Agricultural robot adaptive end effector system and method based on in-situ additive manufacturing

Abstract

The application discloses an agricultural robot adaptive end execution system and method based on field additive manufacturing, and comprises the following steps: S1, scanning and acquiring the form of target crops to obtain key characteristic parameters; S2, comparing the crop characteristic parameters with the performance parameters of the current end execution device to determine whether the two are adaptive; if yes, directly performing a grabbing operation, and if not, triggering an online customization process; S3, generating a flexible clamp unit three-dimensional model adaptive to the target crops through a parameterized design algorithm based on the crop characteristic parameters, and printing and generating the adaptive flexible clamp unit through field additive manufacturing; and S4, completing the unloading and recycling of old clamps and the locking and reloading of new clamps, and returning to a work point to perform an adaptive nondestructive grabbing operation. Compared with the prior art, the application has the advantages that a kind of agricultural robot adaptive end execution system and method based on field additive manufacturing is provided, which has strong operation universality, reduces fruit damage, and increases work efficiency.

Description

Technical Field

[0001] This invention relates to the field of agricultural automation equipment technology, specifically to an adaptive end effector system and method for agricultural robots based on on-site additive manufacturing. Background Technology

[0002] Fruit and vegetable harvesting is one of the most labor-intensive and time-consuming parts of agricultural production.

[0003] Currently, agricultural robots have begun to be applied to the automated harvesting and sorting of cash crops such as tomatoes, strawberries, and apples.

[0004] In agricultural robot systems, the end effector is the core component that comes into direct contact with the crop, and its performance directly determines the success rate of harvesting and the integrity of the fruit.

[0005] Currently, the mainstream solutions for fruit and vegetable picking in unstructured environments (such as complex farmland) can be mainly divided into the following two categories:

[0006] The first type is a general-purpose flexible clamp. These clamps are usually made of highly elastic materials such as silicone and PDMS. They use pneumatic networks or wire drives to achieve bending and deformation. By inflating or pulling the wire, the soft fingers cover the target object. Because the material itself is flexible, it can adapt to fruits of different shapes to a certain extent and is not easy to cause mechanical damage.

[0007] The second type is an automatic tool changer system with a tool magazine. This is a solution transplanted from industrial robot systems. The robot carries a limited "tool magazine" or has a fixed tool magazine in the field. When the robot needs to handle different tasks (such as switching from picking tomatoes to pruning branches), the robotic arm moves to the tool magazine, removes the current end effector through a quick-change interface, and installs another prefabricated end effector.

[0008] Although the above solutions have solved the problem of automated harvesting to some extent, they still have problems in practical applications, such as poor adaptability, downtime maintenance due to high-frequency losses, and lack of on-site responsiveness.

[0009] In summary, current technologies lack a device capable of generating and manufacturing end effectors online based on the real-time morphological characteristics of crops in the field. How to endow agricultural robots with the capabilities of "self-repair" and "on-demand tool customization" is a key technical problem that urgently needs to be solved in the field of agricultural robotics. Summary of the Invention

[0010] The technical problem to be solved by the present invention is to overcome the above-mentioned technical defects and provide an adaptive end effector system and method for agricultural robots based on on-site additive manufacturing, which has strong operational versatility, reduces fruit damage, and increases work efficiency.

[0011] To solve the above-mentioned technical problems, the technical solution provided by the present invention is: an adaptive end effector method for agricultural robots based on on-site additive manufacturing, comprising the following steps:

[0012] S1: Scan to obtain the morphology of the target crop and acquire key feature parameters;

[0013] S2: Compare the crop characteristic parameters with the current end effector's performance parameters to determine if they are compatible;

[0014] If the data is compatible, the crawling task will be executed directly; otherwise, an online customization process will be triggered.

[0015] S3: Based on crop characteristic parameters, a three-dimensional model of a flexible clamping unit adapted to the target crop is generated through a parametric design algorithm, and the adapted flexible clamping unit is generated through on-site additive manufacturing printing.

[0016] S4: Complete the unloading and recycling of the old fixture and the locking and installation of the new fixture, then return to the work point to perform adaptive non-destructive gripping operations.

[0017] Preferably, the key feature parameters in S1 include crop size, surface curvature, estimated hardness, and epidermal vulnerability.

[0018] The adaptation judgment logic in S2 is as follows:

[0019] If the current clamp opening is smaller than the crop size or the clamp stiffness does not match the crop's epidermal vulnerability, it is determined to be an incompatible fit.

[0020] Preferably, the three-dimensional model in S3 is generated parametrically, establishing a mapping relationship between crop features and fixture parameters, including:

[0021] Crop size is mapped to the length of the clamp finger segment and the diameter of the open envelope.

[0022] The curvature of the crop surface is mapped to the arc of the clamp contact surface.

[0023] Crop epidermal vulnerability is mapped to the lattice filling density inside the fixture.

[0024] Preferably, the parametric design in S3 includes:

[0025] Select the corresponding basic topology skeleton based on the overall outline of the target crop;

[0026] The fixture morphology evolution is completed based on the mapped parameters, and a micro-anti-slip texture is generated along the curved UV direction of the inner contact surface of the fixture.

[0027] Based on the vulnerability of the epidermis, a variable density lattice layout design was completed. A low-density flexible lattice structure was set in the fingertip area where the clamp contacts the crop, and a high-density rigid support structure was set at the root of the clamp connection.

[0028] Preferably, in S3, on-site additive manufacturing uses flexible consumables to complete the printing process;

[0029] S4 includes unlocking, unloading, and locking / replacing the clamp using a standardized quick-change locking structure.

[0030] Another aspect of this invention discloses an adaptive end effector system for agricultural robots based on on-site additive manufacturing, comprising a mobile operation robot with wireless communication connection and an intelligent additive manufacturing base station in the field;

[0031] The mobile operation robot also integrates a multi-degree-of-freedom robotic arm, a visual perception module, an edge computing unit, and a quick-change locking mechanism.

[0032] The field-based intelligent additive manufacturing base station includes an additive manufacturing unit, a consumable storage bin, a finished product exchange platform, and a used parts recycling bin.

[0033] The additive manufacturing unit manufactures the corresponding end effector based on feedback data from the edge computing unit.

[0034] Preferably, the end effector of the multi-degree-of-freedom robotic arm is connected to the end effector via a quick-change locking mechanism, and the end effector includes a root structure and a fingertip structure;

[0035] The root structure is equipped with a corresponding male connector to cooperate with the quick-change locking mechanism.

[0036] Preferably, the edge computing unit extracts key physical parameters of the crop, including size, hardness estimates, and epidermal vulnerability, to generate a three-dimensional model of the fixture as feedback data;

[0037] The additive manufacturing unit is either an FDM fused deposition modeling 3D printer or an SLA photopolymerization 3D printer that is compatible with flexible consumables.

[0038] Preferably, the edge computing unit has a built-in parametric design algorithm library that stores a basic topological skeleton white model adapted to different crop contours.

[0039] Preferably, the mobile operation robot is either a tracked robot or a wheeled robot.

[0040] The advantages of this invention compared to the prior art are:

[0041] This invention breaks through the hardware limitations of traditional agricultural robot end-effectors, which are pre-designed at the factory. It enables hardware to be software-based, allowing for the generation of end-effectors that can be adapted to crops of any shape and size. This completely solves the problem of poor versatility of traditional grippers, significantly expands the application boundaries of robots, and allows for the customization of differentiated flexible lattice structures. This greatly reduces the rate of mechanical damage to fruits and significantly improves the appearance and economic value of agricultural products. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of an adaptive end effector system for agricultural robots based on on-site additive manufacturing.

[0043] Figure 2 This is a schematic diagram of the adaptive end effector structure.

[0044] Figure 3 This is a flowchart illustrating an adaptive end effector method for agricultural robots based on on-site additive manufacturing.

[0045] Figure 4 This is a schematic diagram of the end effector replacement structure. Detailed Implementation

[0046] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. Identical components are indicated by the same reference numerals.

[0047] It should be noted that the terms “front,” “back,” “left,” “right,” “up,” and “down” used in the following description refer to the directions shown in the attached diagram, while the terms “inside” and “outside” refer to the directions toward or away from the geometric center of a specific component, respectively.

[0048] To make the content of this invention easier to understand, the technical solutions in the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings.

[0049] Combined with appendix Figure 1 As shown, this invention provides an adaptive end effector system for agricultural robots based on on-site additive manufacturing, which mainly consists of the following two core components:

[0050] Mobile Operation Robot 100:

[0051] The robot is equipped with an autonomous navigation chassis (such as tracked or wheeled) and can adapt to unstructured terrain in the field.

[0052] The robot is equipped with a multi-degree-of-freedom robotic arm, whose front end integrates a depth vision camera and a force sensor for scanning the physical features of crop 200 in multiple dimensions.

[0053] The robot integrates an edge computing module to process visual data and communicate with the base station in real time.

[0054] 300 Field Additive Manufacturing and Maintenance Base Stations

[0055] Base stations are deployed in the ridges or edge areas of farmland, serving as the main control and maintenance unit of the system.

[0056] The base station integrates an additive manufacturing unit 301 (i.e., a 3D printer), which preferably uses FDM (Fused Deposition Modeling) or SLA (Stereolithography) technology, specifically for the rapid manufacturing of flexible material components.

[0057] The base station is also equipped with a hopper for storing spare consumables, a recycling bin 320 for recycling discarded fixtures, and an automatic positioning interface for assisting robots in replacing their end effectors.

[0058] like Figure 2 As shown, the end effector of this invention adopts a modular layered design, which consists of the following layers from top to bottom:

[0059] Robotic arm flange interface 110:

[0060] As a standard connector, it is fixed to the end of the robot arm and is responsible for transmitting power and control signals.

[0061] Quick-change locking mechanism 120:

[0062] It employs an industrial-grade pneumatic or electromagnetic quick-change device. Its upper part connects to a flange interface, and its lower part is equipped with standardized mechanical locking jaws and circuit contacts. This mechanism allows the robot to complete the physical separation and assembly of the end effector within seconds.

[0063] 3D Printed Flexible Grip Unit 130:

[0064] This is the core consumable component of the present invention. It is not a universal standard part, but is printed on-site according to the real-time needs of the work object, including:

[0065] Root structure: Designed with a standard male connector that is fully compatible with the quick-change locking mechanism 120, typically using high-density hard printing parameters to ensure connection rigidity.

[0066] Fingertip Structure 131: This is the part that comes into direct contact with the crop. By altering the internal infill path of the 3D print, specific lattice buffer structures are created. For example, for fragile strawberries, the inside of the fingertip is printed as a sparse honeycomb structure to provide extremely high flexibility; for hard walnuts, it is printed as a dense triangular structure to provide greater gripping force.

[0067] like Figure 3As shown, the control logic of the system of the present invention includes four closed-loop steps: "perception-decision-manufacturing-execution".

[0068] Visual perception and parameter extraction:

[0069] The robot arrives at the work site and scans the target crop using visual sensors.

[0070] The algorithm extracts key physical parameters of crops: size D, estimated hardness H, epidermal vulnerability S, and spatial location.

[0071] Intelligent decision-making:

[0072] The system compares the extracted crop parameters with the performance parameters of the currently installed fixture.

[0073] Judgment logic: If the opening of the current clamp is less than the crop size D, or if the clamp stiffness is too large and may damage the crop epidermis S, then it is judged as "mismatch".

[0074] If a match is found, harvesting will proceed directly; otherwise, the "online manufacturing" process will be triggered.

[0075] In parametric model generation:

[0076] The edge computing module calls the built-in parametric design algorithm to automatically generate a new 3D model of the fixture (.stl or .gcode file) based on crop data D, H, S.

[0077] The specific implementation process of this design algorithm is as follows:

[0078] Dimensionality reduction and parameter mapping of physical quantity data:

[0079] Feature extraction: The algorithm extracts the crop's bounding box size (extraction parameter D), surface curvature distribution (extraction parameter C), and epidermal vulnerability estimated through spectral or historical data (extraction parameter S) from point clouds and images.

[0080] Mapping matrix: Establish a control parameter matrix where the crop size D is directly mapped to the absolute length and open envelope diameter of the clamp finger segments; the crop curvature C is mapped to the basic arc of the inner contact surface of the clamp; and the skin fragility S is mapped to the subsequent material equivalent stiffness coefficient.

[0081] Geometry-driven based on topological skeleton:

[0082] Retrieving the white model: The system stores several basic "white model" structures at its core (e.g., two-finger symmetrical type, three-finger wrapping type). The system of this invention preferentially selects a basic framework based on the overall outline of the crop;

[0083] Morphological Evolution and Surface Texture: The dimensional parameters extracted in the previous step are injected into the skeleton to drive the model to stretch or scale. To ensure that there is friction without damaging the fruit during gripping, the system automatically generates microscopic uneven anti-slip textures (such as vertical stripes along the direction of tension) along the UV direction of the inner curved surface of the gripper, following the logic of force transmission. This texture generation along the UV direction of the curved surface can smoothly conform to the surface of irregular fruits.

[0084] Microlattice filling based on surface damage:

[0085] Variable density lattice layout: After the system obtains the vulnerability S of the crop's epidermis, it will hollow out and rearrange the structure inside the three-dimensional model of the fixture.

[0086] In the fingertip area that comes into direct contact with the fruit, the system automatically generates a low-density flexible lattice or a biomimetic porous structure; while at the root near the quick-change interface, a dense, high-strength support structure is arranged.

[0087] Structural array calculation: Similar to the precise arrangement of components in a limited space, the thickness of each lattice support skeleton is precisely calculated, and a detailed internal structural array is generated, so that the printed single part can achieve complex mechanical properties such as a soft and flexible outer edge and a strong inner side that transmits torque.

[0088] During manufacturing, the robot wirelessly sends the generated printing instructions to base station 300.

[0089] The additive manufacturing unit 301 of the base station immediately starts up, using flexible materials (such as TPU) to print customized fixture units.

[0090] In one embodiment:

[0091] like Figure 4 As shown, after the new fixture has been printed and cooled inside the base station, the system executes the physical replacement process:

[0092] 1. Return to base station: The robot moves to the front of base station 300.

[0093] 2. Unloading: The robotic arm extends the old, worn or mismatched gripper into the old gripper recycling bin 320 of the base station. The quick-change mechanism 120 unlocks, and the old gripper falls down for recycling.

[0094] 3. Loading: The robotic arm moves to the finished product storage and exchange platform 310, aligns with the newly printed fixture 130, and presses down and locks the quick-change mechanism.

[0095] 4. Reset: The robot returns to the field with "tailor-made" new tools to continue performing efficient and non-destructive harvesting operations.

[0096] Working principle:

[0097] This invention's system uses a mobile robot to perceive crop characteristics such as size, hardness, and epidermal vulnerability in real time. Through edge computing, it generates a suitable 3D model of the end effector online based on the "crop characteristics - gripper parameters" mapping relationship. A flexible gripper is then customized using 3D printing at a distributed additive manufacturing base station in the field, and automatically installed via a quick-change mechanism. This forms an integrated closed loop of perception, computation, manufacturing, operation, and maintenance, enabling on-demand customization, on-site manufacturing, and adaptive non-destructive gripping of the agricultural robot's end effector, breaking through the limitations of traditional fixed hardware.

[0098] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0099] All standard parts used in this invention can be purchased from the market, and irregular parts can be customized according to the description and drawings. The specific connection methods of each part adopt conventional methods such as bolts, rivets, and welding that are mature in the prior art. The machinery, parts and equipment adopt conventional models in the prior art, and the circuit connection adopts conventional connection methods in the prior art, which will not be described in detail here. The contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0100] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. An adaptive end effector method for agricultural robots based on on-site additive manufacturing, characterized in that: Includes the following steps: S1: Scan to obtain the morphology of the target crop and acquire key feature parameters; S2: Compare the crop characteristic parameters with the current end effector's performance parameters to determine if they are compatible; If the data is compatible, the crawling task will be executed directly; otherwise, an online customization process will be triggered. S3: Based on crop characteristic parameters, a three-dimensional model of a flexible clamping unit adapted to the target crop is generated through a parametric design algorithm, and the adapted flexible clamping unit is generated through on-site additive manufacturing printing. S4: Complete the unloading and recycling of the old fixture and the locking and installation of the new fixture, then return to the work point to perform adaptive non-destructive gripping operations.

2. The adaptive end effector method for agricultural robots based on on-site additive manufacturing according to claim 1, characterized in that: The key feature parameters in S1 include crop size, surface curvature, estimated hardness, and epidermal vulnerability. The adaptation judgment logic in S2 is as follows: If the current clamp opening is smaller than the crop size or the clamp stiffness does not match the crop's epidermal vulnerability, it is determined to be an incompatible fit.

3. The adaptive end effector method for agricultural robots based on on-site additive manufacturing according to claim 1, characterized in that: The three-dimensional model in S3 is generated parametrically, establishing a mapping relationship between crop features and fixture parameters, including: Crop size is mapped to the length of the clamp finger segment and the diameter of the open envelope. The curvature of the crop surface is mapped to the arc of the clamp contact surface. Crop epidermal vulnerability is mapped to the lattice filling density inside the fixture.

4. The adaptive end effector method for agricultural robots based on on-site additive manufacturing according to claim 3, characterized in that: The parametric design in S3 includes: Select the corresponding basic topology skeleton based on the overall outline of the target crop; The fixture morphology evolution is completed based on the mapped parameters, and a micro-anti-slip texture is generated along the curved UV direction of the inner contact surface of the fixture. Based on the vulnerability of the epidermis, a variable density lattice layout design was completed. A low-density flexible lattice structure was set in the fingertip area where the clamp contacts the crop, and a high-density rigid support structure was set at the root of the clamp connection.

5. The adaptive end effector method for agricultural robots based on on-site additive manufacturing according to claim 1, characterized in that: In S3, on-site additive manufacturing uses flexible consumables to complete the printing process; S4 includes unlocking, unloading, and locking / replacing the clamp using a standardized quick-change locking structure.

6. An adaptive end effector system for agricultural robots based on on-site additive manufacturing, performing the method as described in any one of claims 1-5, characterized in that: This includes mobile robots with wireless communication connectivity and smart additive manufacturing base stations in the field; The mobile operation robot also integrates a multi-degree-of-freedom robotic arm, a visual perception module, an edge computing unit, and a quick-change locking mechanism. The field-based intelligent additive manufacturing base station includes an additive manufacturing unit, a consumable storage bin, a finished product exchange platform, and a used parts recycling bin. The additive manufacturing unit manufactures the corresponding end effector based on feedback data from the edge computing unit.

7. The adaptive end effector system for agricultural robots based on on-site additive manufacturing according to claim 6, characterized in that: The end effector of the multi-degree-of-freedom robotic arm is connected to the end effector via a quick-change locking mechanism. The end effector includes a root structure and a fingertip structure. The root structure is equipped with a corresponding male connector to cooperate with the quick-change locking mechanism.

8. The adaptive end effector system for agricultural robots based on on-site additive manufacturing according to claim 6, characterized in that: The edge computing unit extracts key physical parameters of the crop, including size, hardness estimates, and epidermal vulnerability, to generate a three-dimensional model of the fixture as feedback data. The additive manufacturing unit is either an FDM fused deposition modeling 3D printer or an SLA photopolymerization 3D printer that is compatible with flexible consumables.

9. The adaptive end effector system for agricultural robots based on on-site additive manufacturing according to claim 8, characterized in that: The edge computing unit has a built-in parametric design algorithm library and stores a basic topological skeleton white model adapted to different crop contours.

10. An adaptive end effector system for agricultural robots based on on-site additive manufacturing according to claim 6, characterized in that: The mobile operation robot can be either a tracked robot or a wheeled robot.

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