Unmanned-forklift automatic loading and unloading system for van-type truck

Abstract

An unmanned-forklift automatic loading and unloading system for a van-type truck. The system comprises: a positioning map module, which is used for binding with a positioning map platform areas at which a plurality of trucks park; a task-issuing module, which is used by a driver to issue a task to a WMS by means of a pad system; a WMS, which issues, on the basis of platform numbers, goods pick-up and placement tasks corresponding to map points; an AGV, which reaches a designated platform by means of a positioning system based on a reflecting plate, scans the interior of a carriage and sends a scanning result to the WMS; and a storage location planning module, which receives the carriage scanning result, which is sent by the AGV, plans storage locations on the basis of the carriage space, and issues a goods pick-up task to the AGV. The AGV is navigated inside the van-type truck by using a visual servo system based on a solid-state laser radar, such that the AGV can realize precise positioning in a complex carriage environment. The navigation precision and stability of the AGV are improved, and the adaptability of the AGV in different van-type truck environments is also enhanced.

Description

An automated loading and unloading system for vans using unmanned forklifts Technical Field

[0001] This invention relates to the field of automated loading and unloading technology for logistics forklifts, specifically to an automated loading and unloading system for unmanned forklifts used in vans. Background Technology

[0002] With the continuous development of the global economy and the rapid rise of the e-commerce industry, the logistics industry is experiencing unprecedented high-speed growth. Against this backdrop, the application of automated logistics equipment, especially Automated Guided Vehicles (AGVs), is becoming increasingly widespread, becoming one of the key technologies for improving logistics efficiency and reducing labor costs. As an important application area of ​​AGVs, the automation level of automated loading and unloading of trucks is of great significance to the intelligent development of the entire logistics industry.

[0003] However, in practical applications, automated loading and unloading tasks on trucks face numerous challenges. First, the variety of truck types and their varying cargo bed layouts necessitates greater adaptability and flexibility from AGVs during positioning, navigation, and loading / unloading. Second, the limited space between goods and the narrow gaps in cargo handling place stringent demands on the precision and stability of AGVs. Furthermore, automated loading and unloading tasks in van environments require AGVs to enter the confined space of the cargo bed to retrieve and place goods, undoubtedly increasing the difficulty and complexity of the task. Summary of the Invention

[0004] (a) Purpose of the invention

[0005] In view of this, the purpose of this invention is to provide an unmanned forklift automatic loading and unloading system for vans, which solves the problems mentioned in the background above.

[0006] (II) Technical Solution

[0007] An automated loading and unloading system for vans using unmanned forklifts, the system comprising:

[0008] The location map module is used to bind multiple truck parking platform areas to the location map;

[0009] The task distribution module is used by drivers to distribute tasks to the WMS system via the pad system;

[0010] The WMS system issues cargo pickup and delivery tasks to the corresponding map locations based on the platform number.

[0011] The AGV (Automated Guided Vehicle) uses a reflector-based positioning system to reach the designated platform, scans the inside of the carriage, and sends the scanning results to the WMS (Warehouse Management System).

[0012] The warehouse location planning module receives the carriage scanning results sent by the AGV, plans the warehouse location according to the carriage space, and sends the picking task to the AGV.

[0013] The AGV (Automated Guided Vehicle) trolley performs the pickup operation according to the pickup task and returns to the designated platform after picking up the goods.

[0014] The visual private service navigation module uses LiDAR to scan the point cloud of the AGV in real time when the AGV is performing loading tasks. It then performs ICP registration algorithm with the pre-acquired template point cloud to obtain the pose of the current target point in real time.

[0015] The pallet posture recognition module, when performing a picking task inside a van, still needs to perform a pallet posture recognition visual task after the AGV reaches the target point to obtain a more accurate pallet pose. First, the pallet is scanned using a solid-state LiDAR, and the point cloud segmentation algorithm is used to obtain the point cloud of the region of interest. Then, the filtered pallet point cloud is obtained based on Euclidean distance clustering. By fitting the pallet plane, the pallet angle and the pallet center coordinates can be obtained.

[0016] The cargo placement pose adjustment module calculates the precise pose of the target point based on the real-time target point pose and controls the AGV to reach the target point.

[0017] The vision-based delivery module performs precise visual delivery tasks after the AGV arrives at the target point, ensuring delivery accuracy.

[0018] The control system receives the position and pose data of the target point for loading and controls the AGV to perform the loading operation.

[0019] Preferably, the WMS system concludes the entire pickup and delivery process after confirming that the loading task has been completed; if not, it continues to execute the warehouse location planning module to the control system steps in claim 1 until the task is completed.

[0020] Preferably, when the AGV uses an onboard solid-state LiDAR to scan the inside of the carriage, it can obtain the spatial structure information inside the carriage and store it as a carriage template point cloud for subsequent registration with the real-time scanned point cloud.

[0021] Preferably, the cargo placement pose adjustment module can scan the inner wall of the carriage offline to obtain a high-density point cloud and calculate the pose of the cargo placement target point.

[0022] An automated loading and unloading method for vans using unmanned forklifts, the method comprising the following steps:

[0023] 1) Bind multiple truck parking platform areas to the location map;

[0024] 2) Tasks are sent to the WMS system via the pad system;

[0025] 3) The WMS system issues loading tasks to specific platforms based on the received tasks;

[0026] 4) The AGV trolley uses a reflector-based positioning system to reach the designated platform, scans the inside of the carriage, and sends the scanning results to the WMS system;

[0027] 5) Based on the received vehicle scanning results, the WMS system plans the storage location and issues the picking task to the AGV vehicle;

[0028] 6) The AGV trolley performs the pickup operation according to the pickup task, and returns to the designated platform after picking up the goods;

[0029] 7) When the AGV is performing loading tasks, the LiDAR is used to scan the point cloud of the carriage in real time, and the ICP registration algorithm is performed with the pre-acquired template point cloud to obtain the pose of the current target point in real time.

[0030] 8) Calculate the precise pose of the target point based on the real-time obtained target point pose, and control the AGV to reach the target point;

[0031] 9) After the AGV reaches the target point, perform a precise visual task to ensure placement accuracy;

[0032] 10) Once the WMS system confirms that the loading task has been completed, the entire pickup and delivery process will end; if it has not been completed, return to step 7 and continue until the task is completed.

[0033] Preferably, when the AGV is performing a loading task, if there is a deviation in the loading posture or the goods are scratched, the loading posture will be automatically adjusted and the loading operation will be re-executed until the accuracy requirements are met.

[0034] As can be seen from the above technical solutions, this application has the following beneficial effects:

[0035] This invention introduces an AGV that utilizes a solid-state LiDAR-based visual servoing system for navigation inside a van, offering significant advantages. First, the solid-state LiDAR's stable scanning frequency and high-precision ranging capabilities enable the AGV to achieve accurate positioning within the complex van environment. Second, the visual servoing system processes the point cloud data acquired by the LiDAR in real time to generate a 3D map of the van's interior, providing the AGV with a clear navigation path. Furthermore, the system can dynamically adjust the AGV's trajectory based on real-time data, ensuring safe and efficient loading and unloading tasks within confined spaces. Therefore, the solid-state LiDAR-based visual servoing system not only improves the AGV's navigation accuracy and stability but also enhances its adaptability to various van environments.

[0036] This invention employs a solid-state LiDAR-based visual servoing system, which boasts relatively low cost, making its widespread application in the logistics industry possible. Simultaneously, the system's high precision ensures the accuracy of AGVs during loading and unloading tasks, effectively preventing operational errors caused by positioning inaccuracies. More importantly, the system can automatically correct for potential deviations when trucks are parked, meaning that even with slight misalignments, the AGV can accurately find the optimal position for loading and unloading goods through adjustments made by the visual servoing system. This function not only improves loading and unloading efficiency but also reduces the precision requirements for truck parking, further simplifying logistics operations. Therefore, the solid-state LiDAR-based visual servoing system, with its low cost, high precision, and automatic deviation correction capabilities, provides strong support for the automation development of the logistics industry. Attached Figure Description

[0037] Figure 1 is a schematic diagram of the automatic loading process of the van according to the present invention.

[0038] Figure 2 is a schematic diagram of the automatic unloading process of the van of the present invention. Detailed Implementation

[0039] The following description is exemplary in nature and is not intended to limit the scope, application, or use of this disclosure. It should be understood that in all these figures, the same or similar reference numerals indicate the same or similar parts and features. The figures are merely schematic representations of the concept and principles of embodiments of this disclosure and do not necessarily show the specific dimensions and scale of the various embodiments of this disclosure. Certain details or structures of embodiments of this disclosure may be exaggerated in particular portions of certain figures.

[0040] Please refer to Figures 1-2, which illustrate one embodiment of the present invention:

[0041] An automated loading and unloading system for vans using unmanned forklifts, the system comprising:

[0042] The location map module is used to bind multiple truck parking platform areas to the location map;

[0043] The task distribution module is used by drivers to distribute tasks to the WMS system via the pad system;

[0044] The WMS system issues cargo pickup and delivery tasks to the corresponding map locations based on the platform number.

[0045] The AGV (Automated Guided Vehicle) uses a reflector-based positioning system to reach the designated platform, scans the inside of the carriage, and sends the scanning results to the WMS (Warehouse Management System).

[0046] The storage location planning module receives the scanning results of the AGV carriage, plans the storage location according to the carriage space, and sends the picking task to the AGV carriage. The on-board solid-state LiDAR first scans the inside of the carriage, then segments the point cloud inside the carriage to obtain the carriage ground plane. Based on the carriage ground plane, it calculates the coordinates of the four vertices of the carriage ground plane rectangle and the carriage angle. After sending this data to the WMS system, the WMS system will plan the loading method and the quantity of loading goods according to the available space in the carriage and the size of the goods to ensure the maximum utilization of space.

[0047] The AGV (Automated Guided Vehicle) trolley performs the pickup operation according to the pickup task and returns to the designated platform after picking up the goods.

[0048] The visual navigation module, when the AGV performs loading tasks, uses LiDAR to scan the truck bed point cloud in real time and performs ICP registration algorithm with a pre-acquired template point cloud to obtain the pose of the current target point in real time. The onboard solid-state LiDAR first scans the inside of the truck bed, using the truck bed point cloud as a template point cloud, and simultaneously calculates the pose of the target point relative to the vehicle body (specifically, the target point's position is defined by an obstacle on one side of the truck bed, and the angle between the forklift and the truck bed's inner wall is used as the pose). During AGV movement, the onboard solid-state LiDAR scans the inside of the truck bed in real time, performing ICP registration algorithm on the template point cloud and the current point cloud to obtain the pose of the current target point relative to the vehicle body. During visual unloading, due to the forklift carrying goods, the point cloud on the side walls of the truck bed is poor, making it difficult to directly calculate the angle between the vehicle body and the truck bed; therefore, the ICP registration algorithm is used to calculate the pose.

[0049] The pallet posture recognition module, when performing a picking task inside a van, still needs to perform a pallet posture recognition visual task after the AGV reaches the target point to obtain a more accurate pallet pose. First, the pallet is scanned using a solid-state LiDAR, and the point cloud segmentation algorithm is used to obtain the point cloud of the region of interest. Then, the filtered pallet point cloud is obtained based on Euclidean distance clustering. By fitting the pallet plane, the pallet angle and the pallet center coordinates can be obtained.

[0050] The cargo placement posture adjustment module calculates the precise posture of the target point based on the real-time target point posture and controls the AGV to reach the target point. When performing cargo placement tasks inside a van, due to the small gaps between multiple rows of goods and the high precision requirements, the cargo placement posture adjustment task still needs to be performed after the AGV reaches the target point. First, the solid-state LiDAR is used to scan and obtain the point cloud at the target point. Based on the point cloud of the side wall of the van next to the target point or the already placed goods, the precise posture of the target point after adjustment is calculated to maintain the cargo placement gap and avoid the goods from scratching.

[0051] The vision-based delivery module performs precise visual delivery tasks after the AGV arrives at the target point, ensuring delivery accuracy.

[0052] The control system receives the position and pose data of the target point for loading and controls the AGV to perform the loading operation.

[0053] Furthermore, once the WMS system confirms that the loading task has been completed, the entire picking and placing process ends; if not, it continues to execute the warehouse location planning module in claim 1 to the control system steps until the task is completed.

[0054] Furthermore, when the AGV uses its onboard solid-state LiDAR to scan the inside of the vehicle compartment, it can acquire spatial structure information of the compartment and store it as a point cloud template for subsequent registration with the real-time scanned point cloud.

[0055] Furthermore, the cargo placement pose adjustment module can scan the inner wall of the carriage offline to obtain a high-density point cloud and calculate the pose of the cargo placement target point.

[0056] An automated loading and unloading method for vans using unmanned forklifts, the method comprising the following steps:

[0057] 1) Bind multiple truck parking platform areas to the location map;

[0058] 2) Tasks are sent to the WMS system via the pad system;

[0059] 3) The WMS system issues loading tasks to specific platforms based on the received tasks;

[0060] 4) The AGV trolley uses a reflector-based positioning system to reach the designated platform, scans the inside of the carriage, and sends the scanning results to the WMS system;

[0061] 5) Based on the received vehicle scanning results, the WMS system plans the storage location and issues the picking task to the AGV vehicle;

[0062] 6) The AGV trolley performs the pickup operation according to the pickup task, and returns to the designated platform after picking up the goods;

[0063] 7) When the AGV is performing loading tasks, the LiDAR is used to scan the point cloud of the carriage in real time, and the ICP registration algorithm is performed with the pre-acquired template point cloud to obtain the pose of the current target point in real time.

[0064] 8) Calculate the precise pose of the target point based on the real-time obtained target point pose, and control the AGV to reach the target point;

[0065] 9) After the AGV reaches the target point, perform a precise visual task to ensure placement accuracy;

[0066] 10) Once the WMS system confirms that the loading task has been completed, the entire pickup and delivery process will end; if it has not been completed, return to step 7 and continue until the task is completed.

[0067] Furthermore, when the AGV is performing a loading task, if there is a deviation in the loading position or if the goods are scratched, the loading position will be automatically adjusted and the loading operation will be re-executed until the accuracy requirements are met.

[0068] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. An automated loading and unloading system for vans using unmanned forklifts, characterized in that, The system includes: The location map module is used to bind multiple truck parking platform areas to the location map; The task distribution module is used by drivers to distribute tasks to the WMS system via the pad system; The WMS system issues cargo pickup and delivery tasks to the corresponding map locations based on the platform number. The AGV (Automated Guided Vehicle) uses a reflector-based positioning system to reach the designated platform, scans the inside of the carriage, and sends the scanning results to the WMS (Warehouse Management System). The warehouse location planning module receives the carriage scanning results sent by the AGV, plans the warehouse location according to the carriage space, and sends the picking task to the AGV. The AGV (Automated Guided Vehicle) trolley performs the pickup operation according to the pickup task and returns to the designated platform after picking up the goods. The visual private service navigation module uses LiDAR to scan the point cloud of the AGV in real time when the AGV is performing loading tasks. It then performs ICP registration algorithm with the pre-acquired template point cloud to obtain the pose of the current target point in real time. The pallet posture recognition module, when performing a picking task inside a van, still needs to perform a pallet posture recognition visual task after the AGV reaches the target point to obtain a more accurate pallet pose. First, the pallet is scanned using a solid-state LiDAR, and the point cloud segmentation algorithm is used to obtain the point cloud of the region of interest. Then, the filtered pallet point cloud is obtained based on Euclidean distance clustering. By fitting the pallet plane, the pallet angle and the pallet center coordinates can be obtained. The cargo placement pose adjustment module calculates the precise pose of the target point based on the real-time target point pose and controls the AGV to reach the target point. The vision-based delivery module performs precise visual delivery tasks after the AGV arrives at the target point, ensuring delivery accuracy. The control system receives the position and pose data of the target point for loading and controls the AGV to perform the loading operation.

2. The automated loading and unloading system for a van using an unmanned forklift according to claim 1, characterized in that: Once the WMS system confirms that the loading task has been completed, the entire picking and placing process ends; if it has not been completed, the system continues to execute the warehouse location planning module in claim 1 to the control system steps until the task is completed.

3. The automated loading and unloading system for a van using an unmanned forklift according to claim 1, characterized in that: When the AGV uses its onboard solid-state LiDAR to scan the inside of the carriage, it can obtain the spatial structure information inside the carriage and store it as a carriage template point cloud for subsequent registration with the real-time scanned point cloud.

4. The automated loading and unloading system for unmanned forklifts for vans according to claim 1, characterized in that: The cargo placement pose adjustment module can scan the inner wall of the carriage offline to obtain a high-density point cloud and calculate the pose of the cargo placement target point.

5. A method for automatic loading and unloading of box trucks using unmanned forklifts, characterized in that: The method includes the following steps: 1) Bind multiple truck parking platform areas to the location map; 2) Tasks are sent to the WMS system via the pad system; 3) The WMS system issues loading tasks to specific platforms based on the received tasks; 4) The AGV trolley uses a reflector-based positioning system to reach the designated platform, scans the inside of the carriage, and sends the scanning results to the WMS system; 5) Based on the received vehicle scanning results, the WMS system plans the storage location and issues the picking task to the AGV vehicle; 6) The AGV trolley performs the pickup operation according to the pickup task, and returns to the designated platform after picking up the goods; 7) When the AGV is performing loading tasks, the LiDAR is used to scan the point cloud of the carriage in real time, and the ICP registration algorithm is performed with the pre-acquired template point cloud to obtain the pose of the current target point in real time. 8) Calculate the precise pose of the target point based on the real-time obtained target point pose, and control the AGV to reach the target point; 9) After the AGV reaches the target point, perform a precise visual task to ensure placement accuracy; 10) Once the WMS system confirms that the loading task has been completed, the entire pickup and delivery process will end; if it has not been completed, return to step 7 and continue until the task is completed.

6. The automatic loading and unloading method for a van using an unmanned forklift according to claim 5, characterized in that: When the AGV performs the loading task, if there is a deviation in the loading posture or the goods are scratched, the loading posture will be automatically adjusted and the loading operation will be re-executed until the accuracy requirements are met.

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