Modularized and networked scientific research assistant robot for chemical experiment and cooperative working method, device, equipment and medium of modular and networked scientific research assistant robot

Abstract

The invention relates to the technical field of artificial intelligence and chemical experiments, in particular to a modularized and networked scientific research assistant robot for a chemical experiment and a cooperative working method, device, equipment and medium thereof. Loading a function software module corresponding to the target experiment function, obtaining state information and task scheduling of the collaborative experiment equipment, generating a global optimization task execution scheme according to the target experiment function and the state information and the task scheduling of the collaborative experiment equipment, and executing the collaborative experiment equipment on the basis of the global optimization task execution scheme. According to the scientific research assistant robot, at least one scientific research assistant robot and at least one cooperative device are cooperatively controlled through a functional software module, and the operation efficiency of the whole laboratory can be improved through a module software upgrading function and networked cooperation.

Description

Technical Field

[0001] This disclosure generally relates to the fields of artificial intelligence and chemical experiment technology, specifically a modular, networked research assistant robot for chemical experiments and its collaborative working methods, devices, equipment and media. Background Technology

[0002] In modern chemical research, especially in organic chemistry, separation and purification are the most common, time-consuming, and highly reproducible experimental steps in daily work. In traditional experiments, these operations are mainly performed manually by laboratory technicians, which leads to problems such as high repetitive labor intensity, large human error, high safety risks, and efficiency bottlenecks.

[0003] Current laboratory automation equipment is mostly specialized equipment designed for specific steps, with limited functionality and unable to cover the entire experimental process. These devices and systems are often "information silos," lacking effective communication and collaboration, and unable to form a cohesive whole. While general-purpose collaborative robots possess some operational capabilities, they lack a deep understanding of the chemical experimental environment, and secondary development is difficult and costly.

[0004] Therefore, the market urgently needs an intelligent robot solution that can move flexibly, collaborate with humans, understand chemical experimental procedures, update and expand experimental capabilities in a simple way, and connect to laboratory networks to work collaboratively with other intelligent agents and share resources. Summary of the Invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a modular, networked research assistant robot for chemical experiments and its collaborative working methods, devices, equipment and media, aiming to solve the problems of low automation, poor flexibility, difficulty in functional expansion and "mutual isolation and inability to collaborate" among automated devices in the prior art. It provides a research assistant robot that can upgrade its functions through modular software and improve the overall laboratory operating efficiency through networked collaboration.

[0006] In a first aspect, embodiments of this application provide a collaborative working method for a modular, networked research assistant robot used in chemical experiments, comprising: Obtain instructions for chemical experiments; The chemical experiment task instructions are parsed to determine at least one target experimental function. At least one functional software module corresponding to the target experimental function is loaded, and the status information and task schedule of at least one collaborative experimental device and at least one research assistant robot are obtained. The functional software module is used to control the collaborative experimental device to execute the target experimental function. The functional software module is configured according to the at least one chemical experiment task in which the at least one research assistant robot is applied. Based on the status information and task schedule of at least one of the target experimental functions, at least one scientific research robot, and at least one of the collaborative experimental devices, a globally optimized task execution plan is generated; the globally optimized task execution plan includes at least the device identifier of at least one collaborative experimental device and its execution content and execution time; Based on the global optimization task execution scheme, at least one research assistant robot and at least one of the collaborative experimental devices are controlled by the functional software modules to execute the global optimization task execution scheme and complete the chemical experiment task.

[0007] In some embodiments, the step of collaboratively controlling the research assistant robot through the functional software modules based on the globally optimized task execution scheme includes: Local environmental information is obtained based on the state information, task queue, collaborative work information, and multimodal perception system of the research assistant robot. Based on the local environment information and the global optimization task execution scheme, a fast path planning scheme is obtained; Based on the aforementioned rapid path planning scheme, the functional software modules collaboratively control one or more of the research assistant robot's motion and positioning system, multi-degree-of-freedom robotic arm, and end effector system.

[0008] In some embodiments, obtaining a fast path planning scheme based on the local environment information and the global optimization task execution scheme includes: Based on the global optimization task execution scheme, obtain the current target experimental function; Based on the local environment information and the collaborative experimental device corresponding to the current target experimental function, for each research assistant robot, a first planned path is obtained to move the research assistant robot to the vicinity of the current collaborative experimental device that performs the current target experimental function, based on the mobile and positioning system of the research assistant robot, wherein at least one research assistant robot collaboratively completes the current target experimental function.

[0009] In some embodiments, the functional software module includes an improved operating procedure corresponding to the target experimental function, and the step of obtaining a fast path planning scheme based on the local environment information and the global optimized task execution scheme includes: Based on the global optimization task execution scheme, obtain the current target experimental function; Based on the local environmental information and the improved operating procedure corresponding to the current target experimental function, for each research assistant robot, a second planned path is obtained to move the target to the target position based on the multi-degree-of-freedom robotic arm and end effector system of the research assistant robot, wherein at least one of the research assistant robots collaboratively completes the current target experimental function.

[0010] In some embodiments, obtaining a fast path planning scheme based on the local environment information and the global optimization task execution scheme includes: The local environment information and the global optimization task execution plan are sent to the shared computing power center so that the fast path planning plan can be generated through the shared computing power center. Receive the fast path planning scheme returned by the shared computing power center.

[0011] In some embodiments, the global optimization task execution scheme includes multiple consecutive global optimization task execution sub-schemes, and the method further includes: The experimental status of the current global optimization task execution sub-scheme is obtained in real time, and a task scheme switching instruction is generated when the current global optimization task execution sub-scheme is completed. In response to a task switching command, the current local environment information of each of the research assistant robots is acquired based on the multimodal perception system of at least one of the research assistant robots; Based on the current local environment information obtained through at least one of the scientific research robots and the next global optimization task execution sub-scheme, a fast path planning scheme for executing the next global optimization task execution sub-scheme is obtained.

[0012] Secondly, embodiments of this application provide a collaborative working device for a modular, networked research assistant robot used in chemical experiments, comprising: The acquisition module is used to acquire chemical experiment task instructions; The parsing module is used to parse the chemical experiment task instructions, determine at least one target experimental function, load at least one functional software module corresponding to at least one target experimental function, and obtain the status information and task schedule of at least one collaborative experimental device, as well as the status information and task schedule of at least one research assistant robot. The functional software module is used to control the collaborative experimental device to execute the target experimental function. The functional software module is configured according to the at least one chemical experiment task in which the at least one research assistant robot is applied. A generation module is used to generate a globally optimized task execution plan based on the status information and task schedule of at least one of the target experimental functions, at least one scientific research robot, and at least one of the collaborative experimental devices; the globally optimized task execution plan includes at least the device identifier of at least one collaborative experimental device and its execution content and execution time; An execution module is used to coordinate and control at least one research assistant robot and at least one of the collaborative experimental devices through the functional software modules based on the globally optimized task execution scheme to complete the chemical experiment task.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in embodiments of this application.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in embodiments of this application.

[0015] Fifthly, embodiments of this application provide a computer program product, including a computer program, characterized in that, when the computer program is executed by a processor, it implements the method described in embodiments of this application.

[0016] The collaborative working method of the modular and networked research assistant robot for chemical experiments proposed in this application involves parsing the chemical experiment task instructions after receiving them, determining the target experimental function, loading the corresponding functional software module, and acquiring the status information and task schedule of the collaborative experimental equipment. Based on the target experimental function, the status information and task schedule of the collaborative experimental equipment, a globally optimized task execution plan is generated. Based on the globally optimized task execution plan, the research assistant robot and at least one collaborative device are collaboratively controlled through the functional software module to complete the chemical experiment task by executing the optimal task execution plan. This application improves the flexibility and adaptability of the research assistant robot by configuring functional software modules to adapt to different experimental tasks. By collaboratively controlling the research assistant robot and collaborative experimental equipment through functional software modules, it not only enables the research assistant robot to undertake high-precision, complex, and repetitive experimental tasks, but also breaks down the problem of information silos in traditional experimental equipment. Furthermore, by generating a globally optimized task execution plan based on the status information and task schedule of the collaborative experimental equipment, it achieves automatic flow and global optimization of complex chemical experimental tasks, greatly improving the overall operating efficiency of the experimental tasks.

[0017] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0018] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This paper illustrates the implementation environment architecture diagram of the collaborative working method of the modular, networked research assistant robot for chemical experiments provided in the embodiments of this application; Figure 2 A schematic diagram of the structure of a research assistant robot provided in one embodiment of this application is shown; Figure 3 A schematic diagram of the structure of a central control system provided in an embodiment of this application is shown; Figure 4 A flowchart illustrating a collaborative working method for a modular, networked research assistant robot for chemical experiments, provided in an embodiment of this application, is shown. Figure 5 A flowchart illustrating a collaborative working method for a modular, networked research assistant robot for chemical experiments, provided in another embodiment of this application, is shown. Figure 6 A schematic diagram of the structure of a collaborative working device for a modular, networked research assistant robot for chemical experiments, provided in an embodiment of this application, is shown. Figure 7 A schematic diagram of the structure of a computer system suitable for implementing an electronic device or server according to embodiments of this application is shown. Detailed Implementation

[0019] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] For the specific implementation environment of the collaborative working method of the modular, networked research assistant robot for chemical experiments proposed in this application, please refer to [link to relevant documentation]. Figure 1 . Figure 1 This paper illustrates the implementation environment architecture diagram of the collaborative working method of the modular, networked research assistant robot for chemical experiments provided in the embodiments of this application.

[0022] like Figure 1 As shown, the implementation environment architecture includes: at least one research assistant robot 100, at least one collaborative experimental device 200, and a shared computing center 300.

[0023] Among them, such as Figure 2 As shown, the research assistant robot 100 includes a central control system 110 and a hardware mechanism 120. The hardware mechanism 120 includes, but is not limited to, a motion and positioning system, a multi-degree-of-freedom robotic arm, an end effector system, and a multimodal perception system.

[0024] like Figure 3 As shown, the central control system 110 includes a user / system interface layer, a modular function layer, and a core system layer. The user / system interface layer is equipped with a human-computer interaction system and a laboratory information management system (LIMS). The human-computer interaction system includes, but is not limited to, a voice recognition system and an input control panel, etc., which are not specifically limited in this application.

[0025] The modular functional software layer consists of a series of independent software modules designed for specific chemical experiments. The number of functions configured in the research assistant robot can be adjusted by installing and uninstalling these software modules. Figure 3The intermediate module functional software layer includes separation, purification, and extraction software modules, as well as other configurable new experimental modules. The core foundational software layer solidifies the research assistant robot's basic understanding and general operational capabilities in the laboratory, including but not limited to instrument / vessel recognition, functional area localization, safety rule understanding, and hardware mechanism control functions, such as grasping / placing, liquid transfer, autonomous navigation, and force control.

[0026] Optionally, standalone software modules for specific chemical experiments can be packaged into applications (apps), and the chemical experiment functions configured for the research assistant robot can be adjusted by installing, upgrading, and uninstalling the application.

[0027] The independent software modules for specific chemical experiments may include improved operating procedures (SOPs) for that chemical experiment. These improved SOPs are optimized and improved versions of the Standard Operating Procedure (SOP) based on the higher positioning accuracy, more stable control, and tireless characteristics of the research assistant robot. For example, when installing a separation column, given the research assistant robot's ability to achieve positioning and installation much faster and with zero error than humans, the improved SOP may only include parameters such as the sample addition angle and speed. For instance, while the SOP specifies "slow addition," the improved SOP is set to "add at a flow rate of 0.5 mL / s at a 45-degree angle along the tube wall." This allows for parameter data that can be accurately executed directly by the research assistant robot's hardware. Alternatively, during crystallization, the research assistant robot can control crystal growth through small, continuous, and programmed perturbations; this part incorporates perturbation control strategies based on the research assistant robot's capabilities. It should be understood that these improvements, while strictly adhering to fundamental chemical principles, aim to maximize the efficiency, repeatability, and success rate of the experiment.

[0028] The collaborative experimental device 200 is an independent chemical experimental device with specific experimental functions. For example, for separation and purification experiments, the collaborative experimental device 200 includes, but is not limited to, a chromatograph and a mass spectrometer. It should be noted that, in the embodiments of this application, the collaborative experimental device 200 includes a network interface for communication, including but not limited to Wi-Fi 6, OPC-UA, Bluetooth, etc., to receive experimental operation instructions sent by the research assistant robot 100 and to send real-time status information and experimental analysis result data to the research assistant robot 100, etc. This application does not make specific limitations.

[0029] The real-time status information of the collaborative experimental equipment 200 specifically includes key parameters that are strongly correlated with the chemical experimental process, such as the current elution gradient of the chromatograph, the remaining elution time, the detector baseline equilibrium state, the column pressure curve, the rotation speed of the rotary evaporator, and the heating temperature of the water bath; the experimental analysis results data include, but are not limited to, the fraction purity detection results and mass spectrometry characteristic peaks.

[0030] The collaborative working method of the modular and networked research assistant robot for chemical experiments proposed in this application can be implemented by the collaborative working device of the modular and networked research assistant robot for chemical experiments, which can be installed on the research assistant robot.

[0031] To further illustrate the technical solutions provided in the embodiments of this application, a detailed description is provided below in conjunction with the accompanying drawings and specific implementation methods. Although the embodiments of this application provide method operation instruction steps as shown in the following embodiments or drawings, the method may include more or fewer operation instruction steps based on conventional or non-creative effort. In steps where there is no logically necessary causal relationship, the execution order of these steps is not limited to the execution order provided in the embodiments of this application. In actual processing or when the device executes the method, it may be executed sequentially or in parallel according to the method shown in the embodiments or drawings.

[0032] It should be noted that the acquisition or use of data in the embodiments of this application requires the user's consent. The relevant data can only be obtained after the user's authorization and permission, and the acquisition or use of the data complies with the laws and regulations of the relevant regions.

[0033] Please refer to Figure 4 , Figure 4 A flowchart illustrating a collaborative working method for a modular, networked research assistant robot used in chemical experiments, according to an embodiment of this application, is shown. Figure 4 As shown, the method includes: Step 401: Obtain the chemical experiment task instruction.

[0034] Specifically, chemical experiment task instructions can be obtained through the user / system interface layer. For example, the instructor's voice instructions can be obtained, and chemical experiment task instructions can be obtained by performing speech recognition on the voice instructions; or the chemical experiment task information input by the instructor through the Laboratory Information Management System (LIMS) can be obtained to generate chemical experiment task instructions. This application does not make any specific limitations.

[0035] Step 402: Parse the chemical experiment task instructions, determine at least one target experimental function, load at least one functional software module corresponding to the at least one target experimental function, and obtain the status information and task schedule of at least one collaborative experimental device, as well as the status information and task schedule of at least one research assistant robot; the functional software module is used to control the collaborative experimental device to execute the target experimental function; the functional software module is configured according to the at least one chemical experiment task in which the research assistant robot is applied.

[0036] In other words, after obtaining the chemical experiment task instruction, the chemical experiment task is parsed, including but not limited to breaking down and analyzing the chemical experiment task to determine at least one target experimental function required to complete the chemical experiment task. It should be understood that the chemical experiment task instruction can be the final result of a series of chemical experiment functions. For example, an experimental task to separate and purify a mixture mainly includes pretreatment steps such as column conditioning and sample loading, as well as core separation steps. Among these, column conditioning, sample loading, and separation are multiple different target experimental functions. Each functional software module can correspond to one or more target experimental functions. For example, column conditioning, sample loading, and separation can each have corresponding functional software modules to ensure the independence of each experimental function. Alternatively, they can correspond to the same functional software module due to their inherent correlation (e.g., serving the same chemical experiment task). This application does not make specific limitations.

[0037] Optionally, the functional software modules can be packaged into applications (apps). By installing, upgrading, and uninstalling these applications, the chemical experimental functions configured for the research assistant robot can be adjusted. This gives the research assistant robot more convenient modularity, allowing for the configuration of relevant functional software modules according to the needs of the chemical laboratory in which it is used. This effectively improves the flexibility and replaceability of the research assistant robot's configuration. Functional software modules may include, but are not limited to, separation functional software modules (separation.app), recrystallization functional software modules (recrystallization.app), etc.

[0038] Collaborative experimental equipment refers to devices used to perform the aforementioned chemical experimental functions, such as mass spectrometers used for separation and detection in separation and purification laboratories. The status information of collaborative experimental equipment includes whether it is idle or active, and its task schedule is a task plan for a specific time frame. Similarly, research assistant robots are auxiliary devices used to assist in completing the aforementioned chemical experimental functions, such as research assistant robots used for high-precision positioning during separation column installation. The status information of research assistant robots includes whether they are idle or active, and their task schedule is a task plan for a specific time frame. It should be understood that by acquiring the status information and task schedules of collaborative experimental equipment and research assistant robots, data support can be provided for generating globally optimized task execution schemes, enabling efficient utilization of collaborative experimental equipment and research assistant robots, and improving the efficiency of chemical experimental task execution. Simultaneously, it effectively breaks down the silo problem of experimental equipment in existing technologies, incorporating the status information and task schedules of collaborative experimental equipment and research assistant robots into the global optimization strategy, making automatic flow and orderly connection between tasks possible, and greatly improving the overall operational efficiency of the laboratory.

[0039] It should also be noted that the functional software module is used to control the collaborative experimental equipment to perform the target experimental functions. That is, in addition to the collaborative experimental equipment communicating with the research assistant robot to send the collaborative experimental equipment's status information and task schedule to the research assistant robot, the research assistant robot also utilizes this communication capability to control the collaborative experimental equipment based on the functional software module, including but not limited to sending experimental parameter information for execution, such as separation speed and separation time, to the collaborative experimental equipment. Therefore, this application utilizes the research assistant robot to configure communication between the collaborative experimental equipment and the research assistant robot to achieve automated experimental control, eliminating the need for manual intervention by laboratory personnel and improving laboratory operating efficiency.

[0040] Step 403: Generate a globally optimized task execution plan based on the status information and task schedule of at least one target experimental function, at least one scientific research robot, and at least one collaborative experimental device; the globally optimized task execution plan shall include at least the device identifier of at least one collaborative experimental device and its execution content and execution time.

[0041] It should be noted that, in this embodiment, the globally optimized task execution scheme is a task execution scheme that completes chemical experiment task instructions with higher precision, less time, and lower cost. The globally optimized task execution scheme includes at least the device identifier of at least one collaborative experimental device and its execution content and execution time, as well as the identifier of at least one scientific research robot and its execution content and execution time.

[0042] In other words, the global optimization task execution scheme uses the device identifiers, execution content, and execution time of at least one collaborative experimental device and at least one research assistant robot to reasonably configure task scheduling for at least one collaborative experimental device and at least one research assistant robot. By reasonably scheduling among at least one collaborative experimental device and at least one research assistant robot, task waiting time is reduced, thereby improving the efficiency of experimental task completion.

[0043] Specifically, when a chemical experiment task requires only one collaborative experimental device, the globally optimized task execution plan includes the device identifier, execution content, and execution time of that collaborative experimental device, such as mass spectrometer, separation and detection, execution time XXXXX-XXXXX. Alternatively, when an experimental task requires multiple collaborative experimental devices, the globally optimized task execution plan includes the device identifier, execution content, and execution time of each collaborative experimental device, such as mass spectrometer 1, separation and detection, execution time XXXXX-XXXXX; mass spectrometer 2, separation and detection, execution time XXXXX-XXXXX; mass spectrometer n, separation and detection, execution time XXXXX-XXXXX, etc. In other words, multiple collaborative experimental devices and multiple research assistant robots can be the same or different; this application does not impose specific limitations.

[0044] Preferably, when multiple collaborative experimental devices are of different types, the global optimization task execution scheme also includes the execution order of at least one collaborative experimental device for performing chemical experimental tasks, and the start and end times of the execution content, so that the research assistant robot can subsequently execute each experimental function in an orderly manner according to the global optimization task execution scheme, thereby completing the chemical experimental task.

[0045] Optionally, the research assistant robot can be configured with optimization algorithms, including but not limited to gradient descent algorithms and genetic optimization algorithms. After obtaining the status information and task schedule of at least one target experimental function and at least one collaborative experimental device, it generates an initial task execution plan by analyzing the status information and task schedule of at least one target experimental function and at least one collaborative experimental device. Then, it uses optimization algorithms to perform iterative analysis to obtain a globally optimized task execution plan.

[0046] Step 404: Based on the globally optimized task execution scheme, at least one research assistant robot and at least one collaborative experimental device are controlled collaboratively through functional software modules to execute the globally optimized task execution scheme to complete the chemical experiment task.

[0047] It should be noted that, in the embodiments of this application, the functional software modules in the research assistant robot are used to realize the coordinated control of the entire chemical experiment task, including but not limited to the control of its own operating functions and the control of the collaborative experimental equipment. The control of its own operating functions includes, but is not limited to, the coordinated control of one or more of the research assistant robot's motion and positioning system, multi-degree-of-freedom robotic arm and end effector system.

[0048] Therefore, the collaborative working method of the modular and networked research assistant robot for chemical experiments proposed in this application, after obtaining the chemical experiment task instruction, parses the instruction to determine the target experimental function, loads the corresponding functional software module, and obtains the status information and task schedule of the collaborative experimental equipment. Based on the target experimental function, the status information and task schedule of the research assistant robot, and the status information and task schedule of the collaborative experimental equipment, a globally optimized task execution plan is generated. Based on the globally optimized task execution plan, at least one research assistant robot and at least one collaborative device are collaboratively controlled through the functional software modules to complete the chemical experiment task by executing the optimal task execution plan. This application improves the flexibility and adaptability of the research assistant robot by configuring functional software modules to adapt to different experimental tasks. By collaboratively controlling the research assistant robot and collaborative experimental equipment through functional software modules, it not only enables the research assistant robot to undertake high-precision, complex, and repetitive experimental tasks, but also breaks down the problem of information silos in traditional experimental equipment. Furthermore, by generating a globally optimized task execution plan based on the status information and task schedule of the collaborative experimental equipment, it achieves automatic flow and global optimization of complex chemical experiment tasks, greatly improving the overall operating efficiency of the experimental tasks.

[0049] In one feasible embodiment, such as Figure 5 As shown, based on a globally optimized task execution scheme, the research assistant robot is collaboratively controlled through functional software modules, including: Step 501: Obtain local environmental information based on the state information, task queue, collaborative work information, and multimodal perception system of the research assistant robot.

[0050] It should be noted that the research assistant robot is equipped with a multimodal perception system, which includes, but is not limited to, an image acquisition unit, a voice acquisition unit, and a temperature acquisition unit, used to collect and perceive local environmental information of the research assistant robot. This local environmental information includes, but is not limited to, information on the type and location of objects around the research assistant robot, the location of collaborative experimental equipment, the location of experimental instruments, the location of chemicals, and dynamic chemical characteristics during the experiment. In a preferred embodiment, the multimodal perception system combines a vision-language-action (VLA) model, using the image acquisition unit to identify changes in liquid level, sudden changes in solution color, solid precipitation states, or eddy formation; and using the infrared thermal imaging unit to perceive the real-time temperature distribution and heat release of the reaction vessel.

[0051] Step 502: Based on local environmental information and the global optimization task execution plan, obtain a fast path planning scheme.

[0052] It should be noted that the rapid path planning scheme includes a first planned path based on the mobile and positioning system of the research assistant robot to move the research assistant robot to the vicinity of the current collaborative device to perform the current target experiment function and / or a second planned path based on the multi-degree-of-freedom robotic arm and end effector system of the research assistant robot to move the target to be moved to the target position.

[0053] In other words, the path planning for a research assistant robot is divided into a first planned path that moves the robot to its current location and a second planned path that moves the robot's multi-degree-of-freedom robotic arm and end effector system. The first planned path moves the robot to the vicinity of the collaborative experimental equipment, while the second planned path performs the specific operation of the target experimental function, such as pipetting. When the research assistant robot is a single, movable unit, rapid path planning includes both the first and second planned paths; for example, the embodied robot needs to move to the vicinity of the collaborative experimental equipment to simulate a lab technician performing specific experimental operations. When the research assistant robot comprises multiple controlled collaborative subsystems, such as multi-degree-of-freedom robotic arms and end effector systems configured near each collaborative experimental equipment, rapid path planning only includes the second planned path. This means that no additional movement of the research assistant robot is required; the multi-degree-of-freedom robotic arms and end effector systems can be used directly for specific operations.

[0054] In a feasible embodiment, a fast path planning scheme is obtained based on local environmental information and a globally optimized task execution scheme, including: obtaining the current target experimental function based on the globally optimized task execution scheme; and, for each research assistant robot, obtaining a first planned path based on the research assistant robot's mobility and positioning system to move the research assistant robot to the vicinity of the current collaborative experimental device for performing the current target experimental function, according to the local environmental information and the collaborative experimental device corresponding to the current target experimental function. In this case, at least one research assistant robot collaboratively completes the current target experimental function.

[0055] It should be understood that research assistant robots may include mobility and positioning systems, such as drive motors and casters, but this application does not make any specific limitations.

[0056] In this embodiment, the local environmental information refers to the local environmental information of the laboratory where the research assistant robot is located, including but not limited to the placement of various collaborative experimental equipment, tables and chairs in the laboratory, so as to plan the first planned path for the research assistant robot to move to the vicinity of the collaborative experimental equipment that performs the current target experimental function as quickly as possible.

[0057] In other words, this application arranges multiple target experimental functions for completing chemical experimental tasks in an orderly manner through a globally optimized task execution scheme. The research assistant robot executes each target experimental function in sequence according to the globally optimized task execution scheme, and moves to the vicinity of the collaborative experimental equipment for completing the target experimental function corresponding to the current execution order according to the first planned path before executing each target experimental function, so as to provide an operational basis for subsequent specific operations using a multi-degree-of-freedom robotic arm and end effector system.

[0058] In another feasible embodiment, the functional software module includes an improved operating procedure corresponding to the target experimental function, and obtains a fast path planning scheme based on local environmental information and a globally optimized task execution scheme. This includes: obtaining the current target experimental function based on the globally optimized task execution scheme, and obtaining a second planned path for each research assistant robot based on the local environmental information and the improved operating procedure corresponding to the current target experimental function, using the multi-degree-of-freedom robotic arm and end effector system of the research assistant robot to move the target to the target position. In this embodiment, at least one research assistant robot collaboratively completes the current target experimental function.

[0059] It should be understood that, in the embodiments of this application, the local environmental information refers to the collaborative experimental equipment and its surrounding operating environment, including but not limited to the positions of multiple placement holes, the positions of liquid reservoirs, and the positions of feeding holes on the collaborative experimental equipment, so as to plan a second planned path for the research assistant robot to move the target to the target position, such as moving the target solution to be moved to the feeding hole and putting it into the liquid reservoir.

[0060] It should also be understood that the improved operating procedures stored in the software module for the research assistant robot to perform the current target experiment are based on standard operating procedures and are suitable for the research assistant robot to perform operations with higher efficiency and accuracy, such as pipetting frequency, pipetting speed, and pipetting drop height. By configuring the research assistant robot with improved operating procedures, it can achieve the speed and precision of human experimenters while ensuring adherence to chemical operation principles, thereby improving the reliability of research results.

[0061] Specifically, after determining the current target experimental function based on the global optimization task execution scheme, the scientific research assistant robot determines multiple operation steps to implement the current target experimental function according to the improved operation procedure corresponding to the current target experimental function. Based on local environmental information and multiple operation steps, a second planned path is obtained for the multi-degree-of-freedom robotic arm and end effector system of the scientific research robot to move the target to the target position, such as a second planned path for controlling the multi-degree-of-freedom robotic arm and end effector system to perform liquid pipetting operations.

[0062] In a preferred embodiment, when the chemical experiment task is complex, resulting in numerous target experimental functions or complex supporting operations for achieving the target experimental functions, the computational load of the fast path planning scheme can easily become large, exceeding the computational capacity of the research assistant robot itself. Based on this, this application also proposes sending local environmental information and a global optimization task execution plan to a shared computing power center, so that the shared computing power center can generate a fast path planning scheme and receive the fast path planning scheme returned by the shared computing power center.

[0063] In other words, this application also equips the research assistant robot with a shared computing power center. This shared computing power center shares computing power with other functions, such as various collaborative experimental devices sharing computing power with the research assistant robot. That is, the computing power of collaborative experimental devices not used in the chemical experiment task can be used as shared computing power to provide computing power assistance to the research assistant robot. In a feasible embodiment, the shared computing power center can also be an edge server or cloud server that communicates with the research assistant robot, that is, the computing power of the edge server or cloud server can be used to provide computing power assistance to the research assistant robot.

[0064] Specifically, the research assistant robot sends local environmental information and a global optimization task execution plan to a shared computing power center. The shared computing power center is a computing task allocation device set up inside or outside the research assistant robot that can communicate with other computing power devices. It can break down the fast path planning scheme requirement into multiple fast path planning sub-schemes and allocate the local environmental information and global optimization task execution plan corresponding to each fast path planning sub-scheme requirement to computing power devices with computing power redundancy. The fast path planning sub-schemes are completed by the computing power devices with computing power redundancy and returned to the research assistant robot through the computing power center so that the research assistant robot can obtain the fast path planning scheme.

[0065] Therefore, this application, through a computing power sharing mechanism, eliminates the need to equip the research assistant robot with a high-performance processor, thereby reducing the hardware cost and power consumption of the research assistant robot and greatly improving the response efficiency of rapid path planning, making it possible to deploy research assistant robots on a large scale.

[0066] Step 503: Based on the rapid path planning scheme, control one or more of the following systems in the mobile positioning system, multi-degree-of-freedom robotic arm, and end effector system of at least one research assistant robot through the collaborative control of functional software modules.

[0067] In other words, after acquiring a rapid path planning scheme, the research assistant robot, based on this scheme, uses functional software modules to collaboratively control one or more of its motion positioning system, multi-degree-of-freedom robotic arm, and end effector system. Specifically, the functional software modules can first control the motion positioning system based on a first planned path to move the research assistant robot to the vicinity of the collaborative experimental equipment, and then control the multi-degree-of-freedom robotic arm and end effector system based on a second planned path to move the target to its target position, thus achieving collaborative control between different hardware mechanisms within the research assistant robot.

[0068] It should be understood that, in the embodiments of this application, the research assistant robot can further control the collaborative experimental equipment to perform experimental operations based on the functional software model after completing the second planning path, such as controlling the mass spectrometer to perform separation and detection after pipetting, so as to realize the collaborative control of the research assistant robot on the research assistant robot and the collaborative experimental equipment.

[0069] Furthermore, the global optimization task execution scheme includes multiple consecutive global optimization task execution sub-tasks. The method also includes: real-time monitoring of the experimental status of the current global optimization task execution sub-scheme; generating a task scheme switching instruction when the current global optimization task execution sub-scheme is completed; responding to the task scheme switching instruction, acquiring the current local environment information of each research assistant robot based on the multimodal perception system of at least one research assistant robot; and acquiring a fast path planning scheme for executing the next global optimization task execution sub-scheme based on the current local environment information acquired through at least one research robot and the next global optimization task execution sub-scheme.

[0070] In other words, during the collaborative experimental setup, the research assistant robot can continuously communicate with the equipment to obtain the experimental status of the current global optimization task execution sub-scheme in real time. Upon completion of the current global optimization task execution sub-scheme, it generates a task scheme switching command. For example, after the centrifuge starts operating, it obtains the centrifuge's operating status in real time, and generates a task scheme switching command for sampling when the centrifuge finishes. Responding to the task scheme switching command, the research assistant robot uses its multimodal perception system to obtain information about its current local environment, such as the sample tank position of the centrifuge. Based on this local environment information and the next global optimization task execution sub-scheme, it obtains a fast path planning scheme for executing the next global optimization task execution sub-scheme, such as obtaining a second planned path for sampling from the centrifuge.

[0071] It should be noted that although the operation of the method of the present invention is described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all the operations shown must be performed in order to achieve the desired result.

[0072] Figure 6 A schematic diagram of the collaborative working device of a modular, networked research assistant robot for chemical experiments, provided in one embodiment of this application, is shown. Figure 6 As shown, the collaborative working device 10 of the modular, networked research assistant robot for chemical experiments includes: Module 11 is used to acquire chemical experiment task instructions; The parsing module 12 is used to parse the chemical experiment task instructions, determine at least one target experimental function, load at least one functional software module corresponding to at least one target experimental function, and obtain the status information and task schedule of at least one collaborative experimental device, as well as the status information and task schedule of at least one research assistant robot. The functional software module is used to control the collaborative experimental device to execute the target experimental function. The functional software module is configured according to at least one chemical experiment task in which at least one research assistant robot is applied. The generation module 13 is used to generate a globally optimized task execution plan based on the status information and task schedule of at least one of the target experimental functions, at least one scientific research robot, and at least one of the collaborative experimental devices; the globally optimized task execution plan includes at least the device identifier of at least one collaborative experimental device and its execution content and execution time; The execution module 14 is used to coordinate and control at least one research assistant robot and at least one of the collaborative experimental devices through the functional software modules based on the globally optimized task execution scheme to complete the chemical experiment task.

[0073] It should be understood that the modules or modules described in the collaborative working device 10 of the modular, networked research assistant robot used for chemical experiments are similar to those in the reference... Figure 4 The steps in the described method correspond accordingly. Therefore, the operations and features described above for the method are also applicable to the collaborative working device 10 of the modular, networked research assistant robot for chemical experiments and the modules contained therein, and will not be repeated here. The collaborative working device 10 of the modular, networked research assistant robot for chemical experiments can be pre-implemented in the browser or other secure applications of an electronic device, or it can be loaded into the browser or other secure applications of an electronic device through download or other means. The corresponding modules in the collaborative working device 10 of the modular, networked research assistant robot for chemical experiments can cooperate with the modules in the electronic device to implement the solutions of the embodiments of this application.

[0074] The division of modules or units mentioned in the detailed description above is not mandatory. In fact, according to the embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0075] The following is for reference. Figure 7 , Figure 7 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown. like Figure 7As shown, the computer system 700 includes a central processing unit (CPU) 701, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 702 or programs loaded from storage section 708 into random access memory (RAM) 703. RAM 703 also stores various programs and data required for system operation instructions. CPU 701, ROM 702, and RAM 703 are interconnected via bus 704. Input / output (I / O) interface 705 is also connected to bus 704. A computing acceleration unit 712, acting as an auxiliary computing unit for the CPU, is connected to CPU 701. The computing acceleration unit 712 includes GPUs, TPUs, NPUs, etc.

[0076] The following components are connected to I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network interface card such as a LAN card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to I / O interface 705 as needed. A removable medium 711, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 710 as needed so that computer programs read from it can be installed into storage section 708 as needed.

[0077] Specifically, according to embodiments of this application, the flowchart above refers to... Figure 2 The described process can be implemented as a computer software program. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program contains program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs the functions defined in the system of this application.

[0078] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operational instructions of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two connected blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified functions or operational instructions, or using a combination of dedicated hardware and computer instructions.

[0080] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for collaborative work of modular, networked laboratory assistant robots for chemical experiments, characterized in that, The method comprises the following steps: obtaining a chemical experiment task instruction; parsing the chemical experiment task instruction to determine at least one target experiment function, loading at least one function software module corresponding to the at least one target experiment function, and obtaining state information and task scheduling of at least one collaborative experiment device and state information and task scheduling of at least one scientific research assistant robot, wherein the function software module is used to control the collaborative experiment device to execute the target experiment function; and the function software module is configured according to at least one chemical experiment task to which at least one scientific research assistant robot is applied; generating a global optimization task execution scheme according to the at least one target experiment function, the state information and task scheduling of the at least one scientific research robot, and the state information and task scheduling of the at least one collaborative experiment device; the global optimization task execution scheme at least includes the device identifier, execution content and execution time of the at least one collaborative experiment device; based on the global optimization task execution scheme, at least one scientific research assistant robot and at least one collaborative experiment device are cooperatively controlled by the function software module to execute the global optimization task execution scheme to complete the chemical experiment task.

2. The method of claim 1, wherein the method further comprises: The method of cooperatively controlling the scientific research assistant robot based on the global optimization task execution scheme comprises the following steps: obtaining local environment information based on the state information, task queue, collaborative work information and multi-modal perception system of the scientific research assistant robot; obtaining a rapid path planning scheme based on the local environment information and the global optimization task execution scheme; based on the rapid path planning scheme, one or more of the movement and positioning system, the multi-degree-of-freedom manipulator and the end effector system of at least one scientific research assistant robot are cooperatively controlled by the function software module.

3. The method of claim 2, wherein the method further comprises: The method of obtaining a rapid path planning scheme based on the local environment information and the global optimization task execution scheme comprises the following steps: obtaining a current target experiment function based on the global optimization task execution scheme; for each scientific research assistant robot, a first planning path based on the movement and positioning system of the scientific research assistant robot to move the scientific research assistant robot to the vicinity of the current collaborative experiment device for executing the current target experiment function is obtained according to the local environment information and the collaborative experiment device corresponding to the current target experiment function, wherein at least one scientific research assistant robot cooperatively completes the current target experiment function.

4. The method of claim 2, wherein the method further comprises: The function software module comprises a modified operation procedure corresponding to the target experiment function, and the method of obtaining a rapid path planning scheme based on the local environment information and the global optimization task execution scheme comprises the following steps: obtaining a current target experiment function based on the global optimization task execution scheme; According to the local environment information and the improved operation procedure corresponding to the current target experiment function, a second planning path for moving a target to be moved to a target position based on a multi-degree-of-freedom mechanical arm and an end effector system of each of the scientific research assistant robots is obtained, and at least one of the scientific research assistant robots cooperates to complete the current target experiment function. 5.The method for the modularized and networked robotic laboratory assistant for chemistry experiments to work in collaboration according to claim 2, wherein, The rapid path planning scheme is obtained based on the local environment information and the global optimization task execution scheme, and the rapid path planning scheme includes: The local environment information and the global optimization task execution scheme are sent to a shared computing center, and the shared computing center generates the rapid path planning scheme based on the local environment information and the global optimization task execution scheme; The rapid path planning scheme returned by the shared computing center is received. 6.The method of claim 2, wherein, The global optimization task execution scheme includes a plurality of continuous global optimization task execution sub-schemes, and the method further includes: The experimental state of the current global optimization task execution sub-scheme is obtained in real time, and a task scheme switching instruction is generated when the current global optimization task execution sub-scheme is completed; In response to the task scheme switching instruction, the current local environment information of each of the scientific research assistant robots is obtained based on a multi-modal perception system of at least one of the scientific research assistant robots; Based on the current local environment information obtained by at least one of the scientific research robots and the next global optimization task execution sub-scheme, a rapid path planning scheme for executing the next global optimization task execution sub-scheme is obtained.

7. A collaborative work device of a modular, networked research assistant robot for chemical experiments, characterized by, The method includes: An acquisition module is configured to acquire a chemical experiment task instruction; An analysis module is configured to analyze the chemical experiment task instruction, determine at least one target experiment function, load at least one function software module corresponding to the at least one target experiment function, and obtain state information and task scheduling of at least one cooperative experiment device and state information and task scheduling of at least one scientific research assistant robot, wherein the function software module is used to control the cooperative experiment device to execute the target experiment function; and the function software module is configured according to at least one chemical experiment task applied to at least one scientific research assistant robot; A generation module is configured to generate a global optimization task execution scheme based on at least one target experiment function, state information and task scheduling of at least one scientific research robot, and state information and task scheduling of at least one cooperative experiment device; The global optimization task execution scheme at least includes a device identifier of at least one cooperative experiment device and execution content and execution time thereof; An execution module is configured to cooperatively control at least one scientific research assistant robot and at least one cooperative experiment device based on the global optimization task execution scheme through the function software module to execute the optimal task execution scheme and complete the chemical experiment task.

8. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the method for cooperative work of the modularized and networked scientific research assistant robots for chemical experiments.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method for cooperative work of the modularized and networked scientific research assistant robots for chemical experiments.

10. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method for collaborative work of a modular, networked research assistant robot for chemical experiments according to any one of claims 1 to 6.

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