阅读说明：本技术 一种基于人工智能的机器人行走方法及装置 (Robot walking method and device based on artificial intelligence ) 是由 黄和明 周红锴 丁玉芬 张洪亮 于 2021-07-14 设计创作，主要内容包括：本发明公开了一种基于人工智能的机器人行走方法及装置。其中,该方法包括：获取实时位置数据；根据所述实时位置数据,获取机器人的终点数据；将所述实时位置数据和所述终点数据输入至行走模型中,生成最优路线；根据所述最优路线进行机器人行走操作。本发明解决了现有技术中当机器人行走的时候,步进电机会根据程序设计来进行传感器识别判断,从而进一步决定如何进行行走,如何规避障碍物的硬性,然而通过上述方法进行的机器人行走操作往往不具备一定的学习性,即每一次机器人的行走过程都是全新的,无规则的,无法适应用户在多次使用同一套机器人系统时的历史数据,降低了使用的效率的技术问题。(The invention discloses a robot walking method and device based on artificial intelligence. Wherein, the method comprises the following steps: acquiring real-time position data; acquiring end point data of the robot according to the real-time position data; inputting the real-time position data and the end point data into a walking model to generate an optimal route; and carrying out robot walking operation according to the optimal route. The invention solves the technical problems that in the prior art, when a robot walks, a stepping motor can carry out sensor identification and judgment according to program design so as to further determine how to walk and how to avoid the rigidity of an obstacle, however, the walking operation of the robot by the method often does not have certain learning, namely, the walking process of the robot each time is brand new and irregular, and the method cannot adapt to historical data of a user when the user uses the same robot system for multiple times, and reduces the use efficiency.)

1. A robot walking method based on artificial intelligence is characterized by comprising the following steps:

acquiring real-time position data;

acquiring end point data of the robot according to the real-time position data;

inputting the real-time position data and the end point data into a walking model to generate an optimal route;

carrying out robot walking operation according to the optimal route;

prior to said acquiring real-time location data, the method further comprises:

and acquiring the activation state of the robot.

2. The method of claim 1, wherein prior to said inputting said real-time location data and said end point data into a walking model to generate an optimal route, said method further comprises:

and training the walking model.

3. The method of claim 1, wherein after said performing a robot walking operation according to said optimal route, said method further comprises:

and displaying the optimal route and the estimated walking time.

4. The utility model provides a robot running gear based on artificial intelligence which characterized in that includes:

the first acquisition module is used for acquiring real-time position data;

the second acquisition module is used for acquiring the terminal data of the robot according to the real-time position data;

the route module is used for inputting the real-time position data and the end point data into a walking model to generate an optimal route;

the walking module is used for carrying out robot walking operation according to the optimal route;

the device further comprises:

and the third acquisition module is used for acquiring the activation state of the robot.

5. The apparatus of claim 4, further comprising:

and the training module is used for training the walking model.

6. The apparatus of claim 4, further comprising:

and the display module is used for displaying the optimal route and the estimated walking time.

7. A non-volatile storage medium, comprising a stored program, wherein the program, when executed, controls an apparatus in which the non-volatile storage medium is located to perform the method of any of claims 1 to 3.

8. An electronic device comprising a processor and a memory; the memory has stored therein computer readable instructions for execution by the processor, wherein the computer readable instructions when executed perform the method of any one of claims 1 to 3.

Technical Field

The invention relates to the field of robots, in particular to a robot walking method and device based on artificial intelligence.

Background

Along with the continuous development of intelligent science and technology, people's life, study and more applied to intelligent equipment and device in work to improve people's life, improve the efficiency that people worked. At present, the walking of robot usually operates through the rule of predetermineeing of step motor, when the robot walks promptly, step motor can carry out sensor discernment judgement according to programming, thereby further decide how to walk, how to avoid the rigidity of barrier, the robot walking operation who nevertheless carries out through above-mentioned method often does not possess certain study nature, the walking process of robot is all brand-new each time, and is irregular, can't adapt to the historical data of user when repetitious usage same set of robot system, has reduced the efficiency of using. In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the invention provides a robot walking method and device based on artificial intelligence, which at least solve the technical problems that in the prior art, when a robot walks, a stepping motor carries out sensor identification and judgment according to program design, so that the walking is further determined, and the hardness of obstacles is avoided, however, the robot walking operation carried out by the method often does not have certain learning performance, namely, the walking process of the robot is brand-new and irregular each time, the method and device cannot adapt to historical data of a user when the user uses the same robot system for multiple times, and the use efficiency is reduced.

According to an aspect of an embodiment of the present invention, there is provided a robot walking method based on artificial intelligence, including: acquiring real-time position data; acquiring end point data of the robot according to the real-time position data; inputting the real-time position data and the end point data into a walking model to generate an optimal route; and carrying out robot walking operation according to the optimal route.

Optionally, before the acquiring the real-time location data, the method further includes: and acquiring the activation state of the robot.

Optionally, before the inputting the real-time location data and the end point data into a walking model and generating an optimal route, the method further includes: and training the walking model.

Optionally, after the robot walking operation is performed according to the optimal route, the method further includes: and displaying the optimal route and the estimated walking time.

According to another aspect of the embodiments of the present invention, there is also provided an artificial intelligence-based robot walking device, including: the first acquisition module is used for acquiring real-time position data; the second acquisition module is used for acquiring the terminal data of the robot according to the real-time position data; the route module is used for inputting the real-time position data and the end point data into a walking model to generate an optimal route; and the walking module is used for carrying out robot walking operation according to the optimal route.

Optionally, the apparatus further comprises: and the third acquisition module is used for acquiring the activation state of the robot.

Optionally, the apparatus further comprises: and the training module is used for training the walking model.

Optionally, the apparatus further comprises: and the display module is used for displaying the optimal route and the estimated walking time.

According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to perform an artificial intelligence based robot walking method when running.

According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute an artificial intelligence based robot walking method when running.

In the embodiment of the invention, the real-time position data is acquired; acquiring end point data of the robot according to the real-time position data; inputting the real-time position data and the end point data into a walking model to generate an optimal route; the method for carrying out robot walking operation according to the optimal route solves the technical problems that in the prior art, when a robot walks, a stepping motor can carry out sensor identification and judgment according to program design, so that the walking is further determined, and the hardness of obstacles is avoided.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

FIG. 1 is a flow chart of a method for artificial intelligence based robot walking according to an embodiment of the present invention;

fig. 2 is a block diagram of a robot walking method based on artificial intelligence according to an embodiment of the present invention.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In accordance with an embodiment of the present invention, there is provided a method embodiment of an artificial intelligence based robot walking method, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.

Example one

Fig. 1 is a flowchart of a robot walking method based on artificial intelligence according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:

step S102, acquiring real-time position data.

Specifically, in order to calculate and analyze the most reasonable walking path for the robot by acquiring the position data of the terminal and the starting point where the robot needs to walk, so as to drive the stepping motor of the robot to walk, the embodiment of the present invention first needs to perform positioning of the real-time position of the robot according to the positioning device of the robot, and acquire the relevant coordinate position data.

It should be noted that, when the robot is located in real time, the real-time position of the robot may be determined according to satellite positioning, or a specific real-time position may be determined according to a relative position generated by interconnection of other robots and the robot, and the position information of the robot may be obtained more accurately by calculating the real-time position according to the relative position, so as to perform subsequent analysis and calculation.

Optionally, before the acquiring the real-time location data, the method further includes: and acquiring the activation state of the robot.

Specifically, in order to reduce energy waste of the robot, in the embodiment of the present invention, before the robot acquires the real-time position of the robot through the position sensor, the operation state of the robot needs to be determined first, that is, the activation state of the robot is acquired, where the activation state may include: activated, not activated.

And step S104, acquiring the end point data of the robot according to the real-time position data.

Specifically, after the robot acquires the local real-time position data, the terminal position set by the server or the user side needs to be acquired according to the real-time position, and the position coordinate data of the terminal position is acquired and stored, so that the robot can perform the intelligent path planning in the following.

The end point data of the robot may be determined according to the starting and ending point positions of the robot, or may be determined according to historical walking data of the robot after a user sets preferences according to the historical data and the walking model, so that computing resources are saved, and the efficiency of the robot in traveling is increased.

And S106, inputting the real-time position data and the end point data into a walking model to generate an optimal route.

Optionally, before the inputting the real-time location data and the end point data into a walking model and generating an optimal route, the method further includes: and training the walking model.

Specifically, after acquiring the starting point and the end point that the robot needs to walk, the embodiment of the present invention may input the starting point position coordinate data and the end point position coordinate data into the walking model, and further generate the optimal route data through the output of the walking model.

It should be noted that the walking model is a DNN-based neural network model, and before the walking model is used, maturity training needs to be performed on the walking model, so that usability and accuracy of the model are increased. While neural networks are based on extensions of the perceptron, DNN can be understood as neural networks with many hidden layers. The multi-layer neural network is substantially the same as the deep neural network DNN, which is also called a multi-layer perceptron (MLP). The DNN is divided by the location of different layers, and the neural network layers can be divided into three categories, an input layer, a hidden layer and an output layer, where generally the first layer is the input layer, the last layer is the output layer, and the middle layers are all hidden layers.

It should be further noted that, in the process of generating the optimal route of the robot, walking may be determined according to a walking model, or regular analysis may be performed according to historical data of walking of a plurality of robots, walking routes exceeding a certain walking efficiency threshold in the historical data are screened, and an optimal route suitable for the walking is finally selected in combination with an actual application scenario.

And S108, performing robot walking operation according to the optimal route.

Specifically, after the optimal route is determined, the robot starts the stepping motor to walk according to the determined optimal route data, and gives a prompt when reaching the end position, so as to indicate that the robot has completed the whole walking task according to the optimal route.

Optionally, after the robot walking operation is performed according to the optimal route, the method further includes: and displaying the optimal route and the estimated walking time.

Through the embodiment, the technical problems that in the prior art, when the robot walks, the stepping motor can carry out sensor identification and judgment according to program design, so that how to walk is further determined, how to avoid the rigidity of the barrier is solved, however, the walking operation of the robot through the method often does not have certain learning performance, namely, the walking process of the robot each time is brand new and irregular, the historical data of a user when the same set of robot system is used for multiple times cannot be adapted, and the use efficiency is reduced are solved.

Example two

Fig. 2 is a block diagram of a robot walking method based on artificial intelligence according to an embodiment of the present invention, and as shown in fig. 2, the apparatus includes:

a first obtaining module 20, configured to obtain real-time position data.

Specifically, in order to calculate and analyze the most reasonable walking path for the robot by acquiring the position data of the terminal and the starting point where the robot needs to walk, so as to drive the stepping motor of the robot to walk, the embodiment of the present invention first needs to perform positioning of the real-time position of the robot according to the positioning device of the robot, and acquire the relevant coordinate position data.

It should be noted that, when the robot is located in real time, the real-time position of the robot may be determined according to satellite positioning, or a specific real-time position may be determined according to a relative position generated by interconnection of other robots and the robot, and the position information of the robot may be obtained more accurately by calculating the real-time position according to the relative position, so as to perform subsequent analysis and calculation.

Optionally, the apparatus further comprises: and the third acquisition module is used for acquiring the activation state of the robot.

Specifically, in order to reduce energy waste of the robot, in the embodiment of the present invention, before the robot acquires the real-time position of the robot through the position sensor, the operation state of the robot needs to be determined first, that is, the activation state of the robot is acquired, where the activation state may include: activated, not activated.

And a second obtaining module 22, configured to obtain end point data of the robot according to the real-time position data.

Specifically, after the robot acquires the local real-time position data, the terminal position set by the server or the user side needs to be acquired according to the real-time position, and the position coordinate data of the terminal position is acquired and stored, so that the robot can perform the intelligent path planning in the following.

The end point data of the robot may be determined according to the starting and ending point positions of the robot, or may be determined according to historical walking data of the robot after a user sets preferences according to the historical data and the walking model, so that computing resources are saved, and the efficiency of the robot in traveling is increased.

And a route module 24, configured to input the real-time location data and the destination data into a walking model, so as to generate an optimal route.

Optionally, the apparatus further comprises: and the training module is used for training the walking model.

Specifically, after acquiring the starting point and the end point that the robot needs to walk, the embodiment of the present invention may input the starting point position coordinate data and the end point position coordinate data into the walking model, and further generate the optimal route data through the output of the walking model.

It should be noted that the walking model is a DNN-based neural network model, and before the walking model is used, maturity training needs to be performed on the walking model, so that usability and accuracy of the model are increased. While neural networks are based on extensions of the perceptron, DNN can be understood as neural networks with many hidden layers. The multi-layer neural network is substantially the same as the deep neural network DNN, which is also called a multi-layer perceptron (MLP). The DNN is divided by the location of different layers, and the neural network layers can be divided into three categories, an input layer, a hidden layer and an output layer, where generally the first layer is the input layer, the last layer is the output layer, and the middle layers are all hidden layers.

It should be further noted that, in the process of generating the optimal route of the robot, walking may be determined according to a walking model, or regular analysis may be performed according to historical data of walking of a plurality of robots, walking routes exceeding a certain walking efficiency threshold in the historical data are screened, and an optimal route suitable for the walking is finally selected in combination with an actual application scenario.

And the walking module 26 is used for performing robot walking operation according to the optimal route.

Specifically, after the optimal route is determined, the robot starts the stepping motor to walk according to the determined optimal route data, and gives a prompt when reaching the end position, so as to indicate that the robot has completed the whole walking task according to the optimal route.

Optionally, the apparatus further comprises: and the display module is used for displaying the optimal route and the estimated walking time.

According to another aspect of the embodiments of the present invention, there is also provided a non-volatile storage medium including a stored program, wherein the program controls a device in which the non-volatile storage medium is located to perform an artificial intelligence based robot walking method when running.

Specifically, the method comprises the following steps: acquiring real-time position data; acquiring end point data of the robot according to the real-time position data; inputting the real-time position data and the end point data into a walking model to generate an optimal route; and carrying out robot walking operation according to the optimal route.

According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including a processor and a memory; the memory is stored with computer readable instructions, and the processor is used for executing the computer readable instructions, wherein the computer readable instructions execute an artificial intelligence based robot walking method when running.

Specifically, the method comprises the following steps: acquiring real-time position data; acquiring end point data of the robot according to the real-time position data; inputting the real-time position data and the end point data into a walking model to generate an optimal route; and carrying out robot walking operation according to the optimal route.

Through the embodiment, the technical problems that in the prior art, when the robot walks, the stepping motor can carry out sensor identification and judgment according to program design, so that how to walk is further determined, how to avoid the rigidity of the barrier is solved, however, the walking operation of the robot through the method often does not have certain learning performance, namely, the walking process of the robot each time is brand new and irregular, the historical data of a user when the same set of robot system is used for multiple times cannot be adapted, and the use efficiency is reduced are solved.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.