Cloud server, robot, multi-machine management system and multi-machine management method

Abstract

The present invention discloses a cloud server, a robot, a multi-machine management system and a multi-machine management method. The cloud server includes: a state group management module for managing the state information of the robots in each group; a map group management module for grouping business maps and managing the business maps in each group in a group management manner; a map splicing module for matching and splicing multiple business maps in the same scene to obtain a first spliced ​​map in the same scene, and / or matching and splicing business maps in the same map group to obtain a second spliced ​​map in the same map group; a first interactive communication module for synchronizing the state information of the first map and / or the second map, as well as the robot to at least one robot, to realize the multi-machine collaborative operation of the at least one robot. According to the above technical solution, the multi-machine coordination effect is good, the work efficiency is high, and the scene adaptability is strong.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a cloud server, a robot, a multi-machine management system and a multi-machine management method. Background Art

[0002] In hotels, supermarkets, shopping malls, factories, hospitals and other places, robots are gradually replacing manual labor in delivery, disinfection, cleaning and other tasks with their efficient, accurate and continuous working capabilities. However, due to the large working area and complex working environment, a single robot cannot complete all tasks, and multiple machines are required to work together to achieve full-scene coverage.

[0003] At present, the multi-robot working mode on the market is mainly based on multiple independent robots working alone. A dedicated mapping robot creates a scene map and copies or distributes it to each independent robot for use. After each independent robot obtains the scene map, it performs corresponding operations based on the obtained scene map and the set business logic, including path planning, robot motion control and business operations, etc., and then achieves full coverage of work scene business operations through saturated coverage of multiple machines.

[0004] However, in the related technologies, the multi-machine working mode of robots is mainly based on multiple independent robots working alone. There is no data interaction or only a small amount of data interaction between robots. The cloud is only responsible for monitoring the motion status of each independent robot and managing business operations. It can only achieve full coverage of business operations in work scenarios through the saturated work of multiple machines. The coordination effect of multiple machines is poor, the work efficiency is low, and the scene adaptability is poor. Summary of the invention

[0005] The main purpose of the present invention is to disclose a cloud server, a robot, a multi-machine management system and a multi-machine management method, so as to at least solve the problems in the related technology that the cloud is only responsible for monitoring the motion status of each independent robot and managing business operations, the multi-machine coordination effect is poor, the work efficiency is low, and the scene adaptability is poor.

[0006] According to one aspect of the present invention, a cloud server is provided.

[0007] The cloud server according to the present invention includes: a status group management module, which is used to manage the status information of robots under each group according to the robot group identifier bound to the user or the scene; a map group management module, which is used to group the business maps according to the scene identifier bound to the robot position information, and manage the business maps under each group in a group management manner, wherein each map group corresponds to at least one scene, and each scene corresponds to multiple business maps; a map splicing module, which is used to match and splice multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or, match and splice the business maps under the same map group to obtain a second spliced ​​map under the same map group; a first interactive communication module, which is used to synchronize the above-mentioned first map and / or the above-mentioned second map, as well as the status information of the above-mentioned robots to at least one robot, so as to realize multi-machine collaborative operation of the above-mentioned at least one robot.

[0008] According to another aspect of the present invention, a robot is provided.

[0009] The robot according to the present invention includes: a map management module, which is used to bind the robot's motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory to the established scene map when performing environmental mapping and business operations starting from the initial point position, so as to obtain the above-mentioned business map; a second interactive communication module, which is used to report the status information and the robot group identifier corresponding to the status information, as well as the business map established by the robot and the scene identifier corresponding to the business map to the above-mentioned cloud server, and receive a first map obtained by matching and splicing multiple business maps under the same scene from the above-mentioned cloud server, and / or a second map obtained by matching and splicing business maps under the same map group, and the robot's status information, wherein the above-mentioned status information includes: robot posture information and business information; a collaborative operation module, which is used to perform positioning based on the above-mentioned first map and / or the above-mentioned second map, and obtain the posture information and business information of all robots related to the current business except the current robot based on the interactive information with the above-mentioned cloud server, and realize multi-machine collaborative operation with the above-mentioned other robots.

[0010] According to yet another aspect of the present invention, a multi-machine management system is provided.

[0011] The multi-machine management system according to the present invention includes: any one of the above-mentioned cloud servers, and a plurality of robots respectively connected to the above-mentioned cloud servers, wherein each robot group includes at least one robot, each robot group corresponds to a user or a business scenario, and each robot group is managed in isolation.

[0012] According to another aspect of the present invention, a multi-machine management method based on a cloud server is provided.

[0013] The multi-machine management method based on a cloud server according to the present invention includes: matching and splicing multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or matching and splicing business maps under the same map group to obtain a second spliced ​​map under the same map group; synchronizing the above-mentioned first map and / or the above-mentioned second map, as well as the status information of the above-mentioned robot to at least one robot, so as to realize the multi-machine collaborative operation of the above-mentioned at least one robot.

[0014] According to yet another aspect of the present invention, a robot-based multi-machine management method is provided.

[0015] The robot-based multi-machine management method according to the present invention includes: starting from the initial point position, when performing environmental mapping and business operations, the robot's motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory are bound to the established scene map to obtain the above-mentioned business map; reporting status information and the robot group identifier corresponding to the status information, as well as the business map established by the robot and the scene identifier corresponding to the business map to the above-mentioned cloud server; receiving a first map obtained by matching and splicing multiple business maps under the same scene from the above-mentioned cloud server, and / or a second map obtained by matching and splicing business maps under the same map group, and the robot's status information, wherein the above-mentioned status information includes: robot posture information and business information; positioning is performed based on the above-mentioned first map and / or the above-mentioned second map, and based on the interactive information with the above-mentioned cloud server, the posture information and business information of all robots related to the current business except the current robot are obtained, and multi-machine collaborative operation is realized with the above-mentioned other robots.

[0016] According to the present invention, the cloud server manages the status information of the robots in each group according to the robot group identifier bound to the user or scene; groups the business map according to the scene identifier bound to the robot position information, manages the business map in each group in a group management manner, matches and splices multiple business maps in the same scene and / or the same map group, and gradually achieves full scene coverage of the map as the robot multi-machine iterates and builds the scene map. Multiple independent robots work based on the spliced ​​map, with good multi-machine coordination effect, high work efficiency, and strong scene adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a structural block diagram of a cloud robot according to an embodiment of the present invention;

[0018] Figure 2 is a structural block diagram of a cloud robot according to a preferred embodiment of the present invention;

[0019] Figure 3 is a structural block diagram of a robot according to an embodiment of the present invention;

[0020] Figure 4 is a schematic diagram of a multi-machine management system according to an embodiment of the present invention;

[0021] Figure 5 is a flow chart of a multi-machine management method based on a cloud server according to an embodiment of the present invention;

[0022] Figure 6 4 is a flowchart of a robot-based multi-machine management method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The specific implementation of the present invention is described in detail below with reference to the accompanying drawings.

[0024] According to an embodiment of the present invention, a cloud robot is provided.

[0025] Figure 1 is a structural block diagram of a cloud robot according to an embodiment of the present invention. Figure 1 As shown, the cloud robot includes: a state group management module 10, which is used to manage the state information of the robots under each group according to the robot group identifier bound to the user or scene; a map group management module 11, which is used to group the business maps according to the scene identifier bound to the robot position information, and manage the business maps under each group in a group management manner, wherein each map group corresponds to at least one scene, and each scene corresponds to multiple business maps; a map splicing module 12, which is connected to the map group management module 12, and is used to match and splice multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or, match and splice the business maps under the same map group to obtain a second spliced ​​map under the same map group; a first interactive communication module 13, which is respectively connected to the state group management module 10, the map group management module 11, and the map splicing module 12, and is used to synchronize the above-mentioned first map and / or the above-mentioned second map, and the state information of the above-mentioned robot to at least one robot, so as to realize the multi-machine collaborative operation of the above-mentioned at least one robot.

[0026] use Figure 1The cloud server shown in the figure manages the status information of the robots in each group according to the robot group ID bound to the user or scene; groups the business maps according to the scene ID bound to the robot position information, manages the business maps in each group in a group management manner, matches and splices multiple business maps in the same scene and / or the same map group, and gradually achieves full scene coverage of the map as the robot multi-machine iterates and builds the scene map. Multiple independent robots work based on the spliced ​​map, with good multi-machine coordination effect, high work efficiency, and strong scene adaptability.

[0027] Preferably, the first interactive communication module 13 is also used to receive the status information reported by the robot in real time and the robot group identifier corresponding to the status information, and to receive the business map established or updated by the robot and the scene identifier corresponding to the business map; the status group management module 10 is also used to group the status information according to the robot group identifier corresponding to the status information reported in real time; the map group management module 11 is also used to group the business map according to the scene identifier corresponding to the business map established or updated.

[0028] In multiple robot collaborative work scenarios, the cloud server receives status information reported in real time by the robot (for example, robot-related information such as business information, posture information, speed information, sensor working status information, battery power, etc.) and the robot group identifier corresponding to the status information, and manages the status information in groups according to the robot group identifier corresponding to the above-mentioned real-time reported status information. In addition, the cloud server receives the business map established or updated by the robot (that is, the above-mentioned business map can be either the first established or the map updated during subsequent operations) and the scene identifier corresponding to the business map, and manages the business map in groups according to the scene identifier corresponding to the above-mentioned established or updated business map. It can be seen that the robot status information and business map maintained and managed by the cloud server can be pre-collected, or can be uploaded and updated in real time from the edge robot in actual business scenarios, with good real-time performance and strong scene adaptability.

[0029] In the preferred implementation process, the above-mentioned cloud server and multiple independent robots can form the architecture of a multi-machine management system, wherein a single robot serves as an independent edge node, the cloud server serves as a central node, and the cloud robot and multiple robots are connected using a star network topology.

[0030] Each robot can be set with independent identification information (e.g., code, etc., recorded as machine code). The scene identification can be set based on the position information of the robot in the business scene. For example, each robot can set an initial starting position in the business scene. The initial starting position is encoded based on artificial features or natural features and recorded as the scene code.

[0031] The map created by the robot can be bound to the scene identifier (for example, the scene code), that is, a single scene code can correspond to multiple maps, and the map is not bound to the robot. The map is mounted to the corresponding group according to the scene code, and the maps under each group are managed in a group management manner. The above group is recorded as a map group.

[0032] All robots in a single user or scene can form a robot group, and the robot group is identified (for example, coded, etc., recorded as the robot group code). All robots in a single user or scene are mounted under the corresponding robot group and managed as a group. The running status of the robot is managed based on the group, recorded as the status group.

[0033] Among them, a single robot group allows at least 1 machine, and the maximum number is determined by the server performance. Multiple robot groups and cloud servers form a multi-machine management system with a star network topology. The robots in each robot group are managed uniformly, and each robot group is isolated from each other. A user can correspond to multiple robot groups. The robots in each group support robots of different sizes and types, but robots of different sizes and types need to use a unified data standard and format.

[0034] Among them, the above-mentioned business map includes but is not limited to: a scene map, and a robot motion trajectory bound to the above-mentioned scene map based on real-time coordinates and business operation information corresponding to each target point in at least one target point on the above-mentioned robot motion trajectory.

[0035] During the preferred implementation process, one or more edge robots in the robot group start from the initial position, start based on the scene code, perform independent environment mapping and business operations, and bind the robot's motion trajectory, business operation and other data to the robot scene map based on real-time coordinates to obtain a business map (including scene map, motion trajectory, business operation and other information), and the business map is bound to the scene code.

[0036] Of course, when the robot is working based on the business map, you can set it to continuously update the business map according to scene changes or not update the business map according to business needs. When you choose to update the business map according to scene changes, after the robot completes its work, it needs to upload the updated map to the server to replace the original business map that has not been updated.

[0037] Preferably, if Figure 2 As shown, the map stitching module 12 may further include: a matching submodule 120, for matching two business maps for a plurality of business maps under the same scene, determining the local relative relationship between the two business maps, until a first global relative relationship of the plurality of business maps under the same scene is obtained, and / or, for matching every two business maps for a plurality of business maps under the same map group, determining the local relative relationship between every two business maps, until a second global relative relationship of the plurality of business maps under the same map group is obtained; a stitching submodule 122, for stitching a plurality of business maps under the same scene according to the first global relative relationship, to obtain the first map, and / or, for matching and stitching the business maps under the same map group according to the second global relative relationship, to obtain the second map, wherein, in the process of stitching a plurality of business maps, the stitching submodule stitches the robot motion trajectory bound in the scene map based on the real-time coordinates, and integrates the business operation information corresponding to each target point in at least one target point on the robot motion trajectory.

[0038] In the preferred implementation process, the cloud server performs matching based on the obtained business maps under the same scene code and / or the business maps under the same map group, and splices the successfully matched business maps based on the matching results. In the process of executing the business map splicing, the robot motion trajectories bound to multiple business maps are spliced, and the business operation information corresponding to each target point in at least one target point on the motion trajectory bound to the business map is integrated to form a spliced ​​map under the same scene code and / or a spliced ​​map of the same map group. When the robot in the multi-machine management system is started, the spliced ​​business map can be requested from the cloud server to achieve full coverage of the business scene of a single map. Multiple independent robots in the same business scene are positioned based on the spliced ​​map, and the posture information of the robot in the current map can be accurately obtained. At the same time, combined with the interactive information with the cloud server, multiple machines will know each other's real-time posture, and multiple machines can achieve multi-machine collaborative work based on posture collaboration.

[0039] In the specific implementation process, the robot senses external information through sensors during operation. Multiple sensors can be used, including infrared sensors, ultrasonic sensors, collision sensors, etc. Such sensors can detect obstacles at close range; LiDAR, monocular or binocular cameras, structured light, TOF, these sensors can detect obstacles at long distances; Among them, the detection range of LiDAR and cameras is relatively large. When building a map, the robot generally refers to the data of these two sensors. According to the differences in the robot's functional platform, the data of other sensors can also be used to build a scene map. The robot's motion trajectory, business operation and other data are bound to the robot's scene map based on real-time coordinates to obtain a business map (including scene maps, motion trajectories, business operations and other information). Afterwards, the matching of the two business maps is performed in the business maps independently established by each robot, for example, the first business map and the second business map are extracted, and the first two-dimensional matrix corresponding to the first business map and the second two-dimensional matrix corresponding to the second business map are obtained; the first two-dimensional matrix and the second two-dimensional matrix are respectively Fourier transformed to generate the first amplitude value matrix corresponding to the first business map and the second amplitude value matrix corresponding to the second business map; the first amplitude value matrix and the second amplitude value matrix are transformed by the phase correlation method to generate an impulse function for representing the translation and rotation between the first business map and the second business map, and the relative transformation relationship between the first business map and the second business map is obtained according to the coordinate value corresponding to the impulse function. According to the above method, the global relative relationship of multiple business maps in the same scene or the same map group is obtained.

[0040] Preferably, if Figure 2As shown, the cloud server may further include: a business management module 14, connected to the first interactive communication module 13, for managing and allocating the business corresponding to the business demand (for example, determining which robot or robots are responsible for processing the current business order), and determining the processing strategy of the robot under specific circumstances (for example, the processing strategy when there is no business order or the robot is low on power); a planning module 15, connected to the first interactive communication module 13 and the map stitching module 12, for executing the routing strategy so that the robot reaches the corresponding target point along the planned optimal path, and managing the sequence of at least one target point on the robot's motion trajectory (for example, adding, deleting, modifying the property setting of the target point, etc.). etc.), and manage the business operation information corresponding to each target point in the at least one target point (for example, binding settings for the target point and the corresponding business operation information, etc.); a scheduling management module 16, connected to the first interactive communication module 13 and the map stitching module 12, is used to formulate a scheduling plan to achieve resource management and allocation (for example, manage resource allocation to achieve robot management, avoid robots from colliding with each other, etc.), perform robot status monitoring and task allocation, and monitor the status of at least one target point on the robot's motion trajectory (for example, monitor whether the target point is occupied by other objects or robots, etc.) and authorize the target points that the robot can reach among the at least one target point.

[0041] Preferably, if Figure 2 As shown, the above-mentioned cloud server can also include: a demand docking module 17, which is connected to the business management module 14 and is also used to dock business needs with the business demand end; a human-computer interaction module 18, which is connected to the business management module 14 and is used to receive user instructions through a human-computer interaction interface (UI user interface) and present business information related to the user instructions.

[0042] According to an embodiment of the present invention, a robot is also provided.

[0043] Figure 3 is a structural block diagram of a robot according to an embodiment of the present invention. Figure 3As shown, the robot includes: a map management module 30, which is used to bind the motion trajectory corresponding to the one or more robots and the business operation information corresponding to each target point in at least one target point on the motion trajectory to the established scene map when performing environmental mapping and business operations starting from the initial point position, so as to obtain the above-mentioned business map; a second interactive communication module 31, which is used to report the state information and the robot group identifier corresponding to the state information, as well as the business map established by the robot and the scene identifier corresponding to the business map to the above-mentioned cloud server, and receive a first map obtained by matching and splicing multiple business maps under the same scene from the above-mentioned cloud server, and / or a second map obtained by matching and splicing business maps under the same map group, and the robot's state information, wherein the above-mentioned state information includes: robot posture information and business information; a collaborative operation module 32, which is used to perform positioning based on the above-mentioned first map and / or the above-mentioned second map, and obtain the posture information and business information of all robots related to the current business except the current robot based on the interactive information with the above-mentioned cloud server, and realize multi-machine collaborative operation with the above-mentioned other robots.

[0044] Figure 3 The robot shown in the figure starts from the initial point position, and when performing environmental mapping and business operations, the robot's motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory are bound to the established scene map to obtain the above-mentioned business map. The cloud server performs map splicing based on the map built by the robot, and as the robot multi-machine cycle iterates to perform scene mapping, the map gradually achieves full scene coverage. Multiple independent robots work based on the spliced ​​map, with good multi-machine coordination effect, high work efficiency, and strong scene adaptability.

[0045] In the preferred implementation process, when the robot is working, for example, when the robot is working for the first time, one or more edge robots in the robot group start from the initial position based on the scene identification (for example, scene code, etc.), perform independent scene mapping and business operations, and bind the robot's motion trajectory, business operation and other data to the robot scene map based on real-time coordinates to obtain a business map (including: scene map, motion trajectory, business operation information, etc.), and bind the business map to the scene code. During the operation of the robot, the status information is uploaded to the cloud server in real time and managed based on the status group; after the robot completes the work, the established business map and the corresponding scene code are uploaded to the cloud server, and the cloud server obtains the business map and the corresponding scene code uploaded by the robot in the robot group, groups the business map based on the scene code, and manages the robot business map and the corresponding scene code in units of map groups.

[0046] Preferably, the second interactive communication module 31 is further used to send the request carrying the current scene identifier to the cloud server when the robot is started and / or working, and receive the business map corresponding to the current scene identifier fed back from the cloud server; the map management module 30 is also used to compare the business map currently stored locally with the business map fed back from the cloud server. Figure 1 When it is consistent, it is determined to continue using the current locally saved business map.

[0047] Preferably, the map management module 30 is further configured to determine to trigger execution of one of the following operations when the currently stored business map is inconsistent with the business map fed back by the cloud server:

[0048] When one or more of the business maps fed back by the cloud server are not saved locally, the one or more business maps are saved locally;

[0049] When one or more of the business maps fed back by the cloud server have the same identification information as the business map stored locally, but the map contents are different after matching, the time information of the business map corresponding to the same identification information is determined, the business map corresponding to the latest time is retained, and other business maps except the retained business map are deleted from the business map corresponding to the same identification information;

[0050] When one or more business maps stored locally are not stored in the cloud server, the one or more business maps are sent to the cloud server.

[0051] In the preferred implementation process, when the robot starts and / or operates based on the scene code, the robot sends a request to the server, and the request carries the current scene identifier. The cloud server receives the request and sends the business map corresponding to the current scene identifier to the robot. The robot compares the business map stored locally with the business map under the scene code of the cloud server to see if they are consistent. If they are consistent, no operation is performed and the local business map continues to be used; if they are inconsistent, one of the following three methods is used to handle it:

[0052] Method 1: If some business maps are saved on the cloud server but not on the edge robot, the robot will download and save the maps from the server.

[0053] Method 2: If some business maps saved on the cloud server have the same name as those saved on the edge robot, but the contents of the business maps are different, the latest business map can be kept on the server or robot side in chronological order, and one or more business maps with earlier time can be deleted;

[0054] Method 3: If some business maps saved by the edge robot are not saved on the cloud server, the robot uploads the business map to the cloud server.

[0055] Preferably, the collaborative operation module 32 is further used to convert business needs into business operation information, convert the processing results of the business operation information into instructions for specific modules and transmit them to various modules of the robot in real time. When the robot runs to one or more target points, the robot is controlled to complete the business operations corresponding to the one or more target points.

[0056] According to an embodiment of the present invention, a multi-machine management system is also provided.

[0057] Figure 4 Schematic diagram of a multi-machine management system according to an embodiment of the present invention. Figure 5 As shown, the multi-machine management system includes: a cloud server 40, and a plurality of robots (such as Figure 4 The robot 1-1, robot 1-2, ..., robot 2-1, robot 2-n) shown in the figure, wherein each of the above robot groups includes at least one robot (such as Figure 3 The robot group 1 shown includes: robot 1-1, robot 1-2, ..., robot 1-n; robot group 2 includes: robot 2-1, robot 2-2, ..., robot 2-n), each robot group corresponds to a user or a business scenario, and each robot group is isolated and managed (such as Figure 4 Isolation management between robot group 1 and robot group 2).

[0058] In the preferred implementation process, the cloud server acts as a central node and is connected to multiple robots respectively. The cloud server and multiple robots form a star network topology. A single edge robot acts as an independent edge node and can mount at least one robot under a user or business scenario to a corresponding robot group (such as a delivery business scenario, a disinfection business scenario, a cleaning business scenario, etc.) according to the robot group identifier bound to the user or scenario (for example, a delivery business scenario, a disinfection business scenario, a cleaning business scenario, etc.). Figure 4 The robot group in the system can be at least one robot for a user or at least one robot in a business scenario. A user or a scenario can also correspond to multiple robot groups. The robots in each robot group are managed uniformly, and the robot groups are managed in isolation. The robots in each group support robots of different sizes and types, but robots of different sizes and types need to use a unified data standard and format.

[0059] It should be noted that, in the multi-machine management system of this embodiment, the preferred implementation method of combining the above-mentioned cloud server and the modules in the above-mentioned robot can be specifically referred to in Figures 1 to 3 The description is not repeated here.

[0060] According to an embodiment of the present invention, a multi-machine management method based on a cloud server is also provided.

[0061] Figure 5 4 is a flow chart of a multi-machine management method based on a cloud server according to an embodiment of the present invention.

[0062] like Figure 5 As shown, the multi-machine management method based on the above cloud server includes:

[0063] Step S501: matching and splicing multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or matching and splicing business maps under the same map group to obtain a second spliced ​​map under the same map group;

[0064] Step S502: Synchronize the first map and / or the second map, and the status information of the robot to at least one robot, so as to realize multi-machine collaborative operation of the at least one robot.

[0065] use Figure 5 The multi-machine management method shown matches and splices multiple business maps under the same scene and / or the same map group, and gradually achieves full scene coverage of the map as the robot multi-machine iterates the scene map. Multiple independent robots work based on the spliced ​​map, with good multi-machine coordination effect, high work efficiency, and strong scene adaptability.

[0066] Preferably, the business map includes: a scene map, and business operation information corresponding to each target point in the robot motion trajectory and at least one target point on the robot motion trajectory bound to the scene map based on real-time coordinates;

[0067] In the above step S501, matching and splicing multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or matching and splicing business maps under the same map group to obtain a second spliced ​​map under the same map group may further include the following processing:

[0068] For multiple business maps under the same scene, matching is performed on every two business maps, and the local relative relationship between every two business maps is determined, until a first global relative relationship between the multiple business maps under the same scene is obtained, and / or, matching is performed on every two business maps under the same map group, and the local relative relationship between every two business maps is determined, until a second global relative relationship between the multiple business maps under the same map group is obtained;

[0069] According to the above-mentioned first global relative relationship, multiple business maps under the same scene are spliced ​​to obtain the above-mentioned first map, and / or, the business maps under the same map group are matched and spliced ​​according to the above-mentioned second global relative relationship to obtain the above-mentioned second map, wherein, in the process of splicing multiple business maps, the above-mentioned splicing submodule will splice the robot motion trajectory bound in the above-mentioned scene map based on real-time coordinates, and integrate the business operation information corresponding to each target point in at least one target point on the above-mentioned robot motion trajectory.

[0070] According to the embodiment provided by the present invention, a robot-based multi-machine management method is also provided.

[0071] Figure 6 1 is a flow chart of a multi-machine management method based on robots according to an embodiment of the present invention. Figure 6 As shown, the robot-based multi-machine management method includes:

[0072] Step S601: Starting from the initial point position, when performing environment mapping and business operations, the robot motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory are bound to the established scene map to obtain the above-mentioned business map;

[0073] Step S602: reporting the status information and the robot group identifier corresponding to the status information, as well as the business map established by the robot and the scene identifier corresponding to the business map to the cloud server;

[0074] Step S603: receiving from the cloud server a first map obtained by matching and splicing multiple business maps under the same scene, and / or a second map obtained by matching and splicing business maps under the same map group, and robot status information, wherein the status information includes: robot posture information and business information;

[0075] Step S604: Perform positioning based on the first map and / or the second map, and obtain the position information and business information of all robots related to the current business except the current robot based on the interaction information with the cloud server, and realize multi-machine collaborative operation with the other robots.

[0076] use Figure 6 The method shown in the figure starts from the initial point position, and when the environment is mapped and the business operation is performed, the robot's motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory are bound to the established scene map to obtain the above-mentioned business map. The cloud server performs map splicing based on the map built by the robot, and gradually achieves full scene coverage of the map as the robot multi-machine cycle iterates to perform scene mapping. Multiple independent robots work based on the spliced ​​map, with good multi-machine coordination effect, high work efficiency, and strong scene adaptability.

[0077] In summary, with the above-mentioned embodiments provided by the present invention, the multi-machine management system based on the cloud server and the edge robot adopts a star topology structure and group management. When the robot performs environmental mapping and business operations, the robot's motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory are bound to the established scene map to obtain the business map. The cloud server performs map splicing based on the business map built by the robot, and as the robot multi-machine cycle iteration performs scene mapping and map update, the map gradually achieves full scene coverage. The robots in the group are positioned based on the spliced ​​map. At the same time, based on the cloud interaction, multiple machines know each other's position in real time, realizing multi-robot collaborative management and multi-robot collaborative operations based on the cloud, and improving the robot's intelligence level and multi-machine collaborative work efficiency.

[0078] The above disclosures are only several specific embodiments of the present invention; however, the present invention is not limited thereto, and any changes that can be conceived by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A cloud server, characterized in that: include: A state group management module is used to manage the state information of the robots in each group according to the robot group identifier bound by the user or scene, wherein the state information includes: robot posture information and business information; A map group management module, used to group business maps according to the scene identifier bound to the robot position information, and manage the business maps under each group in a group management manner, wherein each map group corresponds to at least one scene, and each scene corresponds to multiple business maps, wherein the business map includes: a scene map, and business operation information corresponding to each target point in the robot motion trajectory and at least one target point on the robot motion trajectory bound to the scene map based on real-time coordinates; A map splicing module, connected to the map group management module, is used to match and splice multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or to match and splice business maps under the same map group to obtain a second spliced ​​map under the same map group; The first interactive communication module is connected to the status group management module, the map group management module, and the map stitching module, respectively, and is used to synchronize the first map and / or the second map, as well as the status information of the robot to at least one robot, so as to realize multi-machine collaborative operation of the at least one robot, wherein the at least one robot converts business requirements into business operation information, converts the processing results of the business operation information into instructions for specific modules and transmits them to the various modules of the robot in real time, and when the robot runs to one or more target points, controls the robot to complete the business operations corresponding to the one or more target points.

2. The cloud server according to claim 1, characterized in that: The first interactive communication module is further used to receive status information reported by the robot in real time and the robot group identifier corresponding to the status information, and receive a business map established or updated by the robot and a scene identifier corresponding to the business map; The state group management module is further used to group and manage the state information according to the robot group identifier corresponding to the state information reported in real time; The map group management module is further used to group and manage the business map according to the scene identifier corresponding to the established or updated business map.

3. The cloud server according to claim 1, characterized in that: The map splicing module further comprises: A matching submodule, for performing matching of each two business maps for a plurality of business maps in the same scene, determining the local relative relationship between each two business maps, until a first global relative relationship of the plurality of business maps in the same scene is obtained, and / or performing matching of each two business maps for a plurality of business maps in the same map group, determining the local relative relationship between each two business maps, until a second global relative relationship of the plurality of business maps in the same map group is obtained; A splicing submodule is used to splice multiple business maps under the same scene according to the first global relative relationship to obtain the first map, and / or to match and splice business maps under the same map group according to the second global relative relationship to obtain the second map, wherein the splicing submodule, in the process of splicing multiple business maps, splices the robot motion trajectory bound to the scene map based on real-time coordinates, and integrates the business operation information corresponding to each target point in at least one target point on the robot motion trajectory.

4. The cloud server according to claim 1, characterized in that: Also includes: A service management module, connected to the first interactive communication module, for managing and allocating services corresponding to service requirements and determining the processing strategy of the robot in specific situations; a planning module, connected to the first interactive communication module and the map stitching module, configured to execute a routing strategy so that the robot reaches a corresponding target point along a planned optimal path, manage a sequence of at least one target point on the robot's motion trajectory, and manage business operation information corresponding to each of the at least one target point; A scheduling management module is connected to the first interactive communication module and the map stitching module, and is used to formulate a scheduling plan to achieve resource management and allocation, perform robot status monitoring and task allocation, monitor the status of at least one target point on the robot's motion trajectory, and authorize the target point that the robot can reach among the at least one target point.

5. The cloud server according to any one of claims 1 to 4, characterized in that: Also includes: Demand docking module, used to dock with the business demand side to meet business needs; The human-computer interaction module is used to receive user instructions through the human-computer interaction interface and present business information related to the user instructions.

6. A robot, characterized in that: include: A map management module is used to bind the robot motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory to the established scene map when performing environment mapping and business operations starting from the initial point position, so as to obtain a business map; The second interactive communication module is connected to the map management module, and is used to report the status information and the robot group identifier corresponding to the status information, as well as the business map established by the robot and the scene identifier corresponding to the business map to the cloud server, and receive the first map obtained by matching and splicing multiple business maps under the same scene from the cloud server, and / or the second map obtained by matching and splicing business maps under the same map group, and the robot's status information, wherein the status information includes: robot posture information and business information; A collaborative operation module is connected to the second interactive communication module, and is used for positioning based on the first map and / or the second map, and obtaining the position information and business information of all robots other than the current robot among all robots related to the current business based on the interactive information with the cloud server, and realizing multi-machine collaborative operation with the other robots, wherein the collaborative operation module is further used to convert business needs into business operation information, convert the processing results of the business operation information into instructions for specific modules and transmit them to each module of the robot in real time, and when the robot runs to one or more target points, control the robot to complete the business operations corresponding to the one or more target points.

7. The robot according to claim 6, characterized in that: The second interactive communication module is further configured to send a request carrying a current scene identifier to the cloud server when the robot is started and / or operating, and receive a business map corresponding to the current scene identifier fed back from the cloud server; The map management module is further used to determine to continue using the currently locally stored business map when the currently locally stored business map is consistent with the business map fed back by the cloud server.

8. The robot according to claim 7, characterized in that: The map management module is further configured to determine to trigger execution of one of the following operations when the currently stored business map is inconsistent with the business map fed back by the cloud server: When one or more of the business maps fed back by the cloud server are not saved locally, the one or more business maps are saved locally; When one or more of the business maps fed back by the cloud server have the same identification information as the business map stored locally, but the map contents are different after matching, the time information of the business map corresponding to the same identification information is determined, the business map corresponding to the latest time is retained, and other business maps except the retained business map are deleted from the business map corresponding to the same identification information; When one or more business maps stored locally are not stored in the cloud server, the one or more business maps are sent to the cloud server.

9. A multi-machine management system, characterized in that: include: A cloud server as described in any one of claims 1 to 5, and a plurality of robots respectively connected to the cloud server, wherein each robot group includes at least one robot, each robot group corresponds to a user or a business scenario, and each robot group is managed in isolation.

10. A multi-machine management method for a cloud server based on any one of claims 1 to 4, characterized in that: include: Matching and splicing multiple business maps under the same scene to obtain a first spliced ​​map under the same scene, and / or matching and splicing business maps under the same map group to obtain a second spliced ​​map under the same map group; The first map and / or the second map, and the status information of the robot are synchronized to at least one robot to achieve multi-machine collaborative operation of the at least one robot.

11. The multi-machine management method according to claim 10, characterized in that: The business map includes: a scene map, and business operation information corresponding to each target point in the robot motion track and at least one target point on the robot motion track bound in the scene map based on real-time coordinates; Matching and splicing multiple business maps under the same scene to obtain a first map after splicing under the same scene, and / or matching and splicing business maps under the same map group to obtain a second map after splicing under the same map group further includes: For multiple business maps under the same scene, respectively, two business maps are matched to determine the local relative relationship between the two business maps, until a first global relative relationship between the multiple business maps under the same scene is obtained, and / or, for multiple business maps under the same map group, each two business maps are matched to determine the local relative relationship between each two business maps, until a second global relative relationship between the multiple business maps under the same map group is obtained; The first map is obtained by splicing multiple business maps under the same scene according to the first global relative relationship, and / or the business maps under the same map group are matched and spliced ​​according to the second global relative relationship to obtain the second map, wherein, in the process of splicing multiple business maps, the splicing submodule splices the robot motion trajectory bound in the scene map based on real-time coordinates, and integrates the business operation information corresponding to each target point in at least one target point on the robot motion trajectory.

12. A multi-machine management method based on the robot according to any one of claims 6 to 8, characterized in that: include: Starting from the initial point position, when performing environment mapping and business operations, the robot motion trajectory and the business operation information corresponding to each target point in at least one target point on the motion trajectory are bound to the established scene map to obtain the business map; Reporting the status information and the robot group identifier corresponding to the status information, as well as the business map established by the robot and the scene identifier corresponding to the business map to the cloud server; Receiving from the cloud server a first map obtained by matching and splicing a plurality of business maps under the same scene, and / or a second map obtained by matching and splicing business maps under the same map group, and robot status information, wherein the status information includes: robot posture information and business information; Positioning is performed based on the first map and / or the second map, and based on the interaction information with the cloud server, posture information and business information of all robots related to the current business except the current robot are obtained, and multi-machine collaborative operation is achieved with the other robots.

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