Method for implementing mobile robot having intelligent body behavior

Abstract

The present application discloses a method for implementing a mobile robot having an intelligent body behavior. First, a feature database of a workpiece to be processed is established manually or by a robot, and then a workshop digital management system sends a task instruction comprising task data, a target machine tool, a device number, and a processing time to the robot by means of a group control scheduling system. Then, on the basis of the instruction, the robot autonomously identifies the workpiece to be processed. When identification fails, the robot continues to scan a next workpiece to be processed, and when the identification succeeds, the robot autonomously plans a route and transfers the workpiece to be processed to the target machine tool for processing. According to the present application, the robot is endowed with self-determination and self-behavior generation capabilities under particular constraints, so that efficient and autonomous operation tasks can be implemented.

Description

A method for implementing embodied intelligent behavior mobile robots

[0001] Cross-references to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202411930117.X, filed on December 26, 2024, entitled "A Method for Implementing an Embodied Intelligent Behavior Mobile Robot", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of automated processing and manufacturing, and in particular to a method for realizing a mobile robot with embodied intelligent behavior. Background Technology

[0004] With the increasing number of applications for mobile robots, especially in civilian and industrial fields, the requirements for their level of intelligence are also constantly increasing. In complex scenarios, mobile robots need to rely on their own perception capabilities to make autonomous decisions, form autonomous behaviors, and complete various tasks. This requires mobile robots not only to possess strong scene perception capabilities, but also to be able to make autonomous decisions based on task requirements and form corresponding movement and operational behaviors. For example, mobile composite robots need to operate autonomously under different working conditions to achieve the expected task objectives.

[0005] However, current mobile robot technology still has significant shortcomings in achieving self-awareness-based self-decision-making capabilities. Although mobile robots can acquire environmental information through sensing devices, they often rely on the unified scheduling and command of a robot scheduling system during autonomous decision-making and behavior generation. Mobile robots using these technologies have low autonomous decision-making capabilities and struggle to complete tasks independently in complex and changing environments. Therefore, mobile robots urgently need further improvements in autonomy and intelligence to overcome the shortcomings of current technologies in autonomous decision-making and behavior generation, and truly achieve efficient and intelligent autonomous operation capabilities. Summary of the Invention

[0006] In view of this, this disclosure presents a method for implementing an embodied intelligent behavior mobile robot, enabling the robot to possess self-judgment capabilities and generate its own behaviors under limited constraints, thereby meeting the requirements of the task. The technical solution of this disclosure is implemented as follows:

[0007] This disclosure presents a method for implementing an embodied intelligent behavior mobile robot, including the following steps:

[0008] S1. Establish a feature database of the workpiece to be processed, either manually or by robot;

[0009] S2. The workshop digital management system sends task instructions to the robots through the group control and scheduling system.

[0010] S3. Task instructions include task data, target machine tool, equipment number, and processing time;

[0011] S4. The robot autonomously identifies the workpiece to be processed according to the task instructions;

[0012] Optionally, the robot extracts the feature data of the workpiece to be processed and compares it with the target features to see if they match.

[0013] Optionally, the robot extracts the feature data of the workpiece to be processed, uploads it to the group control and scheduling system, and compares it with the target features to see if they match.

[0014] S5. If the recognition fails, the robot continues to scan the next workpiece to be processed;

[0015] S6. If recognition is successful, the robot autonomously plans a route and transfers the workpiece to be processed to the target machine tool.

[0016] Optionally, the robot transfers the workpiece to be processed to the target machine tool and works with the target machine tool to complete the installation and preparation of the workpiece.

[0017] S7. The target machine tool processes the workpiece;

[0018] In steps S3 to S7, the group control and scheduling system is in the state of monitoring the robot;

[0019] Steps S1 to S7 also include methods for robots to avoid each other, including the following steps:

[0020] N1. When two robots enter a certain distance range, they establish a communication relationship and send a handshake signal.

[0021] N2. The two robots determine the avoidance plan based on the interactive driving route and direction. One robot needs to perform priority calculation to make a decision and transmit the decision information to the other robot.

[0022] N3. The low-priority robot performs the avoidance maneuver. After the avoidance maneuver is completed, both robots resume normal driving.

[0023] Optionally, S1, either manually or robotically, establishes a feature database of the workpiece to be processed, including:

[0024] A handheld vision device can be used to photograph the workpiece and extract feature data to build a feature database, or a robot can use a portable vision camera to photograph the workpiece and extract feature data to build a feature database.

[0025] Optionally, the robot can use its onboard vision camera to photograph the workpiece and extract feature data to build a feature database.

[0026] Optionally, the feature database includes feature data for all types of workpieces to be processed.

[0027] Alternatively, all types of workpieces can be machined by all types of machine tools.

[0028] A method for implementing embodied intelligent behavior mobile robots also includes a method for mutual avoidance among robots, wherein the priority calculation rules in step N2 include:

[0029] The task time and cycle time priority rule is that the robot with the shortest remaining task execution time or production cycle time has high priority and enjoys the right of way, while the other robot has low priority and performs an avoidance action.

[0030] The shortest remaining distance priority rule applies: the robot with the shortest remaining travel distance has high priority and enjoys the right of way, while the other robot has low priority and performs an avoidance maneuver.

[0031] The avoidance rules calculate the impact factor. Assuming the other robot does not avoid the other, the two robots calculate the time it takes to reach the target point when they avoid the other robot, and calculate the impact factor of this avoidance on subsequent driving and operation. The robot with the smaller impact factor has low priority and performs the avoidance action, while the robot with the larger impact factor has high priority and enjoys the right of way.

[0032] Other rules specify certain robots as high priority and others as low priority, with low-priority robots avoiding designated high-priority robots.

[0033] Optionally, the calculation formula for the avoidance rule in calculating the impact factor is as follows: λ=(T0-T1) / T1

[0034] Where T0 is the time to reach the target point calculated due to the avoidance action, and T1 is the scheduled time for this task;

[0035] λ represents the influence factor. Robots with a high λ value are high-priority robots, and robots with a low λ value are low-priority robots.

[0036] Based on this invention, mobile robots can autonomously move and perform tasks under the "weak" scheduling of a scheduling system, relying on their own intelligent control system to complete designated tasks. This intelligent control system enables robots to make independent judgments and decisions, improving operational autonomy and flexibility. Furthermore, as the intelligence level of the robot increases, the scheduling burden on the group control scheduling system is reduced, eliminating the need for cumbersome centralized control. This burden reduction effect greatly optimizes the operational efficiency of the scheduling system. In practical applications, multiple robots can coordinate their work more efficiently in the same work scenario, significantly improving overall work efficiency and productivity. In this way, mobile robots not only possess higher autonomous operation capabilities but also demonstrate stronger adaptability and collaborative capabilities in complex and changing environments, further promoting the level of industrial automation. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments or related technologies of this disclosure, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are only one embodiment of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 is a flowchart of a method for implementing an embodied intelligent behavior mobile robot disclosed in this disclosure in a scenario where the raw material warehouse has not achieved digital and intelligent management.

[0039] Figure 2 shows the method for establishing the feature database in Figure 1;

[0040] Figure 3 illustrates the mutual avoidance method of robots in this disclosure;

[0041] Figure 4 illustrates a method for implementing an embodied intelligent behavior mobile robot disclosed in this disclosure, where the robot autonomously searches for suitable raw materials based on task orders. Detailed Implementation

[0042] The technical solutions of this disclosure will now be clearly and completely described with reference to the embodiments and accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0043] Unless otherwise defined, all technical and scientific terms used in this disclosure have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs; the terminology used in the detailed description is for the purpose of describing particular embodiments only and is not intended to limit this disclosure; the terms “comprising” and “having” and any variations thereof in the specification, claims and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0044] In the description of the specific embodiments of this disclosure, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary or secondary relationship of the indicated technical features. In the description of the embodiments of this disclosure, "a plurality of" means two or more, unless otherwise explicitly defined.

[0045] In this disclosure, the reference to "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this disclosure. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this disclosure can be combined with other embodiments.

[0046] In the description of the embodiments of this disclosure, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this disclosure generally indicates that the preceding and following related objects have an "or" relationship.

[0047] It should be noted that, for ease of description, all identical technical features are labeled with the same symbols in the following embodiments.

[0048] With the increasing number of applications for mobile robots, especially in civilian and industrial fields, the requirements for their level of intelligence are also constantly increasing. In complex scenarios, mobile robots need to rely on their own perception capabilities to make autonomous decisions, form autonomous behaviors, and complete various tasks. This requires mobile robots not only to possess strong scene perception capabilities, but also to be able to make autonomous decisions based on task requirements and form corresponding movement and operational behaviors. For example, mobile composite robots need to operate autonomously under different working conditions to achieve the expected task objectives.

[0049] However, current mobile robot technology still has significant shortcomings in achieving self-awareness-based self-decision-making capabilities. Although mobile robots can acquire environmental information through sensing devices, they often rely on the unified scheduling and command of a robot scheduling system during autonomous decision-making and behavior generation. Mobile robots using these technologies have low autonomous decision-making capabilities and struggle to complete tasks independently in complex and changing environments. Therefore, mobile robots urgently need further improvements in autonomy and intelligence to overcome the shortcomings of current technologies in autonomous decision-making and behavior generation, and truly achieve efficient and intelligent autonomous operation capabilities.

[0050] Therefore, this disclosure presents a method for realizing embodied intelligent behavior in mobile robots, enabling robots to have self-judgment capabilities and generate their own behaviors under limited constraints, thereby meeting the requirements of the task. The following will be explained step by step through examples, ultimately realizing the embodied intelligent behavior capabilities of mobile robots applied in industrial manufacturing scenarios.

[0051] Example 1

[0052] In scenarios where raw material warehouses lack digital and intelligent management, as shown in Figures 1 and 2, a method for implementing an embodied intelligent behavior mobile robot includes the following steps:

[0053] S1. Establish a feature database of the workpiece to be processed, either manually or by robot;

[0054] S2. The robot performs visual photography and recognition of the workpiece to be processed;

[0055] S3. The robot extracts the feature data of the workpiece to be processed and uploads it to the robot group control system;

[0056] S4. The robot group control system issues instructions to process the workpiece as a finished or semi-finished part based on the uploaded feature data, and determines the corresponding machine tool type and quantity.

[0057] S5. Based on feedback from the robot group control system, the workshop digital system designates the machine tool to process the workpiece, and the robot performs the transfer task, moving the workpiece to the machine tool.

[0058] S6. The robot group control system controls the machine tool to process the workpiece into finished or semi-finished parts.

[0059] S7. After the workpiece is processed, the robot group control system controls the robot to take out the finished or semi-finished part;

[0060] S8. The robot transfers finished parts to the finished product warehouse, or transfers semi-finished parts to the next machine tool for further processing;

[0061] In the above settings, the typical characteristics of the scenario targeted by the embodiment are that the raw material warehouse has not achieved digital and intelligent management, the raw materials are placed in a disorderly manner, the robot picks them up randomly, and identification and decision-making are required.

[0062] With the above setup, this disclosure is applied in industrial manufacturing workshop scenarios, primarily targeting production lines. Robots operating in this environment have defined requirements and timelines, exhibiting both certainties and uncertainties. Therefore, robots need to possess embodied intelligence capabilities, enabling them to self-judge and adjust their behavior regarding objects, the environment, and their own actions to ensure smooth operation and task execution.

[0063] In this embodiment, the group control and scheduling system serves only as an intermediary platform for robot task execution data. In most cases, it monitors the robot by collecting operational data, rather than performing continuous remote control, scheduling, or planning of robot routes as traditional scheduling systems do.

[0064] In this embodiment, S4, the robot group control system determines the corresponding machine tool type based on the uploaded feature data. The specific steps are as follows:

[0065] S4.1 The robot group control system compares the feature data of the uploaded workpiece to be processed with the data number of the feature database established in S1 to obtain the corresponding feature data number, and then determines the corresponding workshop machine tool type.

[0066] In this embodiment, S5, the workshop digital system, based on feedback from the robot group control system, designates the machine tool for processing, and the robot performs the transfer task, moving the workpiece to be processed to the machine tool. The specific steps are as follows:

[0067] S5.1 After specifying the machine tool to be processed, the information is transmitted to the robot group control system. The robot group control system issues instructions, and the robot establishes a communication relationship with the specified machine tool through the machine tool communication module.

[0068] S5.2 The robot transfers the workpiece to be processed to the designated machine tool and works with the machine tool to complete the installation and preparation of the workpiece.

[0069] In the above setup, the robot swarm control system compares the uploaded feature data of the workpiece to be processed with the data in the feature database to obtain the matching feature data number, for example, feature data number k. This means that the workpiece currently being processed by the robot corresponds to feature data number k in the database. The robot swarm control system has established a mapping relationship between feature data numbers and workshop machine tool type numbers. For example, feature data number k corresponds to machine tool type k. Different machine tool types indicate different workpieces being processed, and multiple machine tools of the same type may have the same or similar processing tasks. The factory digital system determines and allocates the workpiece to which machine tool it will be processed on.

[0070] After the factory's digital system identifies the specific machine tool for processing the workpiece, it transmits this information to the robot swarm control system. The swarm control system then issues commands to the robot, which establishes a communication relationship with the corresponding machine tool through its machine tool communication module. When the robot moves the workpiece to the vicinity of the machine tool, based on this two-way communication relationship, the robot and the machine tool collaborate to complete the installation and preparation of the workpiece.

[0071] It is important to note that the workpiece from which feature data is extracted may not correspond to only one type of machine tool; it may correspond to other types of machine tools. This depends on the specific type of machining task for the workpiece.

[0072] In this embodiment, the instructions in step S5.1 include task data, target machine tool, equipment number, and processing time.

[0073] In this embodiment, the feature database in step S1 includes feature data of all types of workpieces to be processed.

[0074] In this embodiment, all types of workpieces can be processed by all types of machine tools.

[0075] In the above setup, for a given production workshop, the type and number of machine tools are fixed, and therefore the type of workpieces to be processed is also determined. Based on this, different types of workpieces (such as shape, size, and other characteristic data) can have their corresponding processing requirements and corresponding machine tool types pre-determined according to these characteristic parameters. This effectively optimizes the production process and improves processing efficiency.

[0076] In this embodiment, a vision camera is used to take visual pictures of all types of workpieces to be processed and to extract feature data.

[0077] In this embodiment, feature data is extracted by taking photos using a handheld visual device or by taking photos using a visual camera device carried by a robot.

[0078] For example, various workpieces can be placed in a queue, and a mobile robot can complete the pre-database creation by walking and taking pictures at the same time, or a person can take pictures one by one with a handheld vision camera to create a database.

[0079] To further explain, for example, a workshop has 300 machine tools of 10 different types. The types of parts to be processed may be 10, fewer, or more than 10 (depending on the machine tool type and the types of workpieces to be produced). Regardless of the number of workpieces, all 300 machine tools of these 10 types can complete the process.

[0080] In the process of establishing the database, the initial database creation work is carried out manually or by robots. Each type of workpiece is retrieved manually or by robots, scanned and photographed using a vision camera to generate feature data, and then mapped to the appropriate machine tool type, gradually building the database. In subsequent production, when a robot retrieves a workpiece from the warehouse, it uses its onboard vision camera to capture and extract feature information, which is then uploaded to the robot group control system. This system can automatically identify which machine tool should process the workpiece.

[0081] As shown in Figure 3, a method for implementing an embodied intelligent behavior mobile robot also includes a method for mutual avoidance between robots. The specific steps are as follows:

[0082] First, when the two robots enter a certain distance range, they establish a communication relationship and send a handshake signal.

[0083] Secondly, the two robots determine the avoidance plan based on the interactive driving route and direction. One robot needs to perform priority calculations to make a decision and then transmit the decision information to the other robot.

[0084] When a low-priority robot initiates obstacle avoidance maneuvering and reaches the avoidance area or completes the avoidance maneuver, the high-priority robot continues driving, and the low-priority robot continues driving.

[0085] In the above settings, the rules for calculating and judging high and low priorities are as follows:

[0086] 1. Prioritize task time and pace

[0087] The robot with the shortest remaining task execution time or production cycle time among the two robots has high priority and enjoys priority passage, requiring the other robot to perform an avoidance maneuver.

[0088] 2. Shortest remaining distance priority principle

[0089] The robot with the shortest remaining travel distance has the highest priority and the right of way, requiring the other robot to perform an avoidance maneuver.

[0090] 3. Avoidance rules based on impact factor calculation

[0091] Each robot calculates its arrival time at the target point, assuming the other does not yield and it yields, and then calculates the impact factor of this yield action on its subsequent driving and maneuvering actions, exchanging these calculations with the other. The robot with the smaller impact factor performs the yield action. The larger the impact factor, the greater the impact of this yield action on its subsequent task execution.

[0092] 4. Based on other rules

[0093] Some robots are designated as having high priority, and in most scenarios, other robots need to give way to them.

[0094] Optionally, the calculation formula in the above-mentioned avoidance rule for calculating the impact factor is as follows: λ=(T0-T1) / T1

[0095] Where T0 is the time to reach the target point calculated due to the avoidance action, and T1 is the scheduled time for this task;

[0096] λ represents the influence factor. Robots with high λ values ​​are high-priority robots, and robots with low λ values ​​are low-priority robots. The avoidance is performed by the low-priority robots.

[0097] Example 2

[0098] In the scenario of achieving digital and intelligent management of raw material warehouses, a method for implementing embodied intelligent behavior mobile robots includes the following steps:

[0099] S1. The workshop digital management platform issues task instructions;

[0100] S2. The robot group control system directs a robot to go to the raw material warehouse to retrieve materials;

[0101] S3. The robot picks up the material and transfers it to the designated machine tool.

[0102] In the above setup, each step is carried out under the scheduling, command, and control of the workshop digital management platform and the robot group control system. The raw material warehouse has achieved digital and intelligent management and can provide the corresponding raw materials according to the instructions of the workshop digital platform.

[0103] Example 3

[0104] As shown in Figure 4, in a scenario where a robot autonomously searches for suitable raw materials based on a task order, a method for implementing an embodied intelligent behavior mobile robot includes the following steps:

[0105] S1. Establish a feature database of the workpiece to be processed, either manually or by robot;

[0106] S2. The workshop digital management system sends task instructions to the robots through the group control and scheduling system.

[0107] S3. Task instructions include task data, target machine tool, equipment number, and processing time;

[0108] S4. The robot autonomously identifies the workpiece 1 to be processed according to the task instructions;

[0109] Optionally, the robot extracts the feature data of the workpiece 1 and compares it with the target features to see if they match.

[0110] Optionally, the robot extracts the feature data of the workpiece 1 to be processed and uploads it to the group control and scheduling system, and compares it with the target features to see if they match.

[0111] S5. If the recognition fails, the robot continues to scan the next workpiece to be processed, such as workpieces 2, 3, and 4.

[0112] S6. If recognition is successful, the robot autonomously plans a route and transfers workpiece 1 to the target machine tool.

[0113] Optionally, the robot transfers the workpiece 1 to the target machine tool and works with the target machine tool to complete the installation and preparation of the workpiece 1.

[0114] S7. The target machine tool processes workpiece 1.

[0115] In steps S3 to S7, the group control scheduling is in a listening state, monitoring the robot's status.

[0116] Steps S1 to S7 also include methods for robots to avoid each other, including the following steps:

[0117] N1. When two robots enter a certain distance range, they establish a communication relationship and send a handshake signal.

[0118] N2. The two robots determine the avoidance plan based on the interactive driving route and direction. One robot needs to perform priority calculation to make a decision and transmit the decision information to the other robot.

[0119] N3. The low-priority robot performs the avoidance maneuver. After the avoidance maneuver is completed, both robots resume normal driving.

[0120] Optionally, S1, either manually or robotically, establishes a feature database of the workpiece to be processed, including:

[0121] A handheld vision device can be used to photograph the workpiece and extract feature data to build a feature database, or a robot can use a portable vision camera to photograph the workpiece and extract feature data to build a feature database.

[0122] Optionally, the robot can use its onboard vision camera to photograph the workpiece and extract feature data to build a feature database.

[0123] Optionally, the feature database includes feature data for all types of workpieces to be processed.

[0124] Alternatively, all types of workpieces can be machined by all types of machine tools.

[0125] With this configuration, scene maps and feature data mapping databases are stored in the robot's internal controller. Upon receiving task data, the robot autonomously calculates the entire process from identifying a suitable workpiece to determining the target machine tool's location, including route planning and execution of maneuvers. During this time, the group control and scheduling system only monitors the robot's behavior trajectory without performing traditional trajectory scheduling commands, thus reducing the computational burden on the scheduling system.

[0126] It should be noted that the above are merely preferred embodiments of this disclosure and are not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the protection scope of this disclosure. Industrial applicability

[0127] By adopting the above scheme, mobile robots can autonomously move and operate based on their own intelligent control systems to complete designated tasks. This intelligent control system enables robots to make independent judgments and decisions, improving operational autonomy and flexibility. Furthermore, as the intelligence level of robots increases, the scheduling burden on the swarm control system is reduced, eliminating the need for cumbersome centralized control. This burden reduction significantly optimizes the operational efficiency of the scheduling system. In practical applications, multiple robots can coordinate their work more efficiently in the same work environment, significantly improving overall work efficiency and productivity. In this way, mobile robots not only possess higher autonomous operation capabilities but also demonstrate stronger adaptability and collaborative capabilities in complex and changing environments, further promoting the level of industrial automation and enhancing practicality.

Claims

1. A method for implementing a body-embodied intelligent behavior mobile robot, characterized in that, The steps include the following: S1. Establish a feature database of the workpiece to be processed, either manually or by robot; S2. The workshop digital management system sends task instructions to the robots through the group control and scheduling system. S3. The task instruction includes task data, target machine tool, equipment number, and processing time; S4. The robot autonomously identifies the workpiece to be processed according to the task instructions; S5. If the identification fails, the robot continues to scan the next workpiece to be processed; S6. If the recognition is successful, the robot autonomously plans a route, transports the workpiece to be processed to the target machine tool, and autonomously installs the workpiece onto the processing table of the target machine tool. S7. The target machine tool processes the workpiece. In steps S3 to S7, the group control and scheduling system is in the state of monitoring the robot; Steps S1 to S7 also include a method for robots to avoid each other, comprising the following steps: N1. When two robots enter a certain distance range, they establish a communication relationship and send a handshake signal. N2. The two robots determine the avoidance plan based on the interactive driving route and direction. One robot needs to perform priority calculation to make a decision and transmit the decision information to the other robot. N3. The low-priority robot performs the avoidance maneuver. After the avoidance maneuver is completed, both robots resume normal driving.

2. The embodied intelligent behavior mobile robot implementation method of claim 1, wherein, S1. Establish a feature database for the workpiece to be processed, either manually or robotically, including: The handheld vision device is used to photograph the workpiece to be processed and extract feature data to establish the feature database, or the robot uses a carried vision camera to photograph the workpiece to be processed and extract feature data to establish the feature database.

3. The embodied intelligent behavior mobile robot implementation method of claim 1 or 2, wherein, The robot uses its onboard vision camera to photograph the workpiece and extract feature data to build the feature database.

4. A method for implementing an embodied intelligent behavior mobile robot according to any one of claims 1-3, characterized in that, The feature database includes feature data for all types of workpieces to be processed.

5. The method for implementing a embodied intelligent behavior mobile robot according to claim 4, characterized in that, All types of workpieces can be machined by all types of machine tools.

6. The embodied intelligent behavior mobile robot implementation method of any one of claims 1-5, wherein, S4. The robot autonomously identifies the workpiece to be processed according to the task instructions, including: The robot extracts the feature data of the workpiece to be processed and compares it with the target features to see if they match.

7. A method for implementing an embodied intelligent behavior mobile robot according to any one of claims 1-6, characterized in that, S4. The robot autonomously identifies the workpiece to be processed according to the task instructions, including: The robot extracts the feature data of the workpiece to be processed and uploads it to the group control and scheduling system, and compares it with the target features to see if they match.

8. The embodied intelligent behavior mobile robot implementation method of any one of claims 1-7, wherein, S6. If recognition is successful, the robot autonomously plans a route to transport the workpiece to be processed to the target machine tool, including: The robot transfers the workpiece to be processed to the target machine tool and cooperates with the target machine tool to complete the installation and preparation of the workpiece.

9. The embodied intelligent behavior mobile robot implementation method according to any one of claims 1-8, wherein, The rules for priority calculation in step N2 include: The task time and cycle time priority rule is that the robot with the shortest remaining task execution time or production cycle time has high priority and enjoys the right of way, while the other robot has low priority and performs an avoidance action. The shortest remaining distance priority rule applies: the robot with the shortest remaining travel distance has high priority and enjoys the right of way, while the other robot has low priority and performs an avoidance maneuver. The avoidance rules calculate the impact factor. Assuming the other robot does not avoid the other, the two robots calculate the time it takes to reach the target point when they avoid the other robot, and calculate the impact factor of this avoidance on subsequent driving and operation. The robot with the smaller impact factor has low priority and performs the avoidance action, while the robot with the larger impact factor has high priority and enjoys the right of way. Other rules specify certain robots as high priority and others as low priority, with low-priority robots avoiding designated high-priority robots.

10. The embodied intelligent behavior mobile robot implementation method of claim 9, wherein, The calculation formula for the avoidance rule in calculating the impact factor is as follows: λ=(T0-T1) / T1 Where T0 is the time to reach the target point calculated due to the avoidance action, and T1 is the scheduled time for this task; λ represents the influence factor. Robots with a high λ value are high-priority robots, and robots with a low λ value are low-priority robots.

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