CN116125998B - Intelligent route guiding method, device, equipment and storage medium based on AI - Google Patents
Intelligent route guiding method, device, equipment and storage medium based on AI Download PDFInfo
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- CN116125998B CN116125998B CN202310419503.1A CN202310419503A CN116125998B CN 116125998 B CN116125998 B CN 116125998B CN 202310419503 A CN202310419503 A CN 202310419503A CN 116125998 B CN116125998 B CN 116125998B
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The present application relates to the field of artificial intelligence technologies, and in particular, to an AI-based intelligent approach method, apparatus, device, and storage medium. The method based on the approach robot comprises the following steps: obtaining a route guiding request; determining a target route guiding robot for executing a current route guiding task in the route guiding robots in the candidate state, and acquiring second initial position information of the target route guiding robot; determining meeting point position information in a pre-modeled electronic map of a current scene based on the first initial position information and the second initial position information; generating a merging approach scheme based on the merging point position information; and when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold, controlling the target route guiding robot to realize navigation operation from the current position to the target position. By adopting the method, the efficiency of people arriving at the destination can be improved.
Description
Technical Field
The present application relates to the field of artificial intelligence technologies, and in particular, to an AI-based intelligent approach method, apparatus, computer device, and storage medium.
Background
Artificial intelligence is a new technical science to research, develop theories, methods, techniques and application systems for simulating, extending and expanding human intelligence. Artificial intelligence attempts to understand the nature of intelligence and produce a new intelligent machine that can react in a similar manner to human intelligence, research in this area including robotics, language recognition, image recognition, natural language processing, and expert systems. Since the birth of artificial intelligence, theories and technologies are mature, application fields are expanding, and it is expected that technological products brought by artificial intelligence in the future will be efficient containers for human intelligence. With the development of technology, artificial intelligence technology will find application in more fields and will develop more and more important value.
Today, urban construction is continuously advancing, and more high-rise buildings are appeared in places in production and living of people, from commercial malls, office buildings for office use, to museums for entertainment, exhibition halls and the like. Indoor environments in which people face are becoming increasingly large and complex, and existing navigation software generally corresponds to driving navigation in an outdoor environment, lacking the relevant services of indoor navigation.
In the prior art, in order to solve the problem of navigation and route finding of people in a complex indoor environment, information display facilities such as building information cards and navigation cards for displaying indoor area distribution conditions and route conditions of large buildings are arranged in the complex indoor environment. And a direction guide information board such as a drop and a hanging board is also arranged in the passageway in the building.
However, the current approach of indoor navigation has the following technical problems:
in the current indoor navigation approach mode, the information displayed by the indoor information display facilities of the building is too fuzzy, so that the navigation approach effect is poor, and the efficiency of assisting people to arrive at a destination is low.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an AI-based intelligent approach method, apparatus, computer device, computer readable storage medium, and computer program product that can actively implement personalized indoor navigation to improve the efficiency of people arriving at a destination.
In a first aspect, the present application provides an AI-based intelligent approach method. The method comprises the following steps:
acquiring a route request, wherein the route request comprises first initial position information of a request terminal;
determining a target route guiding robot for executing a current route guiding task from route guiding robots in a candidate state, and acquiring second initial position information of the target route guiding robot, wherein the target route guiding robot is the route guiding robot which is the shortest in time consumption when being converged with the request terminal;
determining meeting point position information in a pre-modeled electronic map of a current scene based on the first initial position information and the second initial position information;
generating a merging approach scheme based on the merging point position information, wherein the merging approach scheme is used for assisting the request terminal and the target approach robot to move in opposite directions;
when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold, controlling the target route guiding robot to realize navigation operation from the current position to the target position;
when the distance between the request terminal and the target approach robot is smaller than a preset convergence threshold, controlling the target approach robot to realize navigation operation from the current position to the target position comprises the following steps:
and acquiring real-time position information of the request terminal, and controlling the distance between the target route guiding robot and the request terminal in the navigation operation so that the distance between the target route guiding robot and the request terminal is smaller than or equal to a preset loss threshold value until a navigation ending instruction sent by the request terminal is received.
In one embodiment, the determining the meeting point location information in the pre-modeled electronic map of the current scene based on the first initial location information and the second initial location information includes:
acquiring the moving speed of the request terminal in a preset time window;
and determining the junction point position information based on the moving speed and the running speed of the target approach robot.
In one embodiment, the generating a merging approach scheme based on the merging point position information, where the merging approach scheme is used to assist the request terminal to move towards the target approach robot includes:
generating a first approach scheme from the first initial position information to the meeting point position information, and transmitting the first approach scheme to the request terminal;
and generating a second approach scheme from the second initial position information to the meeting point position information, and transmitting the second approach scheme to the target approach robot.
In one embodiment, the method further comprises:
and after the target route guiding robot completes the movement task corresponding to the second route guiding scheme, if the distance between the target route guiding robot and the request terminal is greater than or equal to the confluence threshold value, controlling the target route guiding robot to move towards the request terminal according to the reverse route of the first route guiding scheme.
In one embodiment, when the distance between the request terminal and the target approach robot is smaller than a preset convergence threshold, controlling the target approach robot to implement the navigation operation from the current position to the target position includes:
acquiring target position information associated with the target position;
the obtaining target location information associated with the target location includes:
acquiring input information of a user, and acquiring target position information corresponding to the input information by using a preset recognition model, wherein the input information comprises one or more of gesture information, voice information, limb action information and terminal input information.
In one embodiment, when the distance between the request terminal and the target approach robot is smaller than a preset convergence threshold, controlling the target approach robot to implement the navigation operation from the current position to the target position includes:
in a navigation operation, the position of the target approach robot is controlled to be maintained on a navigation route from the current position to the target position, and the target approach robot is maintained in front of the traveling direction of the request terminal.
In a second aspect, the present application further provides an AI-based intelligent approach device. The device comprises:
the request acquisition module is used for acquiring a route guiding request, wherein the route guiding request comprises first initial position information of a request terminal;
the equipment selecting module is used for determining a target route guiding robot for executing a current route guiding task from the route guiding robots in the candidate state, acquiring second initial position information of the target route guiding robot, and converging the target route guiding robot with the request terminal to be the route guiding robot with the shortest time consumption;
the meeting point determining module is used for determining meeting point position information in a pre-modeled electronic map of the current scene based on the first initial position information and the second initial position information;
the converging scheme generating module is used for generating a converging approach scheme based on the converging point position information, and the converging approach scheme is used for assisting the request terminal and the target approach robot to move in opposite directions;
the target navigation module is used for controlling the target route guiding robot to realize navigation operation from the current position to the target position when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold value;
the target navigation module comprises:
the robot following module is used for acquiring the real-time position information of the request terminal, and controlling the distance between the target route guiding robot and the request terminal in the navigation operation so that the distance between the target route guiding robot and the request terminal is smaller than or equal to a preset loss threshold value until a navigation ending instruction sent by the request terminal is received.
In one embodiment, the meeting point determination module includes:
the terminal speed module is used for acquiring the moving speed of the request terminal in a preset time window;
and the robot speed module is used for determining the junction point position information based on the moving speed and the running speed of the target approach robot.
In one embodiment, the merging scheme generating module includes:
the first guiding scheme module is used for generating a first guiding scheme from the first initial position information to the meeting point position information and transmitting the first guiding scheme to the request terminal;
and the second approach scheme module is used for generating a second approach scheme from the second initial position information to the junction position information and transmitting the second approach scheme to the target approach robot.
In one embodiment, the apparatus further comprises:
and the robot reverse searching module is used for controlling the target route guiding robot to move towards the request terminal according to the reverse route of the first route guiding scheme if the distance between the target route guiding robot and the request terminal is greater than or equal to the convergence threshold after the target route guiding robot completes the movement task corresponding to the second route guiding scheme.
In one embodiment, the target navigation module includes:
a target position information module for acquiring target position information associated with the target position;
the target location information module includes:
the information input module is used for acquiring input information of a user, acquiring target position information corresponding to the input information by utilizing a preset recognition model, wherein the input information comprises one or more of gesture information, voice information, limb action information and terminal input information.
In one embodiment, the target navigation module includes:
and the relative position maintaining module is used for controlling the position of the target guiding robot to be maintained on a navigation route from the current position to the target position in navigation operation, and maintaining the target guiding robot in front of the travelling direction of the request terminal.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the steps of an AI-based intelligent routing method according to any one of the embodiments of the first aspect when the processor executes the computer program.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of an AI-based intelligent route guidance method as described in any of the embodiments of the first aspect.
In a fifth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of an AI-based intelligent approach method as described in any of the embodiments of the first aspect.
The intelligent route guiding method, the intelligent route guiding device, the intelligent route guiding computer equipment, the intelligent route guiding storage medium and the intelligent route guiding computer program product based on the AI can achieve the following beneficial effects in the corresponding background technology through deducing the technical characteristics in the independent rights:
when the user has the road guiding requirement, the road guiding request of the user is actively acquired, the target road guiding robot which is the shortest in time for merging with the user is determined, then the user is respectively guided to move towards the road guiding robot, the road guiding robot is controlled to move towards the user, the merging efficiency between the user and the road guiding robot is improved in multiple aspects, finally whether the user is merged with the road guiding robot or not is determined through the position information, and the navigation road guiding operation towards the target position is realized after the merging is determined. In implementation, the robot guiding efficiency can be improved, the guiding robot is used for guiding the road, one-to-one fine guiding is further facilitated, and user experience is improved. On the other hand, the guiding robot performs personalized guiding service, which is helpful to reduce the possibility of user walking by mistake and finally improve the efficiency of the user reaching the target position.
Drawings
FIG. 1 is an application environment diagram of an AI-based intelligent approach method in one embodiment;
FIG. 2 is a flow diagram of an AI-based intelligent approach method in one embodiment;
fig. 3 is a block diagram of an AI-based intelligent routing device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
In the prior art, in order to solve the problem of navigation and route finding of people in a complex indoor environment, information display facilities such as building information cards and navigation cards for displaying indoor area distribution conditions and route conditions of large buildings are arranged in the complex indoor environment. And a direction guide information board such as a drop and a hanging board is also arranged in the passageway in the building.
However, the current approach of indoor navigation has the following technical problems:
in the current indoor navigation approach mode, the information displayed by the indoor information display facilities of the building is too fuzzy, so that the navigation approach effect is poor, and the efficiency of assisting people to arrive at a destination is low.
Based on this, the AI-based intelligent approach method provided in the embodiment of the present application may be applied to an application environment as shown in fig. 1. Wherein the terminal 102 communicates with both the lead robot 104 and the requesting terminal 106 via a network. The data storage system may store data that the terminal 102 needs to process. The data storage system may be integrated on the terminal 102 or may be located on a cloud or other network server. The request terminal 106 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like. The terminal 102 may be implemented as a stand-alone server or as a server cluster of multiple servers.
In one embodiment, as shown in fig. 2, an intelligent approach method based on AI is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes the following steps:
step 201: and obtaining a route guidance request, wherein the route guidance request comprises first initial position information of a request terminal.
Step 202: and determining a target route guiding robot for executing the current route guiding task from the route guiding robots in the candidate state, and acquiring second initial position information of the target route guiding robot, wherein the target route guiding robot is the route guiding robot which is the shortest in time consumption when converging with the request terminal.
Step 203: and determining meeting point position information in a pre-modeled electronic map of the current scene based on the first initial position information and the second initial position information.
Step 204: and generating a merging approach scheme based on the merging point position information, wherein the merging approach scheme is used for assisting the request terminal and the target approach robot to move in opposite directions.
Step 205: and when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold, controlling the target route guiding robot to realize navigation operation from the current position to the target position.
Step 206: and acquiring real-time position information of the request terminal, and controlling the distance between the target route guiding robot and the request terminal in the navigation operation so that the distance between the target route guiding robot and the request terminal is smaller than or equal to a preset loss threshold value until a navigation ending instruction sent by the request terminal is received.
In the intelligent approach method based on the AI, the following beneficial effects can be achieved:
when the user has the road guiding requirement, the road guiding request of the user is actively acquired, the target road guiding robot which is the shortest in time for merging with the user is determined, then the user is respectively guided to move towards the road guiding robot, the road guiding robot is controlled to move towards the user, the merging efficiency between the user and the road guiding robot is improved in multiple aspects, finally whether the user is merged with the road guiding robot or not is determined through the position information, and the navigation road guiding operation towards the target position is realized after the merging is determined. In implementation, the robot guiding efficiency can be improved, the guiding robot is used for guiding the road, one-to-one fine guiding is further facilitated, and user experience is improved. On the other hand, the guiding robot performs personalized guiding service, which is helpful to reduce the possibility of user walking by mistake and finally improve the efficiency of the user reaching the target position.
In one embodiment, step 203 comprises:
step 301: and acquiring the moving speed of the request terminal in a preset time window.
Step 302: and determining the junction point position information based on the moving speed and the running speed of the target approach robot.
In this embodiment, the meeting point is determined by the movement speed, so that the position of the meeting point is in line with the actual movement requirement, and the navigation route guiding efficiency of the user in the indoor route searching is improved.
In one embodiment, the step 204 includes:
step 401: generating a first approach scheme from the first initial position information to the meeting point position information, and transmitting the first approach scheme to the request terminal;
step 402: and generating a second approach scheme from the second initial position information to the meeting point position information, and transmitting the second approach scheme to the target approach robot.
In this embodiment, the approach scheme is sent to the request terminal and the approach robot, respectively, so that the user can move opposite to the approach robot, thereby helping to improve the merging efficiency of the user and the approach robot.
In one embodiment, the method further comprises:
step 501: and after the target route guiding robot completes the movement task corresponding to the second route guiding scheme, if the distance between the target route guiding robot and the request terminal is greater than or equal to the confluence threshold value, controlling the target route guiding robot to move towards the request terminal according to the reverse route of the first route guiding scheme.
In this embodiment, the convergence condition between the request terminal and the approach robot is determined by the distance between the request terminal and the approach robot, and the approach robot is controlled to actively approach the request terminal when the request terminal and the approach robot are not converged, so that the convergence efficiency of the user and the approach robot is further improved.
In one embodiment, the step 205 includes:
step 601: acquiring target position information associated with the target position;
the step 601 includes:
step 602: acquiring input information of a user, and acquiring target position information corresponding to the input information by using a preset recognition model, wherein the input information comprises one or more of gesture information, voice information, limb action information and terminal input information.
In this embodiment, input information of a user is acquired in multiple ways, which is conducive to improving the efficiency of acquiring input information, and meanwhile, is conducive to improving the flexibility of inputting information by the user, so that the user can select an information input way according to actual situations.
In one embodiment, the step 205 includes:
step 701: in a navigation operation, the position of the target approach robot is controlled to be maintained on a navigation route from the current position to the target position, and the target approach robot is maintained in front of the traveling direction of the request terminal.
In this embodiment, the loss threshold is set, so that the distance between the control of the guiding robot and the user is kept within the loss threshold, which is conducive to enabling the guiding robot to control the moving speed in the guiding process, so that the guiding robot does not deviate from the distance range capable of generating route guidance for the user, and is conducive to improving the stability of the guiding navigation process.
It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides an AI-based intelligent guiding device for realizing the AI-based intelligent guiding method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of one or more AI-based intelligent guiding devices provided below may be referred to above for the limitation of the AI-based intelligent guiding method, which is not repeated here.
In one embodiment, as shown in fig. 3, there is provided an AI-based intelligent road guiding apparatus, including: the device comprises a request acquisition module, a device selection module, a meeting point determination module, a meeting scheme generation module and a target navigation module, wherein:
the request acquisition module is used for acquiring a route guiding request, wherein the route guiding request comprises first initial position information of a request terminal;
the equipment selecting module is used for determining a target route guiding robot for executing a current route guiding task from the route guiding robots in the candidate state, acquiring second initial position information of the target route guiding robot, and converging the target route guiding robot with the request terminal to be the route guiding robot with the shortest time consumption;
the meeting point determining module is used for determining meeting point position information in a pre-modeled electronic map of the current scene based on the first initial position information and the second initial position information;
the converging scheme generating module is used for generating a converging approach scheme based on the converging point position information, and the converging approach scheme is used for assisting the request terminal and the target approach robot to move in opposite directions;
the target navigation module is used for controlling the target route guiding robot to realize navigation operation from the current position to the target position when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold value;
the target navigation module comprises:
the robot following module is used for acquiring the real-time position information of the request terminal, and controlling the distance between the target route guiding robot and the request terminal in the navigation operation so that the distance between the target route guiding robot and the request terminal is smaller than or equal to a preset loss threshold value until a navigation ending instruction sent by the request terminal is received.
In one embodiment, the meeting point determination module includes:
the terminal speed module is used for acquiring the moving speed of the request terminal in a preset time window;
and the robot speed module is used for determining the junction point position information based on the moving speed and the running speed of the target approach robot.
In one embodiment, the merging scheme generating module includes:
the first guiding scheme module is used for generating a first guiding scheme from the first initial position information to the meeting point position information and transmitting the first guiding scheme to the request terminal;
and the second approach scheme module is used for generating a second approach scheme from the second initial position information to the junction position information and transmitting the second approach scheme to the target approach robot.
In one embodiment, the apparatus further comprises:
and the robot reverse searching module is used for controlling the target route guiding robot to move towards the request terminal according to the reverse route of the first route guiding scheme if the distance between the target route guiding robot and the request terminal is greater than or equal to the convergence threshold after the target route guiding robot completes the movement task corresponding to the second route guiding scheme.
In one embodiment, the target navigation module includes:
a target position information module for acquiring target position information associated with the target position;
the target location information module includes:
the information input module is used for acquiring input information of a user, acquiring target position information corresponding to the input information by utilizing a preset recognition model, wherein the input information comprises one or more of gesture information, voice information, limb action information and terminal input information.
In one embodiment, the target navigation module includes:
and the relative position maintaining module is used for controlling the position of the target guiding robot to be maintained on a navigation route from the current position to the target position in navigation operation, and maintaining the target guiding robot in front of the travelling direction of the request terminal.
The various modules in the AI-based intelligent road diversion device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements an AI-based intelligent approach method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structures of the terminal described above are merely block diagrams of partial structures related to the present application and do not constitute a limitation of the computer device to which the present application is applied, and that a specific computer device may include more or less components than those shown in the drawings, or may combine some components, or have different arrangements of components.
In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.
In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
It should be noted that, user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (7)
1. An intelligent approach method based on AI, which is characterized in that the method is realized based on an approach robot, and the method comprises the following steps:
acquiring a route request, wherein the route request comprises first initial position information of a request terminal;
determining a target route guiding robot for executing a current route guiding task from route guiding robots in a candidate state, and acquiring second initial position information of the target route guiding robot, wherein the target route guiding robot is the route guiding robot which is the shortest in time consumption when being converged with the request terminal;
determining meeting point position information in a pre-modeled electronic map of a current scene based on the first initial position information and the second initial position information;
generating a merging approach scheme based on the merging point position information, wherein the merging approach scheme is used for assisting the request terminal and the target approach robot to move in opposite directions;
when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold, controlling the target route guiding robot to realize navigation operation from the current position to the target position;
when the distance between the request terminal and the target approach robot is smaller than a preset convergence threshold, controlling the target approach robot to realize navigation operation from the current position to the target position comprises the following steps:
acquiring real-time position information of the request terminal, and controlling the distance between the target route guiding robot and the request terminal in navigation operation so that the distance between the target route guiding robot and the request terminal is smaller than or equal to a preset loss threshold value until a navigation ending instruction sent by the request terminal is received;
the determining meeting point location information in the pre-modeled electronic map of the current scene based on the first initial location information and the second initial location information includes:
acquiring the moving speed of the request terminal in a preset time window;
determining the meeting point position information based on the moving speed and the running speed of the target approach robot;
generating a merging approach scheme based on the merging point position information, wherein the merging approach scheme is used for assisting the request terminal and the target approach robot to move towards each other and comprises the following steps:
generating a first approach scheme from the first initial position information to the meeting point position information, and transmitting the first approach scheme to the request terminal;
generating a second approach scheme from the second initial position information to the meeting point position information, and transmitting the second approach scheme to the target approach robot;
the method further comprises the steps of:
and after the target route guiding robot completes the movement task corresponding to the second route guiding scheme, if the distance between the target route guiding robot and the request terminal is greater than or equal to the confluence threshold value, controlling the target route guiding robot to move towards the request terminal according to the reverse route of the first route guiding scheme.
2. The AI-based intelligent approach method of claim 1, wherein controlling the target approach robot to implement a navigation operation from a current location to a target location when the distance between the request terminal and the target approach robot is less than a preset convergence threshold comprises:
acquiring target position information associated with the target position;
the obtaining target location information associated with the target location includes:
acquiring input information of a user, and acquiring target position information corresponding to the input information by using a preset recognition model, wherein the input information comprises one or more of gesture information, voice information, limb action information and terminal input information.
3. The AI-based intelligent approach method of claim 1, wherein controlling the target approach robot to implement a navigation operation from a current location to a target location when the distance between the request terminal and the target approach robot is less than a preset convergence threshold comprises:
in a navigation operation, the position of the target approach robot is controlled to be maintained on a navigation route from the current position to the target position, and the target approach robot is maintained in front of the traveling direction of the request terminal.
4. An AI-based intelligent routing device, the device comprising:
the request acquisition module is used for acquiring a route guiding request, wherein the route guiding request comprises first initial position information of a request terminal;
the equipment selecting module is used for determining a target route guiding robot for executing a current route guiding task from the route guiding robots in the candidate state, acquiring second initial position information of the target route guiding robot, and converging the target route guiding robot with the request terminal to be the route guiding robot with the shortest time consumption;
the meeting point determining module is used for determining meeting point position information in a pre-modeled electronic map of the current scene based on the first initial position information and the second initial position information;
the converging scheme generating module is used for generating a converging approach scheme based on the converging point position information, and the converging approach scheme is used for assisting the request terminal and the target approach robot to move in opposite directions;
the target navigation module is used for controlling the target route guiding robot to realize navigation operation from the current position to the target position when the distance between the request terminal and the target route guiding robot is smaller than a preset convergence threshold value;
the meeting point determination module includes:
the terminal speed module is used for acquiring the moving speed of the request terminal in a preset time window;
the robot speed module is used for determining the meeting point position information based on the moving speed and the running speed of the target approach robot;
the converging scheme generating module comprises:
the first guiding scheme module is used for generating a first guiding scheme from the first initial position information to the meeting point position information and transmitting the first guiding scheme to the request terminal;
the second approach scheme module is used for generating a second approach scheme from the second initial position information to the meeting point position information and transmitting the second approach scheme to the target approach robot;
the apparatus further comprises:
and the robot reverse searching module is used for controlling the target route guiding robot to move towards the request terminal according to the reverse route of the first route guiding scheme if the distance between the target route guiding robot and the request terminal is greater than or equal to the convergence threshold after the target route guiding robot completes the movement task corresponding to the second route guiding scheme.
5. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 3 when the computer program is executed.
6. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 3.
7. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 3.
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