after a certain distance from the identification result. Extend the forks (3) to pick up the goods. During the process of the forks (3) extending to pick up the goods, check whether the micro-motion detection mechanism of the fork tip is triggered at all times. If it is triggered, stop picking up the goods immediately. After the forks (3) are extended to the position, lift the forks (3) and tilt them up. After picking up the goods, the vehicle moves forward, the forks (3) are reset, and the height is checked at the fork (3) height check point to see if the height is safe. During unloading: The main controller of the vehicle body (1) receives the task issued by the scheduling system, then resets the forks, and then controls the vehicle body to travel to the designated unloading point according to the route and speed issued by the scheduling system; at the specified action point, the forks (3) are raised in advance to the identification height of the unloading point. After reaching the identification point, the multi-line laser radar (7) installed under the mast mechanism is used to identify the position of the front face of the corresponding cargo below. The position is judged according to the identification result, and adjustments are made within the specified adjustment limits. The position is adjusted within the safe adjustment threshold range. After the position is adjusted to the correct position, the vehicle reverses according to the relative distance of the identification as the target point, and stops after a certain distance from the target point. The fork (3) extension distance is determined by the relative position of the vehicle parking point and the target point. After the fork (3) is extended to the target point, the left and right positions of the fork (3) are adjusted according to the relative lateral distance between the current position of the vehicle and the target point. Then the fork (3) is lowered to the unloading height and the fork is leveled. Wait 1 second to ensure that the fork is horizontal. The height of the fork (3) is adjusted by the pull line encoder so that the fork (3) is within the safe threshold of the height that can be safely withdrawn. The fork (3) is withdrawn. During the withdrawal of the fork (3), the micro-motion detection mechanism of the fork tip is constantly detected. If it is triggered, the withdrawal is stopped immediately. After the unloading is completed, the vehicle moves forward, the fork (3) is reset, and the height is checked at the fork (3) height check point to see if the height is safe.

2. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: During the pickup and unloading steps, the vehicle (1) navigates to its destination using a fusion positioning system consisting of a navigation lidar (4), a front blind spot lidar (5), and a QR code recognition camera (6).

3. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: The specific process of pose recognition and decision-making in the pickup step is as follows: After the forks (3) are raised to the recognition height, the vehicle detection signal is triggered, and the multi-line lidar (7) is started. The multi-line lidar (7) scans the front face of the vehicle. The point cloud is processed by the algorithm to extract the center points of the two support legs and calculate the longitudinal deviation x, lateral deviation y and heading angle deviation θ of the vehicle relative to the vehicle body (1). It is determined whether the deviation is within the threshold range. If it exceeds the threshold, it is determined to be "abnormal posture". The warning log is reported and the dispatch system intervenes and issues an instruction to make the vehicle return to the standby point. If it is within the threshold, the automatic adjustment process will begin.

4. The control method for a robot in a densely stacked work scenario according to claim 3, characterized in that: The automatic posture adjustment and retrieval process is as follows: Position adjustment: The controller unit calculates the amount of adjustment required based on the identified lateral deviation y, and adjusts the position of the vehicle body to align the target fork (7) with the carrier socket; if it exceeds the lateral movement adjustment range of the fork (3), the vehicle enters the forward adjustment mode, moves forward a certain distance and then reverses to the identification point, and continues to identify until the lateral deviation is within the lateral movement range of the fork (3) so that the target fork (3) is aligned with the carrier socket; Fine-tuning of heading: Controlling the vehicle to make a small-angle spin to correct the heading angle deviation θ. Height confirmation: Confirm the height of the fork carriage (3) is within the range where it can be safely inserted into the carrier hole using a pull-wire encoder; Reversing to retrieve goods: Reverse according to the longitudinal deviation x, and stop at a certain distance from the goods to prepare for retrieval; Lateral shift retrieval: The controller unit controls the target gantry to move forward based on the identified required lateral shift distance, and the collision plate trigger serves as an auxiliary judgment for the retrieval to be in place; Lifting and Resetting: After the goods are successfully picked up, the forks (3) are raised to a safe height, then the mast is moved back to the zero position, the forks (3) are lowered to a safe operating height, and the vehicle leaves the warehouse.

5. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: Before the pickup step, there is also a task receiving step: when the task is received, first check whether the data of the lidar is normal, mainly including the navigation lidar (4) on the roof and front of the vehicle, the front blind spot lidar (5), and the multi-line lidar (7) on the mast mechanism (2); check whether the forks (3), the main controller and the controller unit are faulty, check whether the positioning data jumps, if there is a fault, it is necessary to broadcast the fault voice and stop the execution of the task.

6. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: If the relative pose is not adjusted to the safe pickup threshold within the specified adjustment limits and number of attempts during the pickup process, a pickup failure will be announced via voice and reported to the scheduling system.

7. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: In the picking process, if the height of the fork (3) is not within the threshold range in a single adjustment, it is raised to a certain height and then lowered again, repeating this process three times until the height reaches the safe threshold.

8. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: When the multi-line lidar (7) is working, when the forks (3) are in the lowest position for identification, the multi-line lidar (7) can identify the left and right support corners below the front face of the vehicle on the ground and adjust the vehicle's posture according to the identification results; when the multi-line lidar (7) is working, when the forks (3) are in the high position for identification, the multi-line lidar (7) can identify the columns and crossbeams at the upper left and right support corners above the front face of the lower vehicle and adjust the vehicle's posture according to the identification results.

9. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: The gantry mechanism (2) is located at the rear of the vehicle body (1). The lateral movement, up and down movement, forward and backward movement, and tilting and flattening mechanism of the forks (3) are located on the gantry mechanism (2). The vehicle body (1) navigates outside the dense stacking area by top navigation laser radar (4) and uses a QR code recognition camera (6) to recognize QR codes for navigation within the dense stacking area.

10. The control method for a robot in a densely stacked work scenario according to claim 1, characterized in that: The multi-line laser radar (7) moves laterally and vertically along with the forks (3); the lower part of the vehicle body (1) is also provided with a forward safety mechanism, which consists of a safety contact edge, an obstacle avoidance laser radar, and a blind spot laser radar, used to ensure the safety of the forklift during automatic forward operation; the multi-line laser radar on the forks (3) is also used to detect the safety of the surrounding environment during reverse operation; the forks (3) are also provided with a collision plate A (8) and a collision plate B (9) for confirming that the goods have been picked up; the fork tips of the forks (3) are provided with micro switches and photoelectric sensors for detecting collisions.

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