Visual guidance self-adaptive bottle opening and closing control method and system

Abstract

The invention discloses a visual guidance self-adaptive bottle opening and closing control method and system. The method comprises the steps that a three-section type self-adaptive clamping platform is controlled to execute self-adaptive clamping operation, bottles of known models are rapidly clamped through preset parameters, and bottles of unknown models are gradually clamped in a self-adaptive mode based on real-time torque feedback. The sizes of bottles are accurately recognized and classified in real time through the hand-eye vision system integrated at the tail end of the robot, and therefore the system can actively pre-judge and plan the optimal clamping section and force control parameters before clamping. The three-section type integral moving clamp is combined with a torque feedback self-adaptive clamping algorithm, so that unstable clamping or bottle body damage is avoided; an elastic buffer device and real-time torque monitoring are integrated through the top cover opening unit, so that rigid collision and motor stalling can be effectively prevented through cover opening and closing operation; through a distributed real-time control architecture based on the industrial Ethernet, the cooperation of vision, a robot, a clamp and an uncovering unit is realized.

Description

Technical Field

[0001] This invention relates to the fields of industrial automation and machine vision technology, and more specifically, to a vision-guided adaptive bottle opening and closing control method and system. Background Technology

[0002] In the fields of chemical pharmaceuticals, biopharmaceuticals, and analytical testing, the automated opening and closing of sample and reagent vials is crucial for improving experimental efficiency, ensuring operational consistency, and maintaining data traceability. Currently, automated processing solutions for various vials of different sizes face the following main technical bottlenecks: First, while traditional mechanical adaptive solutions (such as those using spring clamping mechanisms) offer some dimensional tolerance, their adaptive range is limited and they lack the ability to actively sense the bottle shape. The system cannot predict the bottle's specific size and shape before clamping, and can only passively adjust through trial-and-error force control feedback. This leads to stability risks during clamping, easily causing the bottle to tip over or break, and is particularly difficult to use with bottles of significantly different heights and diameters.

[0003] Secondly, with the introduction of machine vision technology, some solutions have begun to utilize vision systems for bottle opening positioning. However, existing vision systems are mostly used only for auxiliary positioning, and their perceived information is not deeply integrated with clamping decisions and control strategies, resulting in a disconnect between "perception and execution." Vision systems typically only provide coarse position coordinates, without using the bottle's precise dimensions to plan the optimal clamping segment, preset clamping parameters, or adjust the motion trajectory in real time. This causes the system to still rely on pre-set fixed programs or delayed force feedback, resulting in limited intelligence and minimal improvement in processing efficiency and adaptability.

[0004] Furthermore, in order to cope with bottle shapes of various sizes, some solutions adopt multiple sets of independent clamps or replaceable grippers, but this significantly increases the complexity of the mechanical structure, the space occupied by the equipment, and the maintenance cost, making it difficult to apply to clean environments with limited space, such as laminar flow hoods and biosafety cabinets.

[0005] In addition, existing systems often use traditional I / O or non-real-time bus communication between various execution units (such as robots, fixtures, and vision controllers), resulting in high data transmission latency and poor coordination. This makes it difficult to achieve millisecond-level synchronization and closed-loop control between visual recognition, robot motion, and fixture force control, which restricts the overall cycle time and operational accuracy of the system. It also makes it difficult to meet the strict requirements of the pharmaceutical industry for real-time recording and complete traceability of data throughout the entire process.

[0006] In summary, existing technologies lack an integrated system and method capable of proactively and accurately sensing bottle dimensions before performing gripping operations, intelligently guiding the robot to grasp the bottle, and simultaneously planning and executing adaptive gripping and cap opening / closing operations. There is an urgent need in this field for a solution integrating intelligent visual perception, forward-looking decision-making, flexible adaptive execution, and highly reliable collaborative control to achieve efficient, compact, and fully automated bottle opening and closing operations while ensuring extremely high compatibility and safety. Summary of the Invention

[0007] To address the aforementioned technical problems in related technologies, this invention proposes a visually guided adaptive bottle opening and closing control method and system, which can overcome the above-mentioned shortcomings of the prior art.

[0008] To achieve the above-mentioned technical objectives, the technical solution of the present invention is implemented as follows: A visually guided adaptive bottle opening and closing control method includes the following steps: S1: Transport the bottle to be processed to the designated operation position; S2: The robot acquires and processes images of the bottle using a hand-eye vision unit installed at the end of the robot, identifies and calculates the bottle's size information, and classifies the bottle according to a preset threshold. S3: The central control unit queries the process parameter database or activates the adaptive learning mode based on the received bottle classification information to determine the corresponding clamping strategy and control parameters. S4: Control the robot to grasp the bottle based on visual positioning information and transport it to the center of the corresponding segment of the three-segment adaptive clamping platform; S5: Control the three-segment adaptive clamping platform to perform adaptive clamping operation. For bottles of known models, use preset parameters for rapid clamping, and for bottles of unknown models, use progressive adaptive clamping based on real-time torque feedback. S6: Control the top opening unit to perform opening or closing operations. During the operation, the top opening unit uses an integrated elastic buffer device to absorb the impact and monitors the operating torque in real time. S7: Record and store the entire process data of this operation, and optimize the visual classification threshold and force control parameters based on historical data.

[0009] Further, in step S2, the identification and calculation of the bottle's size information specifically includes: extracting the maximum bounding rectangle outline of the bottle body, and calculating the actual height H and diameter D of the bottle based on camera calibration parameters; the classification involves comparing the calculated (H, D) with a preset threshold vector to classify the bottle into small, medium, and large bottles.

[0010] Furthermore, in step S3, the activation of the adaptive learning mode includes: adopting a progressive speed adjustment strategy to automatically adjust the clamping speed according to the relative distance between the bottle and the clamp, with the speed decreasing as the distance increases.

[0011] Furthermore, in step S5, the progressive adaptive clamping based on real-time torque feedback for bottles of unknown model specifically includes: S51: Control the clamp to approach the bottle rapidly at a first speed; S52: When the distance reaches the first threshold, reduce to the second speed for slow positioning and start monitoring the servo motor torque; S53: When the distance reaches the second threshold, the speed is further reduced to the third speed for fine adjustment, and torque changes are monitored at high frequency; S54: When a sudden torque change is detected, it is determined that the clamp is in contact with the bottle body, and then torque feedback closed-loop clamping is performed at the fourth speed; S55: When the real-time torque reaches the safety threshold estimated based on the bottle grade, stop clamping and record the current clamping parameters as the optimized parameters for this type of bottle.

[0012] Furthermore, in step S6, the use of the integrated elastic buffer device to absorb the impact specifically includes: during the process of the cap opening device descending to contact the bottle cap or lifting, when subjected to axial reaction force, the spring in the elastic buffer device is compressed to generate a buffer stroke to absorb the mechanical impact.

[0013] A vision-guided adaptive bottle opening and closing control system includes: The central control unit, as the core of the system, is used to issue communication coordination, logical sequence processing and motion control commands to all devices in the system; The hand-eye vision unit is fixedly installed at the end of the multi-axis collaborative robot unit and is used to collect and identify the size information of the bottle located at the operation station. Its output is connected to the central control unit. A multi-axis collaborative robot unit is used to receive motion commands from the central control unit and drive its end effector to grasp and transport bottles; The three-section adaptive clamping platform has its servo driver communicatively connected to the central control unit to receive positioning and clamping commands and adaptively clamp the placed bottle. The top opening unit, whose controller is communicatively connected to the central control unit, is used to receive opening and closing instructions and perform opening or closing operations on the clamped bottle. The central control unit, hand-eye vision unit, multi-axis collaborative robot unit, three-section adaptive clamping platform and top opening unit communicate bidirectionally in real time via industrial Ethernet.

[0014] Furthermore, the three-section adaptive clamping platform includes: An integrated three-section clamp with clamping surfaces of different sizes in the upper, middle and lower sections to accommodate bottles of different sizes; A servo motor drives the three-section clamp to perform horizontal clamping motion via a transmission mechanism; The servo motor driver supports industrial Ethernet communication and features a high-precision position control mode and torque limiting function, enabling adaptive clamping control based on torque feedback.

[0015] Furthermore, the top opening unit includes: Servo electric cylinders provide vertical feed motion; An integrated bottle cap servo gripper is installed at the end of the servo electric cylinder for gripping and twisting bottle caps; An elastic buffer device is connected between the servo cylinder and the integrated bottle cap servo gripper. It has a compressible spring inside to provide a buffer stroke when subjected to axial force and prevent rigid collisions.

[0016] Furthermore, the hand-eye vision unit includes a high-resolution industrial camera and a vision processor. The vision processor runs an image processing algorithm to extract the outer contour of the bottle and calculate its height and diameter, and then classifies the bottle size according to a preset threshold.

[0017] Furthermore, the central control unit is a PLC controller that integrates a process parameter database, which stores the clamping target position, clamping torque, and cap opening torque parameters corresponding to different bottle types; the system realizes distributed real-time collaborative control between units through industrial Ethernet.

[0018] The beneficial effects of this invention are as follows: This invention utilizes a hand-eye vision system integrated into the robot's end effector to accurately identify and classify bottle sizes in real time, enabling the system to proactively predict and plan the optimal gripping segment and force control parameters before clamping. Through a three-segment integrated moving gripper combined with an adaptive clamping algorithm based on torque feedback, the system can safely and smoothly accommodate a wide range of bottle sizes with a single drive source, avoiding unstable clamping or bottle breakage. The top cap opening unit integrates an elastic buffer device and real-time torque monitoring, effectively absorbing axial impacts and preventing rigid collisions and motor stalling during cap opening and closing operations. A distributed real-time control architecture based on industrial Ethernet enables millisecond-level collaboration and data synchronization between the vision system, robot, gripper, and cap opening unit. Through recording operational data and self-learning optimization, the system continuously accumulates experience, optimizes parameters, and constantly improves processing efficiency and intelligent adaptability. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of the vision-guided adaptive bottle opening and closing control method according to an embodiment of the present invention; Figure 2 This is an overall architecture diagram of the vision-guided adaptive bottle opening and closing control system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structural layout of the vision-guided adaptive bottle opening and closing control system according to an embodiment of the present invention; Figure 4 This is a structural diagram of a three-section adaptive clamping platform of a vision-guided adaptive bottle opening and closing control system according to an embodiment of the present invention; Figure 5 This is a partial view of the three-section adaptive clamping platform of the vision-guided adaptive bottle opening and closing control system according to an embodiment of the present invention; Figure 6 This is a detailed drawing of the elastic buffer device of the top opening unit of the visually guided adaptive bottle opening and closing control system according to an embodiment of the present invention; Figure 7 This is a detailed view of the initial state of the elastic buffer device of the top opening unit of the vision-guided adaptive bottle opening and closing control system according to an embodiment of the present invention. Figure 8 This is a detailed diagram of the working state of the elastic buffer device of the top opening unit of the visually guided adaptive bottle opening and closing control system according to an embodiment of the present invention. Figure 9 This is a flowchart of the cap-opening operation of the vision-guided adaptive bottle opening and closing control method according to an embodiment of the present invention; Figure 10 This is a diagram of the core algorithm for processing bottles of unknown model using the visually guided adaptive bottle opening and closing control method according to an embodiment of the present invention. Figure 11 This is a flowchart of the flexible cap opening protection of the visually guided adaptive bottle opening and closing control method according to an embodiment of the present invention; In the diagram: 101, Central control unit; 102, Top opening unit; 103, Three-section adaptive clamping platform; 104, Hand-eye vision unit; 105, Multi-axis collaborative robot unit; 201, Adaptive gripper motor; 202, Three-section gripper; 203, Solvent bottle fixing platform; 301, Servo cylinder; 302, Mounting backplate; 303, Elastic buffer device; 304, Integrated servo gripper; 305, Buffer spring. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention are within the scope of protection of the present invention.

[0022] like Figure 1 As shown in the figure, a vision-guided adaptive bottle opening and closing control method according to an embodiment of the present invention includes the following detailed steps: S1: Bottle Delivery and Preparation S1.1: External handling equipment transports medicine and reagent bottles from the storage area to the designated operating position at the workstation; S1.2: After the system detects that the bottle is in place, it prepares to start the processing procedure.

[0023] S2: Visual Size Prediction and Communication S2.1: The multi-axis collaborative robot carrying the end-effector camera moves to the preset image capture position to acquire images of the target bottle; S2.2: The vision processor preprocesses the acquired image, performs edge detection, extracts the maximum bounding rectangle contour of the bottle body, and calculates the actual height H and diameter D of the bottle based on the camera calibration parameters; S2.3: Compare the calculated (H, D) with the preset threshold vector and classify it as "small bottle", "medium bottle" or "large bottle"; S2.4: The bottle grade information obtained from the classification is sent to the Siemens S7-1515 PLC in real time via industrial Ethernet.

[0024] S3: Fixture Segment Pre-positioning and Strategy Selection S3.1: After receiving the bottle grade, the PLC queries the database to determine whether it is a known bottle model; S3.2: If the model is known, directly call the stored optimization parameters (target position, clamping speed, torque threshold). S3.3: If the model is unknown, activate the adaptive exploration clamping mode and adopt a progressive speed adjustment strategy.

[0025] S4: Robot Vision-Guided Grasping S4.1: The PLC sends grasping instructions and target position information to the multi-axis collaborative robot unit via industrial Ethernet; S4.2: Based on the precise position information of the bottle provided by the vision system, the robot performs trajectory planning and drives the end gripper to grasp the bottle; S4.3: The robot precisely and smoothly transports the bottle and places it into the center of the pre-positioned fixture segment.

[0026] S5: Intelligent Adaptive Clamping Control S5.1: For bottles of known models, use parametric rapid clamping, supplemented by visual verification; S5.2: For bottles of unknown model, initiate the adaptive clamping process: automatically adjust the clamping speed based on the relative distance provided by vision (the closer the distance, the slower the speed); monitor the change in servo motor torque in real time, detect the torque mutation point (indicating contact with the bottle body), stop clamping when the torque reaches the preset safety threshold, and record the success parameters; S5.3: Monitor the torque change rate in real time during clamping, and immediately stop the machine safely if any abnormality is detected.

[0027] S6: Flexible lid opening operation S6.1: The servo electric cylinder of the top opening unit slowly descends to the preset gripping height; S6.2: The resilient device provides cushioning upon contact with the bottle cap to prevent rigid impact; S6.3: The integrated bottle cap gripper closes with a preset gripping torque to firmly hold the bottle cap; S6.4: The rotary servo motor of the gripper starts, executing the opening and rotation action. The PLC monitors the torque curve during the rotation process in real time to ensure operational safety; S6.5: After the cover is opened, the servo electric cylinder is raised to a safe height through elastic buffer.

[0028] S7: Data Recording, Traceability, and Parameter Optimization S7.1: The system will record the entire process data of this operation into the database or the host computer system; S7.2: Based on historical data, the system automatically fine-tunes the size threshold for visual classification and optimizes the best clamping / opening torque parameters for each type of bottle; S7.3: Generate operation records and electronic batch reports that comply with GMP standards to facilitate quality traceability.

[0029] A vision-guided adaptive bottle opening and closing control system includes: a central control unit, a hand-eye vision unit, a multi-axis collaborative robot unit, a three-section adaptive clamping platform, and a top opening unit.

[0030] The central control unit, serving as the system's main control core, establishes bidirectional real-time communication connections with all other units via industrial Ethernet. The hand-eye vision unit is fixedly installed at the end of the multi-axis collaborative robot unit, with its output connected to the central control unit, used to collect and identify the bottle size information located at the operating station. The communication interface of the multi-axis collaborative robot unit is connected to the central control unit to receive motion commands, and its end effector is used to grasp and transport bottles. The servo driver of the three-section adaptive clamping platform is communicatively connected to the central control unit to receive positioning and clamping commands. Its working area is located at the end of the transport path of the multi-axis collaborative robot unit, used to receive and clamp the placed bottles. The controller of the top cap opening unit is communicatively connected to the central control unit to receive opening and closing commands. Its actuator is vertically arranged directly above the three-section adaptive clamping platform, used to perform opening or closing operations on the clamped bottles.

[0031] Central control unit: Based on Siemens S7-1515 PLC, a distributed control system based on industrial Ethernet protocol is built; the PLC acts as the master station, responsible for the communication coordination, logical sequence processing and motion control command issuance of all devices in the system; it integrates a process parameter database to store key parameters such as the clamping target position, clamping torque and cap opening torque corresponding to different bottle types (such as "large", "medium" and "small" grades).

[0032] The hand-eye vision unit includes a high-resolution industrial camera and a vision processor, which are directly mounted on the end effector of the multi-axis collaborative robot. The vision processor runs a dedicated image processing algorithm to extract the outer contour of the bottle and accurately estimate its height and diameter based on the calibrated pixel-physical size relationship. The system compares the calculated size results with preset thresholds to classify the bottles into three levels: "large", "medium" and "small", and sends the level information to the central control unit via industrial Ethernet.

[0033] Multi-axis collaborative robot unit: It includes a multi-axis collaborative robot with an adaptive gripper at its end for grasping bottles; the robot controller, as an intelligent device on the network, receives movement instructions and target position coordinates from the central control unit; based on visual judgment, the robot accurately transports and places the bottle into the center of the corresponding segment of the three-segment gripper.

[0034] Three-section adaptive clamping platform: Mechanical structure: The core of this platform is an integrated three-section clamp. The entire clamp acts as a rigid body, driven by a servo motor via a ball screw for precise horizontal clamping motion; Layered design: The upper, middle, and lower sections of the clamp are designed with clamping surfaces and center holes of different sizes to accommodate bottles of large, medium, and small sizes respectively; Servo drive: It adopts a servo drive system supporting industrial Ethernet communication, featuring high-precision position control and torque limiting functions. Adaptive clamping: The adaptive clamping system monitors the servo motor torque changes in real time, detects torque abrupt changes to determine the contact state, and dynamically adjusts the clamping force based on torque feedback to achieve closed-loop adaptive control.

[0035] Top-opening unit: Includes a vertically mounted servo cylinder and an integrated bottle cap servo gripper with rotation function mounted at the end of the cylinder. Elastic buffer device: The opening device integrates an elastic device with retractable function to effectively prevent motor stalling caused by rigid collisions during bottle cap lifting. This unit also has an industrial Ethernet communication interface to receive commands from the central control unit and execute a series of actions such as lowering, gripping the bottle cap, rotating to open / close the cap, and lifting. The gripper has torque feedback function to ensure appropriate gripping force and monitors torque in real time during twisting to prevent slippage or excessive tightening.

[0036] To facilitate understanding of the above technical solutions of the present invention, the following detailed description of the above technical solutions of the present invention will be provided through specific usage methods.

[0037] 1. System hardware configuration and industrial Ethernet data exchange Central Control Unit: Employs a Siemens S7-1515 PLC with industrial Ethernet master station functionality. Vision System: Utilizes a high-resolution industrial camera and vision processor, communicating with the PLC via industrial Ethernet. Robot Unit: Selects a multi-axis collaborative robot whose controller supports industrial Ethernet intelligent device functionality. Servo Drives: Select servo drives supporting industrial Ethernet communication for the gripper platform and top-opening unit.

[0038] 2. Mechanical structure design schematic diagram 2.1 System Overall Structure Layout Diagram like Figure 3 As shown, the overall structural layout of the vision-guided adaptive bottle opening and closing system of the present invention includes the following core units: central control unit (101), top opening unit (102), three-section adaptive clamping platform (103), hand-eye vision unit (104), and multi-axis collaborative robot unit (105).

[0039] The central control unit (101), serving as the system's brain, is typically installed within the equipment cabinet and exchanges real-time data with each execution unit via industrial Ethernet. The top-opening unit (102) is vertically mounted on the upper part of the system frame, directly above the three-section adaptive clamping platform (103). The three-section adaptive clamping platform (103), located at the workstation's operating center, is used to clamp and secure solvent bottles to be processed. A multi-axis collaborative robot unit (105) is deployed to the side of the clamping platform, its end equipped with a hand-eye vision unit (104) and an adaptive gripper (not separately labeled) for grasping bottles.

[0040] The layout of this diagram clearly demonstrates the high degree of integration and compactness of this invention. The units are rationally distributed around the operation center, maximizing space utilization. This integrated layout design allows the system to be easily integrated into space-constrained clean environments such as laminar flow hoods and fume hoods.

[0041] 2.2 Three-section adaptive clamping platform structure diagram Figure 4-5 The specific mechanical structure of the three-section adaptive clamping platform is shown. It mainly consists of an adaptive gripper motor (201), a three-section clamp (202), and a solvent bottle fixing stage (203). The solvent bottle fixing stage (203) is a mechanical structure platform for placing reagent bottles.

[0042] The adaptive gripper motor (201) is preferably a servo motor, which drives the entire three-section clamp (202) to perform precise horizontal clamping movements through a transmission mechanism such as a ball screw (not shown). The three-section clamp (202) is an integral rigid component, with clamping surfaces of different sizes designed in its upper, middle and lower parts (see the independent figure on the right), which are used to adapt to solvent bottles of different sizes, such as large, medium and small.

[0043] Figure 4-5 The right side provides individual views and operational status demonstrations of key components. The independent diagram of the three-section clamp (202) clearly shows its layered structure. Most importantly, the solvent bottle adaptation demonstration diagram visually illustrates the system's workflow using three typical solvent bottle sizes: 25mL, 100mL, and 500mL. After the vision system identifies the bottle size, it controls the movement of the three-section clamp (202) to precisely align and clamp the corresponding clamping sections (upper section - large bottle, middle section - medium bottle, lower section - small bottle). This fully demonstrates the system's adaptive clamping capability for multiple bottle sizes, solving the core advantage of multi-size compatibility with a single drive source.

[0044] 2.3 Detailed drawing of the elastic buffer device for the top opening unit Figure 6-8The structure of the elastic buffer device of the top opening unit is shown in detail. The unit mainly includes a servo electric cylinder (301), a mounting back plate (302), an elastic buffer device (303), and an integrated servo gripper (304).

[0045] The servo electric cylinder (301) is fixed to the system frame via a mounting backplate (302) to provide precise vertical feed motion. An elastic buffer device (303) is connected to the end of the integrated servo gripper (304) to provide it with extension and retraction.

[0046] Figure 6-8 The diagram on the right clearly illustrates the two key states of the spring inside the elastic buffer device. The left side represents the "initial state," where the buffer spring is in its free reference position and the device is in its fully extended state. The right side represents the "operating state," where, when the cap-opening device descends to contact the bottle cap and receives an upward reaction force, the outer shell overcomes the spring force and undergoes relative displacement, compressing the spring and generating a buffer stroke, thereby effectively absorbing mechanical impact and preventing rigid collisions.

[0047] This innovative design allows the cap-opening unit to absorb potential axial displacement errors and impact energy through spring compression during the upward process after the cap is unscrewed. This effectively avoids motor stalling, bottle tipping, or damage to the cap / bottle body caused by rigid tension, greatly improving the reliability and safety of the system.

[0048] 3. Implementation of key control algorithms The core control logic of this invention is implemented through a program in a central control unit (PLC). Its core lies in the intelligent connection and decision-making of multiple processes, such as visual recognition, robot motion, adaptive clamping, and flexible lid opening. The following uses flowcharts supplemented by textual descriptions to elaborate on the technical implementation and effects of the main control process and two key sub-processes (adaptive clamping control and flexible lid opening protection).

[0049] 3.1 Main Control Flow like Figure 9 As shown, the main control flow coordinates the entire process from the bottle's arrival to the completion of opening the cap, and is a high-order scheduling program based on a state machine. Its core decision point lies in selecting between two paths based on the visual recognition results: "parameterized rapid operation for known models" or "adaptive learning operation for unknown models."

[0050] The process begins with a system self-check to ensure all hardware units are ready. Once the bottle is in place, visual size recognition and classification is the first critical decision-making node, its output determining the strategy basis for all subsequent actions. The process queries a database to categorize bottles into "known" and "unknown" types, thus achieving branching control. For known models, the system directly calls historical optimized parameters for efficient and rapid standardized operation; for unknown models, it switches to an adaptive learning branch to explore and obtain optimal parameters. Robot gripping, placement, and clamping control are the core execution links. Clamping control employs either "parametric rapid clamping" or "adaptive learning clamping" methods depending on the aforementioned branching, demonstrating the system's intelligent decision-making capabilities. Finally, flexible cap opening ensures operational safety, while data recording and optimization enable the system to continuously learn. This main process, through structured state switching, seamlessly closes the loop between visual perception, strategy decision-making, and precise execution, achieving highly flexible and highly reliable fully automated processing.

[0051] 3.2 Adaptive Clamping Control Flow like Figure 10 As shown, this sub-process is the core algorithm for handling bottles of unknown models. Its technical advantage lies in the fact that it can reliably clamp unfamiliar bottle types safely and smoothly through a closed loop of "perception-decision-execution" without the need for preset parameters.

[0052] This process is a typical progressive, multi-stage search and stabilization control process. Stage 1 (rapid approach) aims to improve efficiency by setting a reasonable relatively fast speed based on the initial bottle classification. The core of Stages 2 (slow positioning) and 3 (fine adjustment) is "deceleration-monitoring," gradually reducing the speed to achieve higher control accuracy and safety. Simultaneously, high-frequency torque monitoring begins in Stage 3 to prepare for contact point detection. "Detecting sudden torque changes" is a key technical judgment in this process. When the clamp contacts the bottle, the servo motor torque changes significantly within a short time (gradient exceeding a threshold). The system uses this to accurately determine the contact moment, avoiding collisions that might occur if relying on preset positions. Stage 4 (torque feedback clamping) continues clamping at a finer speed after confirming contact, using torque as feedback for closed-loop control. The process stops when the torque reaches a safety threshold estimated based on the bottle grade, ensuring secure clamping while preventing overpressure that could cause bottle breakage. The continuously monitored torque change rate is an important safety indicator, enabling timely identification of abnormalities such as jamming and collisions and triggering an emergency stop. This process combines "distance" and "torque" information to dynamically adjust the speed, achieving compliant and safe clamping of unknown objects, and is the core manifestation of the system's adaptive capability.

[0053] 3.3 Flexible lid opening protection process like Figure 11As shown, the process controls the execution of the top cap opening unit. Its technical effect is to effectively prevent rigid collisions and motor stalling caused by axial position errors, adhesion, or resistance during the process of twisting the cap by combining mechanical elastic buffering and software control logic.

[0054] This process is a typical force / position-aware safety operation loop. The low-speed descent of the servo cylinder is a safety prerequisite, providing ample response time for the system. Real-time monitoring of contact force (F) and elastic compression (Δ) constitutes dual software protection. During the descent phase, when normal contact force is detected (indicating contact with the bottle cap) and the elastic buffer stroke is sufficient, normal contact is determined, and the program continues to execute the gripping and twisting actions. If the detected force or compression exceeds the limit, it is determined to be abnormal (such as bottle tipping or severe positional deviation), and an immediate emergency stop is initiated—a crucial protection against hardware damage. The process is equally important during the lifting phase after twisting. If there is axial adhesion between the bottle cap and the bottle neck, the lifting action of the cylinder will be hindered. At this time, the integrated elastic buffer device will mechanically compress, absorbing this overshoot energy and displacement, preventing rigid tension, and thus protecting the servo cylinder motor from stalling. The software simultaneously monitors this process to ensure that the buffering is within the designed stroke. This process minimizes the risk of mechanical interference during the cap-opening operation through a collaborative mechanism of "software prediction (monitoring force / position) + hardware buffer (elastic device)," greatly improving the system's reliability and compatibility with different bottle caps.

[0055] In summary, by utilizing the technical solutions described above, the system achieves real-time and accurate bottle size identification and classification through a hand-eye vision system integrated into the robot's end effector. This allows the system to proactively predict and plan the optimal gripping segment and force control parameters before clamping. The three-segment integrated moving gripper combined with an adaptive clamping algorithm based on torque feedback enables the system to safely and smoothly accommodate a wide range of bottle sizes with a single drive source, avoiding unstable clamping or bottle breakage. The top cap opening unit integrates an elastic buffer device and real-time torque monitoring, effectively absorbing axial impacts and preventing rigid collisions and motor stalling. A distributed real-time control architecture based on industrial Ethernet enables millisecond-level collaboration and data synchronization between the vision system, robot, gripper, and cap opening unit. Through recording and self-learning optimization of operational data, the system continuously accumulates experience, optimizes parameters, and continuously improves processing efficiency and intelligent adaptability.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visually guided adaptive bottle opening and closing control method, characterized in that, Includes the following steps: S1: Transport the bottle to be processed to the designated operation position; S2: The robot acquires and processes images of the bottle using a hand-eye vision unit installed at the end of the robot, identifies and calculates the bottle's size information, and classifies the bottle according to a preset threshold. S3: The central control unit queries the process parameter database or activates the adaptive learning mode based on the received bottle classification information to determine the corresponding clamping strategy and control parameters. S4: Control the robot to grasp the bottle based on visual positioning information and transport it to the center of the corresponding segment of the three-segment adaptive clamping platform; S5: Control the three-segment adaptive clamping platform to perform adaptive clamping operation. For bottles of known models, use preset parameters for rapid clamping, and for bottles of unknown models, use progressive adaptive clamping based on real-time torque feedback. S6: Control the top opening unit to perform opening or closing operations. During the operation, the top opening unit uses an integrated elastic buffer device to absorb the impact and monitors the operating torque in real time. S7: Record and store the entire process data of this operation, and optimize the visual classification threshold and force control parameters based on historical data.

2. The visually guided adaptive bottle opening and closing control method according to claim 1, characterized in that, In step S2, the identification and calculation of the bottle's size information specifically includes: extracting the maximum bounding rectangle outline of the bottle body, and calculating the actual height H and diameter D of the bottle based on camera calibration parameters; the classification involves comparing the calculated (H, D) with a preset threshold vector to classify the bottle into small, medium, and large bottles.

3. The visually guided adaptive bottle opening and closing control method according to claim 1, characterized in that, In step S3, the activation of the adaptive learning mode includes: adopting a progressive speed adjustment strategy to automatically adjust the clamping speed according to the relative distance between the bottle and the clamp, with the speed decreasing as the distance increases.

4. The visually guided adaptive bottle opening and closing control method according to claim 1 or 3, characterized in that, In step S5, the progressive adaptive clamping based on real-time torque feedback for bottles of unknown model specifically includes: S51: Control the clamp to approach the bottle rapidly at a first speed; S52: When the distance reaches the first threshold, reduce to the second speed for slow positioning and start monitoring the servo motor torque; S53: When the distance reaches the second threshold, the speed is further reduced to the third speed for fine adjustment, and torque changes are monitored at high frequency; S54: When a sudden torque change is detected, it is determined that the clamp is in contact with the bottle body, and then torque feedback closed-loop clamping is performed at the fourth speed; S55: When the real-time torque reaches the safety threshold estimated based on the bottle grade, stop clamping and record the current clamping parameters as the optimized parameters for this type of bottle.

5. The visually guided adaptive bottle opening and closing control method according to claim 1, characterized in that, In step S6, the absorption of impact using the integrated elastic buffer device specifically includes: during the process of the cap opening device descending to contact the bottle cap or lifting, when subjected to axial reaction force, the spring in the elastic buffer device is compressed to generate a buffer stroke to absorb mechanical impact.

6. A vision-guided adaptive bottle opening and closing control system, characterized in that, include: The central control unit, as the core of the system, is used to issue communication coordination, logical sequence processing and motion control commands to all devices in the system; The hand-eye vision unit is fixedly installed at the end of the multi-axis collaborative robot unit and is used to collect and identify the size information of the bottle located at the operation station. Its output is connected to the central control unit. A multi-axis collaborative robot unit is used to receive motion commands from the central control unit and drive its end effector to grasp and transport bottles; The three-section adaptive clamping platform has its servo driver communicatively connected to the central control unit to receive positioning and clamping commands and adaptively clamp the placed bottle. The top opening unit, whose controller is communicatively connected to the central control unit, is used to receive opening and closing instructions and perform opening or closing operations on the clamped bottle. The central control unit, hand-eye vision unit, multi-axis collaborative robot unit, three-section adaptive clamping platform and top opening unit communicate bidirectionally in real time via industrial Ethernet.

7. The vision-guided adaptive bottle opening and closing control system according to claim 6, characterized in that, The three-section adaptive clamping platform includes: An integrated three-section clamp with clamping surfaces of different sizes in the upper, middle and lower sections to accommodate bottles of different sizes; A servo motor drives the three-section clamp to perform horizontal clamping motion via a transmission mechanism; The servo motor driver supports industrial Ethernet communication and features a high-precision position control mode and torque limiting function, enabling adaptive clamping control based on torque feedback.

8. The vision-guided adaptive bottle opening and closing control system according to claim 6, characterized in that, The top opening unit includes: Servo electric cylinders provide vertical feed motion; An integrated bottle cap servo gripper is installed at the end of the servo electric cylinder for gripping and twisting bottle caps; An elastic buffer device is connected between the servo cylinder and the integrated bottle cap servo gripper. It has a compressible spring inside to provide a buffer stroke when subjected to axial force and prevent rigid collisions.

9. The vision-guided adaptive bottle opening and closing control system according to claim 6, characterized in that, The hand-eye vision unit includes a high-resolution industrial camera and a vision processor. The vision processor runs an image processing algorithm to extract the outer contour of the bottle and calculate its height and diameter, and then classifies the bottle size according to a preset threshold.

10. The vision-guided adaptive bottle opening and closing control system according to claim 6, characterized in that, The central control unit is a PLC controller that integrates a process parameter database. The database stores the clamping target position, clamping torque, and cap opening torque parameters corresponding to different bottle types. The system realizes distributed real-time collaborative control between units through industrial Ethernet.

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