A method and system for long distance navigation enhancement of a robot

Abstract

The application relates to the technical field of robot navigation, in particular to a robot long-distance navigation enhancement method and system, the method comprising the following steps: a central machine decomposes a robot navigation task request into a plurality of sub-navigation tasks, and sends each sub-navigation task to a corresponding space intelligent machine; each space intelligent machine monitors corresponding environment information, and extracts environment entity information corresponding to each sub-navigation task from each environment information; when the space intelligent machine monitors a target robot, the environment entity information is sent to the target robot, and obstacle information on a path is monitored in real time to adjust the walking path in real time; when the target robot passes through a monitoring area of any other space intelligent machine, the central machine is used to realize switching of the sub-navigation tasks; through cooperation control of the central machine and the distributed space intelligent machines, the application can continuously and accurately position the robot in a long distance, and realize dynamic optimization of long-distance path planning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of robot navigation, in particular to a robot long-distance navigation enhancement method and system. BACKGROUND

[0002] In the field of robot navigation, a space intelligent machine generally refers to a robot system capable of intelligent navigation through environmental perception, space modeling and autonomous decision-making capabilities. The core is to enable the robot to understand the physical space through the cooperation of sensors, algorithms and artificial intelligence, thereby realizing robot navigation. However, the space intelligent machine can only realize the local space navigation of the robot, and the robot usually only relies on its own perception system to make local task judgments, lacks a global space perspective, resulting in problems such as insufficient navigation positioning accuracy, short path planning, and insufficient real-time state monitoring capability in long-distance and large-scale space navigation of existing robot navigation technology, affecting navigation safety and efficiency. SUMMARY

[0003] In view of the above technical problems, the present application provides a robot long-distance navigation enhancement method and system, which can continuously and accurately position the robot over a long distance through the coordinated control of the central machine and the distributed space intelligent machine, realize dynamic optimization of long-distance path planning, and improve navigation safety and efficiency.

[0004] According to a first aspect of the present application, a robot long-distance navigation enhancement method is provided, comprising the following steps:

[0005] S100, when receiving a robot navigation task request, the robot navigation task request is disassembled into a plurality of sub-navigation tasks by a pre-deployed central machine, and each sub-navigation task is sent to a corresponding space intelligent machine; the space intelligent machine is provided with a plurality of and is distributed in a distributed manner, and each space intelligent machine communicates with the central machine.

[0006] S200, according to the local space model pre-constructed by each space intelligent machine, the environment information corresponding to each space intelligent machine is monitored respectively, and the environment entity information corresponding to each sub-navigation task is extracted from the environment information corresponding to each space intelligent machine; the environment entity information includes task entity position and initial obstacle position.

[0007] S300, control the target robot to walk according to the initial global path pre-constructed by the central machine, control the walking path of the target robot according to each sub-navigation task, and when any sub-navigation task corresponding space intelligent machine monitors the target robot, the environment entity information corresponding to the space intelligent machine itself is sent to the target robot, so that the target robot avoids the initial obstacle position and reaches the task entity position according to the adjusted path.

[0008] S400, real-time monitoring all obstacle information on the adjusted path, when monitoring the real-time obstacle information, sending the real-time obstacle information to the central machine and optimizing the walking path of the target robot through the central machine, so that the target robot continues to execute the corresponding sub-navigation task;

[0009] S500, when receiving the information that the target robot executes any sub-navigation task and monitoring the target robot passing through any other space intelligent machine monitoring area, realizing the switching of the sub-navigation task through the central machine, and returning to execute the S300 step until the execution of the several sub-navigation tasks is completed.

[0010] According to the second aspect of the application, a robot long-distance navigation enhancement system is provided, the system comprises:

[0011] The task decomposition module is used for decomposing the robot navigation task request into several sub-navigation tasks through the pre-deployed central machine when receiving the robot navigation task request, and sending each sub-navigation task to the corresponding space intelligent machine; the space intelligent machine is provided with several and adopts distributed deployment, and each space intelligent machine communicates with the central machine.

[0012] The information extraction module is used for monitoring the environment information corresponding to each space intelligent machine respectively according to the local space model pre-constructed by each space intelligent machine, and extracting the environment entity information corresponding to each sub-navigation task from the environment information corresponding to each space intelligent machine respectively; the environment entity information includes the task entity position and the initial obstacle position.

[0013] The first sending module is used for controlling the target robot to walk according to the initial global path pre-constructed by the central machine, and when any sub-navigation task corresponding space intelligent machine monitors the target robot, sending the environment entity information corresponding to the space intelligent machine itself to the target robot, so that the target robot avoids the initial obstacle position and walks to the task entity position according to the adjusted path.

[0014] The second sending module is used for real-time monitoring all obstacle information on the adjusted path, when monitoring the real-time obstacle information, sending the real-time obstacle information to the central machine and optimizing the walking path of the target robot through the central machine, so that the target robot continues to execute the corresponding sub-navigation task.

[0015] The task switching module is used for realizing the switching of the sub-navigation task through the central machine when receiving the information that the target robot executes any sub-navigation task and monitoring the target robot passing through any other space intelligent machine monitoring area, and returning to the first sending module until the execution of the several sub-navigation tasks is completed.

[0016] The application has at least the following beneficial effects:

[0017] The application provides a robot long-distance navigation enhancement method, which decomposes a robot navigation task request into a plurality of sub-navigation tasks by a hub machine, and sends each sub-navigation task to a corresponding space intelligent machine, adopts a mode that the hub machine and the plurality of space intelligent machines work cooperatively, the hub machine can switch the space intelligent machines as needed to ensure the smooth progress of each sub-navigation task; meanwhile, the environment information corresponding to each space intelligent machine is monitored in real time, and the environment entity information corresponding to each sub-navigation task is extracted from the environment information corresponding to each space intelligent machine respectively, when the space intelligent machine monitors a target robot, the environment entity information is sent to the target robot to provide the robot with accurate positioning service and navigation prompt based on environment characteristics and global perception, so that the robot can avoid obstacles and accurately find the entity position corresponding to the task to be processed, thereby accurately and quickly executing each sub-navigation task, and the path suddenly appearing obstacle information is monitored in real time during the task execution process, so as to dynamically optimize the walking path of the robot in real time, and ensure the smooth execution of the sub-navigation task; when the target robot passes through the monitoring area of any other space intelligent machine, the switching of the sub-navigation task is realized through the hub machine, the long-distance continuous accurate positioning is realized, the path planning of the integral navigation task and the state connection of the robot are realized at the same time, and the navigation safety and the navigation efficiency are improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0019] Figure 1 A flowchart of a robot long-distance navigation enhancement method provided for the first embodiment of the present application is provided.

[0020] Figure 2 A flowchart of obtaining a sub-navigation task provided for the first embodiment of the present application is provided.

[0021] Figure 3 A flowchart of obtaining a target local path provided for the first embodiment of the present application is provided.

[0022] Figure 4 A flowchart of determining the deployment position of the hub machine provided for the first embodiment of the present application is provided.

[0023] Figure 5 A flowchart of extracting environment entity information provided for the first embodiment of the present application is provided.

[0024] Figure 6 A flowchart of updating the initial global path provided for the first embodiment of the present application is provided.

[0025] Figure 7 A flow chart for switching the space intelligent machine is provided for the first embodiment of the present application.

[0026] Figure 8 A structural schematic diagram of a robot long-distance navigation enhancement system is provided for the second embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] Embodiment one

[0029] The first embodiment of the present application provides a robot long-distance navigation enhancement method, as shown in the figure, the method comprises the following steps: Figure 1

[0030] S100, when receiving a robot navigation task request, the robot navigation task request is disassembled into several sub-navigation tasks by a pre-deployed hub machine, and each sub-navigation task is sent to the corresponding space intelligent machine; it can be understood that the robot navigation task request refers to a request including at least one long-distance navigation task. In a specific implementation scenario, for example, large intelligent park robot patrol navigation, warehouse logistics center long-distance high-precision goods transportation navigation, hospital or shopping mall long-distance navigation of guide robot.

[0031] Specifically, the space intelligent machine is provided with several and adopts distributed deployment, and each space intelligent machine communicates with the hub machine.

[0032] In a specific embodiment, as shown in the figure, S100 comprises the following steps: Figure 2

[0033] S101, when receiving a robot navigation task request, an initial global path corresponding to the robot navigation task request is constructed according to the global map generated by the hub machine and the real-time obstacle information; it can be understood that according to the navigation starting point, the end point and the navigation task, the initial global path is constructed in combination with the obstacle information in the global map.

[0034] Among them, the global map and real-time obstacle information are generated by the hub machine after fusing the local space model pre-constructed by each space intelligent machine.

[0035] ​​S102, according to the overlap between the initial global path and each space intelligent machine monitoring area, the initial global path is divided to obtain the target local path corresponding to each space intelligent machine.

[0036] Further, as shown in S102, the step further comprises the following steps: Figure 3

[0037] S1021, when any initial local path in the initial global path only overlaps with one space intelligent machine monitoring area, the initial local path is determined as the intermediate local path corresponding to the space intelligent machine with overlapping monitoring area; the initial local path is a path randomly divided from the initial global path.

[0038] S1022, when any initial local path in the initial global path overlaps with no less than one space intelligent machine monitoring area, the initial local path is determined as the intermediate local path corresponding to the space intelligent machine with the longest path overlapping with the initial local path.

[0039] S1023, connecting the intermediate local path corresponding to each space intelligent machine to obtain the target local path corresponding to each space intelligent machine.

[0040] The above, through the overlap between the initial global path built by the hub mechanism and the space intelligent machine monitoring area, each space intelligent machine can be reasonably allocated the path monitored by the main monitoring source, which also considers the overlap of different space intelligent machine monitoring areas, so that the robot uses the main monitoring source when walking into the overlapping area, preventing the error caused by multiple space intelligent machines reporting robot information, thereby improving the navigation accuracy and efficiency.

[0041] S103, according to the target local path corresponding to each space intelligent machine, the robot navigation task request is decomposed into several sub-navigation tasks; wherein, the target local path and the sub-navigation task one by one.

[0042] The above, using the way of hub machine and multiple space intelligent machines working together, when realizing the reasonable cooperation of the two, the total navigation task is decomposed, and the corresponding sub-navigation task is allocated to each space intelligent machine, which can make the hub machine switch the space intelligent machine as needed when the robot navigates, to ensure the smooth progress of each sub-navigation task, thereby realizing the long distance accurate and continuous navigation of the robot.

[0043] Further, as shown in S102, the step further comprises the following steps: Figure 4

[0044] ​​S01, according to the position information of each space intelligent machine, a deployment candidate point is determined by using the closeness centrality algorithm; the specific calculation method of the closeness centrality algorithm is known to those skilled in the art, and will not be described here.

[0045] S02, the wireless signal RSSI value of each space intelligent machine and the deployment candidate point is obtained, and when the wireless signal RSSI value corresponding to each space intelligent machine is not less than the preset RSSI threshold value, the deployment candidate point is determined as the deployment position of the hub machine, otherwise, the space intelligent machine farthest from the deployment candidate point is selected from the space intelligent machines; it can be understood that the wireless signal RSSI value is used to reflect the signal strength between the space intelligent machine and the deployment candidate point.

[0046] S03, the selected space intelligent machine is connected with the deployment candidate point, and the position of the deployment candidate point is moved along the connection line to the position of the selected space intelligent machine according to the preset step length, until the wireless signal RSSI value corresponding to all space intelligent machines is not less than the preset RSSI threshold value; the preset step length is set by those skilled in the art according to actual needs, for example, 1 meter, 2 meters, etc.

[0047] In this embodiment, the deployment candidate point is first determined according to the closeness centrality algorithm, and the deployment candidate point determined in this way can obtain the smallest average communication delay, thereby improving the navigation accuracy and smoothness, but considering that the space intelligent machines far away from each other may exist single point communication bottleneck phenomenon, therefore, the deployment position is moved to the space intelligent machine farthest away, and by sacrificing part of the average communication delay, this problem is solved, so that the hub machine can receive and integrate the transmission information of each space intelligent machine in time, and the navigation accuracy and navigation efficiency are improved.

[0048] S04, when there is always a space intelligent machine corresponding to a wireless signal RSSI value less than the preset RSSI threshold value, the centroid position corresponding to the space intelligent machines is calculated according to the preset importance degree of each space intelligent machine, and the centroid position is determined as the deployment position of the hub machine; wherein the preset importance degree of the space intelligent machine is inversely proportional to the overlapping area of the monitoring area corresponding to the space intelligent machine; it can be understood that the more the overlapping area of the monitoring area of any space intelligent machine with other space intelligent machines, the lower the corresponding preset importance degree.

[0049] Specifically, the centroid position corresponding to the space intelligent machines meets the following conditions:

[0050] , wherein (x, y) represents the coordinates of the centroid position corresponding to the space intelligent machines, w j represents the preset importance degree of the jth space intelligent machine, x j represents the horizontal coordinate of the jth space intelligent machine, y jrepresents the longitudinal coordinate of the jth space intelligent machine, and n is the total number of space intelligent machines. In a specific implementation, the coordinates can be longitude and latitude coordinates or plane coordinates.

[0051] In the above, when the single-point communication bottleneck cannot be avoided, the deployment position of the hub machine is set according to the importance of the space intelligent machine. The more the overlapping area of the monitoring areas of the space intelligent machines, the greater the possibility of being replaced by other space intelligent machines, and therefore a smaller importance is set for it. The hub machine deployed in this way not only ensures a smaller average communication delay, but also reduces the disadvantages of the single-point communication bottleneck, and realizes the smooth progress of the overall information interaction.

[0052] S200, according to the local space model constructed in advance by each space intelligent machine, respectively monitor the environment information corresponding to each space intelligent machine, and extract the environment entity information corresponding to each sub-navigation task from the environment information corresponding to each space intelligent machine; the environment entity information includes task entity position and initial obstacle position.

[0053] Further, the task entity position refers to the entity position corresponding to the execution object when the target robot executes the task. For example, if an article in a specific place needs to be taken away, the execution object is the article in the specific place.

[0054] In a specific embodiment, as shown in Figure 5 S200 step includes the following steps:

[0055] S201, for any sub-navigation task, a plurality of entity keywords are extracted from the text corresponding to the sub-navigation task according to a pre-trained entity keyword extraction model. The entity keyword extraction model can be a bert model or a natural language processing model, and those skilled in the art know the training method of the entity keyword extraction model, which will not be described here.

[0056] S202, a plurality of identified entities in the monitoring area of each space intelligent machine are marked from the environment information corresponding to each space intelligent machine.

[0057] S203, when there are same entities in the plurality of identified entities in the monitoring area of the space intelligent machine corresponding to the sub-navigation task and the plurality of entity keywords corresponding to the sub-navigation task, the same entities are determined as target entities, and the entity information of the target entity extracted from the environment information corresponding to the space intelligent machine is determined as the environment entity information corresponding to the sub-navigation task; it can be understood that the entity information of the target entity includes the position information of the target entity.

[0058] By extracting the environment entity information corresponding to each sub-navigation task, the robot can be provided with accurate positioning services and navigation prompts based on environmental characteristics and global perception in real time. The robot can directly go to the specified location according to the navigation prompts, ensuring efficient completion of the task.

[0059] S300, the target robot walks according to the initial global path pre-constructed by the central machine, and when any sub-navigation task corresponding to the spatial intelligent machine is monitored, the spatial intelligent machine sends the environment entity information corresponding to itself to the target robot, so that the target robot avoids the initial obstacle position and reaches the task entity position according to the adjusted path.

[0060] By sending the environment entity information to the target robot, the robot can avoid obstacles during navigation and accurately find the entity position corresponding to the task to be processed, so that the target robot can accurately and quickly execute each sub-navigation task.

[0061] S400, real-time monitoring of all obstacle information on the adjusted path, when real-time obstacle information is monitored, sending the real-time obstacle information to the central machine and optimizing the walking path of the target robot through the central machine, so that the target robot continues to execute the corresponding sub-navigation task.

[0062] Since the situation in space is dynamically changing, considering the situation that new obstacles may appear in the walking path of the target robot, including congestion or other impassable situations, leading to task execution stagnation, the walking path of the robot is dynamically adjusted in real time to determine the optimal path, thereby improving the execution efficiency of the task.

[0063] S500, when receiving the information that the target robot completes any sub-navigation task and monitoring the target robot passing through other any spatial intelligent machine monitoring area, switching the sub-navigation task through the central machine, and returning to execute S300 step until several sub-navigation tasks are executed; it can be understood that: other any spatial intelligent machine refers to any spatial intelligent machine other than the spatial intelligent machine corresponding to the current sub-navigation task. In implementation, when the target robot reaches the new spatial intelligent machine monitoring area, the new spatial intelligent machine will monitor the target robot and send the environment entity information corresponding to itself to the target robot, and then continue to execute according to the subsequent steps of S300 step until all sub-navigation tasks are executed.

[0064] In one specific embodiment, as shown in Figure 6 S500 step further includes the following steps:

[0065] S501, when receiving the information that the target robot completes any sub-navigation task, obtaining the current position of the target robot. In a specific implementation, the position information of the target robot is obtained in real time.

[0066] S502, sending the current position of the target robot to the hub machine in real time, so that the hub machine updates the initial global path corresponding to the robot navigation task request according to the current position of the target robot and the local space model corresponding to each space intelligent machine, to obtain the target global path.

[0067] When the target robot walks according to the pre-planned initial global path, due to the situation that the path is blocked by obstacles or people move, the robot will bypass the obstacles, so that the walking path of the robot deviates. In order to ensure the connection of navigation, the initial global path should be updated according to the current position of the robot to ensure the continuous and accurate navigation of the robot.

[0068] Further, as shown in Figure 7 S500 further includes the following steps:

[0069] S510, when monitoring the target robot passing through any other space intelligent machine monitoring area, sending an area positioning conversion request to the hub machine.

[0070] S520, when the hub machine receives the area conversion positioning request, switching the corresponding space intelligent machine to a new positioning source, and updating the position information of the target robot according to the switched space intelligent machine, to complete the state connection of the target robot; It can be understood that: the corresponding space intelligent machine refers to the space intelligent machine corresponding to the monitoring area currently passed by the target robot.

[0071] When the robot crosses the space intelligent machine coverage area, the hub machine cooperates with multiple space intelligent machines to coordinate the path planning of the overall navigation task and the state connection of the robot, realize the dynamic optimization of long-distance path planning, and ensure the smooth completion of the navigation task.

[0072] In summary, the application provides a robot long-distance navigation enhancement method, which decomposes a robot navigation task request into a plurality of sub-navigation tasks by a hub machine, and sends each sub-navigation task to a corresponding space intelligent machine, adopts a mode of collaborative work of the hub machine and the plurality of space intelligent machines, the hub machine can switch the space intelligent machine as needed to ensure the smooth progress of each sub-navigation task, and simultaneously monitors the environment information corresponding to each space intelligent machine in real time, and extracts the environment entity information corresponding to each sub-navigation task from the environment information corresponding to each space intelligent machine, when the space intelligent machine monitors the target robot, the environment entity information is sent to the target robot to provide the robot with accurate positioning service and navigation prompt based on the environment characteristics and global perception, so that the robot can avoid obstacles and accurately find the entity position corresponding to the task to be processed to accurately and quickly execute each sub-navigation task, and the path suddenly appearing obstacle information is monitored in real time during the task execution process to dynamically optimize the walking path of the robot and ensure the smooth execution of the sub-navigation task, when the target robot passes through the monitoring area of any other space intelligent machine, the hub machine is used to switch the sub-navigation task to realize long-distance continuous accurate positioning, and the path planning of the integrated navigation task and the state of the robot are coordinated to improve the navigation safety and efficiency.

[0073] Embodiment two

[0074] The embodiment two of the application provides a robot long-distance navigation enhancement system, as shown in the figure, the system comprises: Figure 8

[0075] A task decomposition module 100 is used for decomposing a robot navigation task request into a plurality of sub-navigation tasks by a pre-deployed hub machine when the robot navigation task request is received, and sending each sub-navigation task to a corresponding space intelligent machine; the space intelligent machine is provided with a plurality of distributed deployments, and each space intelligent machine communicates with the hub machine.

[0076] Specifically, the task decomposition module 100 comprises:

[0077] A construction module 101 is used for constructing an initial global path corresponding to the robot navigation task request according to the global map generated by the hub machine and the real-time obstacle information when the robot navigation task request is received; wherein the global map and the real-time obstacle information are generated by the hub machine after fusing the local space model pre-constructed by each space intelligent machine.

[0078] A division module 102 is used for dividing the initial global path according to the overlap of the initial global path and the monitoring area of each space intelligent machine to obtain the target local path corresponding to each space intelligent machine.

[0079] ​Further, the dividing module 102 further comprises:

[0080] The first determining module 1021 is configured to determine the initial local path as the intermediate local path corresponding to the space intelligent machine with the overlapping monitoring area when any initial local path in the initial global path only overlaps with one space intelligent machine monitoring area.

[0081] The second determining module 1022 is configured to determine the initial local path as the intermediate local path corresponding to the space intelligent machine with the longest path overlapping with the initial local path when any initial local path in the initial global path overlaps with no less than one space intelligent machine monitoring area.

[0082] The connecting module 1023 is configured to connect the intermediate local paths corresponding to each space intelligent machine to obtain the target local path corresponding to each space intelligent machine.

[0083] The disassembling module 103 is configured to disassemble the robot navigation task request into a plurality of sub-navigation tasks according to the target local path corresponding to each space intelligent machine; wherein the local path and the sub-navigation task are in one-to-one correspondence.

[0084] Further, the system further comprises a hub machine deployment module, and the hub machine deployment module comprises:

[0085] The computing module 01 is configured to determine the deployment candidate point by using the closeness centrality algorithm according to the position information of each space intelligent machine.

[0086] The processing module 02 is configured to obtain the wireless signal RSSI value of each space intelligent machine and the deployment candidate point, and determine the deployment candidate point as the deployment position of the hub machine when the wireless signal RSSI value corresponding to each space intelligent machine is not less than the preset RSSI threshold value, and otherwise, select the space intelligent machine farthest from the deployment candidate point from the plurality of space intelligent machines.

[0087] The moving module 03 is configured to connect the selected space intelligent machine and the deployment candidate point, and move the position of the deployment candidate point along the connection line to the position of the selected space intelligent machine according to the preset step length until the wireless signal RSSI value corresponding to all space intelligent machines is not less than the preset RSSI threshold value.

[0088] The third determining module 04 is configured to calculate the centroid position of the plurality of space intelligent machines according to the preset importance degree of each space intelligent machine when there is always a space intelligent machine with a corresponding wireless signal RSSI value less than the preset RSSI threshold value, and determine the centroid position as the deployment position of the hub machine; wherein the preset importance degree of the space intelligent machine is inversely proportional to the overlapping area of the monitoring area corresponding to the space intelligent machine.

[0089] The information extraction module 200 is configured to monitor environment information corresponding to each space intelligent machine respectively according to a local space model previously constructed by each space intelligent machine, and extract environment entity information corresponding to each sub-navigation task from the environment information corresponding to each space intelligent machine respectively; the environment entity information includes a task entity position and an initial obstacle position.

[0090] Specifically, the information extraction module 200 includes:

[0091] The keyword extraction module 201 is configured to extract a plurality of entity keywords from text corresponding to a sub-navigation task according to a pre-trained entity keyword extraction model.

[0092] The marking module 202 is configured to mark a plurality of identified entities in a monitoring area of each space intelligent machine from the environment information corresponding to each space intelligent machine.

[0093] The matching module 203 is configured to determine a same entity as a target entity when the plurality of identified entities in the monitoring area of the space intelligent machine corresponding to the sub-navigation task are the same as a plurality of entity keywords corresponding to the sub-navigation task, and determine entity information of the target entity extracted from the environment information corresponding to the space intelligent machine as environment entity information corresponding to the sub-navigation task.

[0094] The first sending module 300 is configured to control the target robot to walk according to an initial global path previously constructed by the hub machine, and when the space intelligent machine corresponding to any sub-navigation task detects the target robot, send environment entity information corresponding to the space intelligent machine itself to the target robot, so that the target robot avoids the initial obstacle position and walks to the task entity position according to the adjusted path.

[0095] The second sending module 400 is configured to monitor all obstacle information on the adjusted path in real time, and when the real-time obstacle information is monitored, send the real-time obstacle information to the hub machine and optimize the walking path of the target robot through the hub machine, so that the target robot continues to execute the corresponding sub-navigation task.

[0096] The task switching module 500 is configured to, when receiving information that the target robot completes any sub-navigation task and monitoring that the target robot passes through a monitoring area of any other space intelligent machine, switch the sub-navigation task through the hub machine, and return to the first sending module until the plurality of sub-navigation tasks are completed.

[0097] Specifically, the task switching module 500 includes:

[0098] The position obtaining module 501 is configured to obtain the current position of the target robot when receiving information that the target robot completes any sub-navigation task.

[0099] The path updating module 502 is configured to send the current position of the target robot to the hub robot in real time, so that the hub robot updates the initial global path corresponding to the robot navigation task request to obtain a target global path according to the current position of the target robot and the local space model corresponding to each space intelligent robot.

[0100] Further, the task switching module 500 further comprises:

[0101] The sending module 510 is configured to send a region positioning conversion request to the hub robot when monitoring that the target robot passes through a monitoring region of any other space intelligent robot.

[0102] The position updating module 520 is configured to switch the corresponding space intelligent robot to a new positioning source when the hub robot receives the region conversion positioning request, and update the position information of the target robot according to the switched space intelligent robot to complete the state connection of the target robot; it can be understood that the corresponding space intelligent robot refers to the space intelligent robot corresponding to the monitoring region currently passed through by the target robot.

[0103] It should be noted that the information interaction, execution process and the like between the above modules are based on the same concept as the method embodiments of the present application, and the specific functions and the technical effects brought by the method embodiments can be referred to the method embodiments part, and will not be repeated here.

[0104] Although some specific embodiments of the present application have been described in detail through examples, those skilled in the art should understand that the above examples are only for illustration, but not for limiting the scope of the present application. Those skilled in the art should also understand that various modifications can be made to the embodiments without departing from the scope and spirit of the present application. The scope of the present application is defined by the appended claims.

Claims

1. A method of long distance navigation enhancement for a robot, the method comprising: The method comprises the following steps: S100, when receiving a robot navigation task request, the robot navigation task request is disassembled into several sub-navigation tasks by a pre-deployed hub machine, and each sub-navigation task is sent to a corresponding space intelligent machine; the space intelligent machine is provided with several and adopts distributed deployment, and each space intelligent machine communicates with the hub machine; S200, according to a local space model pre-constructed by each space intelligent machine, the environment information corresponding to each space intelligent machine is monitored respectively, and the environment entity information corresponding to each sub-navigation task is extracted from the environment information corresponding to each space intelligent machine respectively; the environment entity information comprises a task entity position and an initial obstacle position; S300, a target robot is controlled to walk according to an initial global path pre-constructed by the hub machine, and when the target robot is monitored by any sub-navigation task corresponding space intelligent machine, the environment entity information corresponding to the space intelligent machine itself is sent to the target robot, so that the target robot avoids the initial obstacle position and walks to the task entity position according to the adjusted path; S400, all obstacle information on the adjusted path is monitored in real time, and when the real-time obstacle information is monitored, the real-time obstacle information is sent to the hub machine and the walking path of the target robot is optimized by the hub machine, so that the target robot continues to execute the corresponding sub-navigation task; S500, when receiving the information that the target robot executes any sub-navigation task and monitoring the target robot passing through other any space intelligent machine monitoring area, the switching of the sub-navigation task is realized through the hub machine, and the step S300 is returned until the several sub-navigation tasks are executed.

2. The method of robotic long-range navigation enhancement of claim 1, wherein, The step S100 comprises the following steps: S101, when receiving a robot navigation task request, an initial global path corresponding to the robot navigation task request is constructed according to a global map and real-time obstacle information generated by the hub machine; wherein the global map and real-time obstacle information are generated by the hub machine after fusing the local space model pre-constructed by each space intelligent machine; S102, the initial global path is divided according to the overlapping condition of the initial global path and the monitoring area of each space intelligent machine, and the target local path corresponding to each space intelligent machine is obtained; S103, the robot navigation task request is disassembled into several sub-navigation tasks according to the target local path corresponding to each space intelligent machine; wherein the target local path and the sub-navigation task correspond one by one.

3. The method of robotic long-range navigation augmentation according to claim 2, wherein, The step S102 further comprises the following steps: S1021, when any initial local path in the initial global path only overlaps with one space intelligent machine monitoring area, the initial local path is determined as the intermediate local path corresponding to the space intelligent machine with the overlapping monitoring area; the initial local path is a path randomly divided from the initial global path; S1022, when any initial local path in the initial global path overlaps with not less than one space intelligent machine monitoring area, the initial local path is determined as the intermediate local path corresponding to the space intelligent machine with the longest path overlapping with the initial local path; S1023, connecting the intermediate local paths corresponding to each space intelligent machine to obtain a target local path corresponding to each space intelligent machine.

4. The method of robotic long-range navigation enhancement of claim 1, wherein, The deployment position of the hub machine is determined through the following steps: S01, determining a deployment candidate point by using a closeness centrality algorithm according to the position information of each space intelligent machine; S02, obtaining the wireless signal RSSI value of each space intelligent machine and the deployment candidate point, and determining the deployment candidate point as the deployment position of the hub machine when the wireless signal RSSI value corresponding to each space intelligent machine is not less than a preset RSSI threshold, otherwise, selecting a space intelligent machine farthest from the deployment candidate point from the space intelligent machines; S03, connecting the selected space intelligent machine with the deployment candidate point, and moving the position of the deployment candidate point along the connection line to the position of the selected space intelligent machine according to a preset step size until the wireless signal RSSI value corresponding to all space intelligent machines is not less than the preset RSSI threshold; S04, when there is always a space intelligent machine corresponding to a wireless signal RSSI value less than the preset RSSI threshold, calculating the centroid position of the space intelligent machines according to the preset importance degree of each space intelligent machine, and determining the centroid position as the deployment position of the hub machine; wherein the preset importance degree of the space intelligent machine is inversely proportional to the overlapping area of the monitoring area corresponding to the space intelligent machine.

5. The method of robotic long-range navigation augmentation of claim 1, wherein, In the S200 step, the environmental entity information corresponding to each sub-navigation task is extracted from the environmental information corresponding to each space intelligent machine, including the following steps: S201, for any sub-navigation task, extracting a plurality of entity keywords from the text corresponding to the sub-navigation task according to a pre-trained entity keyword extraction model; S202, marking a plurality of recognized entities in the monitoring area of each space intelligent machine from the environmental information corresponding to each space intelligent machine; S203, when there is a same entity in the plurality of recognized entities in the monitoring area of the space intelligent machine corresponding to the sub-navigation task and the plurality of entity keywords corresponding to the sub-navigation task, determining the same entity as a target entity, and determining the entity information of the target entity extracted from the environmental information corresponding to the space intelligent machine as the environmental entity information corresponding to the sub-navigation task.

6. The method of robotic long-range navigation enhancement of claim 2, wherein, The S500 step further includes the following steps: S501, when receiving information that the target robot completes any sub-navigation task, obtaining the current position of the target robot; S502, sending the current position of the target robot to the hub machine in real time, so that the hub machine updates the initial global path corresponding to the robot navigation task request according to the current position of the target robot and the local space model corresponding to each space intelligent machine, to obtain a target global path.

7. The method of robotic long-range navigation augmentation of claim 1, wherein, The S500 step further includes the following steps: S510, when monitoring that the target robot passes through the monitoring area of any other space intelligent machine, sending a region positioning conversion request to the hub machine; S520, when the hub machine receives the region conversion positioning request, switching the corresponding space intelligent machine to a new positioning source, and updating the position information of the target robot according to the switched space intelligent machine to complete the state connection of the target robot.

8. A robotic long distance navigation enhancement system, characterized by, The system comprises: a task decomposition module, configured to, when receiving a robot navigation task request, decompose the robot navigation task request into a plurality of sub-navigation tasks by a pre-deployed central machine, and send each sub-navigation task to a corresponding space intelligent machine; the space intelligent machine is provided with a plurality of and is distributedly deployed, and each space intelligent machine communicates with the central machine; an information extraction module, configured to monitor environment information corresponding to each space intelligent machine respectively according to a local space model pre-constructed by each space intelligent machine, and extract environment entity information corresponding to each sub-navigation task from the environment information corresponding to each space intelligent machine respectively; the environment entity information comprises a task entity position and an initial obstacle position; a first sending module, configured to control a target robot to walk according to an initial global path pre-constructed by the central machine, and when any sub-navigation task corresponding space intelligent machine monitors the target robot, send the environment entity information corresponding to the space intelligent machine itself to the target robot, so that the target robot avoids the initial obstacle position and walks to the task entity position according to an adjusted path; a second sending module, configured to monitor all obstacle information on the adjusted path in real time, and when monitoring real-time obstacle information, send the real-time obstacle information to the central machine and optimize the walking path of the target robot through the central machine, so that the target robot continues to execute the corresponding sub-navigation task; a task switching module, configured to, when receiving information that the target robot executes any sub-navigation task and monitoring that the target robot passes through a monitoring area of any other space intelligent machine, realize switching of the sub-navigation task through the central machine, and return to the first sending module until the plurality of sub-navigation tasks are executed.

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