Methods, apparatus, computer equipment and storage media for task handover of composite robots

Abstract

This invention discloses a method, apparatus, computer equipment, and storage medium for task succession of a composite robot. The method includes: real-time acquisition of operational data from the composite robot and uploading it to a robot scheduling and management system; comparing the acquired operational data with a preset normal operating parameter threshold library to determine the anomaly type and level, and accurately locating the anomaly to specific coordinates using navigation markers; generating a rescue path using a combination of fixed-route navigation and free navigation; determining a task succession scheme based on the functional matching degree of the rescue robot, and generating a decision scheme; decomposing the decision scheme into standardized control commands and issuing them to the rescue robot, which then executes the task succession operation according to the control commands. By implementing the method of this invention, fully automated rescue response and task succession can be achieved in abnormal situations involving composite robots, improving the efficiency, stability, and safety of automated production in factories.

Description

Technical Field

[0001] This invention relates to the field of composite robot task succession technology, and more specifically to composite robot task succession methods, apparatus, computer equipment and storage media. Background Technology

[0002] In modern industrial production, factory environments are typically characterized by dense equipment, narrow passageways, and fixed, repetitive work processes. In traditional production models, material handling and process coordination have long relied on manual labor. However, this model faces numerous problems, such as rising labor costs, difficulty in improving operational efficiency, and a high risk of collisions when frequently moving back and forth in narrow passageways. These issues make it difficult to meet the modern factory's production demands for "high efficiency, safety, and standardization."

[0003] To address these challenges, in recent years, fixed-route hybrid robots integrating AMR mobile chassis, robotic arms, and multi-dimensional perception modules have gradually become a key component in factory automation transformation. Their core logic is to target structured operational scenarios within factories (such as material transfer channels between production lines, fixed connection routes between storage areas and workshops), using pre-set navigation markers such as magnetic strips, QR codes, guide rails, and SLAM positioning stations. This allows the hybrid robot to travel along fixed paths, achieving point-to-point automated transportation (including material transport and process assistance), providing fundamental support for the construction of unmanned factories.

[0004] Compared with traditional manual handling methods, fixed-route composite robots have significant technological advantages:

[0005] High-precision driving: It can travel precisely along a preset trajectory in narrow passages, with a positioning accuracy of within ±10mm, effectively avoiding equipment collisions or passage blockages caused by human operation errors;

[0006] Automated process integration: By linking with factory MES (Manufacturing Execution System), PLC (Programmable Logic Controller) and other systems, it can automatically complete material loading and unloading and process connection according to the production rhythm, which can significantly improve the standardization of processes and operational efficiency.

[0007] However, existing fixed-route composite robots still face many challenges in emergency rescue operations when abnormal situations occur (such as robotic arm malfunctions, AGV chassis malfunctions, etc.). Current solutions mainly suffer from the following shortcomings:

[0008] Manual intervention: Relying on manual handling of abnormal situations is inefficient and unsuitable for unmanned factory environments, making it difficult to meet the needs of fully automated production;

[0009] The existing AGV rescue and dispatch auxiliary equipment has limited functionality: it can only perform fixed, routine operations, such as towing malfunctioning robots to designated maintenance areas, but it cannot take over unfinished production tasks (such as incomplete material transfers or process connection operations). During peak operating periods in unmanned factories, this deficiency will directly lead to production interruptions, making it impossible to guarantee task continuity, and thus seriously affecting the overall production efficiency and delivery cycle of the factory.

[0010] In summary, existing composite robots have significant limitations in handling the dynamic, urgent, and complex nature of task succession scenarios in unmanned factories. Therefore, there is an urgent need for an intelligent decision-making solution that can adapt to environmental changes, optimize task decisions, and improve collaborative efficiency. This solution aims to address the shortcomings of existing technologies in task succession scenarios in unmanned factories and meet the pressing needs of modern factories for efficient, safe, and standardized production. Summary of the Invention

[0011] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, apparatus, computer equipment and storage medium for task succession of composite robots.

[0012] To achieve the above objectives, the present invention adopts the following technical solution:

[0013] Methods for task succession in composite robots include:

[0014] The system collects real-time operational data from the composite robot and uploads it to the robot scheduling and management system. When the composite robot malfunctions and fails to report data proactively, other normally operating composite robots automatically identify and report the abnormal location when passing through the abnormal point.

[0015] The robot scheduling and management system compares the collected operation data with a preset normal operation parameter threshold library to determine the anomaly type and level, and uses navigation markers to accurately locate the anomaly to specific coordinates.

[0016] Based on the specific coordinates of the abnormal location and the current position of the rescue robot, a rescue path is generated by combining fixed route navigation and free navigation.

[0017] The tasks that the abnormal composite robot has not completed are prioritized and the task succession plan is determined based on the functional matching degree of the rescue robot, thus generating a decision plan.

[0018] The decision-making plan is broken down into standardized control commands and issued to the rescue robot, which then performs the task takeover operation according to the control commands.

[0019] The present invention also provides a composite robot task succession device, comprising:

[0020] The data acquisition and uploading unit is used to collect the operation data of the composite robot in real time and upload it to the robot scheduling and management system. When the composite robot malfunctions and fails to actively report data, other normally operating composite robots will automatically identify and report the abnormal location when passing through the abnormal point.

[0021] The comparison and positioning unit is used by the robot scheduling and management system to compare the collected operation data with the preset normal operation parameter threshold library, determine the abnormality type and abnormality level, and accurately locate the abnormal location to specific coordinates in combination with navigation marks;

[0022] The generation unit is used to generate a rescue path based on the specific coordinates of the abnormal location and the current position of the rescue robot, using a combination of fixed route navigation and free navigation.

[0023] The sorting generation unit is used to sort the unfinished tasks of the abnormal composite robot according to priority, determine the task replacement plan based on the functional matching degree of the rescue robot, and generate a decision plan.

[0024] The disassembly execution unit is used to break down the decision-making plan into standardized control commands and issue them to the rescue robot. The rescue robot then performs the task takeover operation according to the control commands.

[0025] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the above-described method.

[0026] The present invention also provides a storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0027] The beneficial effects of this invention compared to existing technologies are as follows: By collecting real-time operational data of the composite robot and uploading it to the Robot Scheduling and Management System (RCS), and by having other normally operating composite robots automatically identify and report the abnormal location when the abnormal robot fails to actively report data, fully automated monitoring and response to abnormal situations are achieved. This mechanism eliminates the reliance on manual intervention, fully adapts to the automated operation requirements of unmanned factories, and significantly improves the efficiency and timeliness of abnormal handling. At the same time, the Robot Scheduling and Management System compares the collected operational data with a preset normal operation parameter threshold library, which can accurately determine the type of abnormality (such as robotic arm jamming, AGV chassis failure, etc.) and the level of abnormality. Combined with navigation markers, the abnormal location is precisely located to specific coordinates (positioning error ≤ ±5mm). This high-precision abnormality identification and positioning capability provides a reliable data foundation for subsequent rescue path planning and task takeover, ensuring that rescue robots can quickly and accurately reach the abnormal location. Furthermore, based on the specific coordinates of the abnormal location and the current position of the rescue robot, a rescue path is generated using a combination of fixed-route navigation and free navigation. On the basis of a pre-set fixed-route network in the factory, fixed routes are prioritized for navigation, ensuring the rescue robot travels along efficient and safe paths. When a fixed route is congested, it automatically switches to free navigation mode, generating a temporary detour path. This hybrid navigation strategy not only fully utilizes the efficiency of fixed routes but also possesses the flexibility of free navigation, effectively avoiding path conflicts with normally operating composite robots and significantly shortening rescue response time, especially in narrow passage scenarios where its advantages are even more pronounced. Simultaneously, during the rescue process, through a real-time feedback mechanism, the rescue robot can dynamically adjust its path according to changes in the on-site environment, further improving the reliability and adaptability of the rescue. For example, when encountering a sudden obstacle, the rescue robot can quickly replan its detour path, ensuring the smooth execution of the mission. Furthermore, tasks not completed by the abnormal composite robot are prioritized, and a task replacement plan is determined based on the functional compatibility of the rescue robot. This mechanism ensures that high-priority tasks (such as emergency material transfer) are handled first. At the same time, tasks are rationally allocated according to the capability adaptability of the rescue robot, achieving seamless task replacement. In this way, production process interruptions caused by equipment malfunctions are avoided, ensuring the continuity of factory production tasks and significantly improving the overall production efficiency of the factory. Meanwhile, the decision-making plan is broken down into standardized control instructions and issued to the rescue robot. The rescue robot executes the task replacement operation according to the control instructions. This integrated decision-making and execution mechanism realizes closed-loop management of the entire process from malfunction detection to task replacement, ensuring the stability and reliability of the rescue and task replacement process.

[0028] The present invention will be further described below with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0029] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a schematic diagram illustrating an application scenario of the composite robot task succession method provided in an embodiment of the present invention.

[0031] Figure 2 A flowchart illustrating the composite robot task succession method provided in an embodiment of the present invention;

[0032] Figure 3 A schematic block diagram of a composite robot task succession device provided in an embodiment of the present invention;

[0033] Figure 4 A schematic block diagram of a computer device provided for an embodiment of the present invention. Detailed Implementation

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0036] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0037] Please see Figure 1 and Figure 2 , Figure 1 This is a schematic diagram illustrating an application scenario of the composite robot task succession method provided in an embodiment of the present invention. Figure 2This is a schematic flowchart illustrating the composite robot task succession method provided in an embodiment of the present invention. This composite robot task succession method is applied to a server, which interacts with a terminal to achieve fully automated rescue response and task succession in case of abnormal situations involving composite robots in unmanned factories. This significantly improves the efficiency, stability, and safety of automated production in factories, demonstrating significant technical effects and socio-economic benefits.

[0038] Figure 2 This is a flowchart illustrating the composite robot task succession method provided in an embodiment of the present invention. Figure 2 As shown, the method includes the following steps S110 to S150.

[0039] S110: Real-time collection of the composite robot's operating data and uploading it to the robot scheduling and management system; when the composite robot malfunctions and cannot actively report data, other normally operating composite robots will automatically identify and report the abnormal location when passing the abnormal point.

[0040] Specifically, during the execution of tasks, the composite robot collects core operational data in real time, including chassis speed, robotic arm joint angle, load weight, and remaining battery power, through its own multi-dimensional perception system (such as high-definition cameras, LiDAR, and sensors), AMR chassis status monitoring unit (such as motor speed sensors and power monitoring modules), and robotic arm precision detection components (such as torque sensors and joint position encoders).

[0041] The composite robot uploads the collected operational data to the factory's Robotic Scheduling and Management System (RCS) at a set frequency (e.g., 100 milliseconds per instance). This mechanism ensures the real-time nature and integrity of the data, providing a foundation for subsequent anomaly detection.

[0042] If a composite robot is unable to proactively report data due to a malfunction (such as power outage or communication module failure), when other normally operating composite robots pass through abnormal locations along fixed routes, their perception systems will automatically identify the abnormal equipment and upload the abnormal location coordinates (combined with SLAM positioning station calibration) to the RCS, forming a passive reporting supplementary mechanism. This supplementary mechanism ensures that abnormal information can still be captured in a timely manner even in extreme cases.

[0043] In other words, the multi-dimensional perception system and high-frequency data upload mechanism ensure the integrity and real-time nature of operational data, providing comprehensive and accurate foundational data for subsequent anomaly monitoring. Simultaneously, real-time data acquisition can quickly capture minute changes during robot operation, promptly identify potential faults, provide early warnings, and reduce the impact of sudden failures on the production process. Furthermore, the automated data acquisition and upload mechanism reduces reliance on manual monitoring, increases the system's automation level, and adapts to the production needs of unmanned factories. Moreover, when abnormal robots cannot actively report data, passive identification and reporting by other robots ensures that no abnormal information is missed, enhancing the system's robustness and reliability. The passive reporting mechanism can also quickly transmit abnormal location information to the RCS, shortening the anomaly response time and improving the system's emergency handling capabilities. Even if some robots malfunction, the system can still maintain normal operation through other robots, reducing the risk of system paralysis due to the failure of a single robot.

[0044] In one embodiment, the real-time acquisition of the composite robot's operational data is uploaded to the robot scheduling and management system; when the composite robot malfunctions and cannot actively report data, other normally operating composite robots automatically identify and report the abnormal location when passing the abnormal point, including:

[0045] Real-time collection of the composite robot's operating data, including chassis speed, robotic arm joint angles, load weight, and remaining battery power;

[0046] Specifically, during task execution, the composite robot collects operational data in real time through various sensors and monitoring units it is equipped with. This data includes:

[0047] Chassis travel speed: The chassis travel speed is monitored in real time by a speed sensor installed on the chassis motor;

[0048] Robotic arm joint angles: The rotation angles of each joint of the robotic arm are obtained in real time using joint position encoders on the robotic arm.

[0049] Load weight: The weight of the goods carried by the robot is monitored in real time by weighing sensors installed at the end of the robotic arm or on the chassis;

[0050] Remaining battery power: The remaining battery power is monitored in real time through the battery management system (BMS) to ensure that the robot can return to charge in time when the battery is low.

[0051] In other words, by collecting multi-dimensional operational data, the system can comprehensively understand the operational status of the composite robot, providing rich data support for subsequent anomaly detection and decision-making. Simultaneously, real-time data acquisition ensures that the system can promptly capture changes in the robot's state, improving the system's response speed and reliability. Furthermore, through continuous monitoring of operational data, the system can detect potential fault signs in advance, such as abnormal joint angles of the robotic arm or rapid battery depletion, thereby taking proactive measures to reduce the impact of sudden failures on the production process.

[0052] The collected operational data is uploaded to the robot scheduling and management system at a preset frequency;

[0053] Specifically, the composite robot uploads the collected operational data to the factory's robot scheduling and management system (RCS) via a wireless communication module (such as Wi-Fi or 5G) at a preset frequency (e.g., once every 100 milliseconds). The RCS system receives and stores this data for subsequent analysis and processing.

[0054] In other words, high-frequency data uploads ensure that the RCS system can acquire continuous and complete operational data, avoiding misjudgments caused by missing data. Simultaneously, through a preset upload frequency mechanism, the RCS system can efficiently manage a large amount of robot operational data, facilitating centralized monitoring and analysis. Furthermore, high-frequency data updates enable the RCS system to quickly respond to changes in robot status, promptly detect anomalies, and trigger corresponding processing procedures, improving the overall system response speed.

[0055] When a composite robot malfunctions and is unable to actively report data, other normally operating composite robots can automatically identify the malfunctioning device through their own sensing systems when passing the malfunction point.

[0056] Specifically, when a composite robot is unable to actively report data due to a malfunction (such as a power outage or communication module failure), other normally operating composite robots can automatically identify the abnormal device through their own sensing systems (such as LiDAR, high-definition cameras, etc.) when passing through the abnormal point. For example, LiDAR can detect the position of the abnormal robot in the channel, and high-definition cameras can identify the abnormal robot's appearance features (such as abnormal posture of the robotic arm).

[0057] In other words, when the malfunctioning robot cannot actively report data, passive identification by other robots ensures that no abnormal information is missed, enhancing the system's robustness and reliability. Simultaneously, utilizing the sensing systems of other robots for anomaly detection enables rapid identification of faulty equipment, shortening response time and improving the system's emergency handling capabilities. Furthermore, the automatic identification mechanism reduces reliance on manual inspections, increasing the automation level of the unmanned factory and lowering labor costs.

[0058] The identified abnormal location coordinates are calibrated using SLAM positioning stations, and the calibrated abnormal location coordinates are uploaded to the robot scheduling and management system.

[0059] Specifically, when other normally operating composite robots detect abnormal equipment, they obtain the current location coordinates through their own SLAM (Simultaneous Localization and Mapping) system and calibrate the abnormal location coordinates using preset SLAM positioning stations. The calibrated abnormal location coordinates are then uploaded to the RCS system via a wireless communication module to ensure the accuracy of the abnormal location.

[0060] In other words, through calibration using the SLAM system and positioning stations, the positioning accuracy of abnormal locations can reach a high level (e.g., error ≤ ±5mm), providing a reliable basis for subsequent rescue route planning. Simultaneously, calibration combined with SLAM positioning stations can effectively reduce positioning deviations caused by environmental interference or sensor errors, improving the system's positioning accuracy and reliability. Furthermore, accurate abnormal location information enables rescue robots to quickly and accurately reach the abnormal points, optimizing the rescue process and improving rescue efficiency.

[0061] S120 The robot scheduling and management system compares the collected operation data with the preset normal operation parameter threshold library to determine the anomaly type and anomaly level, and combines navigation marks to accurately locate the anomaly location to specific coordinates.

[0062] Specifically, the RCS system has a built-in "normal operation parameter threshold library" for the composite robot, which is based on historical operating data and preset factory standards. RCS compares and analyzes the real-time uploaded operating data with the threshold library. For example, if the joint torque fluctuation of the robotic arm exceeds ±5 N·m or the chassis travel deviation exceeds ±3 mm, it is automatically judged as abnormal.

[0063] The system determines the anomaly type (such as robotic arm jamming, chassis malfunction, or sensing module failure) and anomaly level (divided into three levels based on the scope of impact: "single device failure," "path blockage," and "task interruption") based on the specific circumstances of the abnormal parameters. For example, a continuous abnormality in the joint torque of the robotic arm may be determined as "robotic arm jamming," while excessive chassis deviation may be determined as "chassis malfunction."

[0064] By combining pre-set navigation markers within the factory (such as magnetic strips, QR codes, guide rails, and SLAM positioning stations), the system accurately pinpoints the location of anomalies to specific coordinates, with a positioning error controlled within ±5mm. This precise positioning capability provides a reliable basis for subsequent rescue route planning.

[0065] In other words, by comparing thresholds and matching features, the type and level of anomalies can be accurately identified, ensuring the rational allocation and efficient use of rescue resources. Simultaneously, the precise location of anomalies using pre-set navigation markers provides a reliable basis for rescue route planning, improving rescue efficiency.

[0066] In one embodiment, the robot scheduling and management system compares the collected operational data with a preset normal operation parameter threshold library to determine the anomaly type and level, and combines navigation markers to accurately locate the anomaly position to specific coordinates, including:

[0067] The robot scheduling and management system receives the uploaded operation data and compares it with a preset normal operation parameter threshold library;

[0068] Specifically, the Robot Scheduling and Management System (RCS) receives real-time operational data from the hybrid robot through its data receiving module. This data includes chassis speed, robotic arm joint angles, load weight, and remaining battery power. The RCS system internally uses a "normal operating parameter threshold library," which is based on the hybrid robot's historical operational data under different task scenarios and the equipment's factory settings. For example:

[0069] The normal driving speed range of the chassis is set to 0.5-1.2 m / s;

[0070] The normal torque threshold for each joint of the robotic arm is set to 5-20 N·m;

[0071] The allowable range for load weight deviation is set to ±0.5 kg;

[0072] The normal range for remaining battery power is set to 20%-100%.

[0073] The RCS system compares and analyzes the received real-time operating data item by item with the corresponding parameters in the threshold library to determine whether the robot's current operating status is normal.

[0074] In other words, by receiving and comparing operational data in real time, the RCS system can quickly and accurately determine whether the composite robot is in an abnormal state, providing timely and accurate basis for subsequent anomaly handling. Simultaneously, comparison based on a preset threshold library can effectively distinguish between normal fluctuations and abnormal situations, reducing false alarms and improving system reliability. Furthermore, the threshold library is set according to different task scenarios, enabling the system to adapt to diverse production environments and task requirements within factories, enhancing the system's versatility and flexibility.

[0075] If any parameter in the running data exceeds the threshold range, the anomaly type is determined;

[0076] Specifically, when the RCS system detects that a parameter in the operational data exceeds a preset threshold range, it determines the anomaly type based on the specific parameter and its numerical range. For example:

[0077] If the joint torque of the robotic arm fluctuates by more than ±5 N·m, it is judged as an abnormality of "robotic arm jamming";

[0078] If the chassis speed is below 0.3 m / s for more than 5 seconds, it is considered an "abnormal chassis malfunction".

[0079] If the remaining battery power is less than 10%, it is considered an "abnormal low power" situation.

[0080] The system will clearly classify exception types according to preset rules and logic, so that targeted handling measures can be taken subsequently.

[0081] In other words, through clearly defined thresholds and rules, the system can accurately identify anomaly types, providing a clear direction for subsequent rescue and handling, and avoiding blind operations. At the same time, quickly determining the anomaly type can shorten the system response time, enabling rescue robots to quickly reach the anomaly location and reducing production downtime. Furthermore, clear anomaly type classification allows the system to develop targeted handling plans based on different anomaly situations, improving the overall efficiency and reliability of the system.

[0082] Determine the anomaly level based on the anomaly type;

[0083] Specifically, after determining the anomaly type, the RCS system further determines the anomaly level based on the degree of impact and urgency of the anomaly type on the production process. For example:

[0084] Single device failure: The anomaly only affects a single robot and does not affect the overall production process (such as a failure of the end effector of a robotic arm).

[0085] Path congestion: An anomaly causes a robot to occupy a critical passage, affecting the passage of other robots (e.g., a chassis malfunction causes a robot to stop on the main passage).

[0086] Task interruption: An exception causes a critical production task to be unable to continue (such as a production line interruption due to material replenishment).

[0087] The system classifies anomalies into different levels according to preset rules in order to allocate rescue resources and priorities in a reasonable manner.

[0088] In other words, by rationally allocating rescue resources according to the anomaly level, prioritizing high-impact anomalies, the continuity of critical production tasks is ensured, and system resource utilization efficiency is improved. Simultaneously, a clear anomaly level classification enables the system to quickly determine processing priorities, improving overall processing efficiency and reducing production losses caused by anomalies. Furthermore, by rationally allocating rescue resources, the system can maintain stable operation when multiple anomalies occur simultaneously, enhancing the emergency fault tolerance capability of the unmanned factory.

[0089] By combining the anomaly level and navigation markers, the anomaly location can be accurately pinpointed to specific coordinates.

[0090] Specifically, after determining the type and level of the anomaly, the RCS system uses pre-set navigation markers within the factory (such as magnetic strips, QR codes, guide rails, and SLAM positioning stations) to accurately pinpoint the location of the anomaly. For example:

[0091] If the abnormal robot stops on the QR code navigation mark, the system will accurately locate the abnormal position to the specific coordinates by reading the coordinates of the QR code and combining them with the robot's position deviation.

[0092] If the malfunctioning robot stops near the SLAM positioning station, the system uses the SLAM algorithm combined with the positioning station data to calculate the precise location of the malfunctioning robot.

[0093] The positioning accuracy can reach a high level (e.g., error ≤ ±5mm), ensuring that the rescue robot can accurately reach the abnormal location.

[0094] In other words, by combining navigation markers and positioning algorithms, the system can accurately pinpoint the anomaly to its specific coordinates, significantly improving rescue efficiency and accuracy. Simultaneously, precise anomaly location information provides a reliable basis for rescue route planning, reducing errors and uncertainties in route planning and ensuring that rescue robots can quickly and safely reach the anomaly location. Furthermore, high-precision positioning reduces the risk of rescue failure due to inaccurate location, enhancing the overall reliability and stability of the system and ensuring the efficient operation of the unmanned factory.

[0095] S130. Based on the specific coordinates of the abnormal location and the current position of the rescue robot, a rescue path is generated by combining fixed route navigation and free navigation.

[0096] Specifically, based on the exact coordinates of the anomaly location and the current position of the rescue robot, a rescue path is generated using a combination of fixed-route navigation and free navigation. The shortest path from the rescue robot's current position to the anomaly location is calculated using the factory's pre-set fixed-route network, avoiding already occupied route segments.

[0097] If the fixed route is blocked (e.g., an abnormal robot occupies the core passage), the free navigation mode is automatically activated. The rescue robot combines real-time environmental perception data (such as passage width and the location of surrounding equipment) to generate a temporary detour route, ensuring that it reaches the abnormal location as quickly as possible.

[0098] In narrow passage scenarios, the rescue path planning combines the orientation and position of the abnormal composite robot. First, it plans the shortest fixed route from the current position of the rescue robot to the node before the abnormal robot's direction of travel. Then, it generates a path from that node to a rescueable position in front of the abnormal robot through free navigation, ensuring that the rescue robot has sufficient operating space.

[0099] In other words, fixed-route navigation ensures the rescue robot's efficient movement along known paths, while free navigation provides flexibility in dealing with complex environments. The combination of the two significantly improves the efficiency of rescue path planning. Simultaneously, the free navigation mode can respond to environmental changes in real time, generating detour paths to avoid conflicts with normally operating robots, thus improving the system's dynamic adaptability. In other words, through optimal path planning, the rescue robot can quickly and safely reach abnormal locations, shortening rescue response time and reducing the risk of production stoppages.

[0100] In one embodiment, generating a rescue path based on the specific coordinates of the abnormal location and the current position of the rescue robot, using a combination of fixed-route navigation and free navigation, includes:

[0101] Based on the specific coordinates of the abnormal location and the current position of the rescue robot, initiate path planning;

[0102] Specifically, once the Robot Dispatch and Management System (RCS) receives the exact coordinates of the abnormal location, the system obtains the current location information of the rescue robot (through its built-in positioning system or navigation module). Subsequently, the RCS system activates the path planning module, using the coordinates of the rescue robot's current location and the abnormal location as input parameters, to begin planning the rescue path.

[0103] In other words, path planning is initiated based on clearly defined anomaly locations and the rescue robot's position, ensuring the system can quickly enter a rescue readiness state and reduce response time. Simultaneously, clearly defined starting and destination points provide accurate input for path planning, improving its reliability and accuracy and laying the foundation for the success of subsequent rescue missions.

[0104] Fixed route navigation is prioritized, and the shortest path from the current location of the rescue robot to the abnormal location is calculated to form a fixed route navigation path;

[0105] Specifically, the path planning module first invokes a fixed-route navigation algorithm. Utilizing a fixed-route network constructed from pre-defined navigation markers within the factory (such as magnetic strips, QR codes, guide rails, and SLAM positioning stations), it calculates the shortest path from the rescue robot's current location to the anomaly point. This path planning algorithm considers the topology of the fixed routes to avoid planning routes that are already occupied or impassable. For example, the system uses a graph search algorithm (such as the A* algorithm) to find the optimal path in the fixed-route network graph and uses that path as the fixed-route navigation path.

[0106] In other words, fixed-route navigation utilizes a pre-defined route network to quickly calculate the shortest path from the current location to the anomaly point, improving path planning efficiency. Furthermore, fixed routes are typically optimized to avoid potential risks in complex environments, ensuring the rescue robot travels along known safe paths and reducing the occurrence of collisions and other accidents. In addition, the calculation of fixed-route navigation paths is relatively simple, reducing the computational resource consumption of the path planning module and improving the overall system performance.

[0107] If there is congestion on the fixed route, the free navigation mode will be automatically activated, and a temporary detour route will be generated by combining environmental perception data.

[0108] Specifically, during path planning, if the path planning module detects a blockage on the fixed route (e.g., an abnormal robot stopping in a passage, making it impassable), the system automatically switches to free navigation mode. At this time, the rescue robot uses its own environmental perception system (such as LiDAR, cameras, etc.) to acquire real-time information about the surrounding environment, including passage width and obstacle locations. The path planning module combines this real-time perception data and uses a dynamic path planning algorithm (such as Dijkstra's algorithm or a sampling-based fast exploration random tree algorithm) to generate a temporary detour path to avoid the congested area.

[0109] In other words, the free navigation mode enables the system to respond to environmental changes in real time, dynamically generating detour paths to avoid congestion on fixed routes, thus improving the system's flexibility and adaptability. Simultaneously, by combining environmental perception data to generate detour paths, it ensures the rescue robot's safe navigation in complex environments, avoiding collisions with other robots or obstacles. Furthermore, even when fixed routes are blocked, the free navigation mode ensures the rescue robot reaches abnormal locations, guaranteeing the continuity of rescue missions and reducing the risk of rescue failure due to path issues.

[0110] By combining fixed route navigation paths with temporary detour paths, a complete rescue route is generated.

[0111] Specifically, the path planning module integrates the calculated fixed route navigation path and temporary detour path to form a complete rescue route. During the integration process, the system ensures the continuity and feasibility of the path, avoiding path interruptions or unreasonable turns. For example, the path planning module performs smooth transition processing at the connection point between the fixed route and the detour path, ensuring that the rescue robot can smoothly switch from the fixed route to the detour path and ultimately reach the abnormal location.

[0112] In other words, by integrating fixed-route navigation paths and free-navigation paths, the advantages of both are fully utilized to ensure that rescue robots can reach anomaly locations in the most efficient way. Simultaneously, by smoothly transitioning the paths, the rescue robots are ensured to follow continuous and feasible routes during rescue missions, reducing driving problems caused by unreasonable paths. Furthermore, a complete rescue path enables rescue robots to reach anomaly locations quickly and accurately, reducing rescue time and improving the emergency response capabilities and production efficiency of unmanned factories.

[0113] S140. Prioritize the tasks not completed by the abnormal composite robot, determine the task replacement plan based on the functional matching degree of the rescue robot, and generate a decision plan.

[0114] Specifically, tasks that the abnormal composite robot has not completed are prioritized and divided into "urgent tasks" (such as production line material replenishment), "routine tasks" (such as semi-finished product transfer), and "low-priority tasks" (such as waste removal). Prioritization ensures that high-priority tasks are processed first, guaranteeing that critical links in the production process are not affected.

[0115] Based on the functional compatibility of the rescue robot (such as the load capacity of its robotic arm and chassis compatibility), the system determines whether it can directly take over the task or whether the task needs to be broken down and assigned to other idle robots. For example, if the load capacity of the rescue robot's robotic arm is insufficient to complete the original task, the system will break the task into multiple sub-tasks and assign them to other robots with the corresponding capabilities to complete collaboratively.

[0116] The final result is an integrated "rescue + replacement" execution plan, i.e. a decision-making plan, which clarifies the rescue path, task replacement steps, and specific operating instructions for each robot.

[0117] In other words, by prioritizing tasks, high-priority tasks are ensured to be processed first, avoiding interruptions in critical production processes due to anomalies and guaranteeing the continuity of the production flow. Simultaneously, task allocation based on the functional compatibility of the rescue robots improves task execution efficiency and success rate, preventing task failures due to insufficient robot capabilities. Furthermore, the task splitting and allocation mechanism enables the system to flexibly handle complex tasks, fully utilize existing resources, and improve overall production efficiency.

[0118] In one embodiment, the step of prioritizing unfinished tasks by the abnormal composite robot and determining a task succession plan based on the functional matching degree of the rescue robot to generate a decision plan includes:

[0119] Prioritize the unfinished tasks of the abnormal composite robot to obtain sorting information;

[0120] Specifically, the Robot Scheduling and Management System (RCS) obtains detailed information about the unfinished tasks from abnormal composite robots, including task type (such as material transportation, loading and unloading, process connection, etc.), task urgency (such as whether it affects critical production lines), and remaining steps of the task.

[0121] The system prioritizes tasks according to preset priority evaluation rules. For example:

[0122] Urgent tasks: Tasks that directly affect the operation of the production line (such as the production line waiting for material replenishment) are given the highest priority;

[0123] Routine tasks: Tasks that have some impact on the production process but are not critical (such as the transfer of semi-finished products) are assigned medium priority;

[0124] Low-priority tasks: Tasks that have little impact on the production process (such as waste removal) are assigned the lowest priority.

[0125] The system sorts all incomplete tasks from highest to lowest priority and generates sorting information for subsequent task allocation decisions.

[0126] In other words, by prioritizing tasks, the system ensures that high-priority tasks (such as critical production line tasks) are processed first, preventing production interruptions due to task backlog and guaranteeing the continuity of the production process. Simultaneously, by clearly defining task priorities, the system can rationally allocate limited emergency resources, prioritizing tasks with the greatest impact on production and improving resource utilization efficiency. Furthermore, the priority-based ranking mechanism allows the system to flexibly respond to different levels of urgency and adapt to complex and ever-changing production environments.

[0127] The evaluation results are obtained by assessing whether the rescue robot can take over the unfinished task based on its functional matching degree.

[0128] Specifically, the system acquires the functional parameters of the rescue robot, including the robotic arm's load capacity, chassis compatibility, and sensor accuracy. Simultaneously, based on the specific requirements of the unfinished task (such as the required load capacity and operational precision), it assesses whether the rescue robot is capable of directly taking over the task. For example, if the task requires a robotic arm load capacity of 10kg, while the rescue robot's maximum robotic arm load capacity is only 8kg, the assessment result is "cannot directly take over the task"; if the rescue robot's functional parameters fully meet the task requirements, the assessment result is "can directly take over the task".

[0129] Based on the above evaluation rules, the system evaluates each incomplete task one by one and generates detailed evaluation results for subsequent task allocation decisions.

[0130] In other words, by assessing functional compatibility, the system ensures that rescue robots possess the capability to complete tasks, thereby increasing the success rate of mission execution. Simultaneously, it avoids assigning tasks to robots with incompatible functionalities, reducing resource waste and time losses due to mission failures. Furthermore, the functional compatibility assessment mechanism enables the system to accurately determine the capabilities of rescue robots, ensuring the rationality of task allocation and enhancing the overall reliability of the system.

[0131] Based on the sorting information and the functional matching evaluation results, a task replacement plan is determined; if the rescue robot can directly take over the task, a task replacement plan is generated; if the rescue robot cannot directly take over the task, the task is split according to the sorting information and the split task is assigned to other idle robots.

[0132] Specifically, if the rescue robot's functional suitability assessment result is "can directly take over the task," the system generates a task takeover plan based on the task priority ranking information. The rescue robot will directly take over the task of the malfunctioning composite robot and continue to execute the unfinished operations.

[0133] If a rescue robot cannot directly take over the task, the system breaks down the unfinished task into multiple sub-tasks based on task priority. For example, a long-distance transport task can be broken down into multiple short-distance transport tasks. The system then assigns these sub-tasks to other idle robots to ensure the task can continue.

[0134] The system comprehensively considers task priorities and the functional compatibility of the rescue robot to generate detailed task succession plans, clearly defining the executor and execution steps for each task.

[0135] In other words, by splitting and allocating tasks, the system can flexibly handle complex tasks, make full use of existing resources, and ensure task continuity. Simultaneously, by rationally distributing tasks across multiple robots, it avoids task stagnation due to the insufficient functionality of a single robot, thus improving the overall operational efficiency of the system. Furthermore, the task splitting and allocation mechanism enhances the collaborative capabilities among multiple robots within the system, enabling it to efficiently respond to complex and ever-changing task requirements.

[0136] By combining the task succession plan and the rescue route planning results, an integrated rescue succession execution plan, i.e., a decision-making plan, is generated.

[0137] Specifically, the system integrates the generated task takeover plan with the rescue path planning results. For example, it matches the path of the rescue robot to the abnormal location with the task execution steps to ensure that the rescue robot can immediately begin the task takeover operation after arriving at the abnormal location.

[0138] The system generates a complete execution plan that includes the rescue path and task handover steps. The plan details the path the rescue robot takes from its current location to the anomaly point, how it takes over the task upon arrival at the anomaly point (such as robotic arm operation steps, task handover steps, etc.), and the execution flow of subsequent tasks.

[0139] The system will output the final integrated rescue and relief execution plan as a decision-making solution for subsequent instruction breakdown and execution.

[0140] In other words, the integrated execution solution tightly integrates rescue routes with task handover steps, optimizing the entire process from rescue to task execution and improving system operational efficiency. Simultaneously, by integrating route and task information, it ensures seamless task handover after the rescue robot arrives at the anomaly location, reducing task interruption time and guaranteeing the continuity of the production process. Furthermore, the integrated execution solution provides clear operational guidance for the rescue robot, reducing errors caused by improper route and task coordination and improving the overall reliability of the system.

[0141] S150. The decision-making plan is broken down into standardized control commands and issued to the rescue robot. The rescue robot performs the task takeover operation according to the control commands.

[0142] Specifically, the decision-making scheme is broken down into standardized control instructions, including path instructions (speed and steering parameters along a fixed route, obstacle avoidance threshold for free navigation), robotic arm operation instructions (grabbing force and motion trajectory), and task connection instructions (such as connecting to the MES system to obtain material information and completing interface matching with the target equipment).

[0143] These instructions are transmitted to the rescue robot in real time via industrial Ethernet. The rescue robot performs task takeover operations according to the control instructions. For example, if the robotic arm malfunctions but the chassis is still movable, the rescue robot uses its robotic arm to grab the goods from the malfunctioning robot and move them to the designated task area; if the chassis malfunctions, the rescue robot uses its robotic arm to move the goods to its own pallet and continue to complete the subsequent tasks.

[0144] The rescue robot uploads operational data in real time during execution (such as travel speed, robotic arm execution accuracy, and task completion progress). The status feedback unit compares this data with preset indicators in the decision-making plan. If a deviation occurs (such as robotic arm failure to grasp or sudden obstacles in the path), a dynamic adjustment mechanism is immediately triggered to re-optimize the command parameters (such as adjusting the grasping angle and updating the detour path) to ensure the stability and reliability of the task execution process.

[0145] In other words, by breaking down complex decision-making processes into standardized control commands, the executability and accuracy of these commands are improved, ensuring that the rescue robot can execute tasks precisely. Simultaneously, through a real-time feedback mechanism, the rescue robot can dynamically adjust its operational commands based on changes in the on-site environment, further enhancing the stability and reliability of task execution. Furthermore, the closed-loop control mechanism from decision-making to execution ensures the stability and reliability of the task handover process, reducing the risk of task failure due to command errors or execution deviations.

[0146] In one embodiment, the step of breaking down the decision-making scheme into standardized control commands and issuing them to the rescue robot, and the rescue robot performing task takeover operations according to the control commands, includes:

[0147] The decision-making scheme is broken down into standardized control instructions, which include path instructions, robotic arm operation instructions, and task connection instructions.

[0148] Specifically, the system breaks down the path planning part of the decision-making scheme into specific path instructions, including parameters such as driving speed, steering angle, and obstacle avoidance threshold. For example, the path instructions may include "drive along a fixed route at a speed of 0.8 m / s, turn 30 degrees when reaching node A, and keep the obstacle avoidance distance above 0.5 m".

[0149] The system breaks down the robotic arm's operation steps into specific control commands, including joint angles, gripping force, and motion trajectory. For example, the robotic arm operation command might include "move the robotic arm to the designated position, set the gripping force to 5N, and move the goods onto the pallet according to the preset trajectory."

[0150] The system breaks down the task connection part into specific instructions, including interacting with the MES system to obtain material information and completing interface matching with other devices. For example, the task connection instruction may include "obtain the material number from the MES system, complete interface matching with the target device, and confirm the task completion status".

[0151] In other words, by breaking down complex decision-making processes into standardized control commands, the rescue robot can accurately understand and execute these commands, improving the executability of the commands and the accuracy of task execution. Simultaneously, by breaking down the decision-making process into commands for three modules—path selection, robotic arm operation, and task handover—the system can perform refined control for different modules, improving the flexibility and reliability of task execution. Furthermore, standardized control commands reduce execution errors caused by ambiguous instructions, ensuring the rescue robot can accurately complete task handover operations and increasing the task success rate.

[0152] Standardized control commands are sent to the rescue robot in real time via industrial Ethernet;

[0153] Specifically, both the rescue robot and the Robot Dispatch and Management System (RCS) are equipped with industrial Ethernet communication modules to ensure high-speed and stable data transmission between them. The RCS system sends standardized, decomposed control commands to the rescue robot in real time via industrial Ethernet. For example, control commands are sent in the form of data packets, each containing specific command content and execution parameters. After receiving the control command, the rescue robot sends an acknowledgment signal to the RCS system via industrial Ethernet to ensure the integrity of the command transmission. If the RCS system does not receive an acknowledgment signal, it will resend the command to ensure successful transmission.

[0154] In other words, Industrial Ethernet provides a high-speed, stable data transmission channel, ensuring that standardized control commands can be transmitted to the rescue robot in real time and accurately, reducing communication latency. Simultaneously, through a command confirmation mechanism, the system ensures the reliability of command transmission, avoiding task failures due to communication malfunctions and improving overall system reliability. Furthermore, real-time data transmission allows the system to dynamically adjust control commands based on actual conditions during task execution, enhancing the system's adaptability and flexibility.

[0155] The rescue robot performs task takeover operations based on the received control commands.

[0156] Specifically, after receiving standardized control commands, the rescue robot's control system parses the commands and executes the corresponding operations according to the command requirements. For example, path commands control the chassis's movement, robotic arm operation commands control the robotic arm's actions, and task connection commands control the interaction with other systems.

[0157] When the rescue robot performs a task takeover operation, multiple modules, including the chassis, robotic arm, and sensing system, work together. The chassis travels to the designated location according to path instructions, the robotic arm completes the grabbing and transportation of goods according to operation instructions, and the sensing system monitors the surrounding environment in real time to ensure operational safety.

[0158] During operation, the rescue robot provides real-time feedback on its operational status (such as travel speed, robotic arm position, and task completion progress) to the RCS system. If deviations occur (such as path obstruction or grasping failure), the RCS system dynamically adjusts control commands based on the feedback information, and the rescue robot adjusts its operations accordingly.

[0159] In other words, through standardized control commands, the rescue robot can accurately execute task handover operations, ensuring the smooth completion of tasks and improving the success rate. Simultaneously, the coordinated work of multiple modules, including the chassis, robotic arm, and sensing system, enables the rescue robot to efficiently complete tasks in complex environments, enhancing its overall execution capabilities. Furthermore, the real-time feedback mechanism allows the system to dynamically adjust control commands during task execution, ensuring the stability and reliability of task execution and reducing the risk of task failure due to environmental changes or unforeseen circumstances. Moreover, the closed-loop control mechanism from command issuance to task execution ensures the stability and reliability of the task handover process, improving the emergency response capabilities and production stability of the unmanned factory.

[0160] In one embodiment, the step of breaking down the decision-making scheme into standardized control commands and issuing them to the rescue robot, followed by the rescue robot performing the task takeover operation according to the control commands, includes:

[0161] During the rescue operation, the robot provides real-time feedback of operational data to the robot dispatch and management system, including driving speed, robotic arm execution accuracy, and task completion progress.

[0162] Specifically, during the task handover operation, the rescue robot collects operational data in real time through its built-in sensors and monitoring units, including chassis speed, robotic arm execution accuracy (such as joint angle deviation and gripping force), and task completion progress (such as the completion status of task steps).

[0163] The robot uploads its operational data to the Robot Scheduling and Management System (RCS) via Industrial Ethernet at a preset frequency (e.g., every 100 milliseconds or once per second). This data is sent in the form of data packets, containing information such as timestamps, task numbers, and data types, so that the RCS system can accurately record and analyze it.

[0164] During data transmission, the robot employs a redundant transmission mechanism to ensure data integrity and accuracy. For example, for critical data (such as task completion progress), the robot will send it multiple times to ensure that the RCS system can receive accurate information.

[0165] In other words, by providing real-time feedback of operational data, the RCS system can monitor the rescue robot's execution status in real time, ensuring transparency in the mission execution process and facilitating the timely detection of potential problems. Simultaneously, the real-time operational data provides the RCS system with a wealth of information, supporting precise analysis and decision-making, and providing a data foundation for subsequent dynamic adjustments. Furthermore, the real-time feedback mechanism can promptly detect deviations or anomalies during execution, reducing the risk of mission failure due to information lag and improving the overall reliability of the system.

[0166] The robot scheduling and management system analyzes the feedback data. If a deviation occurs, it triggers a dynamic adjustment mechanism to re-optimize the control command parameters.

[0167] Specifically, the RCS system receives operational data uploaded by the rescue robot and stores it in the system database. The system categorizes and labels the data for easy analysis later. The RCS system performs real-time analysis of the feedback data, comparing the actual operational data with preset control command parameters. For example, it checks whether the chassis speed meets expectations, whether the robotic arm's execution accuracy is within allowable limits, and whether the task completion progress is on time. If deviations are detected (such as lower-than-expected speed or robotic arm failure to grasp), the system records the specific details of the deviation.

[0168] Once a deviation is detected, the RCS system immediately triggers a dynamic adjustment mechanism. Based on the type and severity of the deviation, the system re-optimizes the control command parameters. For example, if the robotic arm fails to grasp, the system may adjust the grasping force or position; if the travel speed is too slow, the system may adjust the chassis's travel speed or path planning. Based on the analysis results, the system generates new optimized commands and reissues these commands to the rescue robot to ensure the mission can continue.

[0169] In other words, through a dynamic adjustment mechanism, the system can automatically optimize control commands based on real-time feedback data, enabling the rescue robot to adapt to environmental changes and task requirements, thus improving the system's adaptability. Simultaneously, it promptly detects and corrects deviations, preventing task failures due to minor issues and improving the success rate and stability of task execution. Furthermore, the dynamic adjustment mechanism can optimize task execution parameters based on actual conditions, ensuring the rescue robot performs tasks in its optimal state, thereby improving task execution efficiency.

[0170] The rescue robot continues to perform its mission according to the optimized instructions until the mission is taken over.

[0171] Specifically, after receiving the optimization instructions from the RCS system, the rescue robot's control system parses the instructions and confirms their content and parameters. Based on the optimized instructions, the robot adjusts its behavior and continues to perform the task takeover operation. For example, it adjusts the chassis speed, changes the robotic arm's grasping posture, or replans the path to ensure the task can be carried out smoothly.

[0172] After completing the task handover operation, the robot sends a task completion confirmation message to the RCS system via industrial Ethernet. Upon receiving the confirmation message, the RCS system records the task completion status and performs subsequent resource scheduling and task allocation. Throughout the entire task execution process, the rescue robot continuously feeds back operational data to the RCS system. The RCS system dynamically adjusts its commands based on the feedback information, forming a closed-loop control mechanism to ensure the successful completion of the task.

[0173] In other words, through closed-loop control and dynamic adjustment, the rescue robot can continue to execute tasks according to optimized instructions, ensuring smooth task handover and reducing task interruptions caused by deviations. Simultaneously, the closed-loop control mechanism ensures the stability of the task execution process, maintaining efficient operation even in complex environments and improving the overall system performance. Furthermore, the dynamic adjustment mechanism enables the system to flexibly respond to various changes during task execution, enhancing its flexibility and adaptability to the changing production demands of unmanned factories. Moreover, by adjusting task execution parameters in a timely manner, the system can rationally utilize the rescue robot's resources, reducing unnecessary energy consumption and time waste, and improving resource utilization efficiency.

[0174] The aforementioned composite robot task succession method achieves fully automated monitoring and response to anomalies by collecting real-time operational data from the composite robots and uploading it to the Robot Scheduling and Management System (RCS). Furthermore, when an abnormal robot fails to proactively report data, other normally operating composite robots automatically identify and report the abnormal location. This mechanism eliminates reliance on manual intervention, fully adapts to the automated operation requirements of unmanned factories, and significantly improves the efficiency and timeliness of anomaly handling. Simultaneously, the Robot Scheduling and Management System compares the collected operational data with a preset normal operation parameter threshold library, accurately determining the anomaly type (such as robotic arm jamming, AGV chassis failure, etc.) and anomaly level. Combined with navigation markers, it precisely locates the anomaly to specific coordinates (positioning error ≤ ±5mm). This high-precision anomaly identification and positioning capability provides a reliable data foundation for subsequent rescue path planning and task succession, ensuring that rescue robots can quickly and accurately reach the anomaly location. Furthermore, based on the specific coordinates of the abnormal location and the current position of the rescue robot, a rescue path is generated using a combination of fixed-route navigation and free navigation. On the basis of a pre-set fixed-route network in the factory, fixed routes are prioritized for navigation, ensuring the rescue robot travels along efficient and safe paths. When a fixed route is congested, it automatically switches to free navigation mode, generating a temporary detour path. This hybrid navigation strategy not only fully utilizes the efficiency of fixed routes but also possesses the flexibility of free navigation, effectively avoiding path conflicts with normally operating composite robots and significantly shortening rescue response time, especially in narrow passage scenarios where its advantages are even more pronounced. Simultaneously, during the rescue process, through a real-time feedback mechanism, the rescue robot can dynamically adjust its path according to changes in the on-site environment, further improving the reliability and adaptability of the rescue. For example, when encountering a sudden obstacle, the rescue robot can quickly replan its detour path, ensuring the smooth execution of the mission. Furthermore, tasks not completed by the abnormal composite robot are prioritized, and a task replacement plan is determined based on the functional compatibility of the rescue robot. This mechanism ensures that high-priority tasks (such as emergency material transfer) are handled first. At the same time, tasks are rationally allocated according to the capability adaptability of the rescue robot, achieving seamless task replacement. In this way, production process interruptions caused by equipment malfunctions are avoided, ensuring the continuity of factory production tasks and significantly improving the overall production efficiency of the factory. Meanwhile, the decision-making plan is broken down into standardized control instructions and issued to the rescue robot. The rescue robot executes the task replacement operation according to the control instructions. This integrated decision-making and execution mechanism realizes closed-loop management of the entire process from malfunction detection to task replacement, ensuring the stability and reliability of the rescue and task replacement process.

[0175] Figure 3 This is a schematic block diagram of a composite robot task succession device 300 provided in an embodiment of the present invention. Figure 3 As shown, corresponding to the above-described composite robot task succession method, the present invention also provides a composite robot task succession device 300. This composite robot task succession device 300 includes a unit for executing the above-described composite robot task succession method, and the device can be configured in a server. Specifically, please refer to... Figure 3 The composite robot task succession device 300 includes:

[0176] The data acquisition and upload unit 301 is used to collect the operation data of the composite robot in real time and upload it to the robot scheduling and management system. When the composite robot is abnormal and unable to actively report data, other normally operating composite robots will automatically identify and report the abnormal location when passing the abnormal point.

[0177] The comparison and positioning unit 302 is used by the robot scheduling and management system to compare the collected operation data with the preset normal operation parameter threshold library, determine the abnormality type and abnormality level, and accurately locate the abnormal location to specific coordinates in combination with navigation marks.

[0178] The generation unit 303 is used to generate a rescue path based on the specific coordinates of the abnormal location and the current position of the rescue robot, using a combination of fixed route navigation and free navigation.

[0179] The sorting generation unit 304 is used to sort the unfinished tasks of the abnormal composite robot according to priority, determine the task replacement plan based on the functional matching degree of the rescue robot, and generate a decision plan.

[0180] The disassembly execution unit 305 is used to decompose the decision-making plan into standardized control commands and issue them to the rescue robot. The rescue robot then performs the task takeover operation according to the control commands.

[0181] In one embodiment, the data acquisition and uploading unit 301 includes:

[0182] The data acquisition module is used to collect real-time operating data of the composite robot, including chassis speed, robotic arm joint angles, load weight, and remaining battery power.

[0183] The upload module is used to upload the collected operational data to the robot scheduling and management system at a preset frequency;

[0184] The identification module is used to automatically identify abnormal devices when other normally operating composite robots pass through the abnormal point using their own perception systems, when the composite robot malfunctions and is unable to actively report data.

[0185] The calibration upload module is used to calibrate the identified abnormal location coordinates in conjunction with the SLAM positioning station, and then upload the calibrated abnormal location coordinates to the robot scheduling and management system.

[0186] In one embodiment, the comparison and positioning unit 302 includes:

[0187] The upload comparison module is used by the robot scheduling and management system to receive the uploaded operation data and compare it with the preset normal operation parameter threshold library;

[0188] The first determination module is used to determine the anomaly type if a parameter in the running data exceeds the threshold range.

[0189] The second determination module is used to determine the anomaly level based on the anomaly type.

[0190] Combined with the positioning module, it is used to accurately pinpoint the location of an anomaly to specific coordinates by combining the anomaly level and navigation markers.

[0191] In one embodiment, the generation unit 303 includes:

[0192] The startup module is used to initiate path planning based on the specific coordinates of the abnormal location and the current position of the rescue robot;

[0193] The calculation module is used to prioritize fixed route navigation, calculate the shortest path from the rescue robot's current location to the anomaly point, and form a fixed route navigation path.

[0194] The activation module is used to automatically activate the free navigation mode and generate a temporary detour route by combining environmental perception data if there is a blockage on the fixed route.

[0195] The generation module is used to combine fixed route navigation paths with temporary detour paths to generate a complete rescue route.

[0196] In one embodiment, the sorting generation unit 304 includes:

[0197] The sorting module is used to prioritize the tasks that the abnormal composite robot has not completed in order to obtain sorting information;

[0198] The evaluation module is used to assess whether the rescue robot can take over the unfinished task based on its functional matching degree, so as to obtain the evaluation result;

[0199] The generation module is used to determine the task replacement plan based on the sorting information and the functional matching degree evaluation results. If the rescue robot can directly take over the task, a task replacement plan is generated. If the rescue robot cannot directly take over the task, the task is split according to the sorting information and the split task is assigned to other idle robots.

[0200] The generation module is used to combine the task succession plan and the rescue route planning results to generate an integrated rescue succession execution plan, i.e., a decision-making plan.

[0201] In one embodiment, the disassembly execution unit 305 includes:

[0202] The decomposition module is used to break down the decision-making scheme into standardized control instructions, which include path instructions, robotic arm operation instructions, and task connection instructions.

[0203] The distribution module is used to distribute standardized control commands to the rescue robot in real time via industrial Ethernet;

[0204] The execution module is used by the rescue robot to perform task takeover operations based on the received control commands.

[0205] In one embodiment, the apparatus includes: a feedback optimization execution unit; the feedback optimization execution unit includes:

[0206] The feedback module is used to provide real-time operational data to the robot scheduling and management system during the rescue robot's execution, including driving speed, robotic arm execution accuracy, and task completion progress.

[0207] The optimization module is used by the robot scheduling and management system to analyze the feedback data. If a deviation occurs, a dynamic adjustment mechanism is triggered to re-optimize the control command parameters.

[0208] The execution module is used by the rescue robot to continue performing tasks according to optimized instructions until the task handover is completed.

[0209] It should be noted that those skilled in the art can clearly understand that the specific implementation process of the above-mentioned composite robot task succession device 300 and each unit can be referred to the corresponding description in the foregoing method embodiments. For the sake of convenience and brevity, it will not be repeated here.

[0210] The aforementioned composite robot task succession device 300 can be implemented as a computer program, which can, for example... Figure 4 It runs on the computer device shown.

[0211] Please see Figure 4 , Figure 4 This is a schematic block diagram of a computer device provided in an embodiment of this application. The computer device 500 can be a server, wherein the server can be a standalone server or a server cluster composed of multiple servers.

[0212] See Figure 4 The computer device 500 includes a processor 502, a memory, and a network interface 505 connected via a system bus 501. The memory may include a non-volatile storage medium 503 and internal memory 504.

[0213] The non-volatile storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions that, when executed, cause the processor 502 to perform a composite robot task succession method.

[0214] The processor 502 provides computing and control capabilities to support the operation of the entire computer device 500.

[0215] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a composite robot task succession method.

[0216] This network interface 505 is used for network communication with other devices. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device 500 to which the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0217] The processor 502 is used to run a computer program 5032 stored in the memory to perform the following steps:

[0218] The system collects real-time operational data from the composite robot and uploads it to the robot scheduling and management system. When a composite robot malfunctions and fails to report data proactively, other normally operating composite robots automatically identify and report the abnormal location as they pass the abnormal point. The robot scheduling and management system compares the collected operational data with a preset normal operating parameter threshold library to determine the abnormality type and level, and uses navigation markers to accurately pinpoint the abnormal location to specific coordinates. Based on the specific coordinates of the abnormal location and the current position of the rescue robot, a rescue path is generated using a combination of fixed-route navigation and free navigation. Unfinished tasks by the abnormal composite robot are prioritized, and a task replacement plan is determined based on the functional compatibility of the rescue robot, generating a decision plan. The decision plan is broken down into standardized control commands and issued to the rescue robot, which then executes the task replacement operation according to the control commands.

[0219] It should be understood that in the embodiments of this application, the processor 502 may be a central processing unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0220] It will be understood by those skilled in the art that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program includes program instructions and can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiments of the above methods.

[0221] Therefore, the present invention also provides a storage medium. This storage medium can be a computer-readable storage medium. The storage medium stores a computer program, wherein when executed by a processor, the computer program causes the processor to perform the following steps:

[0222] The system collects real-time operational data from the composite robot and uploads it to the robot scheduling and management system. When a composite robot malfunctions and fails to report data proactively, other normally operating composite robots automatically identify and report the abnormal location as they pass the abnormal point. The robot scheduling and management system compares the collected operational data with a preset normal operating parameter threshold library to determine the abnormality type and level, and uses navigation markers to accurately pinpoint the abnormal location to specific coordinates. Based on the specific coordinates of the abnormal location and the current position of the rescue robot, a rescue path is generated using a combination of fixed-route navigation and free navigation. Unfinished tasks by the abnormal composite robot are prioritized, and a task replacement plan is determined based on the functional compatibility of the rescue robot, generating a decision plan. The decision plan is broken down into standardized control commands and issued to the rescue robot, which then executes the task replacement operation according to the control commands.

[0223] The storage medium can be any computer-readable storage medium capable of storing program code, such as a USB flash drive, portable hard drive, read-only memory (ROM), magnetic disk, or optical disk.

[0224] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0225] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of each unit is merely a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0226] The steps in the method of this invention can be adjusted, merged, or reduced in order according to actual needs. The units in the device of this invention can be merged, divided, or reduced according to actual needs. Furthermore, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0227] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a terminal, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention.

[0228] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method of composite robot task takeover, characterized by, The application comprises the following steps: Real-time acquisition of the running data of the composite robot and uploading to the robot dispatch management system; When the composite robot cannot actively report data due to an abnormality, other normally operating composite robots automatically identify and report the abnormal position when passing through the abnormal point; The robot dispatch management system compares the collected running data with the preset normal running parameter threshold library to determine the abnormal type and abnormal level, and accurately locates the abnormal position to the specific coordinates in combination with the navigation mark; According to the specific coordinates of the abnormal position and the current position of the rescue robot, a rescue path is generated by combining fixed route navigation with free navigation; The tasks not completed by the abnormal composite robot are sorted according to priority, and a task replacement scheme is determined according to the functional matching degree of the rescue robot to generate a decision scheme; The decision scheme is disassembled into standardized control instructions and issued to the rescue robot, which executes the task replacement operation according to the control instructions; The robot dispatch management system receives the uploaded running data and compares it with the preset normal running parameter threshold library; If a parameter in the running data exceeds the threshold range, the abnormal type is determined; The abnormal level is determined according to the abnormal type; The abnormal position is accurately located to the specific coordinates in combination with the abnormal level and the navigation mark; The tasks not completed by the abnormal composite robot are sorted according to priority, and a task replacement scheme is determined according to the functional matching degree of the rescue robot to generate a decision scheme, comprising: The tasks not completed by the abnormal composite robot are sorted according to priority to obtain sorting information; According to the functional matching degree of the rescue robot, it is evaluated whether it can replace the unfinished task to obtain an evaluation result; According to the sorting information and the functional matching degree evaluation result, the task replacement scheme is determined; if the rescue robot can directly replace the task, the task replacement scheme is generated; if the rescue robot cannot directly replace the task, the task is split according to the sorting information, and the split task is assigned to other idle robots; The rescue replacement integrated execution scheme, i.e. the decision scheme, is generated in combination with the task replacement scheme and the rescue path planning result. The application comprises the following steps:

2. The composite robotic task takeover method of claim 1, wherein, Real-time acquisition of the running data of the composite robot, including chassis running speed, mechanical arm joint angle, load weight and battery remaining capacity; The collected running data is uploaded to the robot dispatch management system at a preset frequency; When the composite robot is abnormal and cannot actively report data, other normally operating composite robots automatically identify the abnormal equipment through their own sensing system when passing through the abnormal point; ​ The identified abnormal position coordinates are combined with the SLAM positioning station for calibration, and the calibrated abnormal position coordinates are uploaded to the robot dispatch management system.

3. The composite robotic task takeover method of claim 1, wherein, The specific coordinates of the abnormal position and the current position of the rescue robot are used to generate a rescue path in a combination of fixed route navigation and free navigation, including: Starting path planning according to the specific coordinates of the abnormal position and the current position of the rescue robot; Preferably, fixed route navigation is used to calculate the shortest path from the current position of the rescue robot to the abnormal point, forming a fixed route navigation path; If the fixed route is blocked, the free navigation mode is automatically activated, and a temporary bypass path is generated in combination with the environmental perception data; The fixed route navigation path and the temporary bypass path are combined to generate a complete rescue path.

4. The composite robotic task takeover method of claim 1, wherein, The decision scheme is decomposed into standardized control instructions, which are sent to the rescue robot, and the rescue robot performs task takeover operation according to the control instructions, including: The decision scheme is decomposed into standardized control instructions, which include path instructions, mechanical arm operation instructions, and task connection instructions; The standardized control instructions are sent to the rescue robot in real time through industrial Ethernet; The rescue robot performs task takeover operation according to the received control instructions.

5. The composite robotic task takeover method of claim 1, wherein, After the decision scheme is decomposed into standardized control instructions and sent to the rescue robot, the rescue robot performs task takeover operation according to the control instructions, including: The rescue robot feeds back running data to the robot dispatch management system in real time during execution, including driving speed, mechanical arm execution accuracy, and task completion progress; The robot dispatch management system analyzes the feedback data, and if there is a deviation, triggers a dynamic adjustment mechanism to optimize the control instruction parameters; The rescue robot continues to perform the task according to the optimized instructions until the task takeover is completed.

6. A composite robot task takeover apparatus, characterized by, It includes: A collection and uploading unit for collecting running data of the composite robot in real time and uploading it to the robot dispatch management system; When the composite robot is abnormal and cannot actively report data, other normally operating composite robots automatically identify and report the abnormal position when passing through the abnormal point; A comparison and positioning unit for the robot dispatch management system to compare the collected running data with the preset normal running parameter threshold library to determine the abnormal type and abnormal level, and to accurately position the abnormal position to specific coordinates in combination with navigation marks; A generation unit for generating a rescue path in a combination of fixed route navigation and free navigation according to the specific coordinates of the abnormal position and the current position of the rescue robot; A sorting and generating unit for sorting the tasks not completed by the abnormal composite robot according to priority, and determining a task takeover scheme according to the function matching degree of the rescue robot to generate a decision scheme; A decomposition and execution unit for decomposing the decision scheme into standardized control instructions and sending them to the rescue robot, and the rescue robot performs task takeover operation according to the control instructions. The comparison and positioning unit includes: An uploading comparison module for the robot dispatch management system to receive uploaded running data and compare it with the preset normal running parameter threshold library; The first determining module is configured to determine an abnormal type if a parameter in the running data exceeds a threshold range; The second determining module is configured to determine an abnormal level according to the abnormal type; The combined positioning module is configured to accurately position the abnormal position to a specific coordinate in combination with the abnormal level and the navigation mark; The sorting generation unit comprises: The sorting module is configured to sort the uncompleted tasks of the abnormal composite robot according to priorities to obtain sorting information; The evaluation module is configured to evaluate whether the rescue robot can take over the uncompleted tasks according to a function matching degree to obtain an evaluation result; The generation module is configured to determine a task takeover scheme according to the sorting information and the evaluation result; if the rescue robot can directly take over the tasks, the generation module generates the task takeover scheme; if the rescue robot cannot directly take over the tasks, the generation module splits the tasks according to the sorting information and allocates the split tasks to other idle robots; The combined generation module is configured to generate a rescue and takeover integrated execution scheme, i.e., a decision scheme, in combination with the task takeover scheme and the rescue path planning result.

7. A computer device, characterized by The computer device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method in any one of claims 1 to 5 when executing the computer program.

8. A storage medium, characterized by The storage medium stores a computer program, and the computer program is executed by the processor to implement the method in any one of claims 1 to 5.

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