Part sorting method, device and equipment, storage medium and computer program product
By scanning and adjusting the position of the target container, point cloud data is generated to identify and sort parts, solving the problem of high part missing rate in disordered stacking scenarios of traditional vision systems and realizing efficient part sorting.
Patent Information
- Application Number
- CN202511765816.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-27
- Publication Date
- 2026-02-24
AI Technical Summary
Traditional vision systems have low recognition rates in scenarios with disordered stacking of parts, resulting in a high rate of missed parts.
Point cloud data is generated by scanning the target container, unidentifiable parts are identified and their positions are adjusted to generate second point cloud data, and the position and orientation information of the parts are determined based on the second point cloud data for sorting.
It enables effective identification and positioning of parts in disordered scenarios, reduces the rate of missed picking, and requires no manual intervention.
Smart Images
Figure CN121551286A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of visual processing technology, and in particular to a parts sorting method, apparatus, equipment, storage medium, and computer program product. Background Technology
[0002] Currently, traditional vision systems often rely on fixed arrangement of parts (such as internal material separation and neat posture) when sorting parts, which cannot handle scenarios where parts are stacked disorderly in parts bins. Therefore, the recognition rate is low and it is easy to miss parts. Based on this, the industry urgently needs a method to reduce the missed part rate when sorting parts. Summary of the Invention
[0003] The main objective of this application is to provide a parts sorting method, apparatus, equipment, storage medium, and computer program product, which aims to solve the technical problem of high parts missing rate in existing parts sorting methods.
[0004] To achieve the above objectives, this application provides a parts sorting method, the method comprising the following steps: The target container is scanned to obtain the first point cloud data, and the target container contains several parts to be sorted; If, based on the first point cloud data, it is determined that there are unidentifiable parts among the parts to be sorted, the position of the unidentifiable parts is adjusted and the target container is scanned again to obtain the second point cloud data. Based on the second point cloud data, the location and orientation information of the parts to be sorted are determined, and the parts to be sorted are sorted according to the location and orientation information.
[0005] In one embodiment, after the step of scanning the target container to obtain the first point cloud data, the method further includes: Obtain the target point cloud data corresponding to the part to be sorted from the first point cloud data; The target point cloud data is matched with the sample point cloud data, and the matching result is used to determine whether there are any unidentifiable parts among the parts to be sorted. The sample point cloud data refers to the point cloud data of the parts to be sorted from different perspectives, and the sample point cloud data is pre-stored in the parts sample database.
[0006] In one embodiment, the step of determining whether there are unidentifiable parts among the parts to be sorted based on the matching results includes: If the target point cloud data contains point cloud data that does not match any of the sample point cloud data, then it is determined that there are unidentifiable parts among the parts to be sorted. If there is no point cloud data in the target point cloud data that does not match any of the sample point cloud data, then it is determined that there are no unidentifiable parts among the parts to be sorted.
[0007] In one embodiment, after the step of scanning the target container to obtain the first point cloud data, the method further includes: If it is determined from the first point cloud data that there are no unidentifiable parts among the parts to be sorted, then the position information and attitude information of the parts to be sorted are determined based on the first point cloud data, and the parts to be sorted are sorted according to the position information and the attitude information.
[0008] In one embodiment, the step of sorting the parts to be sorted based on the location information and the attitude information includes: A target sorting plan is generated based on the location information and the attitude information, and the execution order of the target sorting plan is determined based on the sorting priority of the parts to be sorted. The target sorting plan is executed sequentially according to the execution order to sort the parts to be sorted.
[0009] In one embodiment, after the step of sorting the parts to be sorted based on the location information and the attitude information, the method further includes: Once the target sorting plan has been fully executed, the target container is scanned to obtain the third point cloud data; If it is determined from the third point cloud data that there are missing parts in the target container, then the missing parts are sorted. If it is determined from the third point cloud data that there are no missing parts in the target container, then it is determined that all the parts to be sorted have been sorted.
[0010] Furthermore, to achieve the above objectives, this application also proposes a parts sorting device, which includes: The first scanning module is used to scan the target container to obtain the first point cloud data. The target container contains several parts to be sorted. The second scanning module is used to adjust the position of the unidentifiable part and then scan the target container again to obtain the second point cloud data if it is determined from the first point cloud data that there is an unidentifiable part among the parts to be sorted. The parts sorting module is used to determine the position and orientation information of the parts to be sorted based on the second point cloud data, and to sort the parts to be sorted according to the position and orientation information.
[0011] In addition, to achieve the above objectives, this application also proposes a parts sorting device, the device comprising: a memory, a processor, and a parts sorting program stored in the memory and executable on the processor, the parts sorting program being configured to implement the steps of the parts sorting method as described above.
[0012] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, storing a parts sorting program, which, when executed by a processor, implements the steps of the parts sorting method described above.
[0013] In addition, to achieve the above objectives, the present invention also provides a computer program product, the computer program product including a parts sorting program, which, when executed by a processor, implements the steps of the parts sorting method as described above.
[0014] This application scans a target container to obtain first point cloud data, the target container containing several parts to be sorted; if, based on the first point cloud data, it is determined that there are unidentifiable parts among the parts to be sorted, the position of the unidentifiable parts is adjusted and the target container is scanned again to obtain second point cloud data; based on the second point cloud data, the position and orientation information of the parts to be sorted are determined, and the parts to be sorted are sorted according to the position and orientation information. In this method, after the initial scanning of the target container to generate the first point cloud data, if there are unidentifiable parts, the system adjusts their orientation by adjusting their position so that the parts can be more clearly presented during rescanning; then, based on the second point cloud data, the position and orientation information of the parts are accurately extracted, thereby ensuring that the parts to be sorted in the target container are effectively identified and located. This process eliminates missed sorting caused by poor initial part orientation, and achieves comprehensive sorting without relying on manual intervention, thus solving the problem of high part missed sorting rate due to part stacking and chaotic orientation in disordered scenarios. Attached Figure Description
[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0017] Figure 1 This is a flowchart illustrating the first embodiment of the parts sorting method of this application; Figure 2 This is a schematic diagram of the parts sorting equipment for the parts sorting method of this application; Figure 3 This is a flowchart illustrating the second embodiment of the parts sorting method of this application; Figure 4 This is a schematic diagram of sample point cloud data for the parts sorting method of this application; Figure 5 This is a flowchart illustrating the third embodiment of the parts sorting method of this application; Figure 6 This is a structural block diagram of the first embodiment of the parts sorting device of this application; Figure 7 This is a schematic diagram of the structure of a parts sorting device in the hardware operating environment involved in the embodiments of this application.
[0018] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0019] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.
[0020] It should be noted that the executing entity of the embodiments of this application can be a computing service device with data processing, network communication, and program execution functions, such as a smart terminal, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as the parts sorting device described above. The following embodiments will be described using a parts sorting device as an example.
[0021] This application provides a parts sorting method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the parts sorting method of this application.
[0022] In this embodiment, the parts sorting method includes the following steps: Step S10: Scan the target container to obtain the first point cloud data. The target container contains several parts to be sorted.
[0023] It should be noted that the aforementioned target container is a container that can be used for classifying, storing, and transporting standardized components, such as component boxes, material bins, and cargo boxes. The aforementioned parts to be sorted refer to industrial parts placed in the target container that need to be identified, located, and grasped by the aforementioned parts sorting equipment, such as bolts, nuts, and washers. The aforementioned first point cloud data is a three-dimensional point set data generated by the aforementioned parts sorting equipment through non-contact scanning of the target container, containing the spatial coordinate information of the parts to be sorted. Furthermore, the aforementioned parts to be sorted can be arranged randomly or in an ordered manner; this embodiment does not impose any restrictions on this.
[0024] It should be understood that the aforementioned parts sorting equipment may include an industrial-grade 3D camera with a short point cloud acquisition time (e.g., 0.38 seconds). In actual operation, the aforementioned industrial-grade 3D camera can be used to scan the target container to achieve 3D digital model extraction and imaging, thereby obtaining the aforementioned first point cloud data.
[0025] Step S20: If it is determined from the first point cloud data that there are unidentifiable parts among the parts to be sorted, then the position of the unidentifiable parts is adjusted and the target container is scanned again to obtain the second point cloud data.
[0026] It should be noted that the aforementioned unidentifiable parts may be those parts that are obscured due to stacking, preventing the parts sorting equipment from accurately identifying them. The second point cloud data mentioned above is a three-dimensional point set data generated by the parts sorting equipment through non-contact scanning of the target container, which also contains the spatial coordinate information of the parts to be sorted.
[0027] It should be understood that the aforementioned first point cloud data can be input into a pre-trained deep learning neural network model (such as a segmentation and classification network for 3D objects) to identify the position, orientation, and type of the parts to be sorted. This enables the model to determine whether there are any unidentifiable parts among the parts to be sorted based on their position, orientation, and type. The deep learning neural network model has previously learned a large amount of 3D data of parts to be sorted in various orientations.
[0028] Furthermore, the aforementioned parts sorting equipment may also include electromagnetic grippers. When it is determined that there are unidentifiable parts among the parts to be sorted, the electromagnetic grippers can be used to adjust the position of the parts. For example, the electromagnetic grippers can be used to gently move the part or push away any obstructions on it; the electromagnetic grippers can also pick up the part and move it to an open space before putting it down.
[0029] Step S30: Determine the position and orientation information of the parts to be sorted based on the second point cloud data, and sort the parts according to the position and orientation information.
[0030] It should be noted that the above location information can be the three-dimensional spatial coordinates of the parts to be sorted, and the above posture information can be the orientation or rotation angle of the parts to be sorted in space.
[0031] In practical implementation, the movement trajectory of the electromagnetic gripper can be determined based on the position information of the parts to be sorted, so that the electromagnetic gripper can move to the target position accurately with the correct posture; the optimal gripping point and gripping direction can be selected based on the posture information of the parts to be sorted, so as to ensure the stability and efficiency of gripping; and the angle of the electromagnetic gripper can be adjusted during the movement based on the posture information, so that it is in the best gripping posture when contacting the parts to be sorted.
[0032] Further, please refer to Figure 2 , Figure 2 This is a schematic diagram of the parts sorting equipment used in the parts sorting method of this application. Figure 2 As shown, in this embodiment, a 3D camera scans the target container containing parts to be sorted, and a six-axis assisted robot controls an electromagnetic gripper to sort the parts.
[0033] This embodiment scans a target container to obtain first point cloud data. The target container contains several parts to be sorted. If, based on the first point cloud data, it is determined that there are unidentifiable parts among the parts to be sorted, the position of the unidentifiable parts is adjusted, and the target container is scanned again to obtain second point cloud data. Based on the second point cloud data, the position and orientation information of the parts to be sorted are determined, and the parts to be sorted are sorted according to the position and orientation information. In this embodiment, after the initial scanning of the target container generates the first point cloud data, if there are unidentifiable parts, the system adjusts their orientation by adjusting their position so that the parts can be more clearly presented during rescanning. Then, based on the second point cloud data, the position and orientation information of the parts are accurately extracted, thereby ensuring that the parts to be sorted in the target container are effectively identified and located. This process eliminates missed sorting caused by poor initial orientation of parts, and can achieve comprehensive sorting without relying on manual intervention, thus solving the problem of high missed sorting rate caused by parts stacking and messy orientation in disordered scenarios.
[0034] Reference Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the parts sorting method of this application.
[0035] In one feasible implementation, after step S10, the following may also be included: Step S11: Obtain the target point cloud data corresponding to the part to be sorted from the first point cloud data.
[0036] Step S12: Match the target point cloud data with the sample point cloud data, and determine whether there are any unidentifiable parts among the parts to be sorted based on the matching results.
[0037] The sample point cloud data refers to the point cloud data of the parts to be sorted from different perspectives, and this sample point cloud data is pre-stored in a part sample database. For details, please refer to... Figure 4 , Figure 4 This is a schematic diagram of sample point cloud data for the parts sorting method of this application.
[0038] In practical implementation, point cloud segmentation can be performed based on deep learning instance segmentation algorithms (such as PointGroup or PointVote). This algorithm identifies individual part instances through clustering and semantic segmentation, and utilizes Euclidean clustering to optimize segmentation accuracy and reduce noise interference. Simultaneously, multi-scale feature extraction networks (such as PointNet++) can be introduced to enhance adaptability to irregularly shaped parts, ensuring accurate separation of parts in stacked scenarios.
[0039] In one feasible implementation, step S12 may include: Step S121: If there are point cloud data in the target point cloud data that do not match the sample point cloud data, then it is determined that there are unidentifiable parts in the parts to be sorted.
[0040] Step S122: If there is no point cloud data in the target point cloud data that does not match any of the sample point cloud data, then it is determined that there are no unidentifiable parts in the parts to be sorted.
[0041] In practical implementation, a similarity confidence score (e.g., using feature matching distance or the probability output by the model) can be set for the matching process between each target point cloud data and sample point cloud data. For example, a dynamic confidence threshold (e.g., 0.85) can be set. Only when the matching confidence of all target point cloud data with at least one sample point cloud data is higher than this threshold is it determined that there are no unidentifiable parts among the parts to be sorted. Otherwise, if the matching confidence of any target is lower than the threshold, it is determined that there are unidentifiable parts among the parts to be sorted.
[0042] In one feasible implementation, after step S10, the following may also be included: Step S21: If it is determined from the first point cloud data that there are no unidentifiable parts among the parts to be sorted, then the position information and attitude information of the parts to be sorted are determined based on the first point cloud data, and the parts to be sorted are sorted according to the position information and the attitude information.
[0043] In practical implementation, once the position and orientation information of the parts to be sorted are determined, it can be done through... Figure 2 The six-axis assisted robot shown plans an initial path based on position and posture information, combines multi-sensor real-time obstacle avoidance and dynamic path adjustment, and adaptively completes parts sorting based on the priority level of grasping success rate (visual positioning + force feedback).
[0044] In this embodiment, target point cloud data corresponding to the part to be sorted is obtained from the first point cloud data; the target point cloud data is matched with sample point cloud data. If there are point cloud data in the target point cloud data that do not match any of the sample point cloud data, it is determined that there are unidentifiable parts among the parts to be sorted; if there are no point cloud data in the target point cloud data that do not match any of the sample point cloud data, it is determined that there are no unidentifiable parts among the parts to be sorted; wherein, the sample point cloud data is the point cloud data of the part to be sorted from different perspectives, and the sample point cloud data is pre-stored in a part sample database; if it is determined from the first point cloud data that there are no unidentifiable parts among the parts to be sorted, the position information and attitude information of the part to be sorted are determined based on the first point cloud data, and the part to be sorted is sorted according to the position information and the attitude information. The method described in this embodiment utilizes a pre-constructed multi-view sample point cloud database, thus eliminating the need for time-consuming manual on-site sample collection as in traditional methods, thereby achieving automation and standardization of sample identification. Furthermore, when it is determined that there are no unidentifiable parts among the parts to be sorted, the precise position and orientation of the parts are determined based on the first point cloud data, and sorting is driven. This achieves fully automated sorting based on reliable identification, effectively avoiding the high residual inspection rate and manual re-inspection requirements caused by identification failures in traditional methods.
[0045] Reference Figure 5 , Figure 5 This is a flowchart illustrating the third embodiment of the parts sorting method of this application.
[0046] In one feasible implementation, step S30 may include: Step S301: Generate a target sorting plan based on the location information and the attitude information, and determine the execution order of the target sorting plan based on the sorting priority of the parts to be sorted.
[0047] Step S302: Execute the target sorting plan sequentially according to the execution order to sort the parts to be sorted.
[0048] It should be noted that the above-mentioned target sorting plan can be a robot motion path and grasping action sequence generated based on position and posture information, including specific instructions such as path point coordinates and gripper opening and closing timing. The sorting priority can be customized, for example, based on the fragility, value, and sorting difficulty of the parts to be sorted. This embodiment does not impose any restrictions on this.
[0049] In practical implementation, AI algorithms (such as instance segmentation and deep learning), vision systems, and robot data interconnection can be combined to enable the robot to automatically plan paths from all directions and multiple angles (response latency < 0.1 seconds) and intelligently avoid obstacles. By integrating vision (localization) and force sensing (contact feedback), the robot can identify the position, posture, and grasping priority of workpieces and dynamically avoid obstacles, thereby achieving disordered parts picking.
[0050] In one feasible implementation, after step S30, the following may also be included: Step S40: After all the target sorting plans have been executed, the target container is scanned to obtain the third point cloud data.
[0051] Step S50: If it is determined from the third point cloud data that there are missing parts in the target container, then the missing parts are sorted.
[0052] Step S60: If it is determined from the third point cloud data that there are no missing parts in the target container, then it is determined that all the parts to be sorted have been sorted.
[0053] It should be noted that the aforementioned third point cloud data is a three-dimensional point set data generated by the aforementioned parts sorting equipment through non-contact scanning of the target container, and it also contains the spatial coordinate information of the parts to be sorted. The aforementioned missing parts refer to the parts in the target container that were not sorted after the operations of steps S301 and S302.
[0054] This embodiment generates a target sorting plan based on the location and attitude information, and determines the execution order of the target sorting plan according to the sorting priority of the parts to be sorted. The target sorting plan is executed sequentially according to the execution order to sort the parts to be sorted. After all target sorting plans have been executed, the target container is scanned to obtain third point cloud data. If the third point cloud data indicates that there are missing parts in the target container, the missing parts are sorted. If the third point cloud data indicates that there are no missing parts in the target container, it is determined that all parts to be sorted have been sorted. This embodiment generates a target sorting plan based on location and attitude information and determines the execution order according to priority, ensuring that sorting operations are executed in the optimal sequence in disordered scenarios, thereby improving sorting efficiency. Simultaneously, after the target sorting plan is executed, the target container is scanned to obtain third point cloud data. Different processing methods are adopted based on the determination result of the third point cloud data: if there are missing parts, supplementary sorting is performed; if not, sorting is confirmed as complete, thereby ensuring that no parts are ultimately missed.
[0055] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the parts sorting device of this application.
[0056] like Figure 6 As shown, the parts sorting device proposed in this application includes: The first scanning module 601 is used to scan the target container to obtain the first point cloud data. The target container contains several parts to be sorted. The second scanning module 602 is used to adjust the position of the unidentifiable part and then scan the target container again to obtain the second point cloud data if it is determined from the first point cloud data that there is an unidentifiable part among the parts to be sorted. The parts sorting module 603 is used to determine the position information and attitude information of the parts to be sorted based on the second point cloud data, and to sort the parts to be sorted according to the position information and the attitude information.
[0057] This embodiment scans a target container to obtain first point cloud data. The target container contains several parts to be sorted. If, based on the first point cloud data, it is determined that there are unidentifiable parts among the parts to be sorted, the position of the unidentifiable parts is adjusted, and the target container is scanned again to obtain second point cloud data. Based on the second point cloud data, the position and orientation information of the parts to be sorted are determined, and the parts to be sorted are sorted according to the position and orientation information. In this embodiment, after the initial scanning of the target container generates the first point cloud data, if there are unidentifiable parts, the system adjusts their orientation by adjusting their position so that the parts can be more clearly presented during rescanning. Then, based on the second point cloud data, the position and orientation information of the parts are accurately extracted, thereby ensuring that the parts to be sorted in the target container are effectively identified and located. This process eliminates missed sorting caused by poor initial orientation of parts, and can achieve comprehensive sorting without relying on manual intervention, thus solving the problem of high missed sorting rate caused by parts stacking and messy orientation in disordered scenarios.
[0058] Based on the first embodiment of the parts sorting device described in this application, a second embodiment of the parts sorting device of this application is proposed.
[0059] In this embodiment, the first scanning module 601 is further configured to obtain target point cloud data corresponding to the part to be sorted from the first point cloud data; match the target point cloud data with sample point cloud data, and determine whether there are any unidentifiable parts among the parts to be sorted based on the matching result; wherein, the sample point cloud data is the point cloud data of the part to be sorted under different perspectives, and the sample point cloud data is pre-stored in the part sample database.
[0060] Furthermore, the first scanning module 601 is also used to determine that there are unidentifiable parts among the parts to be sorted if there are point cloud data in the target point cloud data that do not match the sample point cloud data; and to determine that there are no unidentifiable parts among the parts to be sorted if there are no point cloud data in the target point cloud data that do not match the sample point cloud data.
[0061] Furthermore, the first scanning module 601 is also configured to, if it is determined from the first point cloud data that there are no unidentifiable parts among the parts to be sorted, determine the position information and attitude information of the parts to be sorted based on the first point cloud data, and sort the parts to be sorted based on the position information and the attitude information.
[0062] Furthermore, the parts sorting module 603 is also used to generate a target sorting plan based on the position information and the posture information, and to determine the execution order of the target sorting plan based on the sorting priority of the parts to be sorted; and to execute the target sorting plan in sequence according to the execution order to sort the parts to be sorted.
[0063] Furthermore, the parts sorting module 603 is also used to scan the target container to obtain third point cloud data after the target sorting plan has been fully executed; if it is determined from the third point cloud data that there are missing parts in the target container, then the missing parts are sorted; if it is determined from the third point cloud data that there are no missing parts in the target container, then it is determined that all the parts to be sorted have been sorted.
[0064] Other embodiments or specific implementations of the parts sorting device of this application can be found in the above-described method embodiments, and will not be repeated here.
[0065] This application provides a parts sorting device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the parts sorting method in the first embodiment described above.
[0066] The following reference Figure 7 The diagram illustrates a structural schematic suitable for implementing the parts sorting equipment of the embodiments of this application. The parts sorting equipment in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), vehicle terminals (e.g., vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The parts sorting equipment shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.
[0067] like Figure 7As shown, the parts sorting equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the parts sorting equipment. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the parts sorting equipment to communicate wirelessly or wiredly with other equipment to exchange data. Although parts sorting equipment with various systems is shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.
[0068] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0069] The parts sorting equipment provided in this application, employing the parts sorting method described in the above embodiments, can solve the technical problem of high parts omission rate in existing parts sorting methods. Compared with the prior art, the beneficial effects of the parts sorting equipment provided in this application are the same as those of the parts sorting method provided in the above embodiments, and other technical features of this parts sorting equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.
[0070] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.
[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0072] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the parts sorting method in the above embodiments.
[0073] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0074] The aforementioned computer-readable storage medium may be included in the parts sorting equipment; or it may exist independently and not be assembled into the parts sorting equipment.
[0075] The aforementioned computer-readable storage medium carries one or more programs that, when executed by the parts sorting equipment, enable the parts sorting equipment to write computer program code for performing the operations of this application in one or more programming languages or a combination thereof. These programming languages include object-oriented programming languages such as Java, Smalltalk, and C++; and also conventional procedural programming languages such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (e.g., via the Internet using an Internet service provider).
[0076] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0077] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0078] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described parts sorting method, which can solve the technical problem of high parts omission rate in existing parts sorting methods. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the parts sorting method provided in the above embodiments, and will not be repeated here.
[0079] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the parts sorting method described above.
[0080] The computer program product provided in this application can solve the technical problem of parts sorting. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the parts sorting method provided in the above embodiments, and will not be repeated here.
[0081] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.
Claims
1. A method for sorting parts, characterized in that, The method includes the following steps: The target container is scanned to obtain the first point cloud data, and the target container contains several parts to be sorted; If, based on the first point cloud data, it is determined that there are unidentifiable parts among the parts to be sorted, the position of the unidentifiable parts is adjusted and the target container is scanned again to obtain the second point cloud data. Based on the second point cloud data, the location and orientation information of the parts to be sorted are determined, and the parts to be sorted are sorted according to the location and orientation information.
2. The parts sorting method as described in claim 1, characterized in that, After the step of scanning the target container to obtain the first point cloud data, the method further includes: Obtain target point cloud data corresponding to the part to be sorted from the first point cloud data; The target point cloud data is matched with the sample point cloud data, and the matching result is used to determine whether there are any unidentifiable parts among the parts to be sorted. The sample point cloud data refers to the point cloud data of the parts to be sorted from different perspectives, and the sample point cloud data is pre-stored in the parts sample database.
3. The parts sorting method as described in claim 2, characterized in that, The step of determining whether there are unidentifiable parts among the parts to be sorted based on the matching results includes: If the target point cloud data contains point cloud data that does not match any of the sample point cloud data, then it is determined that there are unidentifiable parts among the parts to be sorted. If there is no point cloud data in the target point cloud data that does not match any of the sample point cloud data, then it is determined that there are no unidentifiable parts among the parts to be sorted.
4. The parts sorting method as described in claim 3, characterized in that, After the step of scanning the target container to obtain the first point cloud data, the method further includes: If it is determined from the first point cloud data that there are no unidentifiable parts among the parts to be sorted, then the position information and attitude information of the parts to be sorted are determined based on the first point cloud data, and the parts to be sorted are sorted according to the position information and the attitude information.
5. The parts sorting method as described in claim 4, characterized in that, The step of sorting the parts to be sorted according to the location information and the attitude information includes: A target sorting plan is generated based on the location information and the attitude information, and the execution order of the target sorting plan is determined based on the sorting priority of the parts to be sorted. The target sorting plan is executed sequentially according to the execution order to sort the parts to be sorted.
6. The parts sorting method as described in claim 5, characterized in that, After the step of sorting the parts to be sorted according to the location information and the attitude information, the method further includes: Once the target sorting plan has been fully executed, the target container is scanned to obtain the third point cloud data; If it is determined from the third point cloud data that there are missing parts in the target container, then the missing parts are sorted. If it is determined from the third point cloud data that there are no missing parts in the target container, then it is determined that all the parts to be sorted have been sorted.
7. A parts sorting device, characterized in that, The parts sorting device includes: The first scanning module is used to scan the target container to obtain the first point cloud data. The target container contains several parts to be sorted. The second scanning module is used to adjust the position of the unidentifiable part and then scan the target container again to obtain the second point cloud data if it is determined from the first point cloud data that there is an unidentifiable part among the parts to be sorted. The parts sorting module is used to determine the position and orientation information of the parts to be sorted based on the second point cloud data, and to sort the parts according to the position and orientation information.
8. A parts sorting device, characterized in that, The device includes: a memory, a processor, and a parts sorting program stored in the memory and executable on the processor, the parts sorting program being configured to implement the steps of the parts sorting method as described in any one of claims 1 to 6.
9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the storage medium stores a parts sorting program, which, when executed by a processor, implements the steps of the parts sorting method as described in any one of claims 1 to 6.
10. A computer program product, characterized in that, The computer program product includes a parts sorting program, which, when executed by a processor, implements the steps of the parts sorting method as described in any one of claims 1 to 6.