Intelligent mobile mechanical arm composite robot system controlled by Internet of Things and cooperative control method

Abstract

The invention discloses an intelligent mobile mechanical arm composite robot system and method controlled by the Internet of Things, and belongs to the field of application of intelligent robots and the Internet of Things. Aiming at the problems of disjunction between global scheduling and local security, confrontation congestion of robots, difficulty in optimizing data dispersion, difficulty in equipment migration and the like in the existing multi-robot system collaboration, a cloud-side-end collaboration architecture is provided, wherein a cloud side is responsible for task allocation and time sequence arrangement; the edge end is responsible for rolling execution and safety control; and the robots perform mutual avoidance, road occupation and relay based on collaborative semantics. The system comprises a cloud job arrangement module, an edge execution control module, a cooperative communication module and a data closed loop module. According to the method, conflict congestion is reduced through a complete collaborative semantic mechanism, a data-driven strategy optimization closed loop is established, decoupling of the method and equipment is realized, and the operation efficiency, safety and mobility of multiple robots in a complex scene are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent robots and Internet of Things applications, specifically to a cloud-edge collaborative operation method for multiple mobile robotic arms, including task allocation and timing arrangement, edge execution and security control, collaborative communication semantics, and data monitoring closed loop. Background Technology

[0002] Existing technologies mainly include pure cloud scheduling solutions, pure local autonomous solutions, and simple message communication solutions, none of which can achieve a good balance in terms of real-time performance, global optimization, and device versatility.

[0003] Existing mobile robotic arm multi-robot systems face four major technical challenges in complex scenarios: 1. Global scheduling is disconnected from local real-time security, and cloud scheduling is affected by network latency, making it unable to cope with temporary obstacles and scenarios where humans and machines are mixed together; 2. The lack of unified cooperation rules among robots makes systems based on simple message communication prone to confrontation and congestion in narrow passages and intersections; 3. Operational and health data are stored separately, lacking a unified analysis platform, making it difficult to continuously optimize control strategies; 4. The equipment interfaces are different, and the control methods are coupled with specific hardware, making it difficult to migrate between different chassis and robotic arm combinations. Summary of the Invention

[0004] This invention aims to solve the aforementioned technical problems and provide a mobile robotic arm composite robot system and method that can balance global optimization and local real-time safety, support efficient multi-robot collaboration, possess continuous learning capabilities, and is easily transferable. Specifically, The core of this invention lies in establishing a three-tiered collaborative architecture of cloud, edge, and device: 1. Cloud layer: Focuses on large-scale optimization problems in offline or near real-time mode, including task allocation, time series orchestration, and long-term policy learning; 2. Edge layer: Responsible for local planning and security control with high real-time requirements, and capable of operating autonomously under limited network conditions; 3. Robot End: Performs specific movement and operation tasks, and interacts with other robots through unified collaborative semantics.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: An IoT-controlled intelligent mobile robotic arm composite robot system, the system comprising: The cloud-based job orchestration and monitoring module is used for task allocation, timing orchestration, and global optimization. The edge execution and safety control module is used for real-time path planning, attitude control, and safety rule enforcement; The collaborative communication and consistency maintenance module is used for task locks, path locks, mutual yielding, and relay mechanisms between robots. The real-time data transmission and strategy closed-loop module is used for job data acquisition, playback learning, and strategy gray-scale updates.

[0006] It should be noted that the cloud-based job orchestration and monitoring module includes: The task modeling unit is used to parse the location, type, time window, priority, and resource requirements of a task. The robot modeling unit is used to manage the robot's position, power, load, and capability information; The environmental modeling unit is used to maintain traffic topology, congestion status, and restricted area information; The allocation optimization unit generates task sequences based on capacity matching, distance, time consumption, energy consumption, and congestion level.

[0007] It should be noted that the edge execution and security control module adopts a fixed window scrolling planning mechanism to perform the following operations in a loop: The computer chassis movement trajectory and robotic arm motion sequence are planned within the window. Execute minimum safe distance, zone speed limit, and emergency stop triggering safety rules in real time; When sensing is lost or the location is abnormal, it enters a safe state and broadcasts an alarm.

[0008] It should be noted that the collaborative semantics supported by the collaborative communication and consistency maintenance module include: A task lock mechanism is used to prevent multiple robots from repeatedly executing the same task. The lane-locking mechanism enables reservation and right-of-way management for narrow passages; The mutual concession mechanism is based on the right-of-way negotiation using a request / grant model. A relay mechanism supports the transfer of tasks between different robots; A queue mechanism is used to maintain the orderly execution sequence of multiple robots.

[0009] It should be noted that the real-time data transmission and strategy closed-loop module is implemented as follows: The edge device continuously reports location trajectory, operation events, health status, and alarm information; Streaming processing, anomaly detection, and key indicator statistics are performed in the cloud. Adjust the allocation weights and safety thresholds based on playback and small-scale trial operation; The strategy is updated gradually through phased releases.

[0010] This invention also provides a multi-robot collaborative control method for an intelligent mobile robotic arm composite robot system based on Internet of Things control, the method comprising the following steps: S1 cloud-based modeling of tasks and robots, outputting task sequences and time schedules; S2 distributes policies and security parameters from the cloud, and initialization is completed at the edge. S3 edges use a fixed window to plan the path and posture, and correct them in real time according to safety rules; S4 robots maintain consistency by yielding, occupying lanes, and relaying tasks based on collaborative semantics. S5 edge continuously reports job and health data, and cloud monitoring and fine-tuning of strategies; When S6 malfunctions or the network deteriorates, it enters degrade mode, and after recovery, it reconciles accounts and resumes tasks.

[0011] It should be noted that the collaborative semantic consistency maintenance in step S4 includes: Use timestamps to unify the order of events across multiple robots; Conflicts are adjudicated according to priority and chronological order. Narrow channels use a token or time-slice mechanism; Synchronize with the cloud clock and enable local consistency rules when offline.

[0012] It should be noted that the exception handling in step S6 includes: When the network deteriorates, switch to local autonomous mode to increase security priority and suspend high-risk actions; If a task fails, it rolls back to a safe state and either re-plans locally or requests an alternative execution. After recovery, compare the task and trajectory records, reissue missing events, and restore a consistent state.

[0013] The beneficial effects of this invention are as follows: 1. Clear division of labor: The cloud is responsible for orchestration, while the edge is responsible for execution and security, taking into account both global optimization and real-time response; 2. Highly efficient collaboration: A complete collaborative semantic mechanism significantly reduces conflicts and congestion in multi-robot systems; 3. Continuous optimization: The closed-loop learning mechanism based on data playback and mini-batch validation enables continuous improvement of system strategies; 4. Easy to adapt: ​​The method and equipment interface are decoupled, supporting rapid access to different models of chassis and robotic arms; 5. High robustness: A robust degradation and recovery mechanism ensures the continuous operation of the system during network fluctuations and equipment failures. Detailed Implementation

[0014] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0015] This invention relates to an IoT-controlled intelligent mobile robotic arm composite robot system, the system comprising: The cloud-based job orchestration and monitoring module is used for task allocation, timing orchestration, and global optimization. The edge execution and safety control module is used for real-time path planning, attitude control, and safety rule enforcement; The collaborative communication and consistency maintenance module is used for task locks, path locks, mutual yielding, and relay mechanisms between robots. The real-time data transmission and strategy closed-loop module is used for job data acquisition, playback learning, and strategy gray-scale updates.

[0016] Furthermore, the cloud-based job orchestration and monitoring module of the present invention includes: The task modeling unit is used to parse the location, type, time window, priority, and resource requirements of a task. The robot modeling unit is used to manage the robot's position, power, load, and capability information; The environmental modeling unit is used to maintain traffic topology, congestion status, and restricted area information; The allocation optimization unit generates task sequences based on capacity matching, distance, time consumption, energy consumption, and congestion level.

[0017] Furthermore, the edge execution and security control module of the present invention adopts a fixed window scrolling planning mechanism to perform the following operations in a loop: The computer chassis movement trajectory and robotic arm motion sequence are planned within the window. Execute minimum safe distance, zone speed limit, and emergency stop triggering safety rules in real time; When sensing is lost or the location is abnormal, it enters a safe state and broadcasts an alarm.

[0018] Furthermore, the collaborative semantics supported by the collaborative communication and consistency maintenance module of the present invention include: A task lock mechanism is used to prevent multiple robots from repeatedly executing the same task. The lane-locking mechanism enables reservation and right-of-way management for narrow passages; The mutual concession mechanism is based on the right-of-way negotiation using a request / grant model. A relay mechanism supports the transfer of tasks between different robots; A queue mechanism is used to maintain the orderly execution sequence of multiple robots.

[0019] Furthermore, the real-time data transmission and strategy closed-loop module of the present invention is implemented as follows: The edge device continuously reports location trajectory, operation events, health status, and alarm information; Streaming processing, anomaly detection, and key indicator statistics are performed in the cloud. Adjust the allocation weights and safety thresholds based on playback and small-scale trial operation; The strategy is updated gradually through phased releases.

[0020] This invention also provides a multi-robot collaborative control method for an intelligent mobile robotic arm composite robot system based on Internet of Things control, the method comprising the following steps: S1 cloud-based modeling of tasks and robots, outputting task sequences and time schedules; S2 distributes policies and security parameters from the cloud, and initialization is completed at the edge. S3 edges use a fixed window to plan the path and posture, and correct them in real time according to safety rules; S4 robots maintain consistency by yielding, occupying lanes, and relaying tasks based on collaborative semantics. S5 edge continuously reports job and health data, and cloud monitoring and fine-tuning of strategies; When S6 malfunctions or the network deteriorates, it enters degrade mode, and after recovery, it reconciles accounts and resumes tasks.

[0021] Furthermore, the collaborative semantic consistency maintenance in step S4 of the present invention includes: Use timestamps to unify the order of events across multiple robots; Conflicts are adjudicated according to priority and chronological order. Narrow channels use a token or time-slice mechanism; Synchronize with the cloud clock and enable local consistency rules when offline.

[0022] Furthermore, the exception handling in step S6 of the present invention includes: When the network deteriorates, switch to local autonomous mode to increase security priority and suspend high-risk actions; If a task fails, it rolls back to a safe state and either re-plans locally or requests an alternative execution. After recovery, compare the task and trajectory records, reissue missing events, and restore a consistent state.

[0023] Example: 1. Overall Process S1 cloud-based modeling of tasks and robots, outputting task sequences and time schedules for each robot; S2 distributes policies and security parameters from the cloud, and initialization is completed at the edge. S3 edges use a fixed window to plan the path and posture, and make real-time corrections according to safety rules; S4 robots maintain consistency by yielding, occupying lanes, and relaying tasks based on collaborative semantics. S5 edge continuously reports job and health data, and cloud monitoring and fine-tuning of strategies; When S6 malfunctions or the network deteriorates, it enters degrade mode, and after recovery, it reconciles accounts and resumes tasks.

[0024] 2. Cloud-based task allocation and timing orchestration Inputs: Task (location, type, time window, priority, estimated duration, resource requirements), robot (location, battery level, load, tool capabilities, speed and battery life), environment (traffic topology, congestion, restricted areas, travel time).

[0025] Steps: Capacity matching and regional grouping; generate allocation and order by integrating distance, time consumption, energy consumption, congestion and time window; output task sequence, start / end time, buffer and alternative solutions; perform local reallocation based on events during operation.

[0026] Objective: To shorten the total travel time, reduce energy consumption and congestion, and meet the requirements of time windows and safety.

[0027] 3. Edge scrolling execution and safety control The system executes in a fixed-duration window; within the window, it calculates chassis movement and robotic arm motion to ensure path following, smooth motion, and controllable timing. Safety rules are always in place: minimum safe distance, area speed limit, emergency stop trigger; enter safe state and broadcast alarm when sensing is lost or location is abnormal; Task primitives: Picking, placing, docking, and identification are performed using state machines or behavior trees; sensor results are used for minor adjustments to pose and parameters.

[0028] 4. Multi-robot collaborative communication and consistency Collaborative semantics: identity and ability; task intent and mutual concession (request / grant of yield, reservation of lane occupancy, queue number); task lock and lane occupancy lock (including validity period); grouping and relay (role, request / confirmation, synchronous trigger); heartbeat and health.

[0029] Consistency: Timestamps unify event order; conflicts are resolved based on priority and time order; tokens or time slices are used for narrow channels; synchronization with cloud clocks; local consistency rules are enabled when offline, with security as the priority.

[0030] 5. Real-time data transmission and monitoring closed loop Reporting: Location and trajectory, operational events, health status, battery and temperature, point cloud / image summary, alarm codes; Cloud: Streaming and time-series storage, anomaly detection, key performance indicator (throughput, waiting time, energy consumption, success rate). Update: Replay and small-scale trial operation, gradually adjust allocation weights and security thresholds, and release in batches.

[0031] 6. Exception handling, degradation and recovery Network degradation: Switch to local autonomous mode, increase security priority, suspend high-risk actions, and preserve on-site information; Task failure: Roll back to a safe state, reschedule locally, or request an alternative execution; supports relay and limited retries; Post-recovery reconciliation: Compare task and trajectory records, reissue missing events, restore consistency, and continue task assignment.

[0032] For those skilled in the art, various corresponding changes and modifications can be made based on the above technical solutions and concepts, and all such changes and modifications should be included within the protection scope of the claims of this invention.

Claims

1. An IoT-controlled intelligent mobile robotic arm composite robot system, characterized in that, The system includes: The cloud-based job orchestration and monitoring module is used for task allocation, timing orchestration, and global optimization. The edge execution and safety control module is used for real-time path planning, attitude control, and safety rule enforcement. The collaborative communication and consistency maintenance module is used for task locks, path locks, mutual yielding, and relay mechanisms between robots. The real-time data transmission and strategy closed-loop module is used for job data acquisition, playback learning, and strategy gray-scale updates.

2. The IoT-controlled intelligent mobile robotic arm composite robot system according to claim 1, characterized in that, The cloud-based job orchestration and monitoring module includes: The task modeling unit is used to parse the location, type, time window, priority, and resource requirements of a task. The robot modeling unit is used to manage the robot's position, power, load, and capability information; The environmental modeling unit is used to maintain traffic topology, congestion status, and restricted area information; The allocation optimization unit generates task sequences based on capacity matching, distance, time consumption, energy consumption, and congestion level.

3. The IoT-controlled intelligent mobile robotic arm composite robot system according to claim 1, characterized in that, The edge execution and security control module adopts a fixed window scrolling planning mechanism to perform the following operations in a loop: The computer chassis movement trajectory and robotic arm motion sequence are planned within the window. Execute minimum safe distance, zone speed limit, and emergency stop triggering safety rules in real time; When sensing is lost or the location is abnormal, it enters a safe state and broadcasts an alarm.

4. The IoT-controlled intelligent mobile robotic arm composite robot system according to claim 1, characterized in that, The collaborative semantics supported by the collaborative communication and consistency maintenance module include: A task lock mechanism is used to prevent multiple robots from repeatedly executing the same task. The lane-locking mechanism enables reservation and right-of-way management for narrow passages; The mutual concession mechanism is based on the right-of-way negotiation using a request / grant model. A relay mechanism supports the transfer of tasks between different robots; A queue mechanism is used to maintain the orderly execution sequence of multiple robots.

5. The IoT-controlled intelligent mobile robotic arm composite robot system according to claim 1, characterized in that, The real-time data transmission and strategy closed-loop module is implemented as follows: The edge device continuously reports location trajectory, operation events, health status, and alarm information; Streaming processing, anomaly detection, and key indicator statistics are performed in the cloud. Adjust the allocation weights and safety thresholds based on playback and small-scale trial operation; The strategy is updated gradually through phased releases.

6. A multi-robot cooperative control method based on the system according to any one of claims 1-5, characterized in that, The method includes the following steps: S1 cloud-based modeling of tasks and robots, outputting task sequences and time schedules; S2 distributes policies and security parameters from the cloud, and initialization is completed at the edge. S3 edges use a fixed window to plan the path and posture, and correct them in real time according to safety rules; S4 robots maintain consistency by yielding, occupying lanes, and relaying tasks based on collaborative semantics. S5 edge continuously reports job and health data, and cloud monitoring and fine-tuning of strategies; When S6 malfunctions or the network deteriorates, it enters degrade mode, and after recovery, it reconciles accounts and resumes tasks.

7. The collaborative control method for the IoT-controlled intelligent mobile robotic arm composite robot system according to claim 6, characterized in that, The collaborative semantic consistency maintenance in step S4 includes: Use timestamps to unify the order of events across multiple robots; Conflicts are adjudicated according to priority and chronological order. Narrow channels use a token or time-slice mechanism; Synchronize with the cloud clock and enable local consistency rules when offline.

8. The collaborative control method for the IoT-controlled intelligent mobile robotic arm composite robot system according to claim 6, characterized in that, The exception handling in step S6 includes: When the network deteriorates, switch to local autonomous mode to increase security priority and suspend high-risk actions; If a task fails, it rolls back to a safe state and either re-plans locally or requests an alternative execution. After recovery, compare the task and trajectory records, reissue missing events, and restore a consistent state.