A heterogeneous robot distributed collaborative assembly method based on intelligent prefabricated components

Abstract

This invention discloses a distributed collaborative assembly method for heterogeneous robots based on intelligent prefabricated components, comprising: initiating an assembly task; the intelligent prefabricated component sensing its own state, autonomously determining task requirements, and issuing a task request to the heterogeneous robots; the robots determining whether to respond based on their own capability models; the component selecting the optimal responding robot and sending a task execution instruction; the robot executing the task and providing feedback, which is recorded by the component and uploaded to a prefabricated building component management platform; constructing a distributed collaborative framework with the intelligent prefabricated component as the decision-making core, realizing a closed-loop process of task issuance, robot bidding, component decision-making, robot execution, and feedback; this invention is applicable to multi-robot collaborative assembly in large-scale complex scenarios, effectively alleviating the scheduling bottleneck and computational pressure of centralized control, improving system scalability and resource utilization efficiency, and significantly enhancing the collaborative capabilities of heterogeneous robots and overall assembly performance.

Description

Technical Field

[0001] This invention belongs to the field of intelligent construction technology, specifically relating to a distributed collaborative assembly method for heterogeneous robots based on intelligent prefabricated components. Background Technology

[0002] In recent years, prefabricated buildings have developed rapidly due to their advantages such as high construction efficiency and controllable quality. To improve assembly construction efficiency, multi-robot collaborative operations have been gradually introduced into prefabricated component assembly tasks, especially in component transportation, positioning, hoisting, and assembly. However, most existing multi-robot collaborative assembly methods adopt a centralized control mode, which reveals significant shortcomings as the scale of assembly tasks and the number of robots increase: ① The central control unit has a heavy computational burden, resulting in slower real-time response speed; ② Task conflicts easily occur when the number of robots increases, leading to waste of system resources; ③ The overall control strategy lacks flexibility and cannot respond to changes in the on-site environment in a timely manner, making it difficult to effectively improve assembly efficiency.

[0003] Therefore, there is an urgent need to propose a new control method applicable to large-scale tasks and collaborative operations of heterogeneous robots, in order to improve the system's operating efficiency, resource utilization, and flexible response capability. Summary of the Invention

[0004] Purpose of the invention: This invention proposes a distributed collaborative assembly method for heterogeneous robots based on intelligent prefabricated components, which solves the efficiency bottleneck in large-scale tasks under centralized control mode and improves the real-time performance, collaboration and scalability of the system.

[0005] Technical solution: The present invention provides a distributed collaborative assembly method for heterogeneous robots based on intelligent prefabricated components, comprising the following steps:

[0006] After the assembly task is started, the intelligent prefabricated component senses its own status information in real time; the intelligent prefabricated component includes a task flow model unit, an RFID tag, an inertial measurement unit, an ultra-wideband positioning device, and a lightweight AI inference model; the RFID tag, inertial measurement unit, and ultra-wideband positioning device respectively acquire component identity information, location information, attitude information, and assembly progress information; the task flow model unit is used to pre-store and manage the BIM assembly task model.

[0007] The intelligent prefabricated components autonomously determine the assembly task requirements based on the BIM assembly task model and issue task request instructions to the heterogeneous robots in the corresponding work areas; the task requests are broadcast to the heterogeneous robots in real time through the communication network, and the task listening phase begins.

[0008] After receiving a task request instruction from a smart prefabricated component, the heterogeneous robot autonomously determines whether it matches the task request instruction based on its own preset capability model. If the match is successful, it sends a confirmation message to the smart prefabricated component and automatically adjusts its own state to wait for the assembly instruction.

[0009] When receiving task responses from heterogeneous robots, the intelligent prefabricated component uses a lightweight AI inference model to prioritize all responding robots and sends task execution instructions only to the target robot with the highest score. At the same time, it sends task release signals to the other responding robots to deactivate their standby status.

[0010] The target robot completes the assembly task according to the task execution instructions and feeds back the task execution results to the intelligent prefabricated component; the intelligent prefabricated component records the completion status of the assembly task and uploads it to the prefabricated building component management platform for storage and analysis.

[0011] Furthermore, the BIM assembly task model is constructed by combining BIM model data with unique component codes to form a "component-task" mapping table. This mapping table contains the unique codes of prefabricated components and the corresponding list of specific assembly tasks, including information such as assembly task type, assembly location coordinates, key operation accuracy requirements, task priority, and task execution time window. This clearly describes the specific assembly tasks and sequence of each component. The assembly task model is defined as follows:

[0012] M={(C i ,T i |i∈[1,n]}

[0013] Where C i Indicates the component number, generated from the BIM component code; T i Indicates component C i The assembly task set is arranged in order of assembly process; each t j ∈T i It includes fields such as task type, assembly location, accuracy requirements, priority, and execution time window, and is defined as follows:

[0014] T i =[t1,t2,...,t m ]

[0015] t j =(type,pos,acc,prio,window).

[0016] Furthermore, the intelligent prefabricated components are based on the BIM assembly task model, and the component task flow is modeled using a finite state machine. The task state is changed according to the current perception state, and the assembly task request instruction is generated to realize the dynamic execution and update driven by the task flow.

[0017] Each component C i The corresponding finite state machine (FSM) i Defined as:

[0018] FSM i =(S i ,T i G i ,s i,0 )

[0019] Among them, S i The status set includes "Pending Assembly", "Task Requesting", "Matching", "Executing", and "Completion Confirmation"; T i G represents the set of assembly tasks, indicating the specific assembly tasks that can be triggered in each state; i This is a state transition function that determines the next state based on the current state and the task triggering conditions; s i,0 This is the initial state of the component, designated as "to be assembled".

[0020] According to component C i Current perception state s t Matching task t j The status has been updated to:

[0021] s t+1 =G i (s t ,t j )

[0022] Simultaneously, a task request is issued based on the task mapping table: if s t ∈S i Furthermore, if the "perception condition is met", then task t is triggered. j The task request instruction is generated; the "sense condition is met" means that the status information collected by the component through its own sensing device is consistent with the task triggering conditions set in the BIM task mapping table.

[0023] Furthermore, the communication methods between the intelligent prefabricated components and the heterogeneous robots include WiFi, ZigBee, or LoRa; the communication adopts a distributed publish-subscribe model to build a distributed communication network and realize the communication interaction between the intelligent prefabricated components and the heterogeneous robots.

[0024] Furthermore, the heterogeneous robots include palletizing robots, transport robots, hoisting robots, and assembly robots, each with its own independent capability model.

[0025] Furthermore, the capability model includes robot type, maximum workload, critical task accuracy, current status information, and endurance; the capability model R for each heterogeneous robot...i Defined as a quintuple:

[0026] R i =(type) i ,load i ,acc i ,state i power i ).

[0027] Furthermore, after receiving a task request instruction, the heterogeneous robot performs an adaptability assessment of the task request using a condition-based judgment rule, requiring that all indicators of its capability model meet the minimum requirements set by the task parameters; if all conditions are met, it is determined to be adaptable and sends task confirmation information back to the component; otherwise, it does not respond; specifically, the condition-based judgment rule is based on the locally maintained capability model R. i According to the task request t j The type matching, load capacity, accuracy requirements, working status and endurance are compared item by item. Then the robot will be adapted.

[0028] Furthermore, the lightweight AI inference model is an edge-deployable multi-layer feedforward neural network model, including an input layer, a hidden layer, and an output layer; specifically, the input layer includes five feature variables, namely the robot capability matching degree r. i d, the distance between the component and the robot i Task load rate i Response delay t i And the historical mission completion quality score q i , forming the input vector X i =[r i ,d i ,l i ,t i ,q i The model inference calculation process is as follows:

[0029] H i =ReLU(W (1) X i +b (1) )

[0030] S i =σ(W (2) H i +b (2) )

[0031] Among them, W (1) W is the hidden layer weight matrix. (2) b is the output layer weight vector. (1) b(2) For the corresponding bias vector, H i Let S be the hidden layer output vector after the input of the i-th robot, and σ be the sigmoid activation function. i ∈[0,1] represents the robot task adaptation score of the i-th robot, indicating the degree of matching between the robot and the current component task; the intelligent prefabricated component selects the robot with the highest score based on the scoring results of multiple responding robots to perform the task.

[0032] Furthermore, the heterogeneous robot is equipped with a single task locking mechanism, that is, each heterogeneous robot can only respond to one component at any time, send a lock confirmation message, and reject or ignore the task requests of other components. Only after receiving the release message can it return to the standby state and respond to the task request of the next component.

[0033] Furthermore, the assembly management platform is a BIM-based data management platform used to store, analyze, and optimize data fed back by intelligent prefabricated components during the assembly task execution process; it adopts the Contract Network protocol for task request distribution and coordination, including: task broadcasting, robot bidding, component evaluation and task assignment, robot execution and result feedback, to build a closed-loop distributed collaborative control mechanism.

[0034] Beneficial Effects: Compared with the prior art, the beneficial effects of the present invention are as follows: The above-mentioned heterogeneous robot distributed collaborative assembly method based on intelligent prefabricated components provided by the present invention takes intelligent prefabricated components as the central node for task decision-making and scheduling. Through the components' own real-time perception of status information and active generation of task requests, and based on the built-in AI inference model, heterogeneous robots are accurately and dynamically allocated, forming a distributed task-driven collaborative control mechanism. This effectively solves the problems of scheduling bottlenecks, concentrated computing pressure, poor scalability, and low resource utilization in the traditional centralized control mode, significantly improving the collaborative efficiency of heterogeneous robots and the overall assembly performance. At the same time, it effectively reduces the dependence of assembly tasks on on-site manual labor, helps to realize intelligent construction with fewer or no personnel, actively responds to the challenge of labor shortage in the construction industry, and promotes the integrated development of building industrialization and intelligent construction. Attached Figure Description

[0035] Figure 1 This is a flowchart of the present invention;

[0036] Figure 2 This is a schematic diagram of a collaborative assembly and construction scenario where intelligent prefabricated components interact with heterogeneous robots, as proposed in this invention.

[0037] Figure 3 This diagram illustrates the distributed collaborative control mechanism for heterogeneous robots based on intelligent prefabricated components proposed in this invention. Detailed Implementation

[0038] The present invention will now be described in further detail with reference to the accompanying drawings:

[0039] like Figure 1 As shown, this invention proposes a distributed collaborative assembly method for heterogeneous robots based on intelligent prefabricated components, comprising the following steps:

[0040] Step 1: After the assembly task is started, the intelligent prefabricated components perceive their own status information in real time.

[0041] The intelligent prefabricated components are embedded with RFID tags, inertial measurement units, and ultra-wideband (UWB) positioning devices to collect their own identity, attitude, location information, and assembly progress information in real time.

[0042] Step 2: The intelligent prefabricated component autonomously determines the assembly task requirements based on the pre-stored BIM assembly task model and issues task request instructions to the heterogeneous robots related to the task requirements; the task request is broadcast to the heterogeneous robots through the communication network and enters the task listening stage.

[0043] Collaborative assembly and construction scenarios involving the interaction of intelligent prefabricated components and heterogeneous robots, such as... Figure 2 As shown, the assembly and construction scenario consists of three areas: a material storage area, a hoisting area, and an assembly area. The assembly and construction objects are multiple intelligent prefabricated components. The heterogeneous robots include palletizing robots, transport robots, hoisting robots, and assembly robots, with varying numbers of each type. To reduce computational complexity and improve decision-making efficiency, when prefabricated components are operating within a specific area, task requests are only sent to the heterogeneous robots relevant to the task in that area.

[0044] The interaction between intelligent prefabricated components and heterogeneous robots is as follows: A palletizing robot picks up components from the material yard and places them on a transport robot (AGV). After loading, the transport robot carries the component to the hoisting area. A hoisting robot lifts the component from the transport robot and transfers it to the assembly area. Only after the component has been hoisted can the transport robot return. The hoisting robot then lifts the component to the assembly point, where it works with the assembly robot to position and assemble the component. Only after the component is positioned and secured can the hoisting robot leave.

[0045] The intelligent prefabricated component has a built-in task flow model unit for pre-storing and managing BIM assembly task models. The BIM assembly task model is constructed by combining BIM model data with the unique code of the component to form a "component-task" mapping table. This mapping table contains the unique code of the prefabricated component and the corresponding list of specific assembly tasks, including assembly task type, assembly location coordinates, key operation accuracy requirements, task priority, and task execution time window information. This clearly describes the specific assembly tasks and sequence for each component. The assembly task model is defined as follows:

[0046] M={(C i ,T i |i∈[1,n]}

[0047] Among them, C i Indicates the component number, generated from the BIM component code; T i Indicates component C i The assembly task set is arranged in order of assembly process; each t j ∈T i It includes fields such as task type, assembly location, accuracy requirements, priority, and execution time window, and is defined as follows:

[0048] T i =[t1,t2,...,t m ]

[0049] t j =(type,pos,acc,prio,window)

[0050] Intelligent prefabricated components are based on a BIM assembly task model. A finite state machine (FSM) is used to model the component task flow, and the task state is transitioned according to the current perceived state to generate assembly task request instructions, achieving dynamic execution and updating driven by the task flow. Each component C... i The corresponding finite state machine (FSM) i Defined as:

[0051] FSM i =(S i ,T i G i ,s i,0 )

[0052] Wherein: S i This is a set of states, including typical states such as "pending assembly," "requesting a task," "matching," "executing," and "confirmation of completion"; T i G represents the set of assembly tasks, indicating the specific assembly tasks that can be triggered in each state; i This is a state transition function that determines the next state based on the current state and the task triggering conditions; s i,0 This is the initial state of the component, usually "to be assembled".

[0053] The state machine driving logic is: based on component C i Current perception state s t Matching task t j The status has been updated to:

[0054] s t+1 =Gi (s t ,t j )

[0055] At the same time, a task request is issued based on the task mapping table: If s t ∈S i When the perception condition is met, task t is triggered. j This generates a task request instruction. "Perception conditions met" means that the status information collected by the component through its own sensing device is consistent with the task triggering conditions set in the BIM task mapping table.

[0056] Step 3: After receiving the task request instruction from the intelligent prefabricated component, the heterogeneous robot autonomously determines whether it matches the task request instruction based on its own preset capability model. If the match is successful, it sends a confirmation message to the intelligent prefabricated component and automatically adjusts its own state to wait for the assembly instruction.

[0057] The intelligent prefabricated components broadcast their assembly task requirements to the heterogeneous robots in real time. j Each robot has an independent capability model. The capability model includes robot type, maximum workload, working accuracy, current status information, and endurance. The capability model R for each heterogeneous robot... i Defined as a quintuple:

[0058] R i =(type) i ,load i ,acc i ,state i power i )

[0059] After receiving a task request instruction, the heterogeneous robot performs an adaptability assessment of the task request using a conditional judgment-based rule. This requires that all indicators of its capability model meet the minimum requirements set for the task parameters. If all conditions are met, it is determined to be adaptable and sends task confirmation information back to the component; otherwise, it does not respond. Specifically, the conditional judgment-based rule means that the robot, based on its locally maintained capability model R... i According to the task request t j The type matching, load capacity, accuracy requirements, working status and endurance are compared item by item. Then the robot will be adapted.

[0060] Step 4: After receiving the feedback confirmation information from the heterogeneous robot, the intelligent prefabricated component uses the embedded lightweight AI inference model to score multiple responding robots, determines the optimal robot to perform the task, and sends the task execution instruction to the determined robot.

[0061] When the intelligent prefabricated component receives multiple robot task responses, it uses an embedded lightweight AI inference model to prioritize all responding robots and sends task execution instructions only to the target robot with the highest score. At the same time, it sends task release signals to other responding robots to release them from standby status.

[0062] The lightweight AI inference model is an edge-deployable multi-layer perceptron (MLP) neural network model, consisting of an input layer, hidden layers, and an output layer. Specifically, the input layer contains five feature variables, namely the robot capability matching degree r. i d, the distance between the component and the robot i Task load rate i Response delay t i And the historical mission completion quality score q i , forming the input vector X i =[r i ,d i ,l i ,t i ,q i The model inference calculation process is as follows:

[0063] H i =ReLU(W (1) X i +b (1) )

[0064] S i =σ(W (2) H i +b (2) )

[0065] Among them, W (1) W is the hidden layer weight matrix. (2) b is the output layer weight vector. (1) b (2) For the corresponding bias vector, H i Let S be the hidden layer output vector after the input of the i-th robot, and σ be the sigmoid activation function. i ∈[0,1] represents the robot task adaptation score of the i-th robot, indicating the degree of matching between the robot and the current component task; the intelligent prefabricated component selects the highest scorer from the scores of multiple responding robots to execute the task; the AI ​​inference model is trained offline and then compressed and burned into the microcontroller (MCU) embedded in the component, supporting rapid on-site inference and autonomous assignment, realizing the end-side intelligence of the component.

[0066] Step 5: The selected robot completes the assembly task according to the task execution instructions and feeds back the task execution results to the intelligent prefabricated component; the intelligent prefabricated component records the completion status of the assembly task and uploads it to the assembly management platform for storage and analysis.

[0067] The communication methods between intelligent prefabricated components and heterogeneous robots include, but are not limited to, WiFi, ZigBee, or LoRa; the communication adopts a distributed publish-subscribe model to build a distributed communication network for realizing communication and interaction between intelligent prefabricated components and heterogeneous robots.

[0068] To avoid duplicate robot responses or resource conflicts when multiple smart prefabricated components broadcast task requests simultaneously, a single task locking mechanism is set for each heterogeneous robot. That is, at any given time, the robot can only respond to one component, send a lock confirmation message, and reject or ignore task requests from other components. Only after receiving a release message and returning to standby state can it respond to the task request of the next component.

[0069] The assembly management platform is a BIM-based data management platform used to store, analyze, and optimize data generated during assembly task execution. This invention employs an improved Contract Net Protocol (CNP) for task request distribution and coordination. The protocol flow includes: task broadcasting, robot bidding, component evaluation and task assignment, robot execution and result feedback, constructing a closed-loop distributed collaborative control mechanism, such as... Figure 3 As shown.

[0070] In summary, this invention effectively solves the problems of scheduling bottlenecks, concentrated computational pressure, poor scalability, and low resource utilization in traditional centralized control modes, and has the potential to improve the collaborative efficiency of heterogeneous robots and the overall assembly performance. At the same time, it effectively reduces the dependence of assembly tasks on on-site manual labor, helps to realize intelligent construction with fewer or no human intervention, actively addresses the challenge of labor shortage in the construction industry, and promotes the integrated development of construction industrialization and intelligent construction.

[0071] The above content merely illustrates the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made based on this technical solution fall within the scope of protection of the technical concept proposed in this invention and are included in the claims of this invention.

Claims

1. A distributed collaborative assembly method for heterogeneous robots based on intelligent prefabricated components, characterized in that, The implementation process is as follows: After the assembly task is started, the intelligent prefabricated component senses its own status information in real time; the intelligent prefabricated component includes a task flow model unit, an RFID tag, an inertial measurement unit, an ultra-wideband positioning device, and a lightweight AI inference model; the RFID tag, inertial measurement unit, and ultra-wideband positioning device respectively acquire the component's identity information, location information, attitude information, and assembly progress information. The task flow model unit is used to pre-store and manage BIM assembly task models; The intelligent prefabricated components autonomously determine the assembly task requirements based on the BIM assembly task model and issue task request instructions to the heterogeneous robots in the corresponding work areas. The task request is broadcast to the heterogeneous robot in real time via the communication network, and the task listening phase begins. After receiving a task request instruction from a smart prefabricated component, the heterogeneous robot autonomously determines whether it matches the task request instruction based on its own preset capability model. If the match is successful, it sends a confirmation message to the intelligent prefabricated component and automatically adjusts its own status, waiting for assembly instructions. When receiving task responses from heterogeneous robots, the intelligent prefabricated component uses a lightweight AI inference model to prioritize all responding robots and sends task execution instructions only to the target robot with the highest score. At the same time, it sends task release signals to the other responding robots to deactivate their standby status. The target robot completes the assembly task according to the task execution instructions and feeds back the task execution results to the intelligent prefabricated component; the intelligent prefabricated component records the completion status of the assembly task and uploads it to the prefabricated building component management platform for storage and analysis.

2. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, The BIM assembly task model is constructed by combining BIM model data with unique component codes to form a "component-task" mapping table. This mapping table contains the unique codes of prefabricated components and the corresponding list of specific assembly tasks, including assembly task type, assembly location coordinates, critical operation accuracy requirements, task priority, and task execution time window information. It is used to clearly describe the specific assembly tasks and sequence of each component. The assembly task model is defined as follows: Where C i Indicates the component number, generated from the BIM component code; T i Indicates component C i The assembly task set is arranged in order of assembly process; each t j ∈T i It includes fields for task type, assembly location, accuracy requirements, priority, and execution time window, and is defined as follows: 。 3. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 2, characterized in that, The intelligent prefabricated components are based on the BIM assembly task model. The finite state machine is used to model the component task process, and the task state is changed according to the current perception state to generate assembly task request instructions, so as to realize the dynamic execution and update driven by the task process. Each component C i The corresponding finite state machine (FSM) i Defined as: Among them, S i This is a set of states, including "Pending Assembly", "Task Requesting", "Matching", "Executing", and "Completion Confirmation"; T i G represents the set of assembly tasks, indicating the specific assembly tasks that can be triggered in each state; i This is a state transition function that determines the next state based on the current state and the task triggering conditions; s i,0 This is the initial state of the component, designated as "to be assembled". According to component C i Current perception state s t Matching task t j The status has been updated to: Simultaneously, a task request is issued based on the task mapping table: if s t ∈S i And if the "perception condition is met", then task t is triggered. j The task request instruction is generated; the "sense condition is met" means that the status information collected by the component through its own sensing device is consistent with the task triggering conditions set in the BIM task mapping table.

4. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, The communication methods between intelligent prefabricated components and heterogeneous robots include WiFi, ZigBee, or LoRa; the communication adopts a distributed publish-subscribe model to build a distributed communication network and realize the communication interaction between intelligent prefabricated components and heterogeneous robots.

5. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, The heterogeneous robots include palletizing robots, transport robots, hoisting robots, and assembly robots, each with its own independent capability model.

6. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 5, characterized in that, The capability model includes robot type, maximum workload, critical task accuracy, current status information, and endurance; the capability model R for each heterogeneous robot... i Defined as a quintuple: 。 7. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, After receiving the task request instruction, the heterogeneous robot uses a condition-based judgment rule to evaluate the adaptability of the task request, requiring that all indicators of its capability model meet the minimum requirements set by the task parameters; if all conditions are met, it is determined to be adaptable and sends task confirmation information back to the component. Otherwise, no response is made; specifically, the rule based on conditional judgment refers to the robot's local maintained capability model R. i According to the task request t j The type matching, load capacity, accuracy requirements, working status and endurance are compared item by item. If the type i =type tj and load i ≥load tj min andacc i ≤acc tj max and state i =Idle and power i ≥power min Then the robot will adapt.

8. The method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, The lightweight AI inference model is an edge-deployable multi-layer feedforward neural network model, including an input layer, a hidden layer, and an output layer; specifically, the input layer includes five feature variables, namely the robot capability matching degree r. i d, the distance between the component and the robot i Task load rate i Response delay t i And the historical mission completion quality score q i , forming the input vector X i =[r i ,d i ,l i ,t i ,q i The model inference calculation process is as follows: Among them, W （1） W is the hidden layer weight matrix. （2） b is the output layer weight vector. （1） b （2） For the corresponding bias vector, H i Let be the hidden layer output vector after the input of the i-th robot. S is the sigmoid activation function. i ∈[0,1] represents the robot task adaptation score of the i-th robot, indicating the degree of matching between the robot and the current component task; the intelligent prefabricated component selects the robot with the highest score based on the scoring results of multiple responding robots to perform the task.

9. A method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, The heterogeneous robot is equipped with a single task locking mechanism, which means that each heterogeneous robot can only respond to one component at any given time, send a lock confirmation message, and reject or ignore task requests from other components. Only after receiving a release message and returning to standby state can it respond to the task request from the next component.

10. A method for distributed collaborative assembly of heterogeneous robots based on intelligent prefabricated components according to claim 1, characterized in that, The prefabricated building component management platform is a BIM-based data management platform used to store, analyze, and optimize data fed back by intelligent prefabricated components during the assembly task execution process. The Contract Network protocol is used for task request distribution and coordination, including task broadcasting, robot bidding, component evaluation and task assignment, robot execution and result feedback, to build a closed-loop distributed collaborative control mechanism.

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