WO2022166505A1 - Robot apparatus for remote and autonomous experiment, and management system and method - Google Patents

Abstract

A robot apparatus for a remote and autonomous experiment, and a management system and method. The apparatus comprises: a robot master control system (101), a robot arm action planning module (102), a camera visual module (103), a mobile module (104), a speech module (105), a multimedia touch screen module (106), a code scanning information module (107), an oscillator module (108), an incubator module (109), a heating/cooling/drying module (110), an extraction, filtration and crystallization module (111), a microscope module (112), a 360-degree rotary table scale/magnification module (113) and a multi-sensor module (114). By means of the apparatus, remote and autonomous experiments, monitoring, intelligent data identification and data analysis, experiments at a distance and isolated experiments can be realized, and the apparatus can be widely applied to experiments and inspections of multiple scenarios, such as biological experiments, physical experiments, medical experiments and chemical experiments.

Description

A remote and autonomous experimental robot device, management system and method technical field

The invention relates to the field of artificial intelligence robots, in particular to a biological experiment, physical experiment, medical experiment, chemical experiment and other multi-scene experiments. , artificial intelligence robot technology such as biophysics, chemical medicine, etc., is widely used in biological experiments, physical experiments, medical experiments, chemical experiments and other multi-scene experiments and inspections.

Background technique

With the promotion of artificial intelligence robots in the fields of biology, physics, medicine, and chemistry, biological, physical, medical, and chemical experiments take a long time to monitor, the precision of experimental operations is low, and misoperations lead to experimental failures. Due to the differences in the professional ability of personnel, the experimental steps, the experimental details, the degree of experimental completion, and the efficiency of each experimental operation. Therefore, standardization, precision and high-efficiency experimental equipment has become an important issue.

A voice control, remote control, autonomous operation, and the design of a remote and autonomous experimental robot device with increased experimental management functions have been developed into a market demand. Remote control of the experimental machine by the administrator, remote voice commands, remote monitoring of the experimental environment, and efficient realization of experimental step management, experimental personnel management, and experimental consumables. Practical techniques such as medical physics. Using robot arms and cameras, machine vision and various intelligent identification methods, remote, autonomous experiments, monitoring, intelligent data identification, data analysis, long-distance experiments, and isolation experiments are realized.

At present, most of the products on the market are single biological reaction machines, and there is no remote control, autonomous operation, remote supervision of the experimental environment, experimental procedures, management of experimental personnel, experimental samples and other devices, and experimental management systems. Experimental robots and experimental management systems that have not yet implemented voice commands, voice interaction, and robotic arm operation experiments. The invention adds the functions of filtration, oscillator stirring, heating, cooling, drying, cell breaking, filtration, integrated functions of biosensor monitoring experiments, remote control and autonomous operation experiments.

It solves the remote control and autonomous operation experiments of the mobile robot arm, realizing voice commands, voice interaction, microscope visual recognition, filtration, oscillator stirring, heating, cooling, drying, cell disruption, filtration, multi-biosensor monitoring, etc. It solves the problem that due to various human factors, the accuracy is poor, the professional ability of each experimental operation is different, and the experimental completion efficiency of the experimental details is quite different. It solves the remote control and autonomous operation experiments, realizes the integration functions of filtration, oscillator stirring, heating, cooling, drying, cell disruption, and biosensor monitoring experiments, and improves the biological, physical, medical, and chemical experimental utility of intelligent robots.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the above-mentioned shortcomings and deficiencies of the prior art, and provide a voice interaction, remote and autonomous control, intelligent image recognition of experimental samples under a microscope, and auxiliary recognition of microorganisms such as bacteria, viruses, and various cells. Through the crawler and rollers, the robot arm moves the robot arm to grab, scan the code, and place the object. The integrated robot device is connected to the experiment management system to realize the remote user-robot-machine user-side voice interaction, voice commands, and the mobile robot arm picks up the experimental utensils. Scan the code, improve the integrated functions of filtration, oscillator stirring, heating, cooling, drying, cell disruption, filtration, biosensor monitoring experiments, remote control and autonomous operation experiments.

The invention provides a visual identification method, which can identify and identify microorganisms such as bacteria, viruses, and various cell structures in the scene. Use the information collection device to collect and manage the face, biological information, RFID information, and identify the experimental machine through the visual camera to identify the digital code, text, two-dimensional code, color identification, special identification, etc.

The invention provides a dual control method combining remote control and robot autonomy.

Further, the present invention provides a remote control and autonomous control, and a robot arm action planning method.

Still further, the present invention provides a voice recognition method such as remote user-robot-robot user voice interaction, voice command, voice recognition, and voice synthesis. The invention provides a code scanning device for scanning codes, identifying and managing experimental consumables, experimental personnel and experimental devices.

The invention provides an experiment management system, which is used for query, experiment reservation, real-time experiment supervision, observation, management of experiment utensils, experiment management of experiment logs, management of consumable samples, management of experiment personnel, remote robotic arm control, voice interaction, and instruction ,call.

The present invention solves the above problems, and the technical solutions adopted to realize these functions

A remote and autonomous experimental robot device, management system, method and method are characterized in that, a remote and autonomous experimental robot device includes:

A robot main control system, the robot main control system is used to control the robot. The robot main system controls the communication of each robot node through the robot main system, and the connected hardware devices drive and act. The communication module of robot nodes realizes inter-node publishing and receiver communication through messages, services, actions and other communication methods.

The microscope visual acquisition and recognition module is connected with the main control system of the robot, which is used for visual acquisition of experimental samples, and the pictures under the microscope assist the intelligent identification of microorganisms and cells.

Incubator device, the incubator device includes: dispenser, test tube, beaker and other devices, used for cell, microorganism and other experiments culture, filtration, separation, centrifugation, cell disruption, extraction, biosynthesis, precipitation, drying .

The oscillator device is connected with the main control system of the robot, and the oscillator device includes: a stirring rod and an oscillatory device, which are used for shaking and stirring in the experiment of the mobile robot arm.

The heating device is connected with the main control system of the robot for heating the experimental sample.

The cooling device is connected to the main control system of the robot for cooling the experimental samples.

The drying device is connected with the main control system of the robot, and the drying device includes: a microwave drying device, a freeze drying device, a fluidized bed drying device, an infrared drying device, an airflow drying device, a spray drying device, and one of the chamber drying devices. one or more devices.

The stainer device is connected to the main control system of the robot and is used for staining experimental samples.

Filtration device, the filter device includes one or more of vacuum filtration device, centrifugal filtration device, tubular centrifugal device, disc centrifugal device and ultracentrifugation device.

The cell crushing device, connected with the main control system of the robot, is used for various crushing methods, including: chemical method, mechanical method, enzymatic hydrolysis method, alkali treatment method, osmotic shock method, and lipolysis method. Mechanical methods include: ultrasonic, high pressure homogenization, grinding, bead grinding.

The extractor is connected to the main control system of the robot, and the extractor device includes: an extractor, a separation tank, an expansion valve, and a compressor.

The crystallization device, the crystallization device, is connected with the main control system of the robot for crystallization.

The multi-sensor device is connected to the main control system of the robot. The multi-sensor device includes: nano biosensors, enzyme biosensors, biological pseudo-array chips, microfluidic chips, DNA sensors, immune biosensors, gas sensors, ion sensors, photoelectric sensors , Strain and Piezoresistive Sensors, Inductive Sensors, Capacitive Sensors, Hall Sensors, Piezoelectric Sensors, Impedance Sensors, Semiconductor Sensors, Acoustic Sensors, Thermal Sensors, Electrochemical Sensors, Photosensitive Sensors.

The 360-degree rotating table and scale are connected to the main control system of the robot for 360-degree observation of experimental details, and the scale is used to measure the experimental objects.

Vision device and magnifying device, said vision device, magnifying vision device, connected with mobile robot arm, robot main control system, used to identify numbers, color labels, according to the shape and position of the vessel to identify steps in the experimental process, vessels in the process , the location of the reactor, etc.

The mobile device is connected with the main control system of the robot, the visual camera, and the obstacle avoidance device. The mobile device includes wheel-type movement and crawler-type movement, which are used to move the robot arm. Mobile robot arm and wheel type, crawler type can be disassembled, mobile base and robot body can be used independently.

The robot arm, connected with the main control system of the robot and the camera, is used for the robot arm to grasp, pick up, take, and place the target items, scan codes, organize, and place items. The improved neural network method is used to adaptively learn and adjust the parameters of the robotic arm to achieve autonomous robotic arm action planning, and to use the robot body control and remote user control to adjust the robotic arm planning parameters.

The voice device is connected with the main control system of the robot, and the voice module includes: a directional voice recognition device and a microphone. It is used for voice interaction between remote users and experimental devices, experimental guidance, voice commands, voice inquiry of experimental steps, and knowledge quiz.

The multimedia touch screen is connected with the main control system of the robot, and the multimedia touch screen is used for experimental steps, experimental process display, demonstration, experimental learning guidance, etc.

The scanning code information collection device is connected with the main control system of the robot, and the scanning code information collection device includes: barcode, two-dimensional code, biological information collector, and RFID information collector. It is used to manage experimental consumables, experimental samples, experimental personnel, and manage experimental steps and related information using barcodes and QR codes.

The visual recognition module is connected with the main control system of the robot, the camera, and the visual recognition amplifier, and the visual recognition module includes a camera and an amplifier. It is used to collect and publish image information, to identify people's face information, experimental biological reaction devices, color labels, experimental vessel information, to locate target objects, target people, and their locations, by identifying color, digital code, text, two-dimensional code, special identification and other comprehensive information. Through the robot system, the main control unit can manage the experimental personnel, experimental consumables and experimental items under the video camera in each experimental scene.

The microscope device is connected with the main control system of the robot, and through the improved machine learning method and the improved deep learning method, the contour, shape, structure, color, texture and other characteristics of the sample images such as cells and microorganisms are extracted to intelligently identify bacteria and viruses. and other types of microorganisms, learn and train image parameters to assist in identifying experimental samples, and visually collect pictures under a microscope to assist in intelligent identification of microorganisms and cells.

The robotic arm is connected with the main system and the visual camera, and the robotic arm is controlled by the action planning module of the main controller to recognize the target through vision, and use multiple robotic arms to grab, pick, and place the target item, scan the code for experimental samples, and experimental consumables. Action planning, the robot arm action planning module configures the position parameters and angle parameters of the robot arm, wrist, and claw to plan grab, move, and place parameters, and the robot arm moves, grabs, places, scans, sorts, and places. Items, configuring the action parameters of the robotic arm include adaptive learning and adjusting parameters and remote control and adjusting the parameters of the robotic arm.

The oscillator device is connected with the main control system of the robot, and the oscillator device includes: a stirring rod and a shaking device, which are used for the shaking and stirring of the mobile robot arm experiment. The shaking device completes the shaking and stirring through the shaking device. The parameters for setting the shaking device include: the number of times of shaking, the time of shaking, the strength of shaking, and the method of shaking.

The heating device is connected with the main control system of the robot and is used for heating the experimental sample. Set the parameters of the shaking device including: heating temperature, heating time, heating position, range, etc.

The cooling device is connected with the main control system of the robot and is used for cooling the experimental sample. Cooling device parameters include: cooling temperature, cooling time, etc.

The drying device is connected with the main control system of the robot and is used for drying the experimental samples. The drying device includes one or more of microwave drying device, freeze drying device, fluidized bed drying device, infrared drying device, airflow drying device, spray drying device, and chamber drying device. Parameters for drying samples include: drying time, drying method, drying intensity, etc.

The filtering device is connected with the main control system of the robot for filtering. The filter device includes one or more of a vacuum suction filtration device, a centrifugal filter device, a tubular centrifugal device, a disc-type centrifugal device, and an ultracentrifugation device.

The cell crushing device is connected with the main control system of the robot and is used for cell crushing. The various crushing devices correspond to various crushing methods including: chemical method, mechanical method, enzymatic hydrolysis method, alkali treatment method, osmotic shock method, and fat dissolving method. Mechanical methods include: ultrasonic, high pressure homogenization, grinding, bead grinding.

The voice module is connected with the main control system of the robot, and the voice module includes a directional voice recognition device and a microphone. By configuring directional recognition device, microphone and other parameters, through voice recognition, voice wake-up, voice-to-text conversion technology, remote user communication, configure language library for remote users. Voice interaction between remote users and robots, voice commands, voice inquiries, and voice knowledge quizzes.

The scanning code information collection device is connected with the main control system of the robot, and the scanning code information collection device includes: scanning code information collection, scanning and reading devices. The scanning code information collection, scanning, and reading device is the main system of the robot is connected with the camera, scanner, reader, information collection and reading device, through the improved machine learning algorithm and improved neural network method, intelligent identification of two-dimensional code, Digital codes, biological information, RFID information and other management personnel, items, equipment and other information.

The 360-degree rotating table, scale and visual amplifier are connected to the main control system of the robot for 360-degree observation of the details of the experimental items. The 360-degree rotating table and scale are used for rotation, assisting the visual recognition of the camera, and the amplifier device observes the details of the experiment in 360 degrees.

The dyer device is connected with the main control system of the robot and is used for dyeing experimental samples.

Described a visual recognition experiment device, experiment label color, number, letter, text, method for special identification, robot arm motion planning method, the method includes the following steps:

S1. Set the corresponding experimental device parameters of the experimental scene and their corresponding position parameters.

S2. Input the experimental device corresponding to the experimental bench, the color of the experimental label, the number, the letter, the text, and the mathematical model of the special logo.

S3. Extract the shape, outline, structure, color, number, letter, text, and special identification image of the vessel under the experimental scene, and use the corresponding image feature as the input value.

S4. Improve the weight optimizer, quickly train the image, and obtain the output value.

S5. According to the output shape, outline, structure, color, number, letter, text, special identification results, accurately identify the target, specify the target and locate the target position.

S6. According to the experiment, set the experimental steps, plan the action of the robot arm according to the experimental steps, move the robot arm to the test device and its position of each experimental step according to the action plan, and move the robot arm to the designated target experimental device under the main system. .

S7. The parameters in the configuration file of each experimental device node include: frequency, maximum and minimum linear velocity, maximum and minimum rotational speed, maximum linear acceleration in x and y directions, maximum angular velocity, error from the target direction, and error from the target position , the weight of reaching the target position, the weight of avoiding obstacles and other parameters.

S8. Configure the robot arm parameters, obstacle positions and their size parameters, update frequency, release frequency, experimental device location, icons, colors and parameters of each device on the experimental platform in each experimental device node. parameters such as the conversion maximum delay.

S9, set the initial parameters of the robot, including the robot id, the target id and its position and angle pose information.

S10. Set the motion plan, select the joint angle, the joint limit setting, the mechanical arm moves to the specified joint shape, the joint limit, the joint trajectory position, the speed component, and the joint speed. Set motion constraints, target trajectory, speed settings, execute planned trajectory, set joint position, joint angle.

S11. Set the Cartesian path on the robotic arm, and the objects that can be picked up by the target pose are set for the robot pose parameters.

S12 , setting the anti-collision matrix of the manipulator, and setting the anti-collision detection module (detection of other parts of the robot itself, detection of obstacles in the scene).

S13. Mechanical arm, claw parameter setting, grasping, pick-and-place, grasping pose parameter setting and matching target pose.

S14, initialize the placement and grasping, the position of the object, grasp the gesture object, and generate the grasping posture (initialize the grasping object, and create the opening and closing posture of the folder). Set the desired gripper approach, parameters to withdraw from the target, and set the grasping attitude.

S15 , a data list of trying to change the attitude is required.

S16. Grab the gesture list. Change the posture and generate the grasping action (set the grasping posture; grasp the ID number; set the objects that are allowed to be touched, and set the grasping list).

The improved machine learning method for classifying and analyzing abnormal data of microorganisms and cells includes the following steps:

S1. Establish a mathematical model of microorganisms and cell specimens.

S2. Extract the shape, color, outline, size and other cell characteristics of microorganisms and cell specimens, including characteristics such as color, shape, and outline.

S3, extracting the characteristics of microorganisms and cell specimen images. Characteristic values of the image such as color, shape, outline size, etc., enter the characteristic value of the detection item.

S4, classify and identify microorganisms, cell types (neutrophils, eosinophils, basophils, lymphocytes, monocytes), calculate and analyze the proportion and identify microorganisms and cells.

Described a kind of improved neural network algorithm microorganism, cell sample identification method, described method comprises the following steps:

S1. Input the mathematical model of the corresponding microorganism and cell.

S2. The shape, outline, color, structure, size, state characteristics (granular, rod, foam) and irregularity of the extracted specimen before and after the experiment under the staining reaction, and image recognition under the microscope, such as nuclear left shift, nuclear right shift, etc.

S3, establishing a mathematical model of the characteristics of the specimen image, and inputting the characteristic value of the detection item.

S4. Improve the weight optimizer, quickly train the image, and obtain the output value.

S5. According to the output results, assist to identify the microorganisms, cells and their respective shapes, contours, color, structure, size, state characteristics (granular, rod, foam) and Irregularity, kernel shift left, kernel shift right, etc.

S6. Auxiliary recording of dynamic real-time experimental data and its changes, classification and analysis of real-time data, and identification of microorganisms and bacteria in pictures under the microscope.

The described experiment management system is connected with the robot main control system, the voice module, and the robot arm for browsing and querying commodities, experiment reservation, real-time experiment supervision, observation, management of experiment utensils, experiment management of experiment logs, management of consumable samples, management of Experimenter, etc. An experiment management system includes: a browsing module, a query module, an experiment reservation module, a real-time experiment supervision, an observation module, a module for managing experiment utensils, a module for managing experiment logs, a module for managing samples of consumables, a module for managing experimenters, and a module for controlling a remote robotic arm, Visual display module, voice call module.

Description of drawings

Fig. 1 is the schematic diagram of the robot module in this application, and accompanying drawing 1 is marked:

101- robot main control system module; 102- robot arm action planning module; 103- camera vision module;

104-mobile module; 105-voice module; 106-multimedia touch screen module;

107-scan code information module; 108-oscillation device; 109-incubator module;

110-heating/cooling/drying module; 111-extraction filtration crystallization module; 112-microscope module;

113-360 degree turntable and amplification module; 114-biosensor module;

Fig. 2 is the robot structure composition diagram of the present application, and accompanying drawing 2 is marked:

201-main control system; 202-multimedia touch screen; 203-vision module; 204-amplifier;

205-mobile device; 206-robot arm; 207-scan code payment device; 208-filter device;

209-extraction device; 210-X-ray crystallization device; 211-oscillator device; 212-cell disruption device;

213-biosensor device; 214-heating device; 215-cooling device; 216-drying device;

217-microscope device; 218-remote client; 219-staining device; 220-voice device;

221 remote client;

specific implementation

This solution mainly realizes the human-robot voice interaction through the parameter setting of the directional voice recognition device and the microphone module, and uses voice recognition, voice-to-text conversion, voice wake-up and other methods to solve voice interaction, voice commands, and voice query of item information.

This program mainly uses the camera, using the improved machine learning method and the deep neural network method, to identify the color, shape, outline and other comprehensive characteristics of the object, classify the experimental reactor, intelligently identify the color, number, letter, and text experimental identification information, and return to the experimenter, Experiment utensil information, solve the experiment information. Robots use information acquisition and reading devices such as code scanners to realize experimenters and experimental sample management.

This scheme mainly uses the returned position information through the robotic arm module to plan actions such as arm grabbing, scanning code, placing, and operating the experimental reactor. Realize autonomous grasping, scanning, moving, placing, and operating the experimental reactor. Use robots to replace people to complete repetitive tasks, improve efficiency and save labor costs. Greatly reduce the pressure of human work and improve work efficiency.

The technical solution in the implementation of the present application is the general idea of solving the above-mentioned technical problems as follows:

In order to better understand the above technical solutions, the present invention will be further described in detail below with reference to the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

Example 1

As shown in FIG. 1 and FIG. 2 , an embodiment of a remote and autonomous experimental robot device, management system and method includes:

The robot main control system 201, the robot main control system 201, is used to control the robot. The robot main system 201 controls the communication of each robot node through the robot main system, and drives and operates each connected hardware device. The communication module of robot nodes realizes inter-node publishing and receiver communication through messages, services, actions and other communication methods.

The microscope 217 is connected to the robot main control system 201 and is used for visual collection of experimental samples, and pictures under the microscope assist in the intelligent identification of microorganisms and cells.

Incubator device 218, the incubator device includes: dispenser, test tube, beaker and other devices, used for cell, microorganism and other experiments culture, filtration, separation, centrifugation, cell disruption, extraction, biosynthesis, precipitation, dry.

Shaker device 211, the oscillator device includes: a stirring rod, a shaking device, which is used for shaking and stirring in the experiment of the moving robot arm.

The heating device 214 is connected to the robot main control system 201 for heating the experimental sample.

The cooling device 215 is connected to the robot main control system 201 and is used for cooling the experimental sample.

The drying device 216 includes: a microwave drying device, a freeze drying device, a fluidized bed drying device, an infrared drying device, an airflow drying device, a spray drying device, and one or more of the chamber drying devices. , for drying experimental samples.

The stainer device 219, the stainer device, is used to stain the sample.

The filter device 208 is connected to the robot main control system 201, and the filter device 208 includes one or more of: a vacuum suction filter device, a centrifugal filter device, a tubular centrifugal device, a disc type centrifugal device, and an ultracentrifugation device A device for filtering experimental samples.

The cell crushing device 212 is connected to the robot main control system 201 and is used for various crushing methods, including chemical method, mechanical method, enzymatic hydrolysis method, alkali treatment method, osmotic shock method, and lipolysis method. Mechanical methods include: ultrasonic, high pressure homogenization, grinding, bead grinding.

The biosensor device 213 is connected to the robot main control system 201. The biosensor device 213 includes: nano biosensors, enzyme biosensors, biological pseudo-array chips, microfluidic chips, DNA sensors, immune biosensors, gas sensors, and ion sensors.

The 360-degree rotating table and scale are connected to the main control system 201 of the robot for 360-degree observation of experimental details and scales to measure the experimental objects.

Visual magnification device and visual devices 203, 204, the described visual device 203, and the magnified visual device 203 are connected with the mobile robot arm 206 and the robot main control system 201, which are used to identify numbers, color labels, according to the shape of the vessel, and position recognition experiments The steps in the process, the utensils in the process, the location of the reactor, etc.

The mobile device 205 is connected with the robot main control system 201, the visual camera 202, and the obstacle avoidance device 205. The mobile device 205 includes wheel-type movement and crawler-type movement for moving the robot arm. Mobile robot arm with wheel type, crawler type can be disassembled.

The robot arm 206 is connected to the robot main control system 201 and the camera 202, and is used for the robot arm 206 to grasp, pick up, pick up, place target items, scan codes, organize, and place items. The action planning method of the robotic arm 206 includes: using the improved neural network method, adaptively learning and adjusting the parameters of the robotic arm to achieve autonomous robotic arm action planning, and using the robot body control and remote user control to adjust the planning parameters of the robotic arm.

The voice device 220 is connected to the robot main control system 201, and the voice module 220 includes: a directional voice recognition device and a microphone. It is used for voice interaction between remote users and experimental devices, experimental guidance, voice commands, voice inquiry of experimental steps, and knowledge quiz.

The multimedia touch screen 202 is connected to the robot main control system 201, and the multimedia touch screen 202 is used for experimental steps, experimental process display, demonstration, experimental learning guidance, and the like.

The scanning code information collecting device 207, the scanning code information collecting device 207 includes: a barcode, a two-dimensional code, a biological information collector, and an RFID information collector. It is used to manage experimental consumables, experimental samples, experimental personnel, and manage experimental steps and related information using barcodes and QR codes.

The extractor 209, the extractor device 209 includes: an extractor, a separation tank, an expansion valve, and a compressor.

The X-ray crystallizing device 210, the X-ray crystallizing device 210 is connected to the robot main control system 201 for crystallizing.

Example 2

As shown in Figure 1 and Figure 2, the experimental robot device visually recognizes the experimental device, the method for the color identification of the experimental label, and the robotic arm movement to grab the experimental device, the steps are as follows:

Set the corresponding experimental device parameters of the experimental scene and their corresponding position parameters, enter the experimental device corresponding to the experimental bench, the experimental label color, numbers, letters, characters, and the mathematical model of the special logo. In the experimental scene, the shape, outline, color, number, letter, text, and special identification image of the vessel are extracted, and the corresponding image feature is used as the input value. Improve the weight optimizer to quickly train images and get output values.

According to the output shape, outline, structure, color, number, letter, and text special identification results, accurately identify the target, specify the target and locate the target position.

Set the experimental steps, plan the action of the robotic arm 206, move the robotic arm 206 to the experimental device and its position of each experimental step according to the action, and under the main system, the robotic arm is positioned and moved to the specified target experimental device. Configuration frequency, maximum and minimum linear velocity, maximum and minimum rotational speed, maximum linear acceleration in x direction and y direction, maximum angular velocity, error from the target direction, error from the target position, weight to reach the target position, avoid obstacles parameters such as weights. Configure the parameters of the robot arm, the position of the obstacle and its size parameters, the update frequency, the release frequency, the position of the experimental device, the icons, colors and parameters of each device on the experimental platform, and the maximum delay of conversion between coordinate conversion frameworks and other parameters.

Set robot initialization parameters, target id and its position and angle pose information, set motion plan, select joint angle, joint limit setting, robot arm moves to specified joint shape, joint limit, joint trajectory position, velocity component, joint speed. Set motion constraints, target trajectory, speed settings, execute planned trajectory, set joint position, joint angle. Set the Cartesian path on the robotic arm, and the objects that can be picked up by the target pose are set for the robot pose parameters. Set the anti-collision matrix of the robot arm, and set the anti-collision detection module (detection of other parts of the robot itself, detection of obstacles in the scene). The experimental actions are as follows:

Initialize the robot arm 206 , grab objects, claws, grab, pick and place, parameter settings for grabbing poses, and grab experimental targets.

Use the scanning device 207 to scan the experimental samples, experimental consumables, and the work permit of the experimenter, place the experimental samples on the 360-degree rotating table and the scale 207, and use the visual magnifier 203 to observe the details of the product.

The robotic arm 206 grabs the object from the 360-degree rotating table observation table 207, moves to the position of the biological experiment reactor under the positioned experiment label, grabs the target, the experimental sample, the experimental consumables, uses the robot arm, and operates and presses the special biological experiment reactor Mark the button and operate the bioreactor, according to the time interval, use the visual amplifier 203 to observe the 360-degree rotating table observation table, the experimental sample in the incubator 218, record the experimental process, and assist in recording the dynamic real-time experimental data and its changes to achieve real-time Data classification analysis, identification of microorganisms, bacteria and other experimental samples under microscope pictures.

The robotic arm 206 grabs the experimental sample from the 360-degree rotating table observation platform 207, and moves it to the biosensor device 213 according to the experimental steps, including nano biosensors, enzyme biosensors, biological pseudo-array chips, microfluidic chips, DNA sensors, Robotic arms such as immune biosensors, gas sensors, and ion sensors detect experimental data according to planned actions.

Example 3

On the basis of Embodiments 1 and 2, the robot main control system 201 module and the visual recognition module 203 interact with the robot arm 206, target setting, recognition, positioning, using the robot arm 206 to correspond, the robot arm 206 action planning to grab, move , scan the code, place the experimental vessel, the experimental sample, and press the action, the embodiment of the robot arm 206 of the present invention is not limited to this, and the specific implementation steps are as follows:

Through the management system and the voice module 220 of the robot main control system 201, call, voice command, voice interaction, browse and query the experimental data. Use the experiment reservation module to reserve an experiment, and remind the experimenter according to the time and appointment. Use the camera and the vision module 203 to supervise, observe, and magnify the observation of experimental vessels and experimental samples in real time. Record, manage experiment logs, manage consumable samples, and manage experimental staff.

Through the multimedia touch screen 202 , the experimental steps, the experimental process, the remote and the experimental guidance, the remote communication of the experimental user, the contact guidance, the experimental detection, and the monitoring of the experiment are displayed.

Claims (10)

A remote and autonomous experimental robot device, management system and method, characterized in that a remote and autonomous experimental robot device comprises: A robot main control system, the robot main control system is used to control the robot. The robot main system controls the communication of each robot node through the robot main system, and each connected hardware device drives and moves. The communication module of robot nodes realizes inter-node publishing and receiver communication through messages, services, actions and other communication methods. The microscope visual acquisition and recognition module is connected with the main control system of the robot, which is used for visual acquisition of experimental samples, and the pictures under the microscope assist the intelligent identification of microorganisms and cells. Incubator device, the incubator device includes: dispenser, test tube, beaker and other devices, used for cell, microorganism and other experiments culture, filtration, separation, centrifugation, cell disruption, extraction, biosynthesis, precipitation, drying . The oscillator device is connected with the main control system of the robot, and the oscillator device includes: a stirring rod and an oscillatory device, which are used for shaking and stirring in the experiment of the mobile robot arm. The heating device is connected with the main control system of the robot for heating the experimental sample. The cooling device is connected to the main control system of the robot for cooling the experimental samples. The drying device is connected with the main control system of the robot, and the drying device includes: a microwave drying device, a freeze drying device, a fluidized bed drying device, an infrared drying device, an airflow drying device, a spray drying device, and one of the chamber drying devices. one or more devices. The stainer device is connected to the main control system of the robot and is used for staining experimental samples. Filtration device, the filter device includes one or more of vacuum filtration device, centrifugal filtration device, tubular centrifugal device, disc centrifugal device and ultracentrifugation device. The cell crushing device, connected with the main control system of the robot, is used for various crushing methods, including: chemical method, mechanical method, enzymatic hydrolysis method, alkali treatment method, osmotic shock method, and lipolysis method. Mechanical methods include: ultrasonic, high pressure homogenization, grinding, bead grinding. The extractor is connected to the main control system of the robot, and the extractor device includes: an extractor, a separation tank, an expansion valve, and a compressor. The crystallization device, the crystallization device, is connected with the main control system of the robot for crystallization. The multi-sensor device is connected to the main control system of the robot. The multi-sensor device includes: nano biosensors, enzyme biosensors, biological pseudo-array chips, microfluidic chips, DNA sensors, immune biosensors, gas sensors, ion sensors, photoelectric sensors , Strain and Piezoresistive Sensors, Inductive Sensors, Capacitive Sensors, Hall Sensors, Piezoelectric Sensors, Impedance Sensors, Semiconductor Sensors, Acoustic Sensors, Thermal Sensors, Electrochemical Sensors, Photosensitive Sensors one or more devices. The 360-degree rotating table and scale are connected to the main control system of the robot for 360-degree observation of experimental details, and the scale is used to measure the experimental objects. Vision device and magnifying device, said vision device, magnifying vision device, connected with mobile robot arm, robot main control system, used to identify numbers, color labels, according to the shape and position of the vessel to identify steps in the experimental process, vessels in the process , the location of the reactor, etc. The mobile device is connected with the main control system of the robot, the visual camera, and the obstacle avoidance device. The mobile device includes wheel-type movement and crawler-type movement, which are used to move the robot arm. Mobile robot arm and wheel type, crawler type can be disassembled, mobile base and robot body can be used independently. The mobile robot arm is connected with the main control system of the robot, the mobile device, and the camera. The robot arm action planning method includes: using an improved neural network method, adaptively learning and adjusting the parameters of the robot arm to achieve autonomous robot arm action planning, and using the robot body control and remote user control to adjust the robot arm planning parameters. The voice device is connected with the main control system of the robot, and the voice module includes: a directional voice recognition device and a microphone. It is used for voice interaction between remote users and experimental devices, experimental guidance, voice commands, voice inquiry of experimental steps, and knowledge quiz. The multimedia touch screen is connected with the main control system of the robot, and the multimedia touch screen is described. It is used for experimental steps, experimental process display, demonstration, experimental learning guidance, etc. The scanning code information collection device includes: barcode, two-dimensional code, biological information collector, and RFID information collector. It is used to manage experimental consumables, experimental samples, experimental personnel, and manage experimental steps and related information using barcodes and QR codes. A remote and autonomous experimental robot device, characterized in that the visual recognition module is connected with the main robot control system, a camera, and a visual recognition amplifier, and the visual recognition module includes a camera and an amplifier. It is used to collect and publish image information, to identify people's face information, experimental biological reaction devices, color labels, experimental vessel information, to locate target objects, target people, and their locations, by identifying color, digital code, text, two-dimensional code, special identification and other comprehensive information. Through the robot system, the main control unit can manage the experimental personnel, experimental consumables and experimental items under the video camera in each experimental scene. The 360-degree rotating table, scale and visual amplifier are connected to the main control system of the robot for 360-degree observation of the details of the experimental items. The 360-degree rotating table and scale are used for rotation, assisting the visual recognition of the camera, and the amplifier device observes the details of the experimental product in 360 degrees. A remote and autonomous experimental robot device, characterized in that the microscope visual acquisition and recognition module is connected to the main control system of the robot, and extracts sample images of cells, microorganisms, etc. through an improved machine learning method and an improved deep learning method. The contour, shape, structure, color and other characteristics of the device, intelligently identify the types of microorganisms such as bacteria and viruses, learn and train image parameters to assist in the identification of experimental samples, and visually collect pictures under the microscope to assist in intelligent identification of microorganisms and cells. A remote and autonomous experimental robot device, characterized in that the mobile robot arm is connected with a main system and a visual camera, the robot arm is controlled by an action planning module of the main controller, recognizes targets through vision, and uses multiple robot arms. Grab, take, and put the target item, scan the code for experimental samples, and experimental consumables. Action planning, the robot arm action planning module configures the position parameters and angle parameters of the robot arm, wrist, and claw to plan grab, move, and place parameters, and the robot arm moves, grabs, places, scans, sorts, and places. Items, pressing, operating the experimental reactor, and configuring the action parameters of the robotic arm include adaptive learning adjustment parameters and remote control to adjust the robotic arm parameters. A remote and autonomous experimental robot device, characterized in that the experimental reactor device includes: an oscillator device, a heating device, a cooling device, a drying device, a filtering device, a cell crushing device, and a cell crushing device. device. Further, the oscillator device is connected to the main control system of the robot, and the oscillator device includes: a stirring rod, a shaking device, which is used for the vibration and stirring of the mobile robot arm experiment, and the shaking device is used to complete the shaking by the stirring device, Stir. The parameters for setting the shaking device include: the number of times of shaking, the time of shaking, the strength of shaking, and the method of shaking. Further, the heating device is connected to the main control system of the robot for heating the experimental sample. Set the parameters of the shaking device including: heating temperature, heating time, heating position, range, etc. Further, the cooling device is connected to the main control system of the robot for cooling the experimental sample. Cooling device parameters include: cooling temperature, cooling time, etc. Further, the drying device is connected to the main control system of the robot for drying the experimental samples. The drying device includes one or more of microwave drying device, freeze drying device, fluidized bed drying device, infrared drying device, airflow drying device, spray drying device, and chamber drying device. Parameters for drying samples include: drying time, drying method, drying intensity, etc. Further, the filtering device is connected with the main control system of the robot for filtering. The filter device includes one or more of a vacuum suction filtration device, a centrifugal filter device, a tubular centrifugal device, a disc-type centrifugal device, and an ultracentrifugation device. Further, the cell crushing device is connected to the main control system of the robot for cell crushing. The various crushing devices correspond to various crushing methods including: chemical method, mechanical method, enzymatic hydrolysis method, alkali treatment method, osmotic shock method, and fat dissolving method. Mechanical methods include: ultrasonic, high pressure homogenization, grinding, bead grinding. Further, the dyer device is connected with the main control system of the robot, and is used for dyeing experimental samples. A remote and autonomous experimental robot device is characterized in that the voice module is connected with the main control system of the robot, and the voice module includes a directional voice recognition device and a microphone. By configuring directional recognition device, microphone and other parameters, through voice recognition, voice wake-up, voice-to-text conversion technology, remote user communication, configure language library for remote users. Voice interaction between remote user-robot-experimenter, voice command, voice inquiry, voice knowledge quiz. A remote and autonomous experimental robot device is characterized in that the scanning code information collection device is connected with the robot main control system, and the scanning code information collection device comprises: scanning code information collection, scanning and reading devices. The scanning code information collection, scanning, and reading device is the main system of the robot connected with the camera, scanner, reader, information collection and reading device, intelligent collection and identification of two-dimensional code, digital code, biological information, RFID information and other management experiment personnel , experimental items, experimental equipment and other information. A remote and autonomous experimental robot device, management system and method, characterized in that an experiment management system is connected with a robot main control system, a voice module, and a robot arm, and is used for browsing and querying commodities, experiment reservation, and real-time experiments. Supervision, observation, management of experimental utensils, experimental management of experimental logs, management of consumables samples, management of experimental personnel, remote robot arm control module, visual display module, voice call module and other functions. A remote and autonomous experimental robot device, management system and method, characterized in that, a visual recognition experimental device, a robotic arm action method for an experimental label, the method comprising the following steps: S1. Set the corresponding experimental device parameters of the experimental scene and their corresponding position parameters. S2. Input the experimental device corresponding to the experimental bench, the color of the experimental label, the number, the letter, the text, and the mathematical model of the special logo. S3. Extract the shape, outline, structure, color, number, letter, text, and special identification image of the vessel under the experimental scene. The corresponding image features are used as input values. S4. Improve the weight optimizer, quickly train the image, and obtain the output value. S5. According to the output shape, outline, structure, color, number, letter, text, special identification result, Accurately identify the target, specify the target and locate the target position. S6. According to the experiment, set the experimental steps, plan the action of the robot arm according to the experimental steps, move the robot arm to the test device and its position of each experimental step according to the action plan, and move the robot arm to the designated target experimental device under the main system. . S7. The parameters in the configuration file of each experimental device node include: frequency, maximum and minimum linear velocity, maximum and minimum rotational speed, maximum linear acceleration in x and y directions, maximum angular velocity, error from the target direction, and error from the target position , the weight of reaching the target position, the weight of avoiding obstacles and other parameters. S8. Configure the parameters of the robot arm, the position of the obstacle and the update frequency of its size parameters in each experimental device node parameters such as delay. S9, set the initial parameters of the robot, including the robot, the target and its position and angle pose information. S10. Set the motion plan, select the joint angle, set the joint limit, move the mechanical arm to the specified joint shape, joint limit, Joint trajectory position, velocity component, joint velocity. Set motion constraints, target trajectory, speed settings, execute planned trajectory, set joint position, joint angle. S11. Set the Cartesian path on the robotic arm, and the objects that can be picked up by the target pose are set for the robot pose parameters. S12 , setting the anti-collision matrix of the manipulator, and setting the anti-collision detection module (detection of other parts of the robot itself, detection of obstacles in the scene). S13. Mechanical arm, claw parameter setting, grasping, pick-and-place, grasping pose parameter setting and matching target pose. S14, initialize the placement and grasping, the position of the object, the grasping gesture object, and generate the grasping posture. (Initialize the grab object and create the open and closed posture of the clip). Set the desired gripper approach, parameters to withdraw from the target, and set the grasping attitude. S15 , a data list of trying to change the attitude is required. S16. Grab the gesture list. Change pose, generate grab action (Set the grasping posture; grasp the ID number; set the objects that are allowed to be touched, and set the grasping list). A remote and autonomous experimental robot device, management system and method, characterized in that, an experimental method for recording experimental data in real time, analyzing, classifying experimental data and its changes, using an improved neural network method to identify microorganisms in real time, cell methods , the method includes the following steps: An improved machine learning method for classifying microbial, cellular anomaly data, the method comprising the steps of: S1. Establish a mathematical model of microorganisms and cell samples, and set the time interval for observation. S2. Extract the shape, color, outline, size and other cell characteristics of microorganisms and cell specimens, including features such as color, shape, and contour, and extract the characteristics of microorganisms and cell specimen images. The characteristic value of the image (color, shape, outline), etc., input the characteristic value of the detection item, and record the sample information and changes in each time interval. S3. Assist in recording dynamic real-time experimental data and its changes, and realize real-time data classification and analysis to identify microorganisms and bacteria in pictures under the microscope. Classification and identification of microorganisms, cell types, calculation, analysis of proportions and identification of microorganisms and cells. Further, an improved neural network method for analyzing and identifying experimental specimens, and intelligently identifying microorganisms and cells, is characterized in that the method includes the following steps: S1. Input the mathematical model of the corresponding microorganism and cell. S2. The shape, outline, color, structure, size, state characteristics (granular, rod, foam) and irregularity of the extracted specimen before and after the experiment under the staining reaction, and image recognition under the microscope such as nuclear left shift, nuclear right shift, etc. S3, establishing a mathematical model of the characteristics of the specimen image, and inputting the characteristic value of the detection item. S4. Improve the weight optimizer, quickly train the image, and obtain the output value. S5. According to the output results, assist to identify the microorganisms, cells and their respective shapes, contours, color, structure, size, state characteristics (granular, rod, foam) and Irregularity, kernel shift left, kernel shift right, etc. S6, assist in recording dynamic real-time experimental data and its changes, and realize real-time data classification and analysis to identify microorganisms and bacteria in pictures under the microscope.

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