KR20220027832A - State determination method and device, robot, storage medium and computer program - Google Patents

Abstract

An embodiment of the present application discloses a method and apparatus for determining a state of a robot, a robot and a storage medium, the method for determining a state of a robot includes the steps of obtaining reference information of the robot - the reference information is measured by the robot corresponding to multiple times state information, the robot includes at least one of actual driving information corresponding to the current time - ; based on the reference information, determining the state noise of the robot; and using the state noise, the robot obtaining actual state information corresponding to the current time. The above method can improve the accuracy of robot state determination.

Description

State determination method and device, robot, storage medium and computer program

[Cross-Reference to Related Applications]

This application is filed based on a Chinese patent application with an application number of 202010872662.3 and an application date of August 26, 2020, and claims priority to the Chinese patent application, the entire contents of the Chinese patent application are incorporated herein by reference. cited as material.

This application relates to the field of robot technology, and more particularly, to a state determination method and apparatus, a robot, a storage medium, and a computer program.

With the development of electronic technology and computer technology, the robot is applied to courier delivery, service guide, hotel food delivery, etc., and is increasingly receiving widespread attention, and the application field of the robot is becoming more and more extensive.

For example, as artificial intelligence (AI) education continues to be hot in recent years, most of the AI education curriculum is extended based on an online curriculum platform, and is supplemented with hardware devices such as smart vehicles and smart robots. In addition to the school role, schools organize competitions based on robots, such as design competitions and autonomous driving competitions. In these competitions, players must design their own circuits and algorithms to face other players' robots in the match.

An embodiment of the present application provides a state determination method and apparatus, a robot, a storage medium, and a computer program.

An embodiment of the present application provides a method for determining the state of a robot, wherein the method includes the steps of obtaining reference information of the robot - the reference information is measurement state information corresponding to a plurality of times when the robot is running, and actual driving corresponding to a current time of the robot contains at least one of information; based on the reference information, determining the state noise of the robot; and using the state noise, the robot obtaining actual state information corresponding to the current time.

Therefore, through obtaining the reference information of the robot, the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to the current time of the robot, and based on the reference information, By determining the state noise of the robot, the state noise is used, and the robot obtains the actual state information corresponding to the current time. In the process of determining the state, there is no need to perform a simulation using a large amount of particles, so the state is determined It is advantageous to improve the speed of In addition, since the state noise is determined according to at least one of the measured state information of a plurality of times and the actual driving information of the current time, it is possible to estimate the noise from at least one of the external measurement angle of the robot and the state angle of the robot itself, so that the state noise is reduced By making it closer to the real case, the accuracy of the subsequently determined real state information is improved.

In some embodiments of the present invention, the step of determining the noise state of the robot based on the reference information includes: determining the noise interference measured by the robot by using the measurement state information corresponding to the current time and a plurality of previous times ; and determining the state transition noise of the robot by using the actual driving information of the current time.

Therefore, since the measurement interference noise of the robot is determined using the measurement state information corresponding to the current time and the plurality of previous times, the noise of the robot can be determined from the external measurement angle, thereby allowing the robot to interfere with external interference in the driving process. can be estimated; Since the state transition noise of the robot is determined using the actual driving information of the current time, the noise of the robot can be determined from the angle of the state of the robot itself, so that the internal interference of the robot in the driving process can be estimated.

In some embodiments of the present invention, the step of determining the measurement interference noise of the robot by using the measurement state information corresponding to the current time and the plurality of previous times includes the discrete degree of the measurement state information of the current time and the previous plurality of times. obtaining a; and determining the measured interference noise by using the discrete degree.

Accordingly, since the degree of discreteness of the measurement state information of the current time and the plurality of previous times is used, and the measured interference noise is determined using the discrete degree, the robot can accurately estimate the external interference in the driving process.

In some embodiments of the invention, the discrete degree of the measurement state information of the current time and the previous plurality of times is the standard difference between the current time and the measurement state information of the previous plurality of times; Correspondingly, the determining of the measured interference noise by using the discrete degree includes using a product between the discrete degree and a preset gain parameter as the measured interference noise.

Therefore, by setting the discrete degree of the measurement state information of the current time and the plurality of previous times as the standard difference of the measurement state information of the current time and the previous plurality of times, it is advantageous in reducing the determination complexity and the amount of calculation of the discrete degree, It is advantageous in improving the speed of state determination; It is advantageous in improving the measurement accuracy of the interference noise by using the product between the discrete degree and the preset gain parameter as the measurement interference noise, and it is advantageous in improving the precision of the state determination.

In some embodiments of the invention, the actual travel information includes travel angle information, motor drive information, and travel speed information of the robot; The determining of the state transition noise of the robot by using the actual driving information of the current time includes: obtaining the state transition noise of the robot by using at least one of the first state noise and the second state noise; Here, the first state noise is obtained by determining using driving angle information and driving speed information, and the second state noise is obtained by determining using motor driving information and driving speed information.

Therefore, by setting the actual driving information to include the robot's driving angle information, motor driving information, and driving speed information, the state transition noise of the robot is obtained using at least one of the first state noise and the second state noise, Since the first state noise is obtained by determining using the driving angle information and the driving speed information, and the second state noise is obtained by determining using the motor driving information and the driving speed information, it is advantageous to improve the accuracy of the state transition noise.

In some embodiments of the invention, the robot includes a driving wheel and a rotating wheel, the driving wheel is used to drive the robot traveling, and the rotating wheel is used to change the traveling direction of the robot; the traveling speed information includes an actual speed difference between driving wheels of the robot, and the traveling angle information includes an actual rotation angle of a rotating wheel of the robot; Correspondingly, using at least one of the first state noise and the second state noise, before obtaining the state transition noise of the robot, mapping processing is performed for the actual rotation angle using the first mapping relationship between the speed difference and the rotation angle. Proceeding to obtain a theoretical speed corresponding to the actual rotation angle; using the difference between the actual speed difference and the theoretical speed difference to determine a first state noise; and the robot includes a driving wheel, wherein the driving wheel is used to drive the robot travel; the traveling speed information includes an actual average speed of driving wheels of the robot, and the motor driving information includes an actual average driving signal value of a motor of the robot; Correspondingly, using at least one of the first state noise and the second state noise, before obtaining the state transition noise of the robot, the second mapping relationship between the average speed and the average driving signal value is used to calculate the actual average driving signal value. performing a mapping process to obtain a theoretical average speed corresponding to an actual average driving signal value; and determining a second state noise using the difference between the actual average velocity and the theoretical average velocity.

Therefore, the robot includes a driving wheel and a rotating wheel, the driving wheel is used to drive the robot driving, the rotating wheel is used to change the driving direction of the robot, and the driving speed information is transferred to the actual speed difference between the driving wheels of the robot. By setting the driving angle information to include the actual rotation angle of the rotating wheel of the robot, the mapping process is performed on the actual rotation angle using the first mapping relationship between the speed difference and the rotation angle, The theoretical speed difference corresponding to the rotation angle is obtained, and the first state noise is determined by using the difference between the actual speed difference and the theoretical speed difference, so from the angle of the rotating wheel of the robot, the first state noise of the robot can be determined there is; The robot includes driving wheels, the driving wheels are used to drive the robot, and the driving speed information is set to include the actual average speed of the driving wheels of the robot, and the motor driving information is set to the actual average driving signal value of the robot's motor. By setting to include, the mapping process is performed on the actual average driving signal value using the second mapping relationship between the average speed and the average driving signal value to obtain a theoretical average speed corresponding to the actual average driving signal value, Since the second state noise is determined by using the difference between the average speed and the theoretical average speed, it is possible to determine the second state noise of the robot from the angle of the driving wheel of the robot.

In some embodiments of the invention, the determining of the first state noise by using the difference between the actual speed difference and the theoretical speed difference comprises: converting the square of the difference between the actual speed difference and the theoretical speed difference to the first state noise. using; Using the difference between the actual average velocity and the theoretical average velocity, determining the second state noise includes using a square of the difference between the actual average velocity and the theoretical average velocity as the second state noise.

Therefore, since the square of the difference between the actual speed difference and the theoretical speed difference is used as the first state noise, and the square of the difference between the actual average speed and the theoretical average speed is used as the second state noise, the first state noise and the second state noise It is advantageous in reducing the complexity and amount of calculation of the two-state noise calculation, and improving the speed of state determination.

In some embodiments of the present invention, the step of obtaining real state information corresponding to the current time by the robot using the state noise includes the actual state information corresponding to the previous time and the measurement state of the current time by using the state noise. and processing the information so that the robot obtains actual state information corresponding to the current time.

Therefore, by using the state noise, the robot processes the measured state information corresponding to the current time and the actual state information corresponding to the previous time, so that the robot can be separated between the measured state information at the current time and the actual state information at the previous time. Since it is advantageous to balance , it is advantageous to improve the precision of robot state determination by allowing the determined and obtained actual state information to be corrected compared to the measured state information.

In some embodiments of the invention, the robot processes the actual state information corresponding to the previous time and the measured state information of the current time by using the state noise, so that the robot obtains the real state information corresponding to the current time, The filtering gain is determined based on the noise, and the robot predicts the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time, obtains the predicted state information corresponding to the current time, and performs Kalman filtering of the filtering gain. and fusing the predicted state information of the current time and the measured state information of the current time by using the robot to obtain the actual state information corresponding to the current time.

Therefore, the filtering gain is determined based on the state noise, and the robot predicts the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time to obtain the predicted state information corresponding to the current time, By using Kalman filtering, the predicted state information of the current time and the measured state information of the current time can be fused to enhance the robustness of the external signal, thereby accurately determining the actual state information corresponding to the current time.

In some embodiments of the invention, after determining the state noise of the robot based on the reference information, the method further includes: if the state noise does not satisfy the preset noise condition, proceeding with a preset prompt.

Accordingly, when the state noise does not satisfy the preset noise condition, a preset prompt is performed, so that the user can detect the abnormal state noise, thereby improving the user experience.

In some embodiments of the invention, the state noise includes measured interference noise obtained by using measured state information of a current time and a plurality of previous times; Correspondingly, the preset noise condition includes that the measured interference noise is less than the first noise threshold; If the state noise does not satisfy the preset noise condition, the step of performing the preset prompt may include outputting a first warning message if the measured interference noise does not satisfy the preset noise condition - the first warning message is the state to prompt that the measurement is subject to interference; and the state noise includes state transition noise obtained using actual driving information at the current time; Correspondingly, the preset noise condition includes that the state transition noise is less than the second noise threshold; If the state noise does not satisfy the preset noise condition, the step of performing the preset prompt may include outputting a second warning message if the state transition noise does not satisfy the preset noise condition - The second warning message is the robot to prompt that a risk of body slipping exists on the

Therefore, when the measured interference noise does not satisfy the preset condition, a first warning message is output to prompt that the state measurement is being interfered, so that when the state measurement is interfered, the user can timely detect it Therefore, it improves the user experience; When the state transition noise does not satisfy the preset condition, a second warning message is output to prompt the robot that there is a risk of vehicle body sliding, so that when a risk of vehicle body sliding appears in the robot, the user can timely By making it detectable, it improves the user experience.

In some embodiments of the invention, the step of obtaining the reference information of the robot may include: obtaining environment image data corresponding to the current time by collecting images of the surrounding environment of the robot; and determining, by the robot, measurement state information corresponding to the current time, based on the environmental image data of the current time; The measured state information and the actual state information include at least one of a position of the robot, a posture of the robot, and a speed of the robot.

Therefore, by collecting images for the environment around the robot, the number of users of the environment image corresponding to the current time is obtained, and based on the environment image data of the current time, the robot determines the measurement state information corresponding to the current time, , by setting the measured state information and the actual state information to include at least one of the position of the robot, the posture of the robot, and the speed of the robot, the robot can obtain the measured state information corresponding to the current time, thereby determining the speed of the robot state It is advantageous to improve

An embodiment of the present application provides an apparatus for determining a state of a robot, the apparatus comprising a measurement state obtaining module, a state noise determining module and an actual state obtaining module, the measurement state obtaining module is configured to obtain reference information of the robot become; Here, the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to the current time of the robot; the state noise determining module is configured to determine, according to the reference information, the state noise of the robot; The real state acquisition module is configured to use the state noise to enable the robot to acquire real state information corresponding to the current time.

An embodiment of the present application provides a robot, including a robot body and a memory and a processor installed in the robot body, the memory and the processor are coupled to each other, the processor executes a program instruction stored in the memory, the state determination method is to implement.

An embodiment of the present application provides a computer-readable storage medium, in which program instructions are stored, and when the program instructions are executed by a processor, the method for determining the state is implemented.

An embodiment of the present application provides a computer program including a computer readable code, and when the computer readable code is operated in a robot, a processor in the robot implements the method for determining the state.

An embodiment of the present application provides a method and apparatus for determining a state, a robot, a storage medium, and a computer program, and obtains reference information of the robot, the reference information is the measurement state information corresponding to a plurality of times of the robot, the current time of the robot includes at least one of the actual driving information corresponding to In the process, there is no need to perform simulation using bulk particles, which is advantageous in improving the speed of state determination. In addition, since the state noise is determined according to at least one of the acquired plurality of measured state information and the current actual driving information, the noise can be estimated from at least one of the external measurement angle of the robot and the state angle of the robot itself, so that the state noise is actually By making the case and more accessible, the accuracy of future determined actual state information is improved.

1 is a flowchart illustrating an embodiment of a method for determining a state of a robot in an embodiment of the present application.
2 is a flowchart illustrating the determination of actual state information of a robot using a Kalman filter in an embodiment of the present application.
3 is an architectural diagram of an embodiment of the state determination apparatus of the robot in the embodiment of the present application.
4 is an architectural diagram of an embodiment of a robot in an embodiment of the present application.
5 is an architectural diagram of an embodiment of a computer-readable storage medium in an embodiment of the present application.

In conjunction with the drawings of the specification below, a detailed description of the method of the embodiment of the present application proceeds.

The following description is for illustrative purposes only, and is not intended to be limiting, and details such as specific system structures, interfaces, and techniques are provided for a thorough understanding of the present application.

In this text, the terms “system” and “network” are often used interchangeably in this text. As used herein, the term “and/or” is only used to describe the correlation of related objects, and indicates that three relationships exist, for example, A and/or B are, A exists alone; It represents three situations where A and B exist simultaneously and B exists alone. In addition, the symbol “/” in the main text generally means that the related object before and after is “or”. In addition, "plurality" in this specification refers to two or two or more.

As AI education continues to be hot in recent years, most of the AI education curriculum is extended based on an online curriculum platform, and is supplemented with hardware devices such as smart vehicles and smart robots. In addition to the school role, schools organize competitions based on robots, such as design competitions and autonomous driving competitions. In these competitions, players must design their own circuits and algorithms to face other players' robots in the match.

In such a match, the robot generally has to receive information from all rounds of the match from the master computer, which includes information very important to the robot's decision, such as the robot's own position, speed and posture. The communication between the robot and the master computer basically uses all serial interface communication, and all players must know the communication rules in advance. Therefore, there may be cases in which some players place an interferer on the robot to transmit an incorrect communication signal to other players, thereby misleading opponents. The most common is to interfere with the judgment of an opponent by sending incorrect position, speed and attitude information. If the algorithm of the interfered robot is not robust, out of control may appear.

In addition to competitions, there are robots operated using communication collection information in some artificial smart exhibition halls. . For example, when the vehicle slides, etc. occurs, the fusion positioning system used in the vehicle may exhibit positioning drift, etc., which is also an inherent positioning hijacking problem. Currently, in the existing method, a lot of abduction resistance is performed using the method of particle filtering. This method requires hundreds of thousands of particle simulations for each smart body, and the amount of calculation increases as the space expands, so there are multiple robots in the field. It is difficult to meet the demand of real-time computation when it exists.

At the same time, in various other application scenarios, the robot may receive interference such as white noise and even interference signals that exist widely in free space in the course of driving, thereby affecting the normal driving of the robot, and in severe cases, the robot may You may even experience things like out of control, slipping, etc.

Based on this, the embodiment of the present application provides a method for determining the state of the robot, thereby improving the accuracy of determining the state of the robot. Referring to FIG. 1, FIG. 1 is a flowchart illustrating an embodiment of a method for determining a state of a robot in an embodiment of the present application. The method steps provided in the embodiments of the present application may be executed through a hardware device such as a robot, or may be executed through a method in which a processor operates computer-executable code. The state determination method may include the following steps.

In step S11, reference information of the robot is obtained.

In an embodiment of the present application, the reference information of the robot may include at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to a current time of the robot.

It should be noted that the state of the robot may change at different times, for example, when the robot moves at the current time compared to the previous adjacent time, and, of course, in other application scenarios, the state of the robot changes. It is not, and a person skilled in the art can determine it according to the actual operation case of the robot. In contrast, the robot determines the actual state at different viewpoints, which is convenient for further operation.

In the embodiment of the present application, in order to determine the actual state information corresponding to the current time, the measurement state information corresponding to the current time may be first obtained, so that based on the steps in the embodiment of the present application, the current time The actual state information corresponding to the current time is obtained by using the corresponding measurement state information. It can be understood that the information corresponding to the specific time described in this specification is not necessarily acquired at the time, but may be acquired around the time. For example, the measurement state information corresponding to the current time may be obtained at the current time; When the communication delay is considered, the measurement state information corresponding to the current time may be obtained at a plurality of times before the current time (eg, 0.5 seconds ago, 1 second ago, etc.), but is not limited thereto.

In some embodiments of the invention, the measurement state information is obtained by performing state measurement on the robot, and in the implementation scenario of the invention, the environment image data corresponding to the current time can be obtained by collecting the surrounding environment of the robot, Based on the environmental image data of the current time, the robot may determine measurement state information corresponding to the current time. For example, image collection may be performed on the surrounding environment of the robot through a photographing device mounted in the robot driving environment; Alternatively, image collection may be performed on the surrounding environment through a photographing device mounted on the robot, but the present invention is not limited thereto. In some embodiments of the invention, in order to accurately describe the state of the robot, the measured state information and the actual state information of the robot may include at least one of a position of the robot, a state of the robot, and a speed of the robot. For example, the position of the robot may include position coordinates (eg, latitude) at which the robot is located, and the state of the robot may include a driving state (eg, acceleration) of the robot. Taking as an example that the measurement state information includes the position of the robot and the speed of the robot, for ease of explanation, the measurement state information corresponding to the current time may be expressed by Formula (1).

(1);

(One);

는 로봇이 현재 시각(

)에 대응되는 측정 상태 정보를 표시하며,

는 측정 상태 정보에서 로봇의 위치를 표시하며,

는 측정 상태 정보에서 로봇의 속도를 표시한다.In the above formula,

is the current time (

) to display the measurement status information corresponding to

indicates the position of the robot in the measurement status information,

indicates the speed of the robot in the measurement status information.

Similarly, taking as an example that the actual state information includes the robot's position and the robot's speed, the actual state information corresponding to the current time may be expressed by Formula (2) for ease of explanation.

(2);

(2);

는 로봇이 현재 시각(

)에 대응되는 실제 상태 정보를 표시하며,

는 실제 상태 정보에서 로봇의 위치를 표시하며,

는 실제 상태 정보에서 로봇의 속도를 표시한다.In the above formula,

is the current time (

) displays the actual status information corresponding to

indicates the position of the robot in the actual state information,

indicates the speed of the robot in the actual state information.

시각인 것으로 예로 들면, 현재 시각 이전의 복수 시각은

시각 이전의

개 시각으로 표시할 수 있으며,

의 값은 실제 응용 수요에 따라 설정될 수 있으며, 예컨대,

은 5, 10, 15등일 수 있으며, 여기서 한정하지 않는다.In addition, the measurement state information corresponding to a plurality of times by the robot may include measurement state information corresponding to a current time and a plurality of previous times by the robot. the current time

For example, a plurality of times before the current time are

before time

Can be displayed in dog time,

The value of can be set according to the actual application demand, for example,

may be 5, 10, 15, etc., but is not limited thereto.

In another exemplary embodiment scenario, the actual driving information may include driving angle information, motor driving information, and driving speed information of the robot. Here, the driving angle information may be obtained from the steering gear control record of the robot, and the robot may include a rotating wheel, and the steering gear of the robot is for driving the rotating wheel of the robot to rotate at a predetermined angle. The motor driving information may be obtained from a motor control record of the robot, and the robot may include a floating driving wheel, and the motor of the robot is for driving the driving wheel of the robot to move at a constant speed. The traveling speed information may be obtained from an encoder of the robot.

In step S12, based on the reference information, the state noise of the robot is determined.

The state noise of the robot refers to noise that the robot generates an influence on the state in the course of traveling, for example, it is a state transition noise generated when the robot transitions from a specified state to another state; Alternatively, it is measurement interference noise generated while the robot receives measurement state information, but is not limited thereto.

In an implementation scenario of one invention, the measurement interference noise of the robot may be determined by using measurement state information corresponding to the current time and the plurality of previous times. Here, a plurality of times before the current time may refer to the above description. Therefore, by being able to determine the noise of the robot from the external measurement angle, it is possible to estimate the external interference in the course of the robot traveling.

In an implementation scenario of one invention, a discrete degree of measurement state information of a current time and a plurality of previous times may be acquired, and the measurement interference noise is determined using the discrete degree. For example, the discrete degree of the measurement state information of the current time and the plurality of previous times may be a standard difference between the current time and the measurement state information of the plurality of previous times. Also, for example, the degree of dispersion of the measurement state information of the current time and the plurality of previous times may be the dispersion of the measurement state information of the current time and the plurality of previous times, but is not limited thereto. Therefore, it is advantageous in reducing the complexity and calculation amount of discrete degree determination, and is advantageous in improving the speed of state determination.

In another inventive embodiment scenario, also the product between the discrete degree and a preset gain parameter may be used as the measured interference noise. The preset gain parameter may be set according to an actual case, and is not limited thereto. In some inventive embodiments, the measured interference noise can be expressed by Equation (3).

(3);

(3);

은 측정 간섭 소음을 표시하며,

는 현재 시각(

) 및 이전의

개 시각에 대응되는 측정 상태 정보를 표시하며,

는 현재 시각(

) 및 이전의

개 시각에 대응되는 측정 상태 정보의 표준 차를 표시하며,

는 기설정된 이득 파라미터를 표시하며, 여기서, 기설정된 이득 파라미터는 0보다 큰 값일 수 있으며, 예를 들어, 0.5, 1, 1.5 등이며, 여기서 한정하지 않는다.In the above formula,

indicates the measured interference noise,

is the current time (

) and before

Displays measurement status information corresponding to the opening time,

is the current time (

) and before

Displays the standard difference of measurement status information corresponding to the time of opening,

denotes a preset gain parameter, where the preset gain parameter may be a value greater than 0, for example, 0.5, 1, 1.5, etc., but is not limited thereto.

시각인 것으로 예를 들면,

시각의 실제 주행 정보를 이용하여, 로봇의 상태 전이 소음을 결정할 수 있으며, 따라서, 로봇 자체 상태의 각도로부터 로봇의 소음을 결정할 수 있음으로써, 로봇이 주행 과정에서의 내부의 간섭을 가늠할 수 있다. 일부 발명의 실시예에 있어서, 제1 상태 소음, 제2 상태 소음 중 적어도 하나에 따라, 로봇의 상태 전이 소음을 얻을 수 있으며, 제1 상태 소음은 주행 각도 정보 및 가속도 정보를 이용하여 얻은 것이며, 제2 상태 소음은 모터 구동 정보 및 주행 속도 정보를 이용하여 결정하여 얻은 것이다.In another embodiment scenario, the state transition noise of the robot may be determined by using the actual driving information of the current time. the current time

Visually, for example,

By using the real driving information of the time, it is possible to determine the noise of the robot's state transition, and thus, the noise of the robot can be determined from the angle of the state of the robot itself, so that the internal interference of the robot in the driving process can be estimated. In some embodiments of the invention, the state transition noise of the robot may be obtained according to at least one of the first state noise and the second state noise, and the first state noise is obtained by using travel angle information and acceleration information, The second state noise is obtained by determining using the motor driving information and the driving speed information.

In one implementation scenario, it can be considered from the angle of the steering gear, and by using the driving angle information and the driving speed information to determine the first state noise of the robot, using the first state noise, the state transition noise of the robot decide For example, the first state noise of the robot may be determined using the travel angle information and the travel speed information, and the first state noise may be used as the state transition noise of the robot.

In another implementation scenario, it can also be considered from the angle of the motor, and by using the motor driving information and the traveling speed information to determine the noise of the second state of the robot, using the second state, the noise of the state transition of the robot can decide For example, the second state noise of the robot may be determined by using the motor driving information and the traveling speed information, and the second state noise may be used as the state transition noise of the robot.

In another implementation scenario, the first state noise of the robot may be determined using the driving angle information and the driving speed information, and the second state noise of the robot may be determined using the motor driving information and the driving speed information. , by using the first state noise and the second state noise to obtain the state transition noise of the robot, the angle of the steering gear and the angle of the motor can be considered at the same time, which is advantageous in improving the accuracy of the state transition noise.

In some embodiments of the invention, when the state transition noise is obtained using the first state noise and the second state noise, weighting processing is performed on the first state noise and the second state noise to obtain the state transition noise can In addition, weights corresponding to the first state noise and the second state noise may be set according to actual cases. For example, when the noise of the first state is more important than the noise of the second state, a weight corresponding to the noise of the first state may be set to be greater than the weight of the noise of the second state; Also, for example, when the noise in the second state is more important than the noise in the first state, a weight corresponding to the noise in the second state may be set to be greater than the weight of the noise in the second state. In addition, the weight corresponding to the first state noise may be set to be the same as the weight corresponding to the second state noise, for example, the weight corresponding to the first state noise is set to 0.5, and the weight corresponding to the second state noise set to 0.5.

으로 표시할 수 있고, 주행 각도 정보는 로봇의 회전 바퀴의 실제 회전 각도를 포함할 수 있으며, 설명의 용이함을 위해, 실제 회전 각도를

로 표시할 수 있으며, 속도 차와 회전 각도 사이의 제1 맵핑 관계를 이용하여(설명의 용이함을 위해 제1 맵핑 관계를

로 표시할 수 있음) 실제 회전 각도(

)에 대해 맵핑 처리를 진행하여, 실제 회전 각도(

)에 대응되는 이론 속도 차를 얻을 수 있음으로써(설명의 용이함을 위해 이론 속도차를

로 표시할 수 있음), 실제 속도 차(

) 및 이론 속도 차(

) 사이의 차이를 이용하여, 제2 상태 소음을 결정할 수 있으며, 예컨대, 실제 속도 차(

) 및 이론 속도 차(

) 사이의 차의 제곱을 제1 상태 소음으로 사용할 수 있다. 제1 맵핑 관계는 미리 수집하여 얻은 속도 차와 회전 각도에 대해 통계 분석을 진행하여 얻을 수 있으며, 예컨대, 로봇이 정상적인 주행 과정에서, M쌍의 속도 차와 회전 각도를 수집하고, 수집하여 얻은 M 쌍의 속도 차와 회전 각도를 피팅하여, 속도 차와 회전 각도 사이의 제1 맵핑 관계를 얻으며, M의 값은 실제 경우에 따라 설정될 수 있으며, 여기서 한정하지 않는다.In some embodiments of the invention, the robot may include a driving wheel and a rotating wheel, the driving wheel is used to drive the robot traveling, and the rotating wheel is used to change the traveling direction of the robot, so that the traveling speed information is may include the actual speed difference between the driving wheels of For example, since the robot includes two driving wheels, the speed difference between the two driving wheels is an actual speed difference. For ease of explanation, the actual speed difference is

may be displayed, and the driving angle information may include the actual rotation angle of the rotating wheel of the robot, and for ease of explanation, the actual rotation angle

It can be expressed as , and using the first mapping relationship between the speed difference and the rotation angle (for ease of explanation, the first mapping relationship

can be expressed as) the actual angle of rotation (

), the actual rotation angle (

) by being able to obtain a theoretical speed difference corresponding to

can be expressed as ), the actual speed difference (

) and the theoretical speed difference (

) can be used to determine the second state noise, for example, the actual speed difference (

) and the theoretical speed difference (

) can be used as the first state noise. The first mapping relationship can be obtained by performing statistical analysis on the speed difference and rotation angle obtained by collecting in advance. For example, in the normal driving process of the robot, M pairs of speed difference and rotation angle are collected, and M obtained by collecting By fitting the pair of speed difference and rotation angle, a first mapping relationship between the speed difference and the rotation angle is obtained, and the value of M may be set according to an actual case, without limitation here.

로 표시할 수 있으며, 모터 구동 정보는 로봇 모터의 실제 평균 구동 신호 값을 포함할 수 있으며, 즉 로봇의 각 구동 바퀴에 대응되는 모터의 신호 평균 값이다. 예컨대, 로봇은 두 개의 구동 바퀴를 포함하며, 구동 신호가 펄스 폭 변조 신호일 경우, 실제 평균 구동 신호 값은 두 개의 구동 바퀴에 대응되는 모터의 펄스 폭 변조 신호 평균 값일 수 있으며, 설명의 용이함을 위해 평균 구동 신호 값을

로 표시할 수 있으며, 평균 속도와 평균 구동 신호 값 사이의 제2 맵핑 관계를 이용하여(설명의 용이함을 위해 제2 맵핑 관계를

로 표시할 수 있음) 실제 평균 구동 신호 값에 대해 맵핑 처리를 진행하여, 실제 평균 구동 신호 값에 대응되는 이론 평균 속도(설명의 용이함을 위해 이론 평균 속도를

로 표시할 수 있음)를 얻을 수 있음으로써, 실제 평균 속도 및 이론 평균 속도 사이의 차이를 이용하여, 제2 상태 소음을 결정할 수 있다. 예컨대, 실제 평균 속도(

) 및 이론 평균 속도(

) 사이의 차의 제곱을 제2 상태 소음으로 사용할 수 있다. 제2 맵핑 관계는 미리 수집하여 얻은 복수 쌍의 평균 속도와 평균 구동 신호 값에 대해 통계 분석을 진행하여 얻을 수 있다. 예컨대, 로봇의 정상적인 주행 과정에서, N쌍의 평균 속도와 평균 구동 신호값을 수집하고, N쌍의 평균 속도와 평균 구동 신호 값을 피팅하여, 평균 속도와 평균 구동 신호 값 사이의 제2 맵핑 관계를 얻으며, N의 값은 실제 경우에 따라 설정될 수 있으며, 여기서 한정하지 않는다.In some inventive embodiments, the traveling speed information may also include an actual average speed of the driving wheels, that is, an average value of the speed of each driving wheel of the robot. For example, since the robot includes two driving wheels, the average speed value of the two driving wheels is the actual average speed, and for ease of explanation, the actual average speed is

may be displayed, and the motor driving information may include the actual average driving signal value of the robot motor, that is, the average value of the motor signal corresponding to each driving wheel of the robot. For example, the robot includes two driving wheels, and when the driving signal is a pulse width modulated signal, the actual average driving signal value may be the average value of the pulse width modulated signal of the motor corresponding to the two driving wheels, for ease of explanation average drive signal value

It can be expressed as , using the second mapping relationship between the average speed and the average driving signal value (for ease of explanation, the second mapping relationship is

(can be expressed as )) The mapping process is performed on the actual average driving signal value, and the theoretical average speed corresponding to the actual average driving signal value (for ease of explanation, the theoretical average speed is

) can be obtained, so that the second-state noise can be determined using the difference between the actual average velocity and the theoretical average velocity. For example, the actual average speed (

) and the theoretical average speed (

) can be used as the second state noise. The second mapping relationship may be obtained by performing statistical analysis on the average speed and average driving signal values of a plurality of pairs obtained by collecting in advance. For example, in the normal driving process of the robot, the average speed and the average driving signal value of N pairs are collected, and the average speed of the N pairs and the average driving signal value are fitted, so that a second mapping relationship between the average speed and the average driving signal value is obtained. , and the value of N may be set according to actual cases, and is not limited thereto.

Through the above steps, it is possible to obtain the state transition noise of the robot, where the state transition noise can be expressed by Equation (4).

(4);

(4);

는 로봇의 상태 전이 소음을 표시하며,

은 제1 상태 소음에 대응되는 가중치를 표시하며,

는 제2 상태 소음에 대응되는 가중치를 표시하며,

는 제1 상태 소음을 표시하며,

는 제2 상태 소음을 표시하며,

는 실제 속도 차를 표시하며,

은 제1 맵핑 관계를 표시하며,

는 실제 회전 각도를 표시하며,

는 실제 평균 속도를 표시하며,

는 제2 맵핑 관계를 표시하며,

은 평균 구동 신호 값을 표시한다.In the above formula,

indicates the state transition noise of the robot,

indicates a weight corresponding to the first state noise,

denotes a weight corresponding to the second state noise,

indicates the first state noise,

indicates the second state noise,

represents the actual speed difference,

denotes a first mapping relationship,

indicates the actual rotation angle,

represents the actual average speed,

represents the second mapping relationship,

indicates the average driving signal value.

In the implementation scenario of the invention, the state transition noise and the measured interference noise may be obtained through the above steps. Of course, in the case of actual application, according to the actual situation, the state transition noise may be obtained through the above steps, and the measured interference noise may be set to a single fixed value. For example, considering an ideal situation, the measured interference noise can be set to 0, that is, the direct state transition noise is used as the state noise of the robot. For example, the measured interference noise may be set to a non-zero value such as 1, 2, 3, etc., for example, also the measured interference noise may be set to white noise, but is not limited thereto. Of course, also according to the actual situation, the measured interference noise can be obtained through the above steps, and the state transition noise can be set to one fixed value, for example, considering the ideal situation, the state transition noise can be set to zero. In other words, the direct measurement interference noise is used as the state noise of the robot, for example, the state transition noise can be set to a non-zero value such as 1, 2, 3, etc. can be set, but is not limited thereto.

In step S13, the robot obtains actual state information corresponding to the current time by using the state noise.

In some embodiments of the present invention, the robot may obtain real state information corresponding to the current time by processing the actual state information corresponding to the previous time and the measured state information of the current time by using the state noise. For example, by combining state noise using Kalman filtering, the robot processes real state information corresponding to the previous time and measured state information of the current time, so that the robot can obtain real state information corresponding to the current time.

In the implementation scenario of the invention, the filtering gain can be determined based on the state noise, and the robot predicts the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time to obtain the predicted state information corresponding to the current time. By using Kalman filtering of the filtering gain, the predicted state information of the current time and the measured state information of the current time are fused, so that the robot obtains the actual state information corresponding to the current time.

In the implementation scenario of the invention, referring to FIG. 2 , FIG. 2 is a flowchart illustrating the determination of the actual state information of the robot using Kalman filtering in the embodiment of the present application, and the method steps provided in the embodiment of the present application are It may be executed by a hardware device such as a robot, or it may be executed by a processor running computer-executable code. Here, it is possible to determine the actual state information of the robot by using Kalman filtering through the following steps.

In step S21, the a priori estimated covariance corresponding to the previous time is processed using the state transition parameter and the state transition noise of the robot to obtain the a priori estimated covariance corresponding to the current time.

시각이며, 현재 시각의 이전 인접 시각을

시각인 것으로 예들 들면, 상기 현재 시각에 대응되는 선험적 추정 공분산을 공식 (5)로 표시할 수 있다.In some embodiments of the invention, the current time is

time, the previous adjacent time of the current time

As the time, for example, the a priori estimated covariance corresponding to the current time may be expressed by Equation (5).

(5);

(5);

는 현재 시각에 대응되는 선험적 추정 공분산을 표시하며,

은 이전 인접 시각에 대응되는 후험적 추정 공분산을 표시하며, 후험적 추정 공분산은 이전 인접 시각의 실제 상태 정보(

)의 공분산을 표시하며, 즉 이전 인접 시각의 실제 상태 정보(

)의 불결정도이다. 설명해야 할 것은, 본 출원의 실시 시나리오에서 설명한 것은 현재 시각에 대응되는 실제 상태 정보(

)를 획득하는 단계이므로, 이전 인접 시각의 실제 상태 정보(

)는 발명의 실시 시나리오에서 개시된 단계를 참조하여 얻을 수 있다. 후험적 추정 공분산의 획득 방식은 본 출원의 실시예에서의 하기 설명을 참조할 수 있다. 또한,

는 매트릭스 형태의 로봇의 상태 전이 파라미터를 표시하며, 상태 전이 파라미터(

)는 로봇의 움직임 모델을 표시하는데 사용된다. 예컨대, 상태 전이 파라미터(

)를 사용하여 로봇이 일정한 가속도로 가속 운동하거나, 로봇이 일정한 속도로 등속 운동하는 것을 나타낼 수 있으며, 사용자에 의해 설정될 수 있으며,

는 상태 전이 파라미터의 전치(transposition)를 표시하며,

는 상태 전이 소음을 표시하며, 계산 방식은 이전 관련 설명을 참조할 수 있다.In the above formula,

denotes the a priori estimated covariance corresponding to the current time,

represents the retrospective estimated covariance corresponding to the previous adjacent time, and the retrospective estimated covariance is the actual state information (

), that is, the actual state information (

) is the indeterminacy of It should be explained that the actual state information corresponding to the current time (

), so the actual state information (

) can be obtained by referring to the steps disclosed in the implementation scenario of the invention. For a method of obtaining a retrospective estimated covariance, reference may be made to the following description in Examples of the present application. also,

represents the state transition parameters of the robot in matrix form, and the state transition parameters (

) is used to indicate the robot's motion model. For example, state transition parameters (

) can be used to indicate that the robot accelerates with a constant acceleration or that the robot moves with a constant velocity at a constant speed, and can be set by the user,

denotes the transposition of the state transition parameter,

indicates the state transition noise, and the calculation method can refer to the previous related description.

In step S22, the a priori estimated covariance corresponding to the current time is processed using the conversion parameter of the measured state information from the actual state information and the measured interference noise, and a filtering gain corresponding to the current time is obtained.

시각이며, 현재 시각의 이전 인접 시각이

시각인 것으로 예를 들면, 상기 현재 시각에 대응되는 필터링 이득은 공식 (6)으로 표시할 수 있다.In some embodiments of the invention, the current time is still

time, the previous adjacent time of the current time

As the time, for example, the filtering gain corresponding to the current time may be expressed by Equation (6).

(6);

(6);

는 현재 시각에 대응되는 필터링 이득을 표시하며,

는 매트릭스 형태의 변환 파라미터를 표시하며, 변환 파라미터(

)는 실제 상태 정보 및 측정 상태 정보 사이의 변환 관계를 설명하는데 사용되며, 예를 들어 실제 상태 정보 및 측정 상태 정보가 선형 관계인 것을 설명하는데 사용되며, 예컨대, 변환 파라미터

는 사용자가 설정을 진행할 수 있으며, 예를 들어 변환 파라미터(

)를 단위 매트릭스로 설정할 수 있으며, 여기서 한정하지 않으며,

는 변환 파라미터의 전치를 표시하며,

는 측정 간섭 소음을 표시하며, 계산 방식은 이전 관련 설명을 참조할 수 있다.

는 현재 시각에 대응되는 선험적 추정 공분산을 표시하며, 선험적 추정 공분산(

)은 현재 시각에 대응되는 예측 상태 정보(

)의 공분산을 표시하며, 즉 현재 시각에 대응되는 예측 상태 정보(

)의 불결정도이며, 계산 방식은 이전 관련 설명을 참조할 수 있다. In the above formula,

indicates the filtering gain corresponding to the current time,

represents the transformation parameters in matrix form, and the transformation parameters (

) is used to describe the conversion relationship between the actual state information and the measured state information, for example, used to describe that the actual state information and the measured state information are a linear relationship, for example, a conversion parameter

can be set by the user, for example, conversion parameters (

) can be set as the unit matrix, without limitation,

denotes the transpose of the conversion parameter,

denotes the measured interference noise, and the calculation method may refer to the previous related description.

represents the a priori estimated covariance corresponding to the current time, and the a priori estimated covariance (

) is the predicted state information corresponding to the current time (

), that is, the predicted state information corresponding to the current time (

), and the calculation method can refer to the previous related description.

Accordingly, the filtering gain corresponding to the current time may be determined through the measured interference noise and state transition noise. In some embodiments of the invention, at least one of the measured interference noise and the state transition noise is calculated and obtained through the above-described steps, for example, the measured interference noise is obtained by calculating using the above-described steps, or the state transition noise is It is calculated and obtained using the above-described steps, or both the measured interference noise and the state transition noise are obtained by calculating using the above-described steps, but is not limited thereto.

In step S23, the robot processes the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time, respectively, using the state transition parameter and the input state transition parameter of the robot, and predicts state information corresponding to the current time to get

시각이고, 현재 시각의 이전 인접 시각을

시각인 것으로 예를 들면, 상기 현재 시각에 대응되는 예측 상태 정보는 공식 (7)로 표시할 수 있다.In some embodiments of the invention, the current time is still

time, the previous adjacent time of the current time

As the time, for example, the predicted state information corresponding to the current time may be expressed by formula (7).

(7);

(7);

는 현재 시각에 대응되는 예측 상태 정보를 표시하며,

는 이전 인접 시각에 대응되는 실제 상태 정보를 표시하며, 예를 들어 전술한 바와 같이, 본 출원의 실시 시나리오에서 설명한 것은 현재 시각에 대응되는 실제 상태 정보(

)를 획득하는 단계이므로, 이전 인접 시각의 실제 상태 정보(

)는 본 출원의 실시 시나리오에서 개시된 단계를 참조하여 얻을 수 있다. 특히,

가 0일 경우, 실제 상태 정보(

) 는 0으로 초기화 설정될 수 있으며,

는 이전 인접 시각에 대응되는 실제 주행 정보를 표시하며, 실제 주행 정보는 로봇의 주행 각도 정보, 모터 구동 정보 및 주행 속도 정보를 포함할 수 있고, 획득 방식은 전술한 발명의 실시예에서의 관련 설명을 참조할 수 있다.

는 로봇의 상태 전이 파라미터를 표시하며, 이전 관련 설명을 참조할 수 있으며,

는 입력 상태 전이 파라미터를 표시하며, 입력 상태 전이 파라미터(

)는 입력된 실제 주행 정보 및 상태 정보 사이의 전환 관계를 설명하는데 사용됨으로써, 입력 상태 전이 파라미터(

)를 통해 입력된 실제 주행 정보를 상태 정보로 전환할 수 있으며, 로봇이 이전 인접 시각에 대응되는 실제 상태 정보와 결합하여, 로봇이 현재 시각에 대응되는 예측 상태 정보를 얻으며, 즉 이론 상으로, 로봇이 현재 시각에 대응되는 상태 정보를 얻는다.In the above formula,

displays the predicted state information corresponding to the current time,

indicates the actual state information corresponding to the previous adjacent time, for example, as described above, the actual state information corresponding to the current time (

), so the actual state information (

) can be obtained by referring to the steps disclosed in the implementation scenario of the present application. especially,

is 0, the actual state information (

) can be initialized to 0,

displays actual driving information corresponding to the previous adjacent time, the actual driving information may include driving angle information, motor driving information, and driving speed information of the robot, and the acquisition method is related to the above-described embodiment of the present invention can refer to

indicates the state transition parameters of the robot, you can refer to the previous related description,

indicates the input state transition parameter, and the input state transition parameter (

) is used to describe the transition relationship between the input actual driving information and the state information, so that the input state transition parameter (

), the input real driving information can be converted into state information, and the robot is combined with the real state information corresponding to the previous adjacent time, so that the robot obtains the predicted state information corresponding to the current time, that is, in theory, The robot obtains status information corresponding to the current time.

In step S24, the predicted state information of the current time and the measured state information of the current time are fused, so that the robot obtains the actual state information corresponding to the current time.

시각이고, 현재 시각의 이전 인접 시각이

시각인 것으로 예를 들면, 상기 현재 시각에 대응되는 실제 상태 정보는 공식 (8)로 표시할 수 있다.In some embodiments of the invention, the current time is still

time, and the previous adjacent time of the current time is

As the time, for example, the actual state information corresponding to the current time may be expressed by formula (8).

(8);

(8);

는 현재 시각에 대응되는 실제 상태 정보를 표시하며,

는 현재 시각에 대응되는 예측 상태 정보를 표시하며,

는 현재 시각에 대응되는 필터링 이득을 표시하며,

는 현재 시각에 대응되는 측정 상태 정보를 표시하며,

는 실제 상태 정보로부터 측정 상태 정보의 변환 파라미터를 표시하며, 이전 관련 설명을 참조할 수 있다. 즉

는 측정 상태 정보와 예측 상태 정보 사이의 잔차를 표시하며, 필터링 이득(

) 및 예측 상태 정보를 이용하여 상기 잔차를 수정하여, 로봇이 현재 시각에 대응되는 실제 상태 정보(

)를 얻는다.In the above formula,

displays the actual status information corresponding to the current time,

displays the predicted state information corresponding to the current time,

indicates the filtering gain corresponding to the current time,

displays measurement status information corresponding to the current time,

indicates the conversion parameter of the measured state information from the actual state information, and the previous related description may be referred to. In other words

denotes the residual between the measured state information and the predicted state information, and the filtering gain (

) and the predicted state information to correct the residual, so that the robot has the actual state information (

) to get

In step S25, the a priori estimated covariance corresponding to the current time is updated using the filtering gain and the transform parameter to obtain the a priori estimated covariance corresponding to the current time.

시각이고, 현재 시각의 이전 인접 시각이

시각인 것으로 예를 들면, 상기 현재 시각에 대응되는 후험적 추정 공분산은 공식 (9)로 표시할 수 있다.In some embodiments of the invention, the current time is still

time, and the previous adjacent time of the current time is

As the time, for example, the retrospective estimated covariance corresponding to the current time may be expressed by Equation (9).

(9);

(9);

는 현재 시각에 대응되는 후험적 추정 공분산을 표시하며,

는 단위 매트릭스를 표시하며,

는 매트릭스 형태의 필터링 이득을 표시하며,

는 매트릭스 형태의 변환 파라미터를 표시하며,

는 매트릭스 형태의 현재 시각에 대응되는 선험적 추정 공분산을 표시한다. 특히,

가 0일 경우, 후험적 추정 공분산(

)은 모두 0인 매트릭스로 초기화 설정될 수 있다.In the above formula,

represents the retrospective estimated covariance corresponding to the current time,

denotes the unit matrix,

represents the filtering gain in matrix form,

represents the transformation parameters in matrix form,

denotes the a priori estimated covariance corresponding to the current time in matrix form. especially,

is 0, the retrospective estimated covariance (

) may be initialized to a matrix of all zeros.

시각)에 대응되는 실제 상태 정보를 결정할 수 있으며, 이러한 방식으로 순환하면, 로봇 주행 과정에서, 로봇이 각 시각에 대응되는 실제 상태 정보를 결정할 수 있다.By updating the a priori estimated covariance corresponding to the current time, the a posteriori estimated covariance corresponding to the current time is obtained, so if the steps in the embodiment of the present application are duplicated, the next time (that is,

time) can be determined, and when cycled in this way, the robot can determine the actual state information corresponding to each time in the course of robot driving.

In the above method, the reference information of the robot is obtained, and the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to the current time of the robot, based on the reference information, By determining the state noise of the robot, the robot obtains actual state information corresponding to the current time by using the state noise. Accordingly, in the process of state determination, there is no need to perform simulations using bulk particles, which is advantageous in improving the speed of state determination. In addition, since the state noise is determined according to at least one of the measured state information of a plurality of times and the actual driving information of the current time, it is possible to estimate the noise from at least one of the external measurement angle of the robot and the state angle of the robot itself, so that the state noise By making it closer to this real case, the accuracy of the real state information determined in the future is improved.

Here, in some embodiments of the invention, in order to improve the user experience by allowing the user to detect an abnormal state noise in a timely manner, and when the state noise does not satisfy a preset noise condition, a preset prompt may be performed. . The preset prompt may be implemented in the form of at least one of sound, light, and text. For example, playing a prompt voice, turning on a prompt, etc., outputting a prompt text, etc., but is not limited thereto.

In the implementation scenario of the invention, the state noise may include the measured interference noise obtained by using a plurality of measured state information, and the acquisition method may refer to the relevant steps in the above-described embodiments of the invention. Also, the preset noise condition may include that the measured interference noise is less than the first noise threshold, and the value of the first noise threshold may be set according to an actual situation. When the measurement interference noise does not satisfy the preset noise condition, it can output a first warning message to prompt that the state measurement is interrupted, so that when the state measurement is interfered, the user can timely detect it , can improve the user experience.

In another embodiment scenario, the state noise includes state transition noise obtained by using actual driving information at the current time, and the acquisition method may refer to the relevant steps in the above-described embodiment of the present invention. Also, the preset noise condition may include that the state transition noise is less than the second noise threshold, and the value of the second noise threshold may be set according to an actual situation, but is not limited thereto. When the state transition noise does not satisfy the preset condition, a second warning message is output to prompt the robot that there is a risk of vehicle body sliding, so that when a risk of vehicle body sliding appears in the robot, the user can timely This can improve the user experience.

The first warning message and the second warning message may be implemented in the form of at least one of sound, light, and text. For example, playing a prompt voice, turning on a prompt, etc., outputting a prompt text, etc., but is not limited thereto.

In the embodiment of the present application, the fusion positioning of the built-in encoder and the external input is implemented using the Kalman filtering system, and the state transition noise estimation is provided according to the left and right wheel speed states, and the external input change using the noise estimation is It is judged whether it is reasonable, and finally, a fusion decision is made according to the judgment result, to implement preventing positioning hijacking, and providing a fault signal, thereby (1) improving the robustness of signal hijacking, and receiving interference reduces the (2) In the event of signal hijacking, a warning may be provided. (3) Compared to the method of performing particle filtering with a bulk particle model commonly used in the prior art, the calculation amount of this method is small and the convergence speed is fast, so that the robot's positioning system meets the demands for precision and speed. can satisfy

Referring to FIG. 3 , FIG. 3 is an architectural diagram of an embodiment of the state determination device 30 of the robot in the embodiment of the present application. The state determination device 30 of the robot includes a measurement state obtaining module 31 , a state noise determining module 32 , and an actual state obtaining module 33 . The measurement state acquisition module 31 is configured to acquire reference information of the robot, wherein the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to the current time of the robot, and ; the state noise determining module 32 is configured to determine, according to the reference information, the state noise of the robot; The real state obtaining module 33 is configured to use the state noise to enable the robot to obtain real state information corresponding to the current time.

In the above method, the reference information of the robot is obtained, and the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to the current time of the robot, and based on the reference information, the robot By determining the state noise of It is advantageous. In addition, since the state noise is determined according to at least one of the measured state information of a plurality of times and the actual driving information of the current time, the noise can be estimated from at least one of the external measurement angle of the robot and the state angle of the robot itself, so that the state noise is reduced By making it closer to the real case, the accuracy of future determined real state information is improved.

In some embodiments of the invention, the state noise determination module 32 includes a measurement interference determination sub-module, configured to determine the measurement interference noise of the robot by using the measurement state information corresponding to the current time and the plurality of previous times, and ; The state noise determination module 32 includes a state transition determination sub-module, configured to determine the state transition noise of the robot by using the actual driving information of the current time.

In distinction from the above-described embodiment of the present invention, since the measured interference noise of the robot is determined by using the measurement state information corresponding to the current time and the plurality of previous times, the noise of the robot can be determined from the external measurement angle, so that the robot It is possible to estimate external interference in this driving process; Since the state transition noise of the robot is determined by using the actual driving information of the current time, the noise of the robot can be determined from the angle of the state of the robot itself, so that the internal interference of the robot in the driving process can be estimated.

In some embodiments of the invention, the measurement interference determining submodule includes a discrete acquiring unit, configured to acquire the discrete degree of measurement state information of the current time and a plurality of previous times; The measurement interference determination submodule includes a noise determination unit, configured to determine the measurement interference noise by using the discrete degree.

To be distinguished from the above-described embodiment of the present invention, by using the discrete degree of the measurement state information of the current time and a plurality of previous times, and using the discrete degree to determine the measurement interference noise, the robot prevents external interference in the running process can be accurately estimated.

In some embodiments of the present invention, the discrete degree of the measurement state information of the current time and the plurality of previous times is a standard difference between the measurement state information of the current time and the plurality of previous times.

Distinguished from the above-described embodiment of the present invention, by setting the discrete degree of the measurement state information of the current time and the previous plurality of times as the standard difference of the measurement state information of the current time and the previous plurality of times, the complexity of determining the discrete degree and It is advantageous in reducing the amount of calculation and is advantageous in improving the speed of state determination.

In some inventive embodiments, the noise determining unit is configured to use a product between the discrete degree and a preset gain parameter as the measured interference noise.

To be distinguished from the above-described embodiment of the present invention, the product between the discrete degree and the preset gain parameter is used as the measurement interference noise, which is advantageous for improving the accuracy of the measurement interference noise and for improving the precision of the state determination.

In some embodiments of the invention, the actual travel information includes travel angle information, motor drive information, and travel speed information of the robot; the state transition determining submodule is configured to use at least one of the first state noise and the second state noise to obtain the state transition noise of the robot; Here, the first state noise is obtained by determining using the driving angle information and the driving speed information, and the second state noise is obtained by determining using the motor driving information and the driving speed information.

To be distinguished from the embodiment of the present invention described above, by setting the actual driving information to include the driving angle information, the motor driving information, and the driving speed information of the robot, using at least one of the first state noise and the second state noise, the robot The state transition noise of It is beneficial to improve accuracy.

In some embodiments of the invention, the robot includes a driving wheel and a rotating wheel, the driving wheel is used to drive the robot traveling, and the rotating wheel is used to change the traveling direction of the robot; the traveling speed information includes an actual speed difference between driving wheels of the robot, and the traveling angle information includes an actual rotation angle of a rotating wheel of the robot; The first state noise determining unit is a first mapping subunit, configured to perform mapping processing on the actual rotation angle by using the first mapping relationship between the speed difference and the rotation angle, to obtain a theoretical speed difference corresponding to the actual rotation angle includes; The first state noise determining unit includes a first state noise determining subunit, configured to determine a first state noise by using a difference between the actual speed difference and the theoretical speed difference.

To be distinguished from the embodiment of the present invention described above, by setting the traveling speed information to include the actual speed difference between the driving wheels of the robot and setting the traveling angle information to include the actual rotation angle of the rotating wheels of the robot, the speed difference and The mapping process is performed on the actual rotation angle using the first mapping relationship between the rotation angles to obtain a theoretical speed difference corresponding to the actual rotation angle, and using the difference between the actual speed difference and the theoretical speed difference, the first Since the state noise is determined, the first state noise of the robot can be determined from the angle of the rotating wheel of the robot.

In some inventive embodiments, the robot includes drive wheels used to drive the robot travel; the traveling speed information includes an actual average speed of driving wheels of the robot, and the motor driving information includes an actual average driving signal value of a motor of the robot; The second state noise determining unit performs mapping processing on the actual average driving signal value by using the second mapping relationship between the average speed and the average driving signal value, so as to obtain a theoretical average velocity corresponding to the actual average driving signal value. a second mapping sub-unit configured; The second state noise determining unit includes a second state noise determining subunit, configured to determine a second state noise by using a difference between the actual average speed and the theoretical average speed.

To be distinguished from the embodiment of the present invention described above, by setting the driving speed information to include the actual average speed of the driving wheels of the robot and setting the motor driving information to include the actual average driving signal value of the robot's motor, the average speed and The mapping process is performed on the actual average driving signal value using the second mapping relationship between the average driving signal values to obtain a theoretical average speed corresponding to the actual average driving signal value, and the difference between the actual average speed and the theoretical average speed Since the second state noise is determined using , the second state noise of the robot can be determined from the angle of the driving wheel of the robot.

In some inventive embodiments, the first state noise determining sub-unit is configured to use the square of the difference between the actual speed difference and the theoretical speed difference as the first state noise; The second state noise determining subunit is configured to use the square of a difference between the actual average speed and the theoretical average speed as the second state noise.

Distinct from the above-described embodiment of the invention, the square of the difference between the actual speed difference and the theoretical speed difference is used as the first state noise, and the square of the difference between the actual average speed and the theoretical average speed is used as the second state noise Accordingly, it is possible to reduce the complexity and amount of calculation of the noise in the first state and the noise in the second state, and it is advantageous to improve the speed of state determination.

In some embodiments of the invention, the real state acquisition module 33 uses the state noise to process the real state information corresponding to the previous time and the measured state information of the current time by using the state noise, so that the real state corresponding to the current time configured to obtain information.

Distinguished from the above-described embodiment, the robot uses the status noise to process the measurement status information corresponding to the current time and the actual status information corresponding to the previous time, so that the robot can measure status information at the current time and the previous It is advantageous to balance between the actual state information at the time of the determination, so that the actual state information obtained by determining is corrected compared to the measured state information, which is advantageous in improving the precision of robot state determination.

In some embodiments of the invention, the real state acquisition module 33 determines the filtering gain based on the state noise, and the robot predicts the real state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time, It is configured to obtain the predicted state information corresponding to the current time, and fuse the predicted state information of the current time and the measured state information of the current time by using Kalman filtering of the filtering gain, to obtain the actual state information corresponding to the current time. .

To be distinguished from the above-described embodiment of the present invention, the filtering gain is determined based on the state noise, and the robot predicts the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time, and the prediction corresponding to the current time Obtain state information and use Kalman filtering of the filtering gain to fuse the predicted state information of the current time and the measured state information at the current time, and strengthen the robustness of the external signal to obtain the actual state information corresponding to the current time. decide accurately.

In some inventive embodiments, the robot state determination device 30 further includes a prompt module configured to proceed with a preset prompt when the state noise does not satisfy a preset noise condition.

Unlike the above-described embodiment of the present invention, when the state noise does not satisfy the preset noise condition, a preset prompt is performed so that the user can detect the abnormal state noise, thereby improving the user experience.

In some embodiments of the invention, the state noise includes measured interference noise obtained by using a plurality of measured state information; Correspondingly, the preset noise condition includes that the measured interference noise is less than the first noise threshold; the prompt module includes a first warning sub-module, configured to output a first warning message when the measured interference noise does not satisfy the preset noise condition, to prompt that the state measurement is subject to interference; and the state noise includes state transition noise obtained using actual driving information at the current time; Correspondingly, the preset noise condition includes that the state transition noise is less than the second noise threshold; the prompt module includes a second warning sub-module configured to output a second warning message when the state transition noise does not satisfy a preset noise condition to prompt the robot that there is a risk of vehicle body slipping one

Distinguished from the above-described embodiment of the present invention, when the measurement interference noise does not satisfy a preset condition, by outputting a first warning message to prompt that the state measurement is interfered, when the state measurement is interfered, the user to detect in time, improving the user experience; When the state transition noise does not satisfy the preset condition, a second warning message is output to prompt the robot that there is a risk of vehicle body sliding, so that when a risk of vehicle body sliding appears in the robot, the user can timely to improve the user experience.

In some embodiments of the invention, the measurement state acquisition module 31 includes a data collection sub-module configured to collect images of the surrounding environment of the robot to obtain environmental image data corresponding to the current time; the measurement state obtaining module 31 includes a measurement state determination sub-module, configured to cause the robot to determine measurement state information corresponding to the current time, based on the environmental image data of the current time; Here, the measured state information and the actual state information include at least one of a position of the robot, a posture of the robot, and a speed of the robot.

To be distinguished from the above-described embodiment of the present invention, the number of users of the environment image corresponding to the current time is obtained through image collection of the surrounding environment of the robot, and based on the environmental image data of the current time, the robot moves to the current time By determining the measurement state information corresponding to By being able to obtain it, it is advantageous for improving the speed of robot state determination.

Referring to FIG. 4 , FIG. 4 is an architectural diagram of an embodiment of the robot 40 in the embodiment of the present application. The robot 40 includes a robot body 41 and a memory 42 and a processor 43 installed in the robot body 41 , the memory 42 and the processor 43 are coupled to each other, and the processor 43 is for executing the program instructions stored in the memory 42 to implement the steps of any one of the state determination method embodiments above.

In some inventive embodiments, the processor 43 controls itself and the memory 42 to implement the steps of any of the above state determination method embodiments. The processor 43 may be referred to as a central processing unit (CPU). The processor 43 may be an integrated circuit chip, and has a signal processing capability. The processor 43 may include a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic device; It can be a discrete gate or transistor logic device, a discrete hardware assembly, or the like. A general-purpose processor may be a microprocessor or any general processor or the like. Also, the processor 43 may be jointly implemented by an integrated circuit chip.

In the above method, since the state noise is determined according to at least one of the acquired plurality of measured state information and the current actual driving information, it is possible to estimate the noise from at least one of the robot external measured angle and the robot itself state angle, By making the noise closer to the real case, the accuracy of future determined real state information is improved.

In some embodiments of the invention, the robot 40 includes a plurality of wheels installed in the robot body 41, a motor for driving the wheels to move, and a steering gear for driving the wheels to rotate so as to change directions. . For example, the robot includes a first set of wheels and a second set of wheels, the first set of wheels is connected to a motor and used as driving wheels, and the second set of wheels is connected to a steering gear and used as rotating wheels. . In addition, in order to obtain the driving information of the robot, the robot 40 may also include a speed measuring component, and the speed measuring component may be installed on the driving wheel and used to obtain the speed of the driving wheel. In one application scenario, the robot 40 includes four wheels, two front wheels are used as rotating wheels, two rear wheels are used as driving wheels, and an encoder is installed in each rear wheel, corresponding to the rear wheel. gain speed Thereby, the robot can obtain the traveling speed through reading the encoder, and obtain the traveling angle through reading the steering gear control record. Also, according to different actual application demands, the robot body 41 can be set to have different appearances. For example, in the case of courier delivery applications, the robot body 41 may be set to have a vehicle-shaped exterior such as a car or a van; Alternatively, in the case of a service guide application, the robot body 41 may be set to have an appearance such as a general humanoid, a cartoon animal, etc., and may be set according to the actual application demand, and will not be described one by one by example here.

In some embodiments of the invention, in order to implement obtaining the measurement state information, the robot 40 may also be equipped with a photographing device, whereby, through the environmental image obtained by photographing by the photographing device, the robot 40 Determines the measurement status information of

Referring to FIG. 5 , FIG. 5 is an architectural diagram of an embodiment of a computer-readable storage medium 50 of an embodiment of the present application. The computer-readable storage medium 50 stores program instructions 501 operable by a processor, and the program instructions 501 are used to implement the steps of any of the above-described state determination method embodiments.

In the above method, since the state noise is determined according to at least one of the acquired plurality of measured state information and the current actual driving information, the noise can be estimated from at least one of the robot external measured angle and the robot itself state angle, By making the noise closer to the real case, the accuracy of future determined real state information is improved.

In some inventive embodiments, embodiments of the present application provide a computer program comprising computer readable code, wherein when the computer readable code is run in a robot, a processor in the robot executes the method .

In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the above-described device embodiment is merely exemplary, for example, division for modules or units is merely logical function division, and when actually implemented, there may be other division manners, for example, units or components may be combined or integrated into other systems, or may ignore or not implement some features. Further, any coupling or direct coupling or communication connection between each other shown or discussed may be implemented via some interface, and the indirect coupling or communication connection through a device or unit may be electrical, mechanical, or other form.

A unit described as a separate member may or may not be physically separate, and a member shown as a unit may or may not be a physical unit, ie, may be located in one place or distributed in a network unit. Some or all of the units may be selected according to actual needs to implement the purpose of the present embodiment solution.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may be an independent physical entity, and two or two or more units may be integrated into one unit. . The integrated unit may be implemented using a form of hardware or may be implemented using a form of a software functional unit.

When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, that is, the part contributing to the prior art or all or part of the technical solution may be implemented in the form of a software product, the computer software product being stored in one storage medium, , one computer device (which may be a personal computer, a server or a network device, etc.) or a processor. The embodiments of the present application include a plurality of instructions used to execute all or some steps of the method of each embodiment do. The above-mentioned storage medium includes various media capable of storing a program code, such as a disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc. includes

An embodiment of the present application provides a state determination method and apparatus, a robot, a storage medium, and a computer program, wherein the method includes the steps of obtaining reference information of the robot - the reference information is a measurement state corresponding to a plurality of times of the robot information, the robot includes at least one of actual driving information corresponding to the current time; determining a state noise of the robot based on the reference information; and obtaining actual state information of the current time corresponding to the robot by using the state noise. According to the method for determining the state of the robot provided in the embodiment of the present application, there is no need to proceed with simulation using bulk particles in the process of determining the state, which is advantageous in improving the speed of state determination. In addition, since the state noise is determined according to at least one of the acquired plurality of measured state information and the current actual driving information, the noise can be estimated from at least one of the robot external measurement angle and the robot itself state angle, so that the state noise is actually By making the case and more approachable, the accuracy of future determined actual state information is improved.

Claims (28)

A method for determining the state of a robot, comprising:
obtaining reference information of the robot, wherein the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to the current time of the robot;
determining a state noise of the robot based on the reference information; and
and obtaining, by the robot, actual state information corresponding to the current time by using the state noise. The method of claim 1,
Based on the reference information, the step of determining the state noise of the robot,
determining the measured interference noise of the robot by using the measurement state information corresponding to the current time and a plurality of previous times; and
and determining the state transition noise of the robot by using the actual driving information of the current time. 3. The method of claim 2,
Using the measurement state information corresponding to the current time and a plurality of previous times, the step of determining the interference noise measured by the robot,
obtaining a discrete degree of measurement state information of the current time and a plurality of previous times; and
and determining the measured interference noise by using the discrete degree. 4. The method of claim 3,
the discrete degree of the measurement status information of the current time and the plurality of previous times is a standard difference between the measurement status information of the current time and the plurality of previous times; correspondingly,
Using the discrete degree, determining the measured interference noise comprises:
and using a product between the discrete degree and a preset gain parameter as the measured interference noise. 5. The method according to any one of claims 2 to 4,
the actual driving information includes driving angle information, motor driving information, and driving speed information of the robot;
The step of determining the state transition noise of the robot by using the actual driving information of the current time includes:
obtaining the state transition noise of the robot by using at least one of a first state noise and a second state noise;
The first state noise is obtained by determining using the traveling angle information and the traveling speed information, and the second state noise is obtained by determining using the determined motor driving information and the traveling speed information. How to determine the state of 6. The method of claim 5,
the robot includes a driving wheel and a rotating wheel, the driving wheel is used to drive the robot traveling, and the rotating wheel is used to change the traveling direction of the robot; the traveling speed information includes an actual speed difference between driving wheels of the robot, and the traveling angle information includes an actual rotation angle of a rotating wheel of the robot;
Correspondingly, before obtaining the state transition noise of the robot using at least one of the first state noise and the second state noise, the method for determining the state of the robot includes:
performing a mapping process on the actual rotation angle using a first mapping relationship between the speed difference and the rotation angle, and obtaining a theoretical speed difference corresponding to the actual rotation angle; and
determining the first state noise by using a difference between the actual speed difference and the theoretical speed difference; and
the robot includes a driving wheel, the driving wheel being used to drive the robot travel; the driving speed information includes an actual average speed of driving wheels of the robot, and the motor driving information includes an actual average driving signal value of a motor of the robot;
Correspondingly, before obtaining the state transition noise of the robot using at least one of the first state noise and the second state noise, the method for determining the state of the robot includes:
performing a mapping process on the actual average driving signal value using a second mapping relationship between the average speed and the average driving signal value to obtain a theoretical average velocity corresponding to the actual average driving signal value; and
and determining the second state noise by using a difference between the actual average velocity and the theoretical average velocity. 7. The method of claim 6,
Using the difference between the actual speed difference and the theoretical speed difference, determining the first state noise comprises:
using the square of the difference between the actual speed difference and the theoretical speed difference as the first state noise;
Using the difference between the actual average speed and the theoretical average speed, determining the second state noise comprises:
and using the square of a difference between the actual average speed and the theoretical average speed as the second state noise. 8. The method according to any one of claims 1 to 7,
The step of obtaining, by the robot, the actual state information corresponding to the current time by using the state noise,
and processing the real state information corresponding to the previous time and the measured state information of the current time by the robot using the state noise to obtain the actual state information corresponding to the current time by the robot How to determine the state of the robot. 9. The method of claim 8,
Using the state noise, the robot processes the actual state information corresponding to the previous time and the measured state information of the current time, so that the robot obtains the actual state information corresponding to the current time,
determining a filtering gain based on the state noise;
obtaining, by the robot, predicted state information corresponding to the current time by predicting the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time; and
and fusing the predicted state information of the current time with the measured state information of the current time using Kalman filtering of the filtering gain, and the robot obtains actual state information corresponding to the current time. How to determine the state of the robot. 10. The method of claim 9,
Determining a filtering gain based on the state noise comprises:
By processing the a priori estimated covariance corresponding to the previous time using the state transition parameter and the state transition noise of the robot, the a priori estimated covariance corresponding to the current time is obtained, and the conversion parameter and measurement of the measured state information from the actual state information processing a priori estimated covariance corresponding to the current time using interference noise to obtain a filtering gain corresponding to the current time; and
The step of obtaining the predicted state information corresponding to the current time by predicting the actual state information corresponding to the previous adjacent time and the actual driving information corresponding to the previous adjacent time, by the robot, comprises: a state transition parameter of the robot and an input state transition Determining the state of the robot, comprising the step of obtaining predicted state information corresponding to the current time by processing the actual state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time, respectively, by using the parameters method. 11. The method according to any one of claims 1 to 10,
After determining the state noise of the robot based on the reference information, the method for determining the state of the robot comprises:
If the state noise does not satisfy a preset noise condition, the method further comprising the step of performing a preset prompt. 12. The method of claim 11,
the state noise includes measurement interference noise obtained using measurement state information of the current time and a plurality of previous times; Correspondingly, the preset noise condition includes that the measured interference noise is less than a first noise threshold; If the state noise does not satisfy a preset noise condition, the step of performing a preset prompt includes:
if the measured interference noise does not satisfy a preset noise condition, outputting a first warning message, wherein the first warning message is for prompting that the state measurement is interfered with; and
the state noise includes state transition noise obtained using actual driving information at the current time; Correspondingly, the preset noise condition includes that the state transition noise is less than a second noise threshold; If the state noise does not satisfy a preset noise condition, the step of performing a preset prompt includes:
outputting a second warning message if the state transition noise does not satisfy the preset noise condition, wherein the second warning message is for prompting the robot that a risk of vehicle body slipping exists A method for determining the state of a robot, characterized in that at least one of. 13. The method according to any one of claims 1 to 12,
The step of obtaining the reference information of the robot comprises:
acquiring environment image data corresponding to the current time by collecting images of the surrounding environment of the robot; and
determining, by the robot, measurement state information corresponding to the current time based on the environmental image data of the current time;
The measured state information and the actual state information includes at least one of a position of the robot, a posture of the robot, and a speed of the robot. A state determination device for a robot, comprising:
a measurement state acquisition module configured to acquire reference information of the robot, wherein the reference information includes at least one of measurement state information corresponding to a plurality of times of the robot and actual driving information corresponding to a current time of the robot;
a state noise determining module, configured to determine a state noise of the robot based on the reference information; and
and a real state obtaining module configured to enable the robot to obtain real state information corresponding to the current time by using the state noise. 15. The method of claim 14,
The state noise determination module,
The measurement interference determination submodule includes: a measurement interference determination submodule, configured to determine the measurement interference noise of the robot by using measurement state information corresponding to a current time and a plurality of previous times; and
and a state transition determination sub-module configured to determine the state transition noise of the robot by using the actual driving information of the current time. 16. The method of claim 15,
Measurement interference determination submodule,
a discrete acquiring unit, configured to acquire discrete degrees of measurement state information of a current time and a plurality of previous times; and
and a noise determining unit configured to determine the measured interference noise by using the discrete degree. 17. The method of claim 16,
the discrete degree of the measurement status information of the current time and the plurality of previous times is a standard difference between the measurement status information of the current time and the plurality of previous times; correspondingly,
The noise determining unit is configured to use a product between the discrete degree and a preset gain parameter as the measured interference noise. 18. The method according to any one of claims 15 to 17,
The actual driving information includes driving angle information, motor driving information, and driving speed information of the robot; the state transition determination submodule is configured to use at least one of a first state noise and a second state noise, configured to obtain a state transition noise;
The first state noise is obtained by determining using the traveling angle information and the traveling speed information, and the second state noise is obtained by determining using the motor driving information and the traveling speed information. state determination device. 19. The method of claim 18,
the robot includes a driving wheel and a rotating wheel, the driving wheel is used to drive the robot traveling, and the rotating wheel is used to change the traveling direction of the robot; the traveling speed information includes an actual speed difference between driving wheels of the robot, and the traveling angle information includes an actual rotation angle of a rotating wheel of the robot;
Correspondingly, the state determining device of the robot further includes a first state noise determining unit, the first state noise determining unit comprising:
a first mapping subunit, configured to perform mapping processing on the actual rotation angle by using the first mapping relationship between the speed difference and the rotation angle to obtain a theoretical speed difference corresponding to the actual rotation angle; and
including a first state noise determining sub-unit, configured to determine a first state noise by using the difference between the actual speed difference and the theoretical speed difference; and
the robot includes a driving wheel, the driving wheel being used to drive the robot travel; the driving speed information includes an actual average speed of driving wheels of the robot, and the motor driving information includes an actual average driving signal value of a motor of the robot;
Correspondingly, the state determining device of the robot further includes a second state noise determining unit, the second state noise determining unit comprising:
a second mapping subunit, configured to perform mapping processing on the actual average driving signal value using a second mapping relationship between the average speed and the average driving signal value to obtain a theoretical average velocity corresponding to the actual average driving signal value; and
and a second state noise determining sub-unit, configured to determine a second state noise by using the difference between the actual average speed and the theoretical average speed. 20. The method of claim 19,
the first state noise determining sub-unit is configured to use the square of the difference between the actual speed difference and the theoretical speed difference as the first state noise;
and the second state noise determining sub-unit is configured to use the square of a difference between the actual average speed and the theoretical average speed as the second state noise. 21. The method according to any one of claims 14 to 20,
The real state acquisition module is configured so that the robot uses the state noise to process the real state information corresponding to the previous time and the measured state information of the current time, so that the robot obtains the real state information corresponding to the current time. A robot's state determination device. 22. The method of claim 21,
The real state acquisition module determines the filtering gain based on the state noise, and the robot predicts the real state information corresponding to the previous adjacent time and the actual driving information of the previous adjacent time to obtain the predicted state information corresponding to the current time, , using Kalman filtering of the filtering gain, by fusing the predicted state information of the current time and the measured state information of the current time, the robot is configured to obtain the actual state information corresponding to the current time. . 23. The method according to any one of claims 14 to 22,
The state determination device of the robot,
When the state noise does not satisfy the preset noise condition, the robot state determination device further comprising a prompt module configured to proceed with a preset prompt. 24. The method of claim 23,
the state noise includes measured interference noise obtained by using a plurality of measured state information; Correspondingly, the preset noise condition includes that the measured interference noise is less than the first noise threshold; the prompt module includes a first warning submodule, configured to output a first warning message when the measured interference noise does not satisfy a preset noise condition, the first warning message prompting that the state measurement is interrupted It is intended to - ; and
The state noise includes state transition noise obtained using actual driving information at the current time; Correspondingly, the preset noise condition includes that a state transition noise is less than a second noise threshold; The prompt module is a second warning sub-module configured to output a second warning message when the state transition noise does not satisfy a preset noise condition, wherein the second warning message prompts the robot that there is a risk of vehicle body slipping It is for - The state determination device of the robot, characterized in that it is at least one of comprising. 25. The method according to any one of claims 14 to 24,
The measurement state acquisition module,
a data collection sub-module configured to collect images of the surrounding environment of the robot to obtain environmental image data corresponding to the current time; and
a measurement state determination submodule, configured to cause the robot to determine measurement state information corresponding to the current time, based on the environmental image data corresponding to the current time;
The measured state information and the actual state information, the robot state determination device, characterized in that it comprises at least one of the position of the robot, the posture of the robot, and the speed of the robot. As a robot,
It includes a robot body and a memory and a processor installed in the robot body, wherein the processor and the memory are coupled to each other, and the processor executes the program instructions stored in the memory, according to any one of claims 1 to 13 A robot, characterized in that for implementing the state determination method according to the A computer readable storage medium comprising:
A computer readable storage medium storing program instructions and implementing the method of determining a state according to any one of claims 1 to 13 when the program instructions are executed by a processor. A computer program comprising:
14. A computer readable code comprising a computer readable code, characterized in that when the computer readable code is operated in a robot, a processor in the robot is for implementing the method for determining a state according to any one of claims 1 to 13. computer program.

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