Intelligent robot and equipment cooperation system and method based on web3d architecture and behavior tree

Abstract

The invention provides an intelligent robot and equipment cooperation system and method based on a web3d framework and a behavior tree, and the system comprises a web3d component framework which is used for constructing a three-dimensional model of an intelligent robot and production equipment, and defining attributes, features and behaviors for the three-dimensional model; the behavior tree module is used for representing behavior logic of docking of the intelligent robot and the equipment in a tree structure; each node of the behavior tree module represents one behavior or judgment condition; by configuring attributes, combination and hierarchical relationships of nodes, complex behavior logic is constructed to dynamically adjust behaviors of the intelligent robot to adapt to different docking scenes; and the data transmission module is used for establishing a signal transmission channel between the intelligent robot and the web3d component architecture and TCP communication connection between the web3d component architecture and the equipment. Efficient and flexible butt joint of the intelligent robot and the equipment can be achieved, and therefore the overall operation efficiency and reliability of the intelligent manufacturing system are improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing technology, and in particular to an intelligent robot and equipment collaboration system based on a web3d architecture and a behavior tree in automated scheduling and equipment docking technology in the field of intelligent manufacturing, and a method for applying the system. Background Art

[0002] In the modern intelligent manufacturing production process, AGV is a key automation equipment for material transportation. Its effective connection with production equipment is an important link to ensure the smooth production process. At present, the existing AGV and equipment connection technologies mainly include the following:

[0003] 1. Docking method based on fixed programs: The movement of AGV and the interaction with the equipment are controlled by pre-written fixed programs, but this method lacks flexibility. When the production environment or tasks change, the program needs to be rewritten, which is costly and time-consuming.

[0004] 2. Simple sensor-guided docking: Use sensors installed on the AGV to sense the location of the equipment, but this method is easily affected by environmental interference and has poor adaptability to complex equipment layouts and multi-device collaboration scenarios.

[0005] 3. Traditional dispatching system docking: Some dispatching systems only consider basic transportation tasks when coordinating AGVs and equipment, without fully considering equipment status changes and the dynamic characteristics of the AGV itself, resulting in low docking efficiency.

[0006] The shortcomings of the prior art are analyzed as follows:

[0007] 1. Poor flexibility: Most existing technologies are difficult to quickly adapt to adjustments in production processes, such as changes in equipment location, the addition of new equipment, or temporary changes in production tasks, resulting in reduced production efficiency and increased production costs.

[0008] 2. Insufficient docking stability: Due to environmental factors and technical limitations, the docking stability between AGV and equipment is difficult to ensure under complex working conditions, which may lead to problems such as docking failure or material transmission errors, affecting production quality.

[0009] 3. Lack of intelligent scheduling capabilities: Traditional methods cannot perform intelligent scheduling based on the real-time status of equipment and the operation of AGVs, and cannot achieve optimal resource allocation and task execution, limiting the effectiveness of the entire intelligent manufacturing system. Summary of the invention

[0010] In order to solve the problems of flexibility, intelligent scheduling and other deficiencies in the prior art, the purpose of the present invention is to provide an intelligent robot and equipment collaboration system and method based on web3d architecture and behavior tree, which can realize efficient and flexible docking of AGV and equipment, thereby improving the overall operation efficiency and reliability of the intelligent manufacturing system.

[0011] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0012] An intelligent robot and device collaboration system based on web3d architecture and behavior tree, including:

[0013] Web3D component architecture, used to build three-dimensional models of intelligent robots and production equipment, and define properties, features and behaviors for the three-dimensional models;

[0014] The behavior tree module is connected to the web3d component architecture and is used to represent the behavior logic of the intelligent robot and the device in a tree structure; each node of the behavior tree module represents a behavior or judgment condition; by configuring the properties, combination and hierarchical relationship of the nodes, a complex behavior logic is constructed to dynamically adjust the behavior of the intelligent robot to adapt to different docking scenarios;

[0015] The data transmission module is used to establish a signal transmission channel between the intelligent robot and the web3d component architecture, and a TCP communication connection between the web3d component architecture and the device; during operation, the intelligent robot generates data and component signals through its own sensors and control systems, and the component signals include the position, speed, state, and task and behavior tree related data signals of the intelligent robot; the component signals are sent to the web3d component architecture through the data transmission module, and the web3d component architecture processes the received signals and its own logic and transmits them to the real device through the TCP protocol to realize communication and collaboration between the intelligent robot and the device.

[0016] According to the present invention, an intelligent robot and device collaboration system based on a web3d architecture and a behavior tree is provided, wherein the web3d component architecture includes:

[0017] A modeling unit, used to construct a three-dimensional model of the intelligent robot and the equipment according to the actual physical characteristics and functional requirements of the intelligent robot and the equipment;

[0018] An attribute definition unit, used to define attributes for the three-dimensional model;

[0019] A feature definition unit, used to define the appearance and modeling functions of the three-dimensional model, so that the system can distinguish and manage different components;

[0020] The behavior definition unit is used to define action logic for the three-dimensional model in different situations.

[0021] According to the present invention, an intelligent robot and device collaboration system based on web3d architecture and behavior tree is provided, wherein the behavior tree module includes:

[0022] Behavior Tree Editor, used to design the structure of the behavior tree and determine the node types and functions of the behavior tree, including conditional judgment nodes and action nodes;

[0023] The node logic code writing unit is used to write the logic code of each node, implement the specific functions represented by the node, and ensure that the node can correctly return the execution result or status information according to the input parameters and the current system status;

[0024] Node connection unit, which establishes the connection relationship between nodes, combines and connects each node according to a specific logical relationship, thus forming a complete behavior tree structure;

[0025] The behavior tree decision unit is used to evaluate each node in turn starting from the root node based on the received component signals to determine the next behavior of the intelligent robot.

[0026] According to an intelligent robot and device collaboration system based on web3d architecture and behavior tree provided by the present invention, the data transmission module includes:

[0027] A signal transmission channel establishment unit, used to establish a signal transmission channel between the intelligent robot and the web3d component architecture;

[0028] A TCP communication connection establishing unit, used to establish a TCP communication connection between the web3d component architecture and the device;

[0029] The signal processing unit is used to write data processing programs in the web3d component architecture and generate instructions that comply with the TCP protocol based on the received signals and the decision results of the behavior tree;

[0030] The instruction sending unit is used to send the generated instruction to the device through the TCP protocol.

[0031] According to an intelligent robot and device collaboration system based on web3d architecture and behavior tree provided by the present invention, the behavior tree module further includes:

[0032] defining context variables, wherein each context variable is associated with one or more properties of the web3d component architecture, and each context variable has a property type, a variable name, and an associated composite property name;

[0033] In the behavior tree node, a mechanism for accessing and using the context variable is provided, so that the behavior tree node can make a decision or perform a corresponding action according to the value of the context variable;

[0034] Dynamically bind context variables to specific properties of the web3d component architecture through the combined property names, so as to obtain or update the values ​​of these properties in real time during the operation of the behavior tree;

[0035] During the execution of the behavior tree, the value of the context variable is automatically obtained according to the needs of the node, and used as the basis for the node's decision or action execution.

[0036] According to an intelligent robot and device collaboration system based on web3d architecture and behavior tree provided by the present invention, the behavior tree module further includes:

[0037] Control nodes are used to execute specific control logic on any of their child nodes, including:

[0038] Action nodes are used to perform specific operations in the intelligent manufacturing system;

[0039] Sequence nodes are configured to control their child nodes to execute in a preset order until all child nodes are executed or they exit when a specific condition is met;

[0040] A parallel node is configured to control the execution of its child nodes simultaneously or in parallel, independent of the execution order of the child nodes;

[0041] Conditional nodes are configured to decide whether to execute their child nodes or to select specific child node branches to execute based on preset conditional judgment logic;

[0042] Select a node and configure it to control its child nodes to execute in sequence, and immediately jump out when encountering the first successfully returned node, without executing subsequent child nodes;

[0043] A reversal node is configured to reverse the execution result or state of its child node;

[0044] A timed trigger node is configured to continuously execute its child nodes until they succeed, that is, to keep retrying if the child nodes fail until they reach a successful state or meet certain conditions.

[0045] According to the present invention, an intelligent robot and device collaboration system based on a web3d architecture and a behavior tree is provided, and the construction of the web3d component architecture specifically includes:

[0046] Select and configure a Web3D development platform and tools that supports the creation, rendering, interaction, and definition and binding of 3D components’ behavior logic;

[0047] Define the properties, characteristics, and behaviors of 3D components based on the actual physical characteristics and functional requirements of intelligent robots and devices;

[0048] Utilize the modeling technology provided by the Web3D development platform and tools to build high-precision three-dimensional models of intelligent robots and devices, ensure that the models are visually consistent with the real objects, and optimize the models to improve rendering performance and interactive response speed;

[0049] Write code for the movement behavior of the intelligent robot to achieve the function of moving accurately in the virtual environment according to the instructions received;

[0050] Model the device's operational behavior and write the corresponding behavioral logic code to implement the device's behavioral operation in a virtual environment;

[0051] Associate and bind the constructed 3D components with the corresponding behavior logic.

[0052] According to a web3d architecture and behavior tree-based intelligent robot and device collaboration system provided by the present invention, the system also includes an intelligent scheduling unit for real-time perception of the status of the intelligent robot and the device, performing intelligent scheduling through the behavior tree, reasonably arranging the task path and docking sequence of the intelligent robot, and realizing optimal allocation of resources.

[0053] A method for intelligent robot and device collaboration based on web3d architecture and behavior tree, the method applies the above-mentioned intelligent robot and device collaboration system based on web3d architecture and behavior tree to realize the connection between intelligent robot and device, comprising the following steps:

[0054] Build three-dimensional models of intelligent robots and devices in the web3d component architecture and define properties, features and behaviors for the models;

[0055] In the behavior tree module, design the structure of the behavior tree according to the collaboration requirements and operation process of the intelligent robot and the device, and write the logic code of each node in the behavior tree to ensure that the node can correctly evaluate and make decisions based on the input signal and preset logic, thus forming a complete behavior tree;

[0056] During the operation of the intelligent robot, the state information is collected in real time through its built-in sensors, and the task instructions are received through the control system. Based on the collected state information and task instructions, the intelligent robot generates component signals containing key information.

[0057] The generated component signal is sent to the web3d component architecture, which parses and processes the signal according to the received signal content and its own logical processing mechanism;

[0058] The instructions processed by the web3d component architecture are transmitted to the device through the TCP protocol; after receiving the instructions, the device performs corresponding operations according to the content of the instructions, and the intelligent robot also performs docking actions according to the instructions, thereby realizing the docking and collaboration between the intelligent robot and the device.

[0059] According to the method for collaboration between an intelligent robot and a device based on a web3d architecture and a behavior tree provided by the present invention, the following steps are further performed:

[0060] During the operation of the system, the status information of various sensors on the intelligent robot is collected in real time, and task instructions from the production management system are received, which include the target equipment information of material transportation;

[0061] Based on the collected sensor information and task instructions, a component signal containing the AGV's current position, speed, load condition, and target device information is generated.

[0062] The component signal is passed to the web3d component architecture, and the web3d component architecture passes the signal to the behavior tree module. The behavior tree module starts from the root node and evaluates the conditions of each node in turn according to the received signal. Based on the result of the condition evaluation, the behavior tree module dynamically determines the next behavior of the intelligent robot.

[0063] The web3d component architecture generates corresponding control instructions based on the decision results of the behavior tree, and sends the control instructions to the device through the TCP transmission protocol; after receiving the instructions, the device performs the operation corresponding to the instructions; the intelligent robot performs the docking action according to the instructions and accurately transports the materials to the specified location of the equipment;

[0064] During the entire docking process, the system continuously monitors the status of the AGV and equipment, and adjusts the decisions of the behavior tree in a timely manner through the feedback mechanism to ensure the smooth progress and accuracy of the docking process.

[0065] It can be seen that compared with the prior art, the present invention has the following beneficial effects:

[0066] 1. High flexibility: The present invention combines behavior trees with web3d component architecture to enable the system to easily cope with changes in the production environment. Whether it is the adjustment of equipment layout, the introduction of new equipment, or the temporary change of production tasks, it only needs to modify and adjust the behavior tree accordingly without large-scale reprogramming, which greatly shortens the system adjustment time and improves production efficiency.

[0067] 2. Precise docking and high stability: The present invention uses the web3d component architecture to accurately model AGVs and equipment. Combined with the intelligent decision-making of behavior trees, the system can achieve precise docking of AGVs and equipment under complex working conditions, reducing production accidents and material losses caused by docking errors, and improving production quality and system stability.

[0068] 3. Intelligent scheduling and resource optimization: The system of the present invention can perceive the status of AGVs and equipment in real time, perform intelligent scheduling through behavior trees, and reasonably arrange the task path and docking sequence of AGVs according to factors such as the busyness of the equipment and the load of the AGVs, thereby achieving optimal allocation of resources and improving the operating efficiency of the entire intelligent manufacturing system.

[0069] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a schematic diagram of an embodiment of an intelligent robot and device collaboration system based on a web3d architecture and a behavior tree of the present invention.

[0071] Figure 2 It is a structural schematic diagram of a behavior tree in an embodiment of an intelligent robot and device collaboration system based on a web3d architecture and a behavior tree of the present invention.

[0072] Figure 3 It is a schematic diagram of the principle of data transmission in an embodiment of an intelligent robot and device collaboration system based on a web3d architecture and a behavior tree of the present invention. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0075] See also Figures 1 to 3This embodiment provides an intelligent robot and device collaboration system based on web3d architecture and behavior tree, including:

[0076] The web3d component architecture is used to build three-dimensional models of intelligent robots and production equipment, and define properties, features and behaviors for the three-dimensional models. It can be seen that this system is built on the web3d component architecture, which provides the basis for three-dimensional visualization and interaction for the entire system. The web3d component has rich properties, features and behaviors, which together constitute the core functional modules of the system. Among them, the properties include but are not limited to the geometric shape and physical properties (such as mass, friction, etc.) of the component, which are used to accurately simulate AGVs and equipment in a virtual environment; the features define the appearance of the model and the modeling function, which facilitates the system to distinguish and manage different components; the behavior defines the action logic of the component in different situations, such as the movement, steering, loading and unloading of AGVs, and the start, stop, signal transmission and reception and communication components of the equipment.

[0077] The behavior tree module is connected to the web3d component architecture and is used to represent the behavior logic of the intelligent robot and the device docking in a tree structure; each node of the behavior tree module represents a behavior or judgment condition; by configuring the attributes, combination and hierarchical relationship of the nodes, a complex behavior logic is constructed to dynamically adjust the behavior of the intelligent robot to adapt to different docking scenarios. It can be seen that this embodiment introduces the behavior tree technology on the basis of the web3d component architecture. The behavior tree is a method of representing the behavior logic in a tree structure, which has a high degree of flexibility. By combining the behavior tree with the 3d component, the docking of the AGV and the device is realized. Each node of the behavior tree represents a behavior or judgment condition. For example, the node can be "detecting whether the device is idle" or "judging whether the AGV has reached the specified area", etc. Through the attribute configuration of the node, the combination and hierarchical relationship of different nodes, a complex behavior logic can be constructed. This flexible combination method can dynamically adjust the behavior of the AGV according to the actual situation of the production site to adapt it to various docking scenarios.

[0078] The data transmission module is used to establish a signal transmission channel between the intelligent robot and the web3d component architecture, and a TCP communication connection between the web3d component architecture and the device; during the operation of the intelligent robot, the intelligent robot generates data and generates component signals through its own sensors and control systems, and the component signals include the position, speed, state, and relevant data signals of the task and behavior tree of the intelligent robot; the component signals are sent to the web3d component architecture through the data transmission module, and the web3d component architecture processes the received signals and its own logic and transmits them to the real device through the TCP protocol to realize the communication and collaboration between the intelligent robot and the device. This embodiment designs a unique data transmission mechanism. During the operation of the agv, the agv generates data and generates component signals through its own sensors and control systems, and these signals include the position, speed, state, and relevant data signals of the task and behavior tree of the agv. The component signals are sent to the 3d component, and the 3d component processes them according to the received signals and its own logic. The processed instructions are transmitted to the real device through TCP (Transmission Control Protocol). The TCP protocol ensures the reliability and accuracy of instruction transmission, ensuring that the agv and the device can communicate and collaborate accurately.

[0079] In this embodiment, the web3d component architecture includes:

[0080] A modeling unit, used to construct a three-dimensional model of the intelligent robot and the equipment according to the actual physical characteristics and functional requirements of the intelligent robot and the equipment;

[0081] An attribute definition unit, used to define attributes for the three-dimensional model;

[0082] A feature definition unit, used to define the appearance and modeling functions of the three-dimensional model, so that the system can distinguish and manage different components;

[0083] The behavior definition unit is used to define action logic for the three-dimensional model in different situations.

[0084] In this embodiment, the behavior tree module includes:

[0085] Behavior Tree Editor, used to design the structure of the behavior tree and determine the node types and functions of the behavior tree, including conditional judgment nodes and action nodes;

[0086] The node logic code writing unit is used to write the logic code of each node, implement the specific functions represented by the node, and ensure that the node can correctly return the execution result or status information according to the input parameters and the current system status; among them, the complex logic code and relationship include: for example, "if the device is in idle state and the AGV has reached the designated area, then start the docking program" such condition-action pairs, by configuring the parameters of the conditional judgment node and the action node and the connection relationship between them, to realize the automated decision-making and operation process in the intelligent manufacturing system.

[0087] The node connection unit establishes the connection relationship between nodes, combines and connects each node according to a specific logical relationship, and thus forms a complete behavior tree structure. For example, set a logical relationship such as "if the device is idle and the AGV reaches the specified area, start the docking program."

[0088] The behavior tree decision unit is used to evaluate each node in turn starting from the root node based on the received component signals to determine the next behavior of the intelligent robot.

[0089] In this embodiment, the data transmission module includes:

[0090] The signal transmission channel establishment unit is used to establish a signal transmission channel between the intelligent robot and the web3d component architecture; the TCP communication connection establishment unit is used to establish a TCP communication connection between the web3d component architecture and the device; the signal processing unit is used to write a data processing program in the web3d component architecture, and generate instructions that comply with the TCP protocol based on the received signal and the decision result of the behavior tree; the instruction sending unit is used to send the generated instructions to the device through the TCP protocol.

[0091] Furthermore, the data transmission module also includes: configuring the format and protocol of signal transmission to ensure that the component signals generated by the AGV can be accurately received and parsed by the 3D component. On the 3D component side, a data processing program is written to enable it to generate instructions that comply with the TCP protocol based on the received signals and the decision results of the behavior tree, and accurately send the instructions to the device.

[0092] In this embodiment, the behavior tree module further includes:

[0093] Context variables are defined, wherein each context variable is associated with one or more attributes of the web3d component architecture, and each context variable has an attribute type, a variable name, and an associated composite attribute name; in a behavior tree node, a mechanism for accessing and using the context variables is provided, so that the behavior tree node can make decisions or perform corresponding actions according to the value of the context variable; through the composite attribute name, the context variables are dynamically bound to specific attributes of the web3d component architecture, so that the values ​​of these attributes can be obtained or updated in real time during the operation of the behavior tree; during the execution of the behavior tree, the value of the context variable is automatically obtained according to the needs of the node, and is used as the basis for the node decision or action execution.

[0094] In this embodiment, the behavior tree module further includes:

[0095] Design the structure of the behavior tree based on the docking requirements of the intelligent manufacturing system and possible scenarios. Determine the node types and functions of the behavior tree.

[0096] Control nodes are used to execute specific control logic on any of their child nodes, including:

[0097] Action nodes (such as AGV movement, steering, docking operations, etc.) are used to perform specific operations in the intelligent manufacturing system, including but not limited to AGV movement nodes (such as moving to a specified location, moving along a specified path, etc.), steering nodes (such as adjusting the driving direction), and docking operation nodes (such as docking with equipment, unloading materials, etc.).

[0098] A sequence node is configured to control its child nodes to execute in a preset order until all child nodes are executed or they exit when a specific condition is met.

[0099] A parallel node is configured to control the execution of its child nodes simultaneously or in parallel, independent of the execution order of the child nodes.

[0100] Conditional nodes (such as equipment status detection, environmental condition judgment, etc.) are configured to decide whether to execute their child nodes based on preset conditional judgment logic, or to choose to execute specific child node branches. They are used to detect equipment status, environmental conditions or other relevant parameters in the intelligent manufacturing system, and return true or false results based on preset conditional judgment logic, including but not limited to equipment status detection nodes (such as idle, busy, fault, etc.) and environmental condition judgment nodes (such as temperature, humidity, light, etc.).

[0101] Select a node and configure it to control its child nodes to execute in sequence. When the first successfully returned node is encountered, it will immediately jump out and no longer execute subsequent child nodes.

[0102] The inversion node is configured to invert the execution result or state of its child node.

[0103] A timed trigger node is configured to continuously execute its child nodes until they succeed, that is, to keep retrying if the child nodes fail until they reach a successful state or meet certain conditions.

[0104] It can be seen that the behavior tree node provides control nodes and specific docking nodes. The control nodes are used to control any of its child nodes, such as: sequence nodes control their child nodes to execute in sequence, parallel nodes control their child nodes to execute in parallel, conditional nodes control execution according to conditions, selection nodes control their child nodes to execute in sequence and jump out when encountering a node that returns successfully, inversion nodes invert the results of their child nodes, and timed trigger nodes execute their child nodes until success (retry until success).

[0105] In this embodiment, the construction of the web3d component architecture specifically includes:

[0106] Select and configure a Web3D development platform and tools that supports the creation, rendering, interaction, and definition and binding of behavioral logic of 3D components; define the properties, features, and behaviors of 3D components based on the actual physical characteristics and functional requirements of intelligent robots and devices, including but not limited to size, shape, color, material, kinematic characteristics, operation mode, status indication, etc.

[0107] Utilize the modeling technology provided by the Web3D development platform and tools to build high-precision three-dimensional models of intelligent robots and equipment, ensure that the models are visually consistent with the real objects, and optimize the models to improve rendering performance and interactive response speed.

[0108] Write code for the movement behavior of the intelligent robot to realize the function of accurate movement in the virtual environment according to the received instructions (such as path planning instructions, speed control instructions, etc.), including but not limited to straight-line movement, turning, obstacle avoidance, docking and other actions.

[0109] Model the operational behavior of the equipment and write the corresponding behavioral logic code to realize the behavioral operation of the equipment in the virtual environment, such as receiving material actions, processing actions, state switching and other complex operations, to ensure that these operations are consistent with the actual operation process.

[0110] Associate and bind the constructed 3D components with the corresponding behavioral logic to ensure that they can respond to external instructions or internal state changes in real time and accurately in the virtual environment, achieving complex interactions and simulation effects.

[0111] It can be seen that when building the web3d component architecture, this embodiment first selects the appropriate web3d development platform and tools, creates the 3d component architecture, and defines the properties, characteristics and behaviors of the 3d components according to the actual physical characteristics and functional requirements of the AGV and the equipment. Then, the three-dimensional model of the AGV and the equipment is constructed using modeling technology, and it is associated with the corresponding behavior logic. For example, code is written for the movement behavior of the AGV so that it can move accurately in the virtual environment according to the received instructions; model the operation behavior of the equipment, such as setting the equipment's receiving material action, processing action, etc.

[0112] In this embodiment, the system also includes an intelligent scheduling unit for sensing the status of intelligent robots and equipment in real time, performing intelligent scheduling through behavior trees, and reasonably arranging the task paths and docking sequences of intelligent robots to achieve optimal allocation of resources.

[0113] Specifically, Figure 1 As shown in the figure, the web3d component architecture is the core framework of the system, carrying the virtual models and behavior logic of AGV and equipment. The behavior tree is embedded in this architecture and works closely with the 3d component to realize the docking function of AGV and equipment. AGV generates component signals through sensors and internal systems, which are transmitted to the 3d component through a specific interface. The 3d component sends instructions to the real device through a TCP connection based on the decision results of the behavior tree, forming a complete closed-loop system.

[0114] Specifically, Figure 2 As shown in the figure, the structure of the behavior tree is explained in detail. The root node represents the goal of the entire behavior, such as "complete the docking task between the AGV and the target device"; the branch nodes include various conditional judgments, such as "whether the device is idle" and "whether the distance between the AGV and the device is within the allowed range", etc. The leaf nodes are specific action instructions, such as "AGV moves forward" and "start the docking procedure". Through this hierarchical structure, the behavior tree can flexibly adjust the behavior of the AGV according to different situations to achieve accurate docking.

[0115] Specifically, Figure 3 As shown in the figure, starting from the task generation, the task is sent to the 3D component, which parses and processes it, finds the behavior tree and generates corresponding instructions according to the logic of the behavior tree. Then, the instructions are encapsulated and transmitted to the device through the TCP protocol. In this process, the reliability of the TCP protocol ensures the accurate transmission of data, prevents data loss or errors, and ensures the coordination between the AGV and the device.

[0116] An embodiment of a method for collaboration between an intelligent robot and a device based on a web3d architecture and a behavior tree

[0117] A method for intelligent robot and device collaboration based on web3d architecture and behavior tree, the method applies the above-mentioned intelligent robot and device collaboration system based on web3d architecture and behavior tree to realize the connection between intelligent robot and device, comprising the following steps:

[0118] Construct three-dimensional models of intelligent robots and devices in the web3d component architecture, and define properties (such as size, color, material, etc.), features (such as shape, structure, etc.) and behaviors (such as movement, steering, docking, etc.) for the models;

[0119] In the behavior tree module, design the structure of the behavior tree according to the collaboration requirements and operation process of the intelligent robot and the device, and write the logic code of each node in the behavior tree to ensure that the node can correctly evaluate and make decisions based on the input signal and preset logic, thus forming a complete behavior tree;

[0120] During the operation of the intelligent robot, the state information (such as position, speed, load, etc.) is collected in real time through its built-in sensors, and task instructions are received through the control system; based on the collected state information and task instructions, the intelligent robot generates component signals containing key information;

[0121] The generated component signal is sent to the web3d component architecture, which parses and processes the signal according to the received signal content and its own logical processing mechanism;

[0122] The instructions processed by the web3d component architecture are transmitted to the device through the TCP protocol; after receiving the instructions, the device performs corresponding operations according to the content of the instructions (such as preparing to receive materials, opening the entrance, starting processing, etc.). At the same time, the intelligent robot (such as AGV) also performs docking actions according to the instructions, such as moving to the specified position, adjusting the direction, docking with the device, etc., thereby realizing the docking and collaboration between the intelligent robot and the device.

[0123] In this embodiment, the method further performs:

[0124] During the operation of the system, the status information of various sensors (such as position sensors, speed sensors, load sensors, etc.) on the intelligent robot is collected in real time, and task instructions from the production management system are received, such as transporting materials to designated equipment, and the task instructions include the target equipment information for material transportation;

[0125] Based on the collected sensor information and task instructions, component signals are generated including the AGV's current position, speed, load condition, and target device information.

[0126] The component signal is passed to the web3d component architecture, and the web3d component architecture passes the signal to the behavior tree module. The behavior tree module starts from the root node and evaluates the conditions of each node in turn according to the received signal. According to the results of the condition evaluation, the behavior tree module dynamically determines the next behavior of the intelligent robot. For example, first determine whether the target device is idle. If the device is idle, further determine whether the distance between the AGV and the device is within the allowable range. Based on the results of these conditional judgments, the behavior tree determines the next behavior of the AGV, such as continuing to move forward, adjusting the direction, or starting the docking procedure.

[0127] After the behavior tree determines the behavior of the AGV, the web3D component architecture generates corresponding control instructions based on the decision results of the behavior tree, and sends the control instructions to the device through the TCP transmission protocol; after receiving the instructions, the device performs the operations corresponding to the instructions, such as the device is ready to receive materials and opens the material entrance; the AGV performs the docking action to accurately transport the materials to the specified location of the device. The intelligent robot performs the docking action according to the instructions and accurately transports the materials to the specified location of the device;

[0128] During the entire docking process, the system continuously monitors the status of the AGV and equipment, and adjusts the decisions of the behavior tree in a timely manner through the feedback mechanism to ensure the smooth progress and accuracy of the docking process.

[0129] In actual applications, in an intelligent manufacturing workshop of an electronic product manufacturing plant, the system and method of this embodiment are used for AGV to transport electronic components to different production equipment (such as placement machines, welding machines, etc.). When a placement machine on the production line needs a new electronic component, the production management system sends a transportation task instruction to the AGV. The AGV obtains the current state information through its own sensor, generates a component signal and sends it to the 3D component. The 3D component obtains the corresponding behavior tree and dynamically adjusts the path and behavior of the AGV according to the idle state of the placement machine, the distance between the AGV and the placement machine, and other information. Finally, the AGV accurately transports the electronic components to the feed port of the placement machine, achieving efficient and precise docking, and improving production efficiency and product quality.

[0130] In summary, this embodiment combines the behavior tree with the web3d component architecture to enable the system to easily cope with changes in the production environment. Whether it is the adjustment of equipment layout, the introduction of new equipment, or the temporary change of production tasks, it only needs to modify and adjust the behavior tree accordingly without large-scale reprogramming, which greatly shortens the system adjustment time and improves production efficiency.

[0131] Furthermore, this embodiment uses the web3d component architecture to accurately model the AGV and equipment, and combines it with the intelligent decision-making of the behavior tree. The system can achieve accurate docking of the AGV and equipment under complex working conditions, reducing production accidents and material losses caused by docking errors, and improving production quality and system stability.

[0132] Furthermore, the system of this embodiment can perceive the status of AGVs and equipment in real time, perform intelligent scheduling through behavior trees, and reasonably arrange the task paths and docking sequences of AGVs according to factors such as the busyness of the equipment and the load conditions of the AGVs, thereby achieving optimal allocation of resources and improving the operating efficiency of the entire intelligent manufacturing system.

[0133] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0134] The above-mentioned embodiments are only preferred embodiments of the present invention and cannot be used to limit the scope of protection of the present invention. Any non-substantial changes and substitutions made by technicians in this field on the basis of the present invention shall fall within the scope of protection required by the present invention.

Claims

1. An intelligent robot and equipment collaboration system based on web3d architecture and behavior tree, characterized in that: include: Web3D component architecture, used to build three-dimensional models of intelligent robots and production equipment, and define properties, features and behaviors for the three-dimensional models; A behavior tree module is connected to the web3d component architecture and is used to represent the behavior logic of the intelligent robot and the device in a tree structure; each node of the behavior tree module represents a behavior or a judgment condition; the behavior logic is constructed by configuring the attributes, combination and hierarchical relationship of the nodes to dynamically adjust the behavior of the intelligent robot to adapt to different docking scenarios; The data transmission module is used to establish a signal transmission channel between the intelligent robot and the web3d component architecture, and a TCP communication connection between the web3d component architecture and the device; during operation, the intelligent robot generates data and component signals through its own sensors and control system, and the component signals include the position, speed, state, and task and behavior tree related data signals of the intelligent robot; the component signals are sent to the web3d component architecture through the data transmission module, and the web3d component architecture processes the received signals and its own logic and transmits them to the real device through the TCP protocol to realize communication and collaboration between the intelligent robot and the device.

2. The system according to claim 1, characterized in that: The web3d component architecture includes: A modeling unit, used to construct a three-dimensional model of the intelligent robot and the equipment according to the actual physical characteristics and functional requirements of the intelligent robot and the equipment; An attribute definition unit, used to define attributes for the three-dimensional model; A feature definition unit, used to define the appearance and modeling functions of the three-dimensional model, so that the system can distinguish and manage different components; The behavior definition unit is used to define action logic for the three-dimensional model in different situations.

3. The system according to claim 1, characterized in that: The behavior tree module includes: Behavior Tree Editor, used to design the structure of the behavior tree and determine the node types and functions of the behavior tree, including conditional judgment nodes and action nodes; The node logic code writing unit is used to write the logic code of each node, implement the specific functions represented by the node, and ensure that the node can correctly return the execution result or status information according to the input parameters and the current system status; Node connection unit, which establishes the connection relationship between nodes, combines and connects each node according to a specific logical relationship, thus forming a complete behavior tree structure; The behavior tree decision unit is used to evaluate each node in turn starting from the root node based on the received component signal to determine the next behavior of the intelligent robot.

4. The system according to claim 1, characterized in that: The data transmission module comprises: A signal transmission channel establishment unit, used to establish a signal transmission channel between the intelligent robot and the web3d component architecture; A TCP communication connection establishing unit, used to establish a TCP communication connection between the web3d component architecture and the device; The signal processing unit is used to write data processing programs in the web3d component architecture and generate instructions that comply with the TCP protocol based on the received signals and the decision results of the behavior tree; The instruction sending unit is used to send the generated instruction to the device through the TCP protocol.

5. The system according to claim 1, characterized in that: The behavior tree module also includes: defining context variables, wherein each context variable is associated with one or more properties of the web3d component architecture, and each context variable has a property type, a variable name, and an associated composite property name; In the behavior tree node, a mechanism for accessing and using the context variable is provided, so that the behavior tree node can make a decision or perform a corresponding action according to the value of the context variable; Dynamically bind context variables to specific properties of the web3d component architecture through the combined property names, so as to obtain or update the values ​​of these properties in real time during the operation of the behavior tree; During the execution of the behavior tree, the value of the context variable is automatically obtained according to the needs of the node, and used as the basis for the node's decision or action execution.

6. The system according to claim 5, characterized in that: The behavior tree module also includes: Control nodes are used to execute specific control logic on any of their child nodes, including: Action nodes are used to perform specific operations in the intelligent manufacturing system; Sequence nodes are configured to control their child nodes to execute in a preset order until all child nodes are executed or they exit when a specific condition is met; A parallel node is configured to control the execution of its child nodes simultaneously or in parallel, independent of the execution order of the child nodes; Conditional nodes are configured to decide whether to execute their child nodes or to select specific child node branches to execute based on preset conditional judgment logic; Select a node and configure it to control its child nodes to execute in sequence, and immediately jump out when encountering the first successfully returned node, without executing subsequent child nodes; A reversal node is configured to reverse the execution result or state of its child node; A timed trigger node is configured to continuously execute its child nodes until they succeed, that is, to keep retrying if the child nodes fail until they reach a successful state or meet certain conditions.

7. The system according to claim 1, characterized in that: The construction of the web3d component architecture specifically includes: Select and configure a Web3D development platform and tools that supports the creation, rendering, interaction, and definition and binding of 3D components’ behavior logic; Define the properties, characteristics, and behaviors of 3D components based on the actual physical characteristics and functional requirements of intelligent robots and devices; Utilize the modeling technology provided by the Web3D development platform and tools to build high-precision three-dimensional models of intelligent robots and devices, ensure that the models are visually consistent with the real objects, and optimize the models to improve rendering performance and interactive response speed; Write code for the movement behavior of the intelligent robot to achieve the function of moving accurately in the virtual environment according to the instructions received; Model the device's operational behavior and write the corresponding behavioral logic code to implement the device's behavioral operation in a virtual environment; Associate and bind the constructed 3D components with the corresponding behavior logic.

8. The system according to any one of claims 1 to 7, characterized in that: The system also includes an intelligent scheduling unit, which is used to perceive the status of intelligent robots and equipment in real time, perform intelligent scheduling through behavior trees, reasonably arrange the task paths and docking sequences of intelligent robots, and achieve optimal allocation of resources.

9. A method for intelligent robot and device collaboration based on web3d architecture and behavior tree, the method using the intelligent robot and device collaboration system based on web3d architecture and behavior tree as described in any one of claims 1 to 8 to achieve intelligent robot and device docking, characterized in that: The following steps are involved: Build three-dimensional models of intelligent robots and devices in the web3d component architecture and define properties, features and behaviors for the models; In the behavior tree module, design the structure of the behavior tree according to the collaboration requirements and operation process of the intelligent robot and the device, and write the logic code of each node in the behavior tree to ensure that the node can correctly evaluate and make decisions based on the input signal and preset logic, thus forming a complete behavior tree; During the operation of the intelligent robot, the state information is collected in real time through its built-in sensors, and the task instructions are received through the control system. Based on the collected state information and task instructions, the intelligent robot generates component signals containing key information. The generated component signal is sent to the web3d component architecture, which parses and processes the signal according to the received signal content and its own logical processing mechanism; The instructions processed by the web3d component architecture are transmitted to the device through the TCP protocol; after receiving the instructions, the device performs corresponding operations according to the content of the instructions, and the intelligent robot also performs docking actions according to the instructions, thereby realizing the docking and collaboration between the intelligent robot and the device.

10. The method according to claim 9, characterized in that Also execute: During the operation of the system, the status information of various sensors on the intelligent robot is collected in real time, and task instructions from the production management system are received, which include the target equipment information of material transportation; Based on the collected sensor information and task instructions, a component signal containing the AGV's current position, speed, load condition, and target device information is generated. The component signal is passed to the web3d component architecture, and the web3d component architecture passes the signal to the behavior tree module. The behavior tree module starts from the root node and evaluates the conditions of each node in turn according to the received signal. Based on the result of the condition evaluation, the behavior tree module dynamically determines the next behavior of the intelligent robot. The web3d component architecture generates corresponding control instructions based on the decision results of the behavior tree and sends the control instructions to the device through the TCP transmission protocol; after receiving the instructions, the device executes the operations corresponding to the instructions; The intelligent robot performs docking actions according to instructions and accurately transports the materials to the designated location of the equipment; During the entire docking process, the system continuously monitors the status of the AGV and equipment, and adjusts the decisions of the behavior tree in a timely manner through the feedback mechanism to ensure the smooth progress and accuracy of the docking process.

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