Robot scheduling method, device and system

Abstract

The invention relates to the technical field of robot control, and discloses a robot scheduling method, device and system. Environment data sent by a robot is received, and the environment data is environment data of an area where the robot is located; according to the environment data, performing map model construction on the area to obtain map information; obtaining local map information, positioning information and a motion path according to the map information; and sending the local map information, the positioning information and the motion path to the robot to support autonomous movement and task execution of the robot in the current area. In this way, the overall operation efficiency and stability of the system are improved.

Description

Technical Field

[0001] The present application relates to the field of robot control technology, and in particular to a robot scheduling method, device, and system. Background Art

[0002] Currently, indoor robots are widely used for tasks such as cleaning, delivery, and guidance, but existing systems still face numerous technical bottlenecks. First, the robots themselves typically rely on embedded devices for computing. However, these devices have limited computing power, making it difficult to efficiently complete complex computational tasks, limiting the robots' adaptability in large-scale dynamic environments. Second, as the number of robots increases, the system becomes more complex, requiring each robot to perform independent calculations and decision-making. This increases maintenance costs and the probability of failure, impacting the stability and reliability of the overall system. Summary of the Invention

[0003] The implementation methods of this application mainly solve the technical problems of limited computing power of single robots and poor overall operating efficiency of the system.

[0004] In order to solve the above technical problems, a technical solution adopted in the embodiment of the present application is: to provide a robot scheduling method, which is applied to the central control device of the robot control system, and the robot control system also includes at least one robot. The method includes: receiving environmental data sent by the robot, wherein the environmental data is the environmental data of the area where the robot is located; based on the environmental data, constructing a map model of the area to obtain map information; based on the map information, obtaining local map information, positioning information and motion path; sending the local map information, positioning information and motion path to the robot to support the robot's autonomous movement and task execution in the current area.

[0005] In some embodiments, the method further includes: when there is a task requirement in the target area, assigning tasks according to the task requirement to obtain task assignment information; determining the target robot according to the task assignment information; and controlling the target robot to go to the target area according to the map information of the target area and then perform the task according to the task assignment information.

[0006] In some embodiments, a map model of the area is constructed based on the environmental data to obtain map information, including: when the robot is a construction survey robot, a global static map is constructed based on the environmental data obtained by the construction survey robot; when the robot is a target robot performing a task, a local map is constructed based on the environmental data obtained by the target robot and the global static map to obtain map information.

[0007] In some embodiments, when there is a task requirement in the target area, task allocation is performed according to the task requirement to obtain task allocation information, including: obtaining the current position information and status information of the robot; determining the task allocation information according to the task requirement, the current position information and status information of the robot.

[0008] In some embodiments, before receiving the environmental data sent by the robot, the method also includes: communicating with the robot through a first communication link; when the communication quality of the first communication link does not meet the control requirements, communicating with the robot according to the second communication link with the optimal signal among multiple alternative second communication links.

[0009] In some embodiments, among multiple alternative second communication links, communicating with the robot according to the second communication link with the optimal signal includes: obtaining the robot's driving intention; in the driving intention, determining the target communication device on the robot's driving route; and determining the second communication link with the optimal signal based on the location of the target communication device.

[0010] In some embodiments, the method further includes: determining to execute emergency measures when communication between the robot and the central control device is interrupted.

[0011] In some embodiments, executing emergency measures includes: safely stopping at the roadside within a preset range; and / or evaluating the task being performed, stopping the task when it can be paused, and putting away the operating equipment for performing the task.

[0012] To solve the above technical problems, another technical solution adopted in the embodiment of the present application is: providing a central control device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0013] In order to solve the above technical problems, another technical solution adopted in the embodiment of the present application is: providing a control system, the control system includes the above-mentioned central control device and at least one robot.

[0014] Different from the related art, the present application provides a robot scheduling method, device and system. By receiving environmental data sent by the robot, wherein the environmental data is the environmental data of the area where the robot is located; constructing a map model of the area based on the environmental data to obtain map information; obtaining local map information, positioning information and motion path based on the map information; sending the local map information, positioning information and motion path to the robot to support the robot's autonomous movement and task execution in the current area. In this way, the central control device can centrally process environmental data, build a high-precision map model, and send the map information, positioning information and motion path to the robot, so that the robot can quickly and accurately locate and improve the stability and accuracy of positioning. At the same time, the map construction method based on central control can realize the sharing of environmental information by multiple robots, avoid the waste of resources caused by repeated mapping, and enhance the collaborative ability of multiple robots. In addition, the central control device centrally processes complex computing tasks, thereby reducing the dependence of a single robot on high-computing-power embedded devices, making the robot body more lightweight, and reducing hardware costs and energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] One or more embodiments are exemplarily illustrated by corresponding drawings, which do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0016] Figure 1 This is a schematic diagram of the structure of a control system provided by an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of the hardware structure of a central control device provided in an embodiment of the present application;

[0018] Figure 3 This is a flowchart of a robot scheduling method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0020] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other and are all within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as those commonly understood by those skilled in the art of the technical field of the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. The term "and / or" used in this specification includes any and all combinations of one or more related listed items.

[0021] Currently, indoor robots are widely used in scenarios such as cleaning, logistics distribution, and guidance services, but they still face challenges with limited computing power and high failure rates. Traditional robots typically rely on embedded devices to perform mapping, path planning, and task scheduling. Limited by hardware performance, these robots struggle to efficiently handle multi-task computations in complex environments. Furthermore, as the number of robots increases, equipment costs and system maintenance burdens rise significantly, and the repeated configuration of independent computing units increases the likelihood of failure. Therefore, improving robot computing power, reducing hardware costs, and ensuring system stability in large-scale applications have become pressing technical challenges in the field of indoor robotics.

[0022] To this end, this paper proposes a control system that centralizes computing tasks to a central control device, leaving only the sensors and basic drive units within the robot itself. The central control device is responsible for global mapping, path planning, and task scheduling, and issues commands to the robots via wireless communication. This reduces the computing power required of individual robots, optimizes computing resources, and improves system stability and scalability.

[0023] See also Figure 1 , Figure 1 This is a schematic diagram of the structure of a control system provided by an embodiment of the present application. Figure 1 As shown, the control system 100 includes: a central control device 10 and at least one robot 20.

[0024] The central control device 10 is the core computing unit of the entire system, primarily responsible for global computing and task scheduling. It functions as a high-performance computing server, centrally processing computing tasks for multiple robots 20 and reducing the computing burden on individual robots. The central control device 10 can take one of the following forms, depending on the application scenario and system architecture: A local server: Deployed in a computer room or fixed location, it provides powerful computing power and stable communications. An edge computing device: Utilizes a high-performance edge computing unit (such as a GPU / FPGA accelerator) to process real-time tasks and reduce data transmission latency. A cloud computing server: Connected via the internet or a local area network, it enables remote computing and scheduling, making it suitable for managing large-scale robot clusters. In this embodiment, the central control device 10 includes, but is not limited to, the following features: Environmental data reception: Sensor data collected by robots (such as lidar, depth cameras, and inertial measurement units) is uploaded to the central control device 10. Global mapping: The central control device 10 utilizes SLAM (Simultaneous Localization and Mapping) technology to integrate data provided by multiple robots to generate an accurate environmental map. Path planning: Based on real-time map information, the central control device 10 calculates the optimal path to ensure efficient robot movement in complex environments. Task Scheduling: Analyzes the status of all robots and allocates tasks appropriately to avoid duplication or inefficient operations. Command Issuance: Based on planning results, action instructions are sent to robots via wireless networks (such as Wi-Fi and 5G). Real-time Monitoring and Adjustment: Continuously receives feedback from robots to optimize paths and task execution strategies.

[0025] See also Figure 2 , Figure 2 Schematic diagram of the hardware structure of the central control device 10 for executing the robot scheduling method provided in the embodiment of the present application. Figure 2 As shown, the central control device 10 includes: at least one processor 11; and a memory 12 in communication with the at least one processor 11. Figure 2 In the example, a processor 11 is used. The memory 12 stores instructions that can be executed by the at least one processor 11. The instructions are executed by the at least one processor 11 so that the at least one processor 11 can execute the robot scheduling method proposed in the embodiment of the present application. The processor 11 and the memory 12 can be connected by a bus or other means. Figure 2 The bus connection is taken as an example.

[0026] Memory 12, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer executable programs, and modules. Processor 11 executes the non-volatile software programs, instructions, and modules stored in memory 12 to execute various server functional applications and data processing, thereby implementing the robot scheduling method.

[0027] The memory 12 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the computing device, etc. In addition, the memory 12 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 12 may optionally include a memory remotely located relative to the processor 11, and these remote memories may be connected to the computing device via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The one or more modules are stored in the memory 12, and when executed by the one or more processors 11, the robot scheduling method is executed.

[0028] Robot 20 is the main body that performs tasks, responsible for sensing, moving, and executing specific tasks in the real environment. Compared to traditional robots, the robot body provided in this embodiment is more lightweight, retaining only basic execution functions and relying on the central control device 10 for decision-making and calculations. The components of robot 20 include, but are not limited to, the following modules: Sensors: used for environmental perception, which may include lidar, depth cameras, infrared sensors, inertial measurement units (IMUs), etc. Drive units: including motors, servos, etc., for motion control of the robot body. Communication module: responsible for wireless communication with the central control device 10 (Wi-Fi, 5G, UWB, etc.). Safety module: when communication with the central control device 10 is interrupted, the robot triggers a safe docking mechanism and stays in a safe area. In this embodiment, robot 20 includes, but is not limited to, the following features: Environmental perception: collecting surrounding environmental data, such as obstacle locations and moving object information. Data upload: sending collected data to the central control device 10 to await instructions. Instruction execution: receiving path and task instructions issued by the central control device 10 and driving the motor to move along the calculated route. Status feedback: Continuously transmits location information, execution progress, and possible abnormal situations to the central control device 10. Safe docking: If communication with the central control device 10 is unavailable, the robot will dock in a safe area (such as a wall or corner) to avoid obstructing passage.

[0029] Through this design, the entire control system 100 can fully utilize central computing resources, improve computing efficiency, reduce robot hardware costs, and enhance system reliability and scalability.

[0030] The following describes the robot scheduling method proposed in the embodiments of the present application through specific examples.

[0031] See also Figure 3 , Figure 3This is a flow chart of a robot scheduling method provided by an embodiment of the present application. The robot scheduling method proposed in the embodiment of the present application is applied to a central control device, and the method includes steps S11 to S14:

[0032] S11: Receive environmental data sent by the robot, where the environmental data is environmental data of the area where the robot is located.

[0033] First, the robot uses its own sensors to collect data about the surrounding environment, including but not limited to the following perception modules: LiDAR: used to measure the distance and position of surrounding obstacles and generate point cloud data. Depth camera: used to obtain three-dimensional environmental information and can identify scenes such as height changes and stairs. Inertial measurement unit (IMU): records the robot's own motion state, such as acceleration, angular velocity, etc., to help infer its own position. Odometer (Odometry): measures the rotation of the robot's wheels and estimates the moving distance. The robot will perform preliminary processing on this data (such as filtering, noise reduction, and time synchronization), and then encapsulate it into an environmental data packet to be sent to the central control device.

[0034] Secondly, the robot needs to package the collected environmental data and transmit it to the central control device via a wireless network (such as Wi-Fi, 5G, UWB). Among them, the environmental data is encapsulated using standardized data formats (such as ROS (Robot Operating System) messages, JSON, Protobuf). Environmental data includes but is not limited to: robot ID (used to identify the source of data), timestamp (synchronize data from different robots), sensor data (LiDAR point cloud, depth image, IMU data, etc.) and preliminary position information (estimated position calculated based on odometer and inertial sensor).

[0035] Finally, the robot transmits the data to the central control device via the wireless communication module. It is understood that the robot sends a connection request to the central control device and ensures network stability. Data transmission can use TCP (for reliability) or UDP (suitable for real-time data). For large data volumes (such as LiDAR point clouds), data compression (such as PCD and Octomap) can be used to optimize transmission efficiency.

[0036] Through the above process, the central control device can efficiently and accurately receive the robot environment data, providing data support for subsequent map construction (step S12) and path planning.

[0037] S12: Construct a map model of the area based on the environmental data to obtain map information.

[0038] Among them, a map model of the area is constructed based on the environmental data to obtain map information, including: when the robot is a survey robot, a global static map is constructed based on the environmental data obtained by the survey robot; when the robot is a target robot performing a task, a local map is constructed based on the environmental data obtained by the target robot and the global static map to obtain map information.

[0039] As you can understand, the central control device builds maps based on the environmental data uploaded by the robots. Different types of robots (survey robots and target robots) play different roles in the map-building process: Survey robots are responsible for building global static maps, which fully depict the environmental structure of the entire area. Target robots are responsible for building local dynamic maps to adapt to environmental changes during actual mission execution, such as personnel movement and obstacle changes.

[0040] As a survey robot explores a new environment, it continuously uploads LiDAR point cloud data, depth camera data, and IMU odometry data. First, the central control device performs the following on this data: Denoising: This filters out measurement errors, such as removing invalid LiDAR points and outliers from the depth camera. Data alignment: This synchronizes multiple sensor data based on timestamps, aligning the point cloud and IMU data within the same time reference frame. Next, the central control device calculates the survey robot's trajectory using Simultaneous Localization and Mapping (SLAM) technology. Specifically, the ICP (Iterative Closest Point) algorithm is used to match point cloud data and estimate robot displacement. IMU + odometry fusion improves pose estimation accuracy and reduces positioning error. Finally, map stitching and optimization are performed, including: Point cloud stitching: This stitches LiDAR data from multiple time points to form a complete map of the environment. Loop closure: This detects whether the robot has returned to its previous position and corrects accumulated errors. Graph optimization is used to adjust the map to ensure overall accuracy. Finally, the central control device obtains a global static map, which can be used for subsequent path planning and task scheduling.

[0041] While performing its mission, the target robot collects real-time environmental data, such as obstacles and dynamic objects, and sends it to the central control device. The central control device then builds a local map based on the existing global static map. First, the target robot uploads LiDAR data, camera data, and IMU data for the current time. The central control device aligns the timestamps and maps the data into the global coordinate system, ensuring alignment between the old and new maps. Next, the central control device performs dynamic object detection and map updates. These include: Background modeling: distinguishing between static obstacles (such as walls) and dynamic obstacles (such as pedestrians and mobile shelves) in the environment. Obstacle detection: LiDAR point cloud difference: comparing historical point clouds with the current point cloud to identify new or missing objects. Deep image analysis: combining visual AI to identify dynamic objects (such as pedestrians and carts). Local map update: New dynamic obstacles are marked as temporary obstacles on the local map to prevent collisions. When the dynamic obstacle is removed (e.g., a pedestrian leaves), the local map automatically restores the traversable area. Finally, the target robot obtains a local dynamic map based on the latest environmental data and uses it to perform its mission.

[0042] After obtaining the global static map and local maps, map storage and distribution operations can be performed. It is understood that the global static map is stored in the database of the central control device and can be updated at any time. The local dynamic map is calculated by the central control device and distributed in real time to the target robot in need, so that it can adapt to environmental changes when performing tasks. A differential update strategy can be used to only distribute the part of the environment that has changed, reducing the data transmission burden. It should be noted that the technology used to construct the global static map and local maps is not limited here and can be selected according to actual circumstances.

[0043] It's important to note that a complete global map is required during initial system deployment or major environmental changes (such as the opening of a new floor or a redesigned layout). Taking office buildings as an example, since office buildings are typically complex environments with multiple rooms and floors, mapping them with a single robot can be time-consuming and limited by the range and speed of a single robot's sensors. Therefore, before the first mission, the system uses multiple robots in parallel to complete a global static map. The specific process includes: Simultaneous Multi-Robot Activation: Multiple robots are activated simultaneously, each exploring a different area from a different initial position. Environmental Data Collection: Robots scan the environment using devices such as LiDAR, cameras, IMUs, and ultrasonic sensors, collecting data such as obstacle distribution, wall structure, door locations, and lane widths. Regional Mapping: Robots transmit local map data to a central control device, which executes a SLAM (Simultaneous Localization and Mapping) algorithm to fuse the data from multiple robots to generate a complete global map. If the mapping data from multiple robots in the same area overlaps, the central control device performs data matching and optimization to remove redundant data and improve mapping accuracy. Build a global map: After data fusion and optimization, the central control device obtains a global static map (including floor structure, office area, corridor, elevator location, etc.). This map is only built once before the first mission is executed and does not need to be recreated for each mission. In daily operation, personnel activities, furniture movement, temporary obstacles, etc. in the office building will cause local maps to change, but the overall structure (such as walls, elevators, fixed desks) usually does not change. Therefore, the central control device does not need to recreate the global map for each mission, but only performs local updates on the dynamically changing parts. It is understandable that the global map (static) is constructed by multiple robots in collaboration and is only performed once to provide basic building structure information. The local map (dynamic) is continuously updated when the robot performs a task to ensure that the path planning adapts to environmental changes.

[0044] In this embodiment, the central control device is responsible for generating a global static map and updating local maps, enabling hierarchical processing of map data. This not only reduces the computational burden on individual robots but also optimizes resource allocation, enabling robots to operate efficiently in vast, highly dynamic environments. Furthermore, multi-robot collaboration is supported, enabling the central control device to dispatch multiple robots in real time, improving the stability, scalability, and intelligence of the entire system.

[0045] S13: Obtain local map information, positioning information and movement path according to the map information.

[0046] After completing the construction of the global static map and local maps, the central control device needs to send the map information to the target robot so that it can accurately locate its own position for subsequent path planning and task execution. Due to the high complexity of positioning calculations, a method of combining central control device calculations with robot execution is used. That is, the high-computation positioning task is processed by the central control device, and the simplified data is then sent to the robot. The specific process is as follows:

[0047] First, the central control device calculates the robot's current position. Understandably, the central control device performs the following computationally intensive positioning operations: It calculates the robot's precise pose (position + orientation) by combining the robot's sensor data with the global map using algorithms such as particle filtering (PF), extended Kalman filtering (EKF), and graph optimization (GTSAM). Global optimization methods (such as Loop Closure Detection) are used to reduce errors and ensure that the robot does not drift due to accumulated errors. Combined with high-precision SLAM map matching, this ensures that the robot can be correctly projected onto the current map.

[0048] Secondly, plan the motion path and emergency path. It is understandable that after determining the current position of the robot, the central control device will execute the path planning algorithm (such as Dijkstra, RRT, etc.) to calculate the optimal path for the robot from the current position to the target point based on the target task and map information. Among them, in order to prevent the robot from losing connection or encountering emergencies during the execution of the task, the central control device will calculate the emergency path in advance: when the robot loses the communication signal, the emergency path ensures that the robot can dock in a safe area (such as the edge of the corridor, obstacle avoidance area). If congestion or obstacles are detected in the main channel, the emergency path provides a detour plan to prevent the robot from falling into a dead end.

[0049] S14: Send local map information, positioning information and motion path to the robot to support the robot's autonomous movement and task execution in the current area.

[0050] It is understandable that due to the large amount of data in the complete SLAM map, the central control device will not send the complete map directly, but will simplify the map: retain the main path: only contain the key structural information required for the robot to move. Remove redundant information: eliminate areas that do not affect movement, such as ceilings, small furniture, etc. Optimize the storage format: use raster maps, topological maps or key point representations to reduce the burden of data transmission. The central control device then encapsulates the data packet, including: positioning information (coordinates + direction), planned motion paths, emergency paths, and simplified maps (local map information), and sends it to the robot via Wi-Fi / 5G / dedicated wireless networks.

[0051] Finally, the robot receives the information and performs motion control and task execution. It is understandable that after the robot receives the positioning information, path and map sent by the central control device, it will: Analyze the current position and update its own motion control system. Load the motion path and move along the planned route. Pre-store the emergency path and enable it when the signal is lost. In addition, the robot combines the path points and its own sensors to perform path tracking control: PID control, MPC (model predictive control) calculates the motor steering and speed control. Adjust the direction in real time to ensure precise movement along the planned path. When encountering sudden obstacles, execute the obstacle avoidance strategy and feedback the situation to the central control device. In addition, the above-mentioned local map information, positioning information, motion path and emergency path can also be used in the robot's task execution process.

[0052] Globally optimized positioning is performed through high-performance computing on the central control device, preventing individual robots from deviating from their trajectories due to accumulated errors, significantly improving navigation accuracy. The robot no longer needs to perform complex SLAM positioning and path planning, but only needs to parse the path instructions issued by the central control device, reducing computing resource consumption and making the robot itself lighter and less expensive. Furthermore, sending a simplified map rather than a complete SLAM map reduces data transmission volume and avoids network latency issues caused by excessive bandwidth usage. Finally, after receiving the emergency path, even if the robot loses connection to the central control device, it can still dock in a safe area or select a backup channel based on the pre-stored emergency path, improving system reliability.

[0053] The embodiment of the present application provides a robot scheduling method, which uniformly manages and schedules robots through a central control device, and has significant beneficial effects. First, it reduces the dependence of a single robot on high-computing-power embedded devices, makes the robot body more lightweight, and reduces hardware costs and energy consumption. Secondly, the central control device can centrally process environmental data, build a high-precision map model, and send local map information, positioning information, and motion paths to the robot, so that the robot can quickly and accurately locate and improve the stability and accuracy of positioning. In addition, the map construction method based on central control can enable multiple robots to share environmental information, avoid the waste of resources caused by repeated mapping, and enhance the collaborative capabilities of multiple robots. Overall, the robot scheduling method of the present application effectively solves the problems of limited computing power, low positioning accuracy, and weak collaborative capabilities in existing robot systems, and improves the intelligence level and operation efficiency of robots in various application scenarios.

[0054] In some embodiments, the method further includes: when there is a task requirement in the target area, performing task allocation according to the task requirement to obtain task allocation information; determining the target robot according to the task allocation information; and controlling the target robot to go to the target area according to the map information of the target area and then perform the task according to the task allocation information.

[0055] Among them, when there is a task requirement in the target area, task allocation is performed according to the task requirement to obtain task allocation information, including: obtaining the current position information and status information of the robot; and determining the task allocation information according to the task requirement, the current position information and status information of the robot.

[0056] First, the central control device needs to assign tasks based on the task requirements and the current status of the robots. The specific process includes: Task requirement acquisition: The central control device obtains task requirements by interacting with the task source (e.g., store manager, production line operator, etc.) or sensor data in the target area. For example, in a cleaning robot scenario, the task requirement might be "clean area A"; in a logistics robot scenario, it might be "pick up goods from warehouse A and deliver them to target location B." Current robot location information acquisition: Robots use sensor systems (such as GPS, IMU, and LiDAR) to obtain their current geographic location and posture in real time. The central control device updates the location database of all robots in real time to ensure accurate information about each robot's position. Robot status information acquisition: Robot status information includes not only its location but also its current operating status (e.g., task completed, standby, moving), remaining battery power, and fault information. The central control device obtains real-time robot status information through communication protocols with the robots (e.g., MQTT, WebSocket, ROS). Task allocation decision-making: The central control device makes task allocation decisions based on the task requirements, the robots' current location information, and their status information. It's understandable that if a task requires cleaning a specific area, the central control device will select the robot closest to that area and with sufficient battery power. If a task requires cargo handling, the central control device will select a robot with an appropriate load capacity and currently in the "Standby" or "Available" state. Task assignment information includes task type, target area, selected robot, and task priority.

[0057] Next, the central control device identifies the target robot for the task based on the task assignment information. The specific process includes: Task type matching: Based on the task type (such as cleaning, handling, inspection, etc.), the central control device matches the task with a robot that meets the requirements. Robot type and configuration, such as load capacity, sensor type, operating speed, and operating hours, will become important criteria for task matching. Robot resource inspection: After determining the task type, the central control device checks all available robots to see if they meet the task requirements. For example, if the task involves carrying heavy objects, the system will prioritize robots with greater load capacity; if the task requires navigating a complex environment, robots equipped with high-precision positioning sensors may be selected. Robot priority determination: If multiple robots meet the task requirements, the central control device will determine the order in which the tasks will be executed based on the robots' current workload and status priority (such as power level, battery life, and task urgency) to ensure maximum resource utilization.

[0058] Finally, the central control device controls the target robot to proceed to the target area to perform the mission based on the map information of the target area. It is understood that the central control device uses the map information of the target area, combined with the robot's current position and the target location, to perform path planning. A path planning algorithm such as the A* algorithm, Dijkstra's algorithm, or Rapid Random Tree (RRT) is used to calculate the shortest, safest path from the robot's current position to the target area. Once the path planning is complete, the central control device sends the path instructions to the target robot via wireless communication. After receiving the instructions, the robot follows the path accordingly. The robot uses its built-in sensor systems (such as LiDAR, ultrasonic sensors, and vision systems) to perform real-time obstacle avoidance and path adjustments to ensure smooth mission execution. Once the target robot arrives at the designated target area, the central control device further instructs the robot to perform specific tasks, such as cleaning, transporting, and guiding, based on the task assignment information. The robot then performs a series of operations based on the task requirements, such as transporting the target object to a designated location or cleaning the target area.

[0059] In this embodiment, efficient task scheduling and allocation are achieved through a central control device. First, task allocation is based on the robot's current state and task requirements, avoiding irrational task allocation and improving resource utilization efficiency. Second, through precise task allocation, robots can respond to task requests more quickly, reducing redundancy and resource waste in path planning when executing tasks, thereby improving overall system efficiency. Finally, by selecting the most appropriate target robot, task execution delays are reduced and the system can better adapt to dynamically changing working environments, thereby enhancing the flexibility and stability of the robot scheduling system.

[0060] In some embodiments, before receiving the environmental data sent by the robot, the method also includes: communicating with the robot through a first communication link; when the communication quality of the first communication link does not meet the control requirements, communicating with the robot according to the second communication link with the optimal signal among multiple alternative second communication links.

[0061] Among them, among multiple alternative second communication links, communicating with the robot according to the second communication link with the optimal signal includes: obtaining the robot's driving intention; in the driving intention, determining the target communication device on the robot's driving route; and determining the second communication link with the optimal signal according to the location of the target communication device.

[0062] During the execution of the robot scheduling method, a high-quality communication link must be established to ensure that the central control device can stably receive environmental data from the robots. Communication is established via a first communication link (primary communication link). When communication quality degrades, a second communication link (backup communication link) is dynamically selected to maintain stable communication.

[0063] It is understandable that when the robot starts and enters the task state, it first tries to establish a communication connection with the central control device. Typically, the first communication link can be Wi-Fi, 5G, 4G, Zigbee or a dedicated wireless communication protocol. The central control device scans and identifies the connection request sent by the robot, and confirms the communication stability through a handshake protocol (such as TCP / IP three-way handshake or MQTT connection establishment). After the communication connection is established, the central control device periodically detects key parameters such as the signal strength, data transmission rate, packet loss rate, and latency of the first communication link. If the link quality continues to be within an acceptable range, the link will continue to be used for communication.

[0064] The robot then transmits environmental data, including LiDAR point cloud data, camera images, infrared sensor data, and IMU odometry data, to the central control device via the first communication link. The central control device analyzes this data in real time to facilitate subsequent tasks such as map building and path planning.

[0065] During the communication process, the central control device will continuously monitor the quality of the communication link. When any of the following situations occurs, the central control device will determine that the communication quality does not meet the control requirements: the signal strength is lower than the preset threshold (such as the Wi-Fi signal is lower than -75dBm), the data packet loss rate is higher than the preset threshold (such as more than 10%), the network delay exceeds the allowable range (such as more than 100ms), and the data throughput is significantly reduced, affecting task execution. It should be noted that this embodiment does not limit the control requirements and can be set according to actual conditions.

[0066] When the quality of the first communication link decreases, the central control device will enable the backup communication mechanism and select the best communication mode from multiple alternative second communication links.

[0067] First, obtain the robot's driving intention. It is understandable that when the robot is performing a task, the central control device will continue to obtain its motion path, target position, task type and other information, which constitutes the robot's driving intention. For example, if the robot is traveling from area A to area B, the driving intention may include: the expected path (such as the path planned based on A* or Dijkstra), the driving speed (such as 0.5m / s or 1.2m / s) and the passing area (such as different signal coverage areas). While sending environmental data, the robot will also upload its own location information and planned path in real time. The central control device uses this information to infer future driving intentions.

[0068] Secondly, determine the target communication device. It can be understood that the target communication device is a wireless network device that the robot can access on its driving path, such as: Wi-Fi access points (APs), 5G / 4G base stations, Internet of Things (IoT) gateways (such as Zigbee or LoRa gateways), and dedicated robot communication nodes (such as ad hoc network Mesh nodes). The robot may pass through multiple different signal coverage areas, so the central control device needs to: predict future communication coverage based on the map and path, and determine whether there are known communication devices in the area the robot is about to enter. Query the communication device database to match the available target communication devices on the robot's driving path. Combined with the signal propagation model, predict the device that the robot is most likely to connect to during driving.

[0069] Finally, the second communication link with the optimal signal is determined. It's understood that the robot may be able to connect to multiple different types of communication links. For example, if Wi-Fi coverage is weak, it switches to 5G. If the 5G network is congested, it switches to LoRa for lower-speed data transmission. If other networks are unavailable, the mesh network between the robots is used for data transmission. Among the candidate communication links, the system evaluates the following parameters to select the optimal link. These parameters include: Signal strength (RSSI): Select the link with the strongest signal. Data transmission rate: Prioritize links with high speed and low latency. Stability: Avoid links with large signal fluctuations. Network load: If a link is too congested (e.g., too many users at a 5G base station), it switches to another link. After determining the optimal link, the central control device instructs the robot to switch communications, typically using: Wi-Fi roaming protocols such as 802.11r and 802.11k. Cellular network switching: Automatically switching base stations between 5G and 4G networks. Ad hoc network protocols such as MANET (Mobile Ad Hoc Network) automatically adjust network topology.

[0070] In this embodiment, an intelligent communication management mechanism is used to ensure that the central control device can always maintain stable communication with the robot. First, by dynamically switching between multiple communication links, communication interruptions caused by signal attenuation, network congestion, and other problems in a single link are avoided, thereby improving the robustness of the system. Secondly, based on the robot's driving intention, future communication needs are predicted, making communication switching more accurate and reducing the risk of sudden network interruptions. In addition, the optimal signal selection mechanism ensures that the robot always uses the most efficient network, improving the real-time and accuracy of task execution. Ultimately, this method effectively improves the reliability of the entire robot scheduling system and lays a stable communication foundation for subsequent environmental data acquisition, map construction, and path planning.

[0071] In some embodiments, the method further includes: determining to execute emergency measures when communication between the robot and the central control device is interrupted.

[0072] Among them, executing emergency measures includes: safely stopping at the roadside within a preset range; and / or evaluating the task being performed, stopping the task when it can be paused, and putting away the operating equipment for performing the task.

[0073] Communication between the central control device and the robots is a critical link in the robot dispatching process. However, in real-world applications, communication interruptions can occur due to signal interference, network coverage blind spots, hardware failures, and other factors. To ensure the robots can continue to operate safely even in the event of communication loss, the system must implement emergency measures to mitigate potential risks and ensure the safety of mission execution.

[0074] First, identify communication interruption events. It's understood that robots and central control devices typically use a heartbeat packet mechanism to monitor communication status. The central control device periodically sends heartbeat packets to the robot, which must respond immediately upon receipt. If no response is received from the robot within a preset time (e.g., 5 seconds), the central control device determines that communication has been lost. The robot also monitors the signal strength (RSSI) and data reception rate. If the RSSI falls below a threshold (e.g., -85dBm), the packet loss rate exceeds 10%, or there is a continuous timeout without receiving instructions from the central control device, the robot determines that communication is abnormal. Upon detecting communication loss, the robot will first attempt to recover automatically, for example, by switching to an alternate communication link (e.g., Wi-Fi to 5G, or an ad hoc network) and reestablishing the connection (e.g., TCP rehandshake). If three reconnection attempts fail (with a 2-second interval between each attempt), communication is considered completely lost and the robot enters emergency mode.

[0075] Secondly, the robot safely stops at the curb within a preset range. As you can see, robots typically operate within designated work areas, such as factory roads, where they must avoid obstructing the passage of other automated equipment or personnel. Between warehouse shelves, they should stop where they do not interfere with the operation of logistics equipment. During outdoor patrol routes, they should stop as close to the edge of the area as possible to avoid being struck by other robots or vehicles. The robot uses its sensor data (LiDAR, ultrasonic, and camera) to identify its surroundings and find suitable stopping points. As you can see, the robot avoids stopping at intersections or high-traffic areas, prioritizing pre-defined safe stopping points (such as parking areas on a map). If no pre-defined stopping points are available nearby, the robot selects a spacious area and uses a path planning algorithm (such as A*) to calculate the optimal stopping route. Ultimately, the robot gradually adjusts its posture based on the planned path to approach the stopping point. For example, it may first reduce its speed (e.g., from 1 m / s to 0.2 m / s). When approaching the stopping point, it checks the surrounding environment to avoid collisions. After stopping, it activates the electronic brake system or enters a low-power mode to reduce energy consumption.

[0076] Additionally, it can assess whether an ongoing task needs to be paused. Robot tasks include, but are not limited to: Carrying tasks, such as an AGV (Automated Guided Vehicle) transporting items; Inspection tasks, such as a factory inspection robot collecting data; Cleaning tasks, such as a cleaning robot sweeping the floor; and Operation tasks, such as a robotic arm assembling a product.

[0077] It is understandable that after the communication is interrupted, the robot needs to evaluate whether the current task can be safely paused. For example, for a handling task, if the item is fixed (such as loaded on a shelf), the handling can be suspended. If the item is suspended (such as a robotic arm is carrying it), it must be placed in a safe place first. For another example, for an inspection task, if the current task can be interrupted (such as reading temperature data), stop the inspection. If a critical test is being performed (such as an electrical equipment fault check), record the current status so that it can be continued after recovery. For another example, for a cleaning task, if the cleaning robot is in an open area, it can pause cleaning. If it is in a small space, it should exit the small area before pausing. For another example, for an operating task, if the robotic arm is assembling a product, it should ensure that the task status can be recovered and put the removed parts back in a safe place.

[0078] After determining that a task can be paused, the robot needs to safely stop its current work. For example, the robot first gradually reduces its movement speed, eventually stopping in a safe area and retracting the operating tools used to perform the task. For example, a cleaning robot retracts its cleaning brushes and shuts down its vacuum system. The robotic arm retracts to its initial position to prevent suspended parts from falling. Finally, the robot also stores the current task status (such as the current task ID, execution progress, and location information) in local storage so that it can resume the unfinished task after communication is restored.

[0079] In this embodiment, it is ensured that the robot can still dock safely in the event of communication loss, and properly handle the task being performed, avoiding safety hazards or damage to items due to unexpected interruptions. First, through the intelligent docking strategy, the robot can find a suitable docking point within a reasonable range without hindering the normal passage of other equipment or personnel. Second, the task evaluation mechanism ensures that the robot can properly pause the task without causing task data loss or execution errors. In addition, the strategy of automatically retracting operating equipment reduces equipment damage and safety risks, and improves the reliability of the system. Overall, this method enhances the risk resistance and task recovery capabilities of the robot system, and ensures the stable operation of the robot in complex environments.

[0080] The following examples in an office building scenario are given to further introduce the implementation process of the robot scheduling method provided in the embodiments of the present application.

[0081] In an office building environment, central control equipment is deployed in the computer room and is responsible for SLAM mapping, path planning, and task scheduling. The robots carry only sensors and communicate via Wi-Fi. If communication is interrupted, the robots trigger a safe docking mechanism to ensure stability and safety. The specific implementation process includes:

[0082] First, the robot collects environmental data. It is understandable that after the robot is turned on, it first tries to connect to the Wi-Fi network and communicates with the central control device through the first communication link (such as 5GHz Wi-Fi). The robot periodically monitors the Wi-Fi signal quality. If the communication quality does not meet the control requirements (such as too high packet loss rate, too large delay), it tries to switch to an alternative Wi-Fi frequency band (such as 2.4GHz Wi-Fi). Subsequently, the robot uses lidar, depth camera, and IMU sensor to collect surrounding environment information and upload it to the central control device in real time. Among them, the environmental information collected by the robot includes but is not limited to: obstacle location, dynamic personnel distribution, elevator status, floor information, etc.

[0083] Secondly, the central control device performs map modeling. It is understandable that if the robot is a construction survey robot, the central control device will use the environmental data it transmits to build a global static map for long-term task planning. If the robot is a target robot performing a task, the central control device will combine its sensor data to build a local map to update dynamic obstacle information (such as the movement of office personnel) in real time. It is understandable that the central host models the layout information of the office building based on the data collected by the robot, including floor structure, office area, conference room, corridor, stairs, elevator, etc. The local map information, positioning information and motion path are then transmitted to the robot to support the robot's autonomous movement and task execution in the current area.

[0084] Again, task scheduling and allocation are performed. It is understandable that when a task requirement arises in the office building (such as delivering documents, cleaning, delivering coffee, patrolling, etc.), the central control device will perform task scheduling. For example, obtain the current location information and status of all robots (whether they are idle, current power level, task execution progress). Calculate the task allocation plan based on the optimal path, task priority and robot status. Generate task allocation information and send it to the target robot. After receiving the task instruction, the robot will confirm its own status and confirm the task receipt with the central control device. If the robot is low on power or in an abnormal state, the task will be rejected and the central control device will reallocate the task to other robots.

[0085] From this moment on, the robot performs the task. It is understandable that the robot calls the map data based on the received task information and plans the optimal path to the target area. During the driving process, the robot will continue to communicate with the central control device to ensure the smooth progress of the task. If the robot encounters a dynamic obstacle (such as a person passing by) during driving, it will use local sensors to adjust the path or request the central control device to plan a new path. In front of the elevator or access control, the robot will determine whether it needs to wait or seek external assistance (such as voice prompts for help pressing the elevator) based on the information of the building control system.

[0086] In addition, the robot handles emergencies after communication with the central control device is interrupted. It is understandable that the robot continuously monitors the Wi-Fi signal. If the signal is lost or the data is not received due to a timeout, the communication exception handling mechanism is triggered. The robot will slowly slow down and look for a suitable parking point: if it is in the corridor, it will park close to the wall to avoid blocking traffic. If it is in front of the elevator, it will wait for the signal to be restored before continuing the task. If it is in the office area, it will park in a fixed waiting area, such as next to the printer, in the rest area, etc. In addition, the robot will locally store the task execution status, including the current location and the progress of the executed tasks, so that it can continue execution after communication is restored.

[0087] Finally, after arriving at the target area, the robot performs a specific task (such as delivering items, logging patrols, cleaning, etc.). Upon completion, the robot sends a task completion report to the central control device and awaits further instructions. If the task is successful, the central control device archives the task results and notifies the relevant users (such as notifying the property management after the cleaning task is completed). If the task fails (such as if the target area is occupied), the central control device reschedules the task execution time or replaces the robot.

[0088] It should be noted that the central control device can dispatch multiple robots to perform tasks at the same time, and is not limited to dispatching only one robot to perform tasks. It can be understood that the central control device and the robots are in a one-to-many scheduling relationship.

[0089] In this embodiment, through unified scheduling by the central control device, tasks can be dynamically assigned, the utilization rate of robots can be improved, and resource waste can be reduced. Using SLAM mapping and path planning, the robot can navigate accurately and move efficiently even in a multi-floor environment. At the same time, Wi-Fi signal optimization and communication interruption emergency measures ensure the safety of the robot when the signal is unstable, avoiding operational failures or task failures due to signal loss. In addition, the task state storage and recovery mechanism enables the robot to continue to perform unfinished tasks even after a short period of communication loss, thereby improving the stability of the system and the success rate of the task. Overall, this method improves the reliability, efficiency and safety of intelligent robot scheduling in office building environments, making it have higher application value in scenarios such as smart office, property management, and logistics distribution.

[0090] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general hardware platform, or of course by hardware. Those skilled in the art can understand that all or part of the processes in the above embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Based on the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the present application as described above. For the sake of simplicity, they are not provided in detail. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A robot scheduling method, characterized in that: A central control device applied to a robot control system, wherein the robot control system further comprises at least one robot, wherein the method comprises: receiving environmental data sent by the robot, wherein the environmental data is environmental data of an area where the robot is located; constructing a map model of the area according to the environmental data to obtain map information; Obtaining local map information, positioning information, and a motion path based on the map information; The local map information, the positioning information and the motion path are sent to the robot to support the robot's autonomous movement and task execution in the current area.

2. The robot scheduling method according to claim 1, characterized in that: The method further comprises: When there is a task demand in the target area, the task is assigned according to the task demand to obtain task assignment information; determining a target robot according to the task assignment information; According to the map information of the target area, the target robot is controlled to go to the target area and then perform the task according to the task allocation information.

3. The robot scheduling method according to claim 1, characterized in that: The step of constructing a map model for the area based on the environmental data to obtain map information includes: When the robot is a site survey robot, a global static map is constructed based on environmental data acquired by the site survey robot; When the robot is a target robot performing a task, a local map is constructed according to the environmental data acquired by the target robot and the global static map to obtain map information.

4. The robot scheduling method according to claim 2, characterized in that: When there is a task demand in the target area, tasks are assigned according to the task demand to obtain task assignment information, including: Obtaining the current position information and status information of the robot; The task allocation information is determined according to the task requirements, the current position information and the status information of the robot.

5. The robot scheduling method according to claim 1, characterized in that: Before receiving the environmental data sent by the robot, the method further includes: communicatively connected to the robot via a first communication link; When the communication quality of the first communication link does not meet the control requirement, the robot is communicated with according to the second communication link with the best signal among a plurality of alternative second communication links.

6. The robot scheduling method according to claim 5, characterized in that: Among a plurality of alternative second communication links, communicating with the robot according to the second communication link with the optimal signal comprises: Obtaining the driving intention of the robot; In the driving intention, determining a target communication device on a route that the robot needs to drive; A second communication link with an optimal signal is determined according to the location of the target communication device.

7. The robot scheduling method according to claim 1, characterized in that: The method further comprises: When communication between the robot and the central control device is interrupted, it is determined to execute emergency measures.

8. The robot scheduling method according to claim 7, characterized in that: Implement emergency response measures, including: Stop safely on the roadside within the preset range; And / or, evaluating the task being performed, stopping the task when it can be paused, and putting away the operating tools for performing the task.

9. A central control device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

10. A control system, characterized in that: The control system comprises the central control device according to claim 9 and at least one robot.

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