WO2022041797A1 - 状态确定方法及装置、机器人、存储介质和计算机程序 - Google Patents

状态确定方法及装置、机器人、存储介质和计算机程序 Download PDF

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Publication number
WO2022041797A1
WO2022041797A1 PCT/CN2021/088224 CN2021088224W WO2022041797A1 WO 2022041797 A1 WO2022041797 A1 WO 2022041797A1 CN 2021088224 W CN2021088224 W CN 2021088224W WO 2022041797 A1 WO2022041797 A1 WO 2022041797A1
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WIPO (PCT)
Prior art keywords
state
robot
noise
information
actual
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Ceased
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PCT/CN2021/088224
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English (en)
French (fr)
Inventor
姚达琛
何悦
李�诚
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Beijing Sensetime Technology Development Co Ltd
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Beijing Sensetime Technology Development Co Ltd
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Priority to JP2021566210A priority Critical patent/JP2022550231A/ja
Priority to KR1020217039198A priority patent/KR102412066B1/ko
Priority to KR1020227019722A priority patent/KR20220084434A/ko
Priority to KR1020227019723A priority patent/KR20220084435A/ko
Publication of WO2022041797A1 publication Critical patent/WO2022041797A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1602—Program controls characterised by the control system, structure, architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1674—Program controls characterised by safety, monitoring, diagnostic
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J13/00—Controls for manipulators
    • B25J13/08—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices
    • B25J13/088—Controls for manipulators by means of sensing devices, e.g. viewing or touching devices with position, velocity or acceleration sensors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J19/00—Accessories fitted to manipulators, e.g. for monitoring, for viewing; Safety devices combined with or specially adapted for use in connection with manipulators
    • B25J19/02—Sensing devices
    • B25J19/04—Viewing devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J5/00—Manipulators mounted on wheels or on carriages
    • B25J5/007—Manipulators mounted on wheels or on carriages mounted on wheels
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1628—Program controls characterised by the control loop
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1628—Program controls characterised by the control loop
    • B25J9/1653—Program controls characterised by the control loop parameters identification, estimation, stiffness, accuracy, error analysis
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1656—Program controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Program controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Program-controlled manipulators
    • B25J9/16—Program controls
    • B25J9/1679—Program controls characterised by the tasks executed
    • B25J9/1692—Calibration of manipulator

Definitions

  • the present application relates to the field of robotics, and in particular, to a state determination method and device, a robot, a storage medium and a computer program.
  • AI Artificial Intelligence
  • Most AI education courses are extended on the basis of online course platforms, supplemented by corresponding hardware equipment, such as smart cars and smart robots.
  • the school will also organize some robot-based competitions, such as electronic design competitions, autonomous driving competitions, etc. In these competitions, players need to design their own circuits and algorithms to compete with other players' robots in the competition.
  • Embodiments of the present application provide a state determination method and device, a robot, a storage medium, and a computer program.
  • An embodiment of the present application provides a method for determining a state of a robot, including: acquiring reference information of the robot; wherein the reference information includes at least one of the following: measurement state information corresponding to several moments of the robot, and actual driving information of the robot corresponding to the current moment; Based on the reference information, the state noise of the robot is determined; using the state noise, the actual state information of the robot corresponding to the current moment is obtained.
  • the reference information of the robot includes at least one of the following: the measurement state information of the robot corresponding to several moments, the actual driving information of the robot corresponding to the current moment, and based on the reference information, the state noise of the robot is determined, so as to utilize the state Noise, the actual state information of the robot corresponding to the current moment can be obtained, and in the process of determining the state, it can be simulated without using a large number of particles, which is beneficial to improve the speed of state determination.
  • the state noise is determined based on the measured state information at several moments and/or the actual driving information at the current moment, the noise can be measured from the external measurement angle of the robot and/or the robot's own state, thereby making the state noise more closely related to the actual situation. close, thereby improving the accuracy of the actual state information determined subsequently.
  • determining the state noise of the robot based on the reference information includes: using the measurement state information corresponding to the current moment and several previous moments to determine the measurement interference noise of the robot; and/or using the actual driving information at the current moment , to determine the state transition noise of the robot.
  • the measurement interference noise of the robot can be determined by using the measurement state information corresponding to the current moment and several moments before it. Therefore, the noise of the robot can be determined from an external measurement perspective, so that the external interference during the driving process of the robot can be measured; the current moment can be used to determine the noise of the robot.
  • the actual driving information of the robot can determine the state transition noise of the robot. Therefore, the noise of the robot can be determined from the perspective of the robot's own state, so that the internal interference of the robot during the driving process can be measured.
  • determining the measurement interference noise of the robot includes: acquiring the degree of dispersion of the measurement state information at the current moment and several moments before it; using the degree of dispersion , determine the measurement interference noise.
  • the external disturbance of the robot during driving can be accurately measured.
  • the degree of dispersion of the measurement state information at the current moment and several moments before it is the standard deviation of the measurement state information at the current moment and several moments before it; correspondingly, using the degree of dispersion, the measurement interference noise is determined, including : Take the product between the dispersion degree and the preset gain parameter as the measurement interference noise.
  • the degree of dispersion of the measurement state information at the current moment and several moments before it as the standard deviation of the measurement state information at the current moment and several moments before it, it can be beneficial to reduce the complexity and calculation amount of determining the degree of dispersion. It is beneficial to improve the speed of state determination; taking the product between the dispersion degree and the preset gain parameter as the measurement interference noise can be beneficial to improve the accuracy of the measurement interference noise and the accuracy of the state determination.
  • the actual travel information includes travel angle information, motor drive information, and travel speed information of the robot; using the actual travel information at the current moment to determine the state transition noise of the robot includes: utilizing the first state noise and the second state At least one of the noises is used to obtain the state transition noise of the robot; wherein, the first state noise is determined by using travel angle information and travel speed information, and the second state noise is determined by using motor drive information and travel speed information Certainly get it.
  • the actual travel information is set to include travel angle information, motor drive information and travel speed information of the robot, so that the state transition noise of the robot is obtained by using at least one of the first state noise and the second state noise, and the first state transition noise is obtained.
  • the state noise is determined by using the travel angle information and the travel speed information
  • the second state noise is determined by using the motor drive information and the travel speed information, which can help improve the accuracy of the state transition noise.
  • the robot includes a driving wheel and a steering wheel, the driving wheel is used to drive the robot, and the steering wheel is used to change the driving direction of the robot;
  • the driving speed information includes the actual speed difference between the driving wheels of the robot, and the driving angle information Including the actual steering angle of the steering wheel of the robot; correspondingly, before using at least one of the first state noise and the second state noise to obtain the state transition noise of the robot, the method further includes: using the difference between the speed difference and the steering angle.
  • the first mapping relationship performs mapping processing on the actual steering angle to obtain a theoretical speed difference corresponding to the actual steering angle; the first state noise is determined by using the difference between the actual speed difference and the theoretical speed difference; and/or the robot includes a driving wheel , the driving wheel is used to drive the robot; the driving speed information includes the actual average speed of the driving wheel of the robot, and the motor driving information includes the actual average driving signal value of the motor of the robot; Correspondingly, when using the first state noise and the second state noise At least one of the methods, before obtaining the state transition noise of the robot, further comprising: using the second mapping relationship between the average speed and the average driving signal value to perform mapping processing on the actual average driving signal value, and obtaining a corresponding value corresponding to the actual average driving signal value.
  • the theoretical average speed of using the difference between the actual average speed and the theoretical average speed, the second state noise is determined.
  • the robot includes a driving wheel and a steering wheel, the driving wheel is used to drive the robot, and the steering wheel is used to change the driving direction of the robot, and the driving speed information is set to include the actual speed difference between the driving wheels of the robot, and the driving angle information is set
  • the actual steering angle is mapped by using the first mapping relationship between the speed difference and the steering angle, and the theoretical speed difference corresponding to the actual steering angle is obtained.
  • the difference between the theoretical speed differences determines the first state noise, so the first state noise of the robot can be determined from the angle of the steering wheel of the robot;
  • the robot includes a driving wheel, the driving wheel is used to drive the robot, and the driving speed information It is set to include the actual average speed of the driving wheel of the robot, and the motor drive information is set to include the actual average driving signal value of the motor of the robot, so that the actual average driving signal value is calculated by using the second mapping relationship between the average speed and the average driving signal value.
  • Perform the mapping process to obtain the theoretical average speed corresponding to the actual average driving signal value, and use the difference between the actual average speed and the theoretical average speed to determine the second state noise, so the robot can be determined from the perspective of the driving wheel of the robot.
  • Second state noise Perform the mapping process to obtain the theoretical average speed corresponding to the actual average driving signal value, and use the difference between the actual average speed and the theoretical average speed to determine the second state noise, so the robot can be determined from the perspective of the driving wheel of the robot.
  • using the difference between the actual speed difference and the theoretical speed difference to determine the first state noise includes: taking the square of the difference between the actual speed difference and the theoretical speed difference as the first state noise; using the actual speed difference The difference between the average speed and the theoretical average speed, and determining the second state noise, includes: taking the square of the difference between the actual average speed and the theoretical average speed as the second state noise.
  • using the state noise to obtain the actual state information of the robot corresponding to the current moment includes: using the state noise to process the actual state information of the robot corresponding to the previous moment and the measured state information of the current moment, and obtaining the robot corresponding to the current moment. actual status information.
  • the robot by using the state noise to process the robot's measurement state information corresponding to the current moment and the actual state information corresponding to the previous moment, it is beneficial for the robot to achieve a balance between the current measurement status information and the actual status information of the previous moment, so that the determination can be obtained.
  • the actual state information of the robot is corrected relative to the measured state information, which can help to improve the accuracy of the robot state determination.
  • using the state noise to process the actual state information of the robot corresponding to the previous moment and the measured state information of the current moment, and obtaining the actual state information of the robot corresponding to the current moment includes: determining a filter gain based on the state noise, and applying the state noise to the robot.
  • the predicted state information corresponding to the current moment is obtained, and the Kalman filter of the filter gain is used to combine the predicted state information of the current moment with the measured state of the current moment. The information is fused to obtain the actual state information of the robot corresponding to the current moment.
  • the filter gain is determined based on the state noise, and the actual state information of the robot corresponding to the previous moment and the actual driving information of the previous moment are predicted to obtain the predicted state information corresponding to the current moment, and the Kalman filter of the filter gain is used to convert the Fusion of the predicted state information at the current moment and the measured state information at the current moment can enhance the robustness to external signals, so as to accurately determine the actual state information corresponding to the current moment.
  • the method further includes: if the state noise does not meet a preset noise condition, performing a preset prompt.
  • the state noise includes: measurement interference noise obtained by using measurement state information at the current moment and several previous moments; correspondingly, the preset noise conditions include: the measurement interference noise is less than a first noise threshold; if the state noise If the preset noise condition is not met, performing a preset prompt includes: if the measured interference noise does not meet the preset noise condition, outputting a first warning message, where the first warning message is used to prompt that the state measurement is disturbed; and/or, The state noise includes: the state transition noise obtained by using the actual driving information at the current moment; correspondingly, the preset noise condition includes: the state transition noise is smaller than the second noise threshold; if the state noise does not meet the preset noise condition, a preset prompt is performed The method includes: if the state transition noise does not meet the preset noise condition, outputting a second early warning message, where the second early warning message is used to prompt the robot that there is a risk of vehicle body slippage.
  • a first warning message is output to prompt the state measurement to be disturbed, so that the user can perceive in time when the state measurement is disturbed, and the user experience can be improved;
  • a second warning message is output to remind the robot that there is a risk of vehicle body slippage, so that when the robot has a vehicle body slippage risk, the user can sense it in time and improve user experience.
  • acquiring the reference information of the robot includes: collecting images of the surrounding environment of the robot to obtain environmental image data corresponding to the current moment; and determining the measurement state information of the robot corresponding to the current moment based on the environmental image data at the current moment ; Both the measured state information and the actual state information include at least one of the following: the position of the robot, the posture of the robot, and the speed of the robot.
  • the measurement status information of the robot corresponding to the current moment is determined, and the measurement status information and actual status information are set. Both include at least one of the position of the robot, the posture of the robot, and the speed of the robot, so that the measurement state information of the robot corresponding to the current moment can be quickly obtained, which can help improve the speed of determining the state of the robot.
  • An embodiment of the present application provides a device for determining a state of a robot, including: a measurement state acquisition module, a state noise determination module, and an actual state acquisition module, where the measurement state acquisition module is configured to acquire reference information of the robot; wherein the reference information includes at least the following One: the robot corresponds to the measured state information at several moments, and the robot corresponds to the actual driving information at the current moment; the state noise determination module is configured to determine the state noise of the robot based on the reference information; the actual state acquisition module is configured to use the state noise to obtain The robot corresponds to the actual state information at the current moment.
  • An embodiment of the present application provides a robot, including a robot body, a memory and a processor disposed on the robot body, the memory and the processor are coupled to each other, and the processor is configured to execute program instructions stored in the memory to realize the above state determination method.
  • An embodiment of the present application provides a computer-readable storage medium, on which program instructions are stored, and when the program instructions are executed by a processor, the foregoing state determination method is implemented.
  • An embodiment of the present application provides a computer program, including computer-readable code, when the computer-readable code is executed in a robot, a processor in the robot executes the method for implementing the above state determination method.
  • the embodiments of the present application provide a state determination method and device, a robot, a storage medium, and a computer program, by obtaining reference information of the robot, and the reference information includes at least one of the following: measurement state information corresponding to several moments of the robot, and current moment corresponding to the robot.
  • the actual driving information of the robot is determined based on the reference information
  • the state noise of the robot is determined based on the reference information, so that the actual state information of the robot corresponding to the current moment can be obtained by using the state noise. determined speed.
  • the state noise is determined according to several obtained measured state information and/or current actual driving information, the noise can be measured from the external measurement angle of the robot and/or the robot's own state angle, so that the state noise is closer to the actual situation , thereby improving the accuracy of the actual state information determined subsequently.
  • FIG. 1 is a schematic flowchart of an embodiment of a method for determining a state of a robot according to an embodiment of the present application
  • FIG. 2 is a schematic flowchart of an embodiment of the present application for determining actual state information of a robot using Kalman filtering
  • FIG. 3 is a schematic frame diagram of an embodiment of a state determination device for a robot according to an embodiment of the present application
  • FIG. 4 is a schematic diagram of a framework of an embodiment of a robot according to an embodiment of the present application.
  • FIG. 5 is a schematic diagram of a framework of an embodiment of a computer-readable storage medium according to an embodiment of the present application.
  • system and “network” are often used interchangeably herein.
  • the term “and/or” in this article is only an association relationship to describe the associated objects, indicating that there can be three kinds of relationships, for example, A and/or B, it can mean that A exists alone, A and B exist at the same time, and A and B exist independently B these three cases.
  • the character "/” in this document generally indicates that the related objects are an “or” relationship.
  • “multiple” herein means two or more than two.
  • AI education has gradually become popular.
  • Most AI education courses are extended on the basis of online course platforms, supplemented by corresponding hardware equipment, such as smart cars and smart robots.
  • schools often organize some robot-based competitions, such as electronic design competitions, autonomous driving competitions, etc. In these competitions, players need to design their own circuits and algorithms to compete with other players' robots in the competition.
  • the robot In this kind of competition, the robot usually needs to receive the global information of the competition from the host computer, including the robot's own position, speed and attitude, which are very important to the robot's decision-making. Because the communication between the robot and the host computer basically uses serial communication, and all players know the communication rules in advance. Therefore, it is inevitable that some players will place jammers on the robot to send wrong communication signals to other players to mislead their opponents. Among them, the most common is to send wrong position, speed and attitude information to interfere with the judgment of the opponent. If the disturbed robot algorithm is not robust enough, loss of control can occur.
  • the robot will inevitably be disturbed during the driving process.
  • the white noise that exists widely in free space, and even interfere with signals, thus affecting the normal driving of the robot.
  • the robot may even appear Loss of control, slippage, etc.
  • FIG. 1 is a schematic flowchart of an embodiment of a method for determining a state of a robot according to an embodiment of the present application.
  • the method steps provided in the embodiments of the present application may be performed by a hardware device such as a robot, or performed by a processor running computer-executable codes.
  • the state determination method may include the following steps:
  • Step S11 Obtain reference information of the robot.
  • the reference information of the robot may include at least one of the following: measurement state information corresponding to several moments of the robot, and actual driving information of the robot corresponding to the current moment.
  • the state of the robot may change at different times. For example, the robot moves at the current moment relative to the previous moment. Of course, in other application scenarios, the state of the robot may not change. Personnel can determine it according to the actual operation of the robot. In response to this, the robot needs to determine its actual state at different times in order to facilitate subsequent operations.
  • the measurement status information corresponding to the current moment may be obtained first, and then based on the steps in the embodiments of the present application, the measurement status information corresponding to the current moment is used to obtain the measurement status information corresponding to the current moment.
  • the actual state information at the moment It can be understood that the information corresponding to a certain moment described in this article is not necessarily obtained at this moment, but may be obtained near this moment.
  • the measurement status information corresponding to the current moment may be obtained at the current moment; when considering the communication delay, the measurement status information corresponding to the current moment may also be several moments before the current moment (for example, the first 0.5 seconds, the previous 1 second, etc.), which is not limited here.
  • the measurement state information is obtained by measuring the state of the robot.
  • the surrounding environment of the robot can be collected to obtain environmental image data corresponding to the current moment, based on the environment at the current moment. Image data to determine the measurement status information of the robot corresponding to the current moment.
  • images of the surrounding environment of the robot may be captured by a camera device installed in the driving environment of the robot; or, images of the surrounding environment may be captured by a camera device installed on the robot, which is not limited herein.
  • the measured state information and the actual state information of the robot may include at least one of: the position of the robot, the state of the robot, and the speed of the robot.
  • the position of the robot may include position coordinates (eg, latitude and longitude) where the robot is located, and the state of the robot may include the driving state (eg, acceleration) of the robot.
  • the measurement state information including the position of the robot and the speed of the robot as an example, for the convenience of description, the measurement state information corresponding to the current moment can be expressed by formula (1):
  • z k represents the measurement state information of the robot corresponding to the current time k
  • p represents the position of the robot in the measurement state information
  • v represents the speed of the robot in the measurement state information
  • the measurement state information of the robot corresponding to several moments may include the measurement state information of the robot corresponding to the current moment and several moments before it.
  • the current time as time k as an example
  • several times before the current time can be expressed as n times before time k, and the value of n can be set according to actual application needs.
  • n can be 5, 10, 15, etc., which is not limited here.
  • the actual travel information may include travel angle information, motor drive information, and travel speed information of the robot.
  • the driving angle information can be obtained from the control record of the steering gear of the robot, and the robot can include a steering wheel, and the steering gear of the robot is used to drive the steering wheel of the robot to turn at a certain angle.
  • the motor driving information can be obtained from the motor control record of the robot, and the robot can also include a driving wheel, and the motor of the robot is used to drive the driving wheel of the robot to move at a certain speed.
  • Travel speed information can be obtained from the robot's encoder.
  • Step S12 Determine the state noise of the robot based on the reference information.
  • the state noise of the robot refers to the noise that affects the state of the robot during driving.
  • the measurement interference noise is not limited here.
  • the measurement interference noise of the robot can be determined by using the measurement state information corresponding to the current moment and several moments before it.
  • the foregoing description may be referred to for several moments before the current moment. Therefore, the noise of the robot can be determined from the perspective of external measurement, so that the external disturbance during the driving of the robot can be measured.
  • the degree of dispersion of the measurement state information at the current moment and several moments before it may be acquired, and the measurement interference noise can be determined by using the degree of dispersion.
  • the degree of dispersion of the measurement state information at the current moment and several moments before it may be the standard deviation of the measurement state information at the current moment and several moments before it.
  • the degree of dispersion of the measurement state information at the current moment and several moments before it may also be the variance of the measurement state information at the current moment and several moments before it, which is not limited here. Therefore, it is beneficial to reduce the complexity and calculation amount of determining the discrete degree, and is beneficial to improve the speed of state determination.
  • the product between the degree of dispersion and the preset gain parameter may also be used as the measurement interference noise.
  • the preset gain parameters can be set according to the actual situation, which is not limited here.
  • the measurement interference noise can be represented by equation (3):
  • R represents the measurement interference noise
  • z kn:k represents the measurement state information corresponding to the current time k and its previous n moments
  • ⁇ (z kn:k ) represents the measurement corresponding to the current time k and its previous n moments
  • K R represents a preset gain parameter, wherein the preset gain parameter may be a value greater than 0, such as 0.5, 1, 1.5, etc., which is not limited here.
  • the state transition noise of the robot can be determined by using the actual driving information at the current moment. Taking the current time as time k as an example, the actual driving information at time k can be used to determine the state transition noise of the robot. Therefore, the noise of the robot can be determined from the perspective of the robot's own state, so as to measure the internal interference of the robot during the driving process. .
  • the state transition noise of the robot can 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 the travel angle information and acceleration information, and the second state noise is obtained. Noise is determined using motor drive information and travel speed information.
  • the first state noise of the robot can be determined by using the traveling angle information and the traveling speed information, so that the state transition noise of the robot can be determined by using the first state noise.
  • the travel angle information and travel speed information can be used to determine the first state noise of the robot, and the first state noise can be used as the state transition noise of the robot.
  • the motor drive information and travel speed information can be used to determine the second state noise of the robot, so that the second state can be used to determine the state transition noise of the robot.
  • the second state noise of the robot may be determined by using the motor drive information and the traveling speed information, and the second state noise may be used as the state transition noise of the robot.
  • the driving angle information and the driving speed information can also be used to determine the first state noise of the robot, and the motor drive information and the driving speed information can be used to determine the second state noise of the robot, so that the first state noise can be used.
  • the noise and the second state noise are used to obtain the state transition noise of the robot, so that the angle of the steering gear and the angle of the motor can be considered at the same time, which is beneficial to improve the accuracy of the state transition noise.
  • the first state noise and the second state noise when used to obtain the state transition noise, the first state noise and the second state noise may be weighted to obtain the state transition noise.
  • the weights corresponding to the first state noise and the second state noise may be set according to actual conditions. For example, when the noise of the first state is more important than the noise of the second state, the 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; for another example, when the noise of the second state is more important than the noise of the first state, The weight corresponding to the noise in the second state may be set to be greater than the weight of the noise in the first state.
  • the weight corresponding to the noise in the first state can also be set equal to the weight corresponding to the noise in the second state.
  • the weight corresponding to the noise in the first state is set to 0.5
  • the weight corresponding to the noise in the second state is also set is 0.5.
  • the robot may include a driving wheel and a steering wheel, the driving wheel is used to drive the robot to travel, and the steering wheel is used to change the driving direction of the robot, and the driving speed information may include the actual speed difference between the driving wheels of the robot.
  • the driving speed information may include the actual speed difference between the driving wheels of the robot.
  • the actual speed difference can be expressed as e w
  • the travel angle information can include the actual steering angle of the steering wheel of the robot.
  • the actual steering angle can be expressed as ⁇
  • the difference between the speed difference and the steering angle can be used.
  • the first mapping relationship between (for convenience of description, the first mapping relationship can be expressed as f 1 ) is mapped to the actual steering angle ⁇ to obtain the theoretical speed difference corresponding to the actual steering angle ⁇ (for the convenience of description, the theoretical speed difference can be The speed difference is expressed as f 1 ( ⁇ )), so that the difference between the actual speed difference ew and the theoretical speed difference f 1 ( ⁇ ) can be used to determine the first state noise, for example, the actual speed difference ew and the theoretical speed difference ew can be used to determine the first state noise.
  • the square of the difference between the speed differences f 1 ( ⁇ ) is taken as the first state noise.
  • the first mapping relationship can be obtained by performing statistical analysis on multiple pairs of speed differences and steering angles collected in advance.
  • M pairs of speed differences and steering angles are collected, and the collected M pairs of speed differences and steering angles are collected.
  • the steering angle is fitted to obtain the first mapping relationship between the speed difference and the steering angle.
  • the value of M can be set according to the actual situation, which is not limited here.
  • the traveling speed information may further include the actual average speed of the driving wheels, that is, the average speed of each driving wheel of the robot.
  • the average speed of the two driving wheels is the actual average speed.
  • the actual average speed can be expressed as v w
  • the motor driving information can include the actual average driving signal value of the robot motor. , that is, the average signal value of the motor corresponding to each driving wheel of the robot.
  • the robot includes two driving wheels.
  • the driving signal is a PWM signal
  • the actual average driving signal value can be the average value of the PWM signals of the motors corresponding to the two driving wheels.
  • the average driving signal value can be is expressed as p w
  • the second mapping relationship between the average speed and the average driving signal value (for convenience of description, the second mapping relationship can be expressed as f 2 ) can be used to map the actual average driving signal value
  • the The theoretical average speed corresponding to the actual average driving signal value (for the convenience of description, the theoretical average speed can be expressed as f 2 (p w )), so that the difference between the actual average speed and the theoretical average speed can be used to determine the second state noise .
  • the square of the difference between the actual average speed v w and the theoretical average speed f 2 (p w ) can be taken as the second state noise.
  • the second mapping relationship may be obtained by performing statistical analysis on multiple pairs of average speed and average driving signal values collected in advance. For example, in the normal driving process of the robot, N pairs of average speed and average driving signal values are collected, and N pairs of average speed and average driving signal values are fitted to obtain the second mapping relationship between the average speed and the average driving signal value. , the value of N can be set according to the actual situation, which is not limited here.
  • the state transition noise of the robot can be obtained, wherein the state transition noise can be expressed by formula (4):
  • Q represents the state transition noise of the robot
  • k 1 represents the weight corresponding to the first state noise
  • k 2 represents the weight corresponding to the second state noise
  • (f 1 ( ⁇ )-e w ) 2 represents the first State noise
  • (f 2 (p w )-v w ) 2 represents the second state noise
  • e w represents the actual speed difference
  • f 1 represents the first mapping relationship
  • ⁇ represents the actual steering angle
  • v w represents the actual average speed
  • f 2 represents the second mapping relationship
  • p w represents the average drive signal value.
  • the state transition noise and the measurement interference noise can be obtained through the above steps.
  • the state transition noise can also be obtained through the above steps according to the actual situation, and the measurement interference noise can be set to a fixed value.
  • the measurement interference noise can be set to 0, that is, the state The transition noise is used as the state noise of the robot.
  • the measurement interference noise can also be set to non-zero values such as 1, 2, 3, etc., for example, the measurement interference noise can also be set to white noise, which is not limited here.
  • the measurement interference noise can also be obtained through the above steps, and the state transition noise can be set to a fixed value.
  • the state transition noise can be set to 0, that is, the measurement interference noise can be directly used as the robot's noise.
  • the state transition noise can also be set to a non-zero value such as 1, 2, 3, etc., for example, the state transition noise can also be set to white noise, which is not limited here.
  • Step S13 Using the state noise, obtain the actual state information of the robot corresponding to the current moment.
  • the actual state information of the robot corresponding to the previous moment and the measured state information of the current moment may be processed by using the state noise, so as to obtain the actual state information of the robot corresponding to the current moment.
  • state noise For example, Kalman filtering combined with state noise can be used to process the actual state information of the robot corresponding to the previous moment and the measured state information of the current moment, so as to obtain the actual state information of the robot corresponding to the current moment.
  • the filter gain can be determined based on the state noise, and the actual state information of the robot corresponding to the previous moment and the actual driving information of the previous moment can be predicted to obtain the predicted state information corresponding to the current moment, and the filter gain can be used.
  • the Kalman filter of fuses the predicted state information at the current moment with the measured state information at the current moment, and obtains the actual state information of the robot corresponding to the current moment.
  • FIG. 2 is a schematic flowchart of determining the actual state information of a robot by using Kalman filtering in an embodiment of the present application.
  • the method steps provided in the embodiment of the present application may be executed by hardware devices such as a robot. Or by means of a processor running computer-executable code.
  • the actual state information of the robot can be determined by Kalman filtering through the following steps:
  • Step S21 using the state transition parameters and state transition noise of the robot to process the posterior estimated covariance corresponding to the previous moment to obtain the prior estimated covariance corresponding to the current moment.
  • the prior estimated covariance corresponding to the current moment can be expressed by formula (5):
  • P k- represents the prior estimated covariance corresponding to the current moment
  • P k-1 represents the posterior estimated covariance corresponding to the previous moment
  • the a posteriori estimated covariance represents the actual state information of the previous moment.
  • the covariance of that is, the actual state information at the previous moment uncertainty.
  • A represents the state transition parameters of the robot in matrix form, and the state transition parameters A are used to represent the motion model of the robot.
  • the state transition parameter A can be used to indicate that the robot accelerates at a certain acceleration, or the robot moves at a constant speed at a constant speed, which can be set by the user
  • a T means the transposition of the state transition parameter
  • Q means the state transition noise
  • the calculation method can be See related descriptions above.
  • Step S22 using the transformation parameter from the actual state information to the measurement state information and the measurement interference noise to process the a priori estimated covariance corresponding to the current moment to obtain the filter gain corresponding to the current moment.
  • the filter gain corresponding to the current moment can be expressed by formula (6):
  • K k represents the filter gain corresponding to the current moment
  • H represents the transformation parameter in matrix form
  • the transformation parameter H is used to describe the transformation relationship between the actual state information and the measured state information. For example, it can be used to describe the actual state information and
  • the measurement state information is a linear relationship.
  • the transformation parameter H can be set by the user.
  • the transformation parameter H can be set as a unit matrix, which is not limited here.
  • H T represents the transposition of the transformation parameter
  • R represents the measurement interference noise
  • P k- represents the prior estimated covariance corresponding to the current moment
  • the prior estimated covariance P k- represents the predicted state information corresponding to the current moment
  • the covariance of that is, the predicted state information corresponding to the current moment
  • the uncertainty of , the calculation method can refer to the relevant description above.
  • the filter gain corresponding to the current moment can be determined by measuring the interference noise and the state transition noise.
  • at least one of the measurement interference noise and the state transition noise is calculated through the aforementioned steps, eg, the measurement interference noise is calculated using the aforementioned steps, or the state transition noise is calculated using the aforementioned steps.
  • the measurement interference noise and the state transition noise are both calculated and obtained by using the preceding steps, which are not limited herein.
  • Step S23 Use the state transition parameters and input state transition parameters of the robot to process the actual state information of the robot corresponding to the previous moment and the actual driving information of the previous moment, respectively, to obtain the predicted state information corresponding to the current moment.
  • the predicted state information corresponding to the current moment can be represented by formula (7):
  • the implementation scenario of this application describes the acquisition of the actual status information corresponding to the current moment. steps, so the actual state information at the previous moment It can be obtained by referring to the steps disclosed in the implementation scenario of this application.
  • the actual state information can be initialized to 0, and u k-1 represents the actual driving information corresponding to the previous moment.
  • the actual driving information may include the driving angle information, motor driving information and driving speed information of the robot. describe.
  • A represents the state transition parameters of the robot, please refer to the previous description
  • B represents the input state transition parameters
  • the input state transition parameters B are used to describe the conversion relationship between the input actual driving information and state information, so that the input state transition parameters B
  • the input actual driving information is converted into state information, and then combined with the actual state information of the robot corresponding to the previous moment to obtain the predicted state information of the robot corresponding to the current moment, that is, theoretically, the state information of the robot corresponding to the current moment.
  • Step S24 Integrate the predicted state information at the current moment with the measured state information at the current moment to obtain the actual state information of the robot corresponding to the current moment.
  • the actual state information corresponding to the current moment can be represented by formula (8):
  • Step S25 Update the prior estimated covariance corresponding to the current moment by using the filter gain and the transformation parameter to obtain the a posteriori estimated covariance corresponding to the current moment.
  • the posterior estimated covariance corresponding to the current moment can be expressed by formula (9):
  • P k represents the posterior estimated covariance corresponding to the current moment
  • I represents the identity matrix
  • K k represents the filter gain in matrix form
  • H represents the transformation parameter in matrix form
  • P k- represents the matrix form corresponding to the current moment.
  • Covariance is estimated a priori.
  • the posterior estimated covariance Pk may be initialized to a matrix set to all zeros.
  • the posterior estimated covariance corresponding to the current moment is obtained. Therefore, by repeating the steps in the embodiments of the present application, the actual value corresponding to the next moment (that is, moment k+1) can be determined. The state information is repeated in this way, and the actual state information of the robot corresponding to each moment can be determined during the driving process of the robot.
  • the reference information of the robot is obtained, and the reference information includes at least one of the following: measurement state information corresponding to several moments of the robot, actual driving information of the robot corresponding to the current moment, and based on the reference information, determine the state noise of the robot, so as to use
  • the state noise is obtained to obtain the actual state information of the robot corresponding to the current moment.
  • simulation can be performed without using a large number of particles, which is beneficial to improve the speed of state determination.
  • the state noise is determined according to the measured state information at several moments and/or the actual driving information at the current moment, the noise can be measured from the external measurement angle of the robot and/or the robot's own state, so that the state noise is consistent with the actual situation. It is closer, thereby improving the accuracy of the actual state information determined subsequently.
  • a preset prompt may also be provided when the state noise does not meet the preset noise condition.
  • the preset prompt can be realized in at least one form of sound, light and text. For example, playing a prompt voice, or lighting a prompt light, or outputting prompt text, etc., which are not limited here.
  • the state noise may include measurement interference noise obtained by using several pieces of measurement state information, and for the acquisition method, reference may be made to the relevant steps in the foregoing disclosed embodiments.
  • the preset noise condition may include that the measured interference noise is less than a first noise threshold, and the value of the first noise threshold may be set according to actual conditions. If the measurement interference noise does not meet the preset noise conditions, a first warning message can be output to prompt the state measurement to be disturbed, so that the user can perceive in time when the state measurement is disturbed, and the user experience can be improved.
  • the state noise may include state transition noise obtained by using actual driving information at the current moment, and for the acquisition method, reference may be made to the relevant steps in the foregoing disclosed embodiments.
  • the preset noise condition may include that the state transition noise is smaller than the second noise threshold, and the value of the second noise threshold may be set according to the actual situation, which is not limited herein. If the state transition noise does not meet the preset noise condition, a second warning message is output to remind the robot that there is a risk of vehicle body slippage, so that the user can perceive the risk of vehicle body slippage in a timely manner and improve user experience.
  • the above-mentioned first warning message and second warning message may be implemented in at least one form of sound, light and text. For example, playing a prompt voice, or lighting a prompt light, or outputting prompt text, etc., which are not limited here.
  • the Kalman filter system is used to realize the fusion positioning of the built-in encoder and the external input, and the state transition noise estimation is given according to the speed state of the left and right wheels, and then the noise estimation is used to judge whether the change of the external input is reasonable, and finally According to the judgment result, the fusion decision is made to avoid the location hijacking and give the fault signal. In this way, it can (1) improve the robustness of the signal hijacking and reduce the interference. (2) When signal hijacking occurs, a warning can be given. (3) Compared with the method for implementing particle filtering with a large number of particle models in the prior art, the method has a small amount of calculation and a fast convergence speed, and can meet the requirements of the robot positioning system for accuracy and speed.
  • FIG. 3 is a schematic frame diagram of an embodiment of a state determining apparatus 30 for a robot according to an embodiment of the present application.
  • the state determination device 30 of the robot includes a measurement state acquisition module 31 , a state noise determination module 32 and an actual state acquisition 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 the following: measurement state information of the robot corresponding to several moments, and actual driving information of the robot corresponding to the current moment; the state noise determination module 32 is configured to be based on the reference information to determine the state noise of the robot; the actual state acquisition module 33 is configured to use the state noise to obtain the actual state information of the robot corresponding to the current moment.
  • the reference information of the robot is obtained, and the reference information includes at least one of the following: measurement state information corresponding to several moments of the robot, actual driving information of the robot corresponding to the current moment, and based on the reference information, determine the state noise of the robot, so as to use
  • the state noise can obtain the actual state information of the robot corresponding to the current moment, and then in the process of determining the state, it can be simulated without using a large number of particles, which is beneficial to improve the speed of state determination.
  • the state noise is determined based on the measured state information at several moments and/or the actual driving information at the current moment, the noise can be measured from the external measurement angle of the robot and/or the robot's own state, thereby making the state noise more closely related to the actual situation. close, thereby improving the accuracy of the actual state information determined subsequently.
  • the state noise determination module 32 includes a measurement interference determination sub-module, configured to determine the measurement interference noise of the robot using measurement state information corresponding to the current moment and several moments before it; the state noise determination module 32 includes a state transition The determining sub-module is configured to determine the state transition noise of the robot by using the actual driving information at the current moment.
  • the measurement interference noise of the robot is determined by using the measurement state information corresponding to the current moment and several moments before it. Therefore, the noise of the robot can be determined from an external measurement perspective, so that the external noise of the robot during driving can be measured.
  • Interference Using the actual driving information at the current moment, the state transition noise of the robot can be determined. Therefore, the noise of the robot can be determined from the perspective of the robot's own state, so that the internal interference of the robot in the driving process can be measured.
  • the measurement interference determination sub-module includes a discrete acquisition unit configured to acquire the degree of dispersion of measurement state information at the current moment and several moments before it; the measurement interference determination sub-module includes a noise determination unit configured to utilize the dispersion degree , determine the measurement interference noise.
  • the external disturbance of the robot during driving can be accurately measured.
  • the degree of dispersion of the measurement status information at the current moment and several moments before it is the standard deviation of the measurement status information at the current moment and several moments before it.
  • the degree of dispersion of the measurement state information at the current moment and several moments before it as the standard deviation of the measurement state information at the current moment and several moments before it, the complexity of determining the degree of dispersion can be reduced. And the amount of calculation is beneficial to improve the speed of state determination.
  • the noise determination unit is configured to measure the interference noise as the product between the degree of dispersion and the preset gain parameter.
  • taking the product between the degree of dispersion and the preset gain parameter as the measurement interference noise can help improve the accuracy of measuring the interference noise and improve the accuracy of state determination.
  • the actual travel information includes travel angle information, motor drive information and travel speed information of the robot; the state transition determination sub-module is configured to use at least one of the first state noise and the second state noise to obtain the robot The state transition noise; wherein, the first state noise is determined by using the travel angle information and the travel speed information, and the second state noise is determined by using the motor drive information and the travel speed information.
  • the actual travel information is set to include travel angle information, motor drive information and travel speed information of the robot, so as to obtain the state transition of the robot by using at least one of the first state noise and the second state noise
  • the first state noise is determined by using the driving angle information and the driving speed information
  • the second state noise is determined by using the motor drive information and the driving speed information, which can help improve the accuracy of the state transition noise.
  • the robot includes a driving wheel and a steering wheel, the driving wheel is used to drive the robot, and the steering wheel is used to change the driving direction of the robot;
  • the driving speed information includes the actual speed difference between the driving wheels of the robot, and the driving angle information Including the actual steering angle of the steering wheel of the robot;
  • the first state noise determination unit includes a first mapping sub-unit, configured to use the first mapping relationship between the speed difference and the steering angle to perform mapping processing on the actual steering angle, and obtain the actual steering angle.
  • the first state noise determination unit includes a first state noise determination subunit, configured to use the difference between the actual speed difference and the theoretical speed difference to determine the first state noise.
  • the travel speed information is set to include the actual speed difference between the driving wheels of the robot
  • the travel angle information is set to include the actual steering angle of the steering wheel of the robot, so that the first difference between the speed difference and the steering angle is used.
  • a mapping relationship is performed on the actual steering angle to obtain the theoretical speed difference corresponding to the actual steering angle, and the difference between the actual speed difference and the theoretical speed difference is used to determine the first state noise. angle to determine the first state noise of the robot.
  • the robot includes drive wheels for driving the robot to travel;
  • the travel speed information includes an actual average speed of the drive wheels of the robot, and the motor drive information includes an actual average drive signal value of the motor of the robot;
  • the second state The noise determination unit includes a second mapping subunit, configured to perform 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 to obtain a theoretical average speed corresponding to the actual average driving signal value;
  • the second state noise determination unit includes a second state noise determination subunit configured to use the difference between the actual average speed and the theoretical average speed to determine the second state noise.
  • the travel speed information is set to include the actual average speed of the driving wheels of the robot
  • the motor drive information is set to include the actual average driving signal value of the motor of the robot, so as to utilize the difference between the average speed and the average driving signal value.
  • the actual average driving signal value is mapped to obtain the theoretical average speed corresponding to the actual average driving signal value, and the difference between the actual average speed and the theoretical average speed is used to determine the second state noise, so it can be From the perspective of the driving wheels of the robot, the second state noise of the robot is determined.
  • the first state noise determination subunit is configured to take the square of the difference between the actual speed difference and the theoretical speed difference as the first state noise; the second state noise determination subunit is configured to use the actual average speed and The square of the difference between the theoretical average velocities is taken as the second state noise.
  • the square of the difference between the actual speed difference and the theoretical speed difference is used as the first state noise
  • the square of the difference between the actual average speed and the theoretical average speed is used as the second state noise, which can reduce the noise.
  • the complexity and amount of calculation of the first state noise and the second state noise are beneficial to improve the speed of state determination.
  • the actual state acquisition module 33 is configured to use state noise to process the actual state information of the robot corresponding to the previous moment and the measured state information of the current moment to obtain the actual state information of the robot corresponding to the current moment.
  • the robot by using the state noise to process the measurement state information of the robot corresponding to the current moment and the actual state information corresponding to the previous moment, it is beneficial for the robot to achieve a balance between the measurement status information at the current moment and the actual status information at the previous moment. , so that the determined actual state information is corrected relative to the measured state information, which can help to improve the accuracy of the robot state determination.
  • the actual state acquisition module 33 is configured to determine the filter gain based on the state noise, and to predict the actual state information of the robot corresponding to the previous moment and the actual driving information of the previous moment to obtain the predicted state corresponding to the current moment information, and use the Kalman filter of the filter gain to fuse the predicted state information at the current moment with the measured state information at the current moment to obtain the actual state information of the robot corresponding to the current moment.
  • the filter gain is determined based on the state noise, and the actual state information of the robot corresponding to the previous moment and the actual driving information of the previous moment are predicted, and the predicted state information corresponding to the current moment is obtained, and the filter gain is used.
  • Kalman filter fuses the predicted state information at the current moment with the measured state information at the current moment, which can enhance the robustness to external signals, so as to accurately determine the actual state information corresponding to the current moment.
  • the state determination device 30 of the robot further includes a prompting module configured to perform a preset prompt when the state noise does not meet the preset noise condition.
  • a preset prompt is performed, which enables the user to perceive the abnormal state noise and improves the user experience.
  • the state noise includes: measurement interference noise obtained by using several pieces of measurement state information; correspondingly, the preset noise condition includes: the measurement interference noise is less than a first noise threshold; the prompt module includes a first warning sub-module, configured In order to output a first warning message when the measured interference noise does not meet the preset noise condition, to prompt that the state measurement is disturbed; and/or, the state noise includes: the state transition noise obtained by using the actual driving information at the current moment; correspondingly, The preset noise conditions include: the state transition noise is less than a second noise threshold; the prompting module includes a second early warning sub-module, configured to output a second early warning message when the state transition noise does not meet the preset noise conditions to prompt the robot to have a vehicle body Risk of skidding.
  • a first warning message is output to prompt the state measurement to be disturbed, so that the user can perceive in time when the state measurement is disturbed, and the user experience can be improved;
  • a second warning message is output to remind the robot that there is a risk of vehicle body slippage, so that when the robot has a vehicle body slippage risk, the user can perceive it in time and improve user experience.
  • the measurement state acquisition module 31 includes a data acquisition sub-module configured to perform image acquisition on the surrounding environment of the robot to obtain environmental image data corresponding to the current moment; the measurement state acquisition module 31 includes a measurement state determination sub-module, It is configured to determine the measurement status information of the robot corresponding to the current moment based on the environmental image data at the current moment; wherein the measurement status information and the actual status information both include at least one of the following: the position of the robot, the posture of the robot, and the speed of the robot.
  • the measurement status information of the robot corresponding to the current moment is determined, and the measurement status is determined.
  • the information and the actual state information are set to include at least one of the position of the robot, the posture of the robot and the speed of the robot, so that the measurement state information of the robot corresponding to the current moment can be quickly obtained, which can help improve the speed of determining the state of the robot. .
  • FIG. 4 is a schematic diagram of a framework of an embodiment of the robot 40 according to the embodiment of the present application.
  • the robot 40 includes a robot body 41, a memory 42 and a processor 43 disposed on the robot body 41, the memory 42 and the processor 43 are coupled to each other, and the processor 43 is used to execute program instructions stored in the memory 42 to implement any of the above Steps of a state determination method embodiment.
  • the processor 43 is configured to control itself and the memory 42 to implement the steps of any of the above state determination method embodiments.
  • the processor 43 may also be referred to as a CPU (Central Processing Unit, central processing unit).
  • the processor 43 may be an integrated circuit chip with signal processing capability.
  • the processor 43 may also be a general-purpose processor, a DSP (Digital Signal Processor, digital signal processor), an ASIC (Application Specific Integrated Circuit, an application-specific integrated circuit), an FPGA (Field-Programmable Gate Array, a field programmable gate array) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
  • the processor 43 may be jointly implemented by an integrated circuit chip.
  • the state noise is determined according to several obtained measured state information and/or current actual driving information, and the noise can be measured from the external measurement angle of the robot and/or the robot's own state angle, so that the state noise can be closer to the actual situation.
  • the accuracy of the actual state information determined subsequently is improved.
  • the robot 40 further includes several wheels disposed on the robot body 41, a motor for driving the wheels to walk, and a steering gear for driving the wheels to turn.
  • the robot includes a first wheel group and a second wheel group, the first wheel group is connected with a motor to serve as a driving wheel, and the second wheel group is connected with a steering gear to serve as a steering wheel.
  • the robot 40 may further include a speed measuring component, which may be disposed on the driving wheel for obtaining the speed of the driving wheel.
  • the robot 40 includes four wheels, wherein the two front wheels are used as steering wheels, the two rear wheels are used as driving wheels, and each rear wheel is provided with an encoder to obtain the speed corresponding to the rear wheel.
  • the robot can obtain the travel speed by reading the encoder, and obtain the travel angle by reading the steering gear control record.
  • the robot body 41 can be set in different shapes according to different practical application requirements. For example, for express delivery applications, the robot body 41 can be set to have the shape of a car, van, etc.; or, for service guidance applications, the robot body 41 can be set to have a general human shape, cartoon animals and other shapes, which can be set according to the actual situation. Set according to application requirements, and will not give examples one by one here.
  • the robot 40 in order to obtain the measurement state information, may further be provided with a camera device, so that the measurement state information of the robot 40 is determined by using the environment image captured by the camera device.
  • FIG. 5 is a schematic diagram of a framework of an embodiment of a computer-readable storage medium 50 according to an embodiment of the present application.
  • the computer-readable storage medium 50 stores program instructions 501 that can be executed by the processor, and the program instructions 501 are used to implement the steps of any of the above-mentioned state determination method embodiments.
  • the state noise is determined according to several obtained measured state information and/or current actual driving information, and the noise can be measured from the external measurement angle of the robot and/or the robot's own state angle, so that the state noise can be closer to the actual situation, Thus, the accuracy of the actual state information determined subsequently is improved.
  • the embodiments of the present application provide a computer program, including computer-readable codes, when the computer-readable codes are executed in a robot, a processor in the robot executes to implement the above method.
  • the disclosed method and apparatus may be implemented in other manners.
  • the device implementations described above are only illustrative.
  • the division of modules or units is only a logical function division. In actual implementation, there may be other divisions.
  • units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented.
  • the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, which may be in electrical, mechanical or other forms.
  • Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed over network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this implementation manner.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
  • the integrated unit if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium.
  • the medium includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (Processor) to execute all or part of the steps of the methods in the various implementation manners of the embodiments of this application.
  • the aforementioned storage medium includes: U disk, mobile hard disk, ROM (Read-Only Memory, read-only memory), RAM (Random Access Memory, random access memory), magnetic disk or optical disk and other media that can store program codes .
  • Embodiments of the present application provide a state determination method and device, a robot, a storage medium, and a computer program.
  • the method includes: acquiring reference information of the robot; wherein the reference information includes at least one of the following: the robot The measured state information corresponding to several moments, the actual driving information of the robot corresponding to the current moment; the state noise of the robot is determined based on the reference information; the actual state noise of the robot corresponding to the current moment is obtained by using the state noise status information.
  • simulation can be performed without using a large number of particles, which is beneficial to improve the speed of state determination.
  • the state noise is determined according to several obtained measured state information and/or current actual driving information, the noise can be measured from the external measurement angle of the robot and/or the robot's own state angle, thereby making the state noise more closely related to the actual situation. close, thereby improving the accuracy of the actual state information determined subsequently.

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Abstract

一种机器人（40）的状态确定方法及装置（30）、机器人（40）及存储介质（50），其中，机器人（40）的状态确定方法包括：获取机器人（40）的参考信息；其中，参考信息包括以下至少一者：机器人（40）对应若干时刻的测量状态信息、机器人（40）对应当前时刻的实际行驶信息；基于参考信息，确定机器人（40）的状态噪声；利用状态噪声，得到机器人（40）对应当前时刻的实际状态信息。

Description

状态确定方法及装置、机器人、存储介质和计算机程序
相关申请的交叉引用
本申请基于申请号为202010872662.3、申请日为2020年08月26日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以全文引用的方式引入本申请。
技术领域
本申请涉及机器人技术领域，特别是涉及一种状态确定方法及装置、机器人、存储介质和计算机程序。
背景技术
随着电子技术和计算机技术的发展，将机器人应用于快件配送、服务引导、酒店送餐等，逐渐受到广泛的关注，机器人的应用领域越来越广泛。
例如，近年来AI(Artificial Intelligence，人工智能)教育逐渐变得火热，大多数AI教育课程都以在线课程平台为基础进行延伸，辅以相应的硬件设备，如智能小车、智能机器人等。除了教学作用，学校还会组织一些基于机器人的比赛，如电子设计大赛、自动驾驶比赛等。在这些比赛中，选手们需要自己设计电路与算法，在比赛中与其他选手的机器人对抗。
发明内容
本申请实施例提供一种状态确定方法及装置、机器人、存储介质和计算机程序。
本申请实施例提供了一种机器人的状态确定方法，包括：获取机器人的参考信息；其中，参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息；基于参考信息，确定机器人的状态噪声；利用状态噪声，得到机器人对应当前时刻的实际状态信息。
因此，通过获取机器人的参考信息，且参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息，并基于参考信息，确定机器人的状态噪声，从而利用状态噪声，得到机器人对应当前时刻的实际状态信息，进而在确定状态的过程中，能够无需利用大量粒子进行模拟，有利于提高状态确定的速度。另外，由于该状态噪声是根据若干时刻的测量状态信息和/或当前时刻的实际行驶信息确定，因此能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从而使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。
在一些公开实施例中，基于参考信息，确定机器人的状态噪声包括：利用 对应当前时刻及其之前若干时刻的测量状态信息，确定机器人的测量干扰噪声；和/或，利用当前时刻的实际行驶信息，确定机器人的状态转移噪声。
因此，利用对应当前时刻及其之前若干时刻的测量状态信息，确定机器人的测量干扰噪声，故此，能够从外部测量角度确定机器人的噪声，从而能够衡量机器人在行驶过程中外部的干扰；利用当前时刻的实际行驶信息，确定机器人的状态转移噪声，故此，能够从机器人自身状态的角度确定机器人的噪声，从而能够衡量机器人在行驶过程中内部的干扰。
在一些公开实施例中，利用对应当前时刻及其之前若干时刻的测量状态信息，确定机器人的测量干扰噪声包括：获取当前时刻及其之前若干时刻的测量状态信息的离散程度；利用所述离散程度，确定测量干扰噪声。
因此，利用当前时刻及其之前若干时刻的测量状态信息的离散程度，并利用离散程度确定测量干扰噪声，能够准确地衡量机器人在行驶过程中外部的干扰。
在一些公开实施例中，当前时刻及其之前若干时刻的测量状态信息的离散程度为当前时刻及其之前若干时刻的测量状态信息的标准差；对应地，利用离散程度，确定测量干扰噪声，包括：将离散程度与预设增益参数之间的积作为测量干扰噪声。
因此，通过将当前时刻及其之前若干时刻的测量状态信息的离散程度设置为当前时刻及其之前若干时刻的测量状态信息的标准差，能够有利于降低确定离散程度的复杂度和计算量，有利于提高状态确定的速度；将离散程度与预设增益参数之间的积作为测量干扰噪声，能够有利于提高测量干扰噪声的准确性，有利于提高状态确定的精度。
在一些公开实施例中，实际行驶信息包括机器人的行驶角度信息、电机驱动信息和行驶速度信息；利用当前时刻的实际行驶信息，确定机器人的状态转移噪声包括：利用第一状态噪声和第二状态噪声中的至少一者，得到机器人的状态转移噪声；其中，所述第一状态噪声是利用行驶角度信息和行驶速度信息确定得到的，所述第二状态噪声是利用电机驱动信息和行驶速度信息确定得到的。
因此，将实际行驶信息设置为包括机器人的行驶角度信息、电机驱动信息和行驶速度信息，从而利用第一状态噪声和第二状态噪声中的至少一者，得到机器人的状态转移噪声，且第一状态噪声是利用行驶角度信息和行驶速度信息确定得到的，第二状态噪声是利用电机驱动信息和行驶速度信息确定得到的，能够有利于提高状态转移噪声的准确性。
在一些公开实施例中，机器人包括驱动轮和转向轮，驱动轮用于驱动机器人行驶，转向轮用于改变机器人的行驶方向；行驶速度信息包括机器人的驱动轮间的实际速度差，行驶角度信息包括机器人的转向轮的实际转向角度；对应地，在利用第一状态噪声和第二状态噪声中的至少一者，得到机器人的状态转移噪声之前，还包括：利用速度差与转向角度之间的第一映射关系对实际转向角度进行映射处理，得到与实际转向角度对应的理论速度差；利用实际速度差与理论速度差之间的差异，确定第一状态噪声；和/或，机器人包括驱动轮，驱 动轮用于驱动机器人行驶；行驶速度信息包括机器人的驱动轮的实际平均速度，电机驱动信息包括机器人的电机的实际平均驱动信号值；对应地，在利用第一状态噪声和第二状态噪声中的至少一者，得到机器人的状态转移噪声之前，还包括：利用平均速度与平均驱动信号值之间的第二映射关系对实际平均驱动信号值进行映射处理，得到与实际平均驱动信号值对应的理论平均速度；利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声。
因此，机器人包括驱动轮和转向轮，驱动轮用于驱动机器人行驶，转向轮用于改变机器人的行驶方向，并将行驶速度信息设置为包括机器人的驱动轮间的实际速度差，行驶角度信息设置为包括机器人的转向轮的实际转向角度，从而利用速度差与转向角度之间的第一映射关系对实际转向角度进行映射处理，得到与实际转向角度对应的理论速度差，并利用实际速度差与理论速度差之间的差异，确定第一状态噪声，故能够从机器人的转向轮的角度，确定机器人的第一状态噪声；机器人包括驱动轮，驱动轮用于驱动机器人行驶，并将行驶速度信息设置为包括机器人的驱动轮的实际平均速度，电机驱动信息设置为包括机器人的电机的实际平均驱动信号值，从而利用平均速度与平均驱动信号值之间的第二映射关系对实际平均驱动信号值进行映射处理，得到与实际平均驱动信号值对应的理论平均速度，并利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声，故能够从机器人的驱动轮的角度，确定机器人的第二状态噪声。
在一些公开实施例中，利用实际速度差与理论速度差之间的差异，确定第一状态噪声，包括：将实际速度差与理论速度差之间的差的平方作为第一状态噪声；利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声，包括：将实际平均速度和理论平均速度之间的差的平方作为第二状态噪声。
因此，将将实际速度差与理论速度差之间的差的平方作为第一状态噪声，将实际平均速度和理论平均速度之间的差的平方作为第二状态噪声，能够降低第一状态噪声和第二状态噪声计算的复杂度和计算量，有利于提高状态确定的速度。
在一些公开实施例中，利用状态噪声，得到机器人对应当前时刻的实际状态信息包括：利用状态噪声对机器人对应之前时刻的实际状态信息和当前时刻的测量状态信息进行处理，得到机器人对应当前时刻的实际状态信息。
因此，通过利用状态噪声处理机器人对应当前时刻的测量状态信息和对应之前时刻的实际状态信息，有利于使机器人在当前时刻的测量状态信息和之前时刻的实际状态信息之间取得平衡，使得确定得到的实际状态信息相对于测量状态信息得到修正，进而能够有利于提高机器人状态确定的精度。
在一些公开实施例中，利用状态噪声对机器人对应之前时刻的实际状态信息和当前时刻的测量状态信息进行处理，得到机器人对应当前时刻的实际状态信息包括：基于状态噪声确定滤波增益，并对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，并利用滤波增益的卡尔曼滤波，将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，得到机器人对应当前时刻的实际状态信息。
因此，基于状态噪声确定滤波增益，并对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，并利用滤波增益的卡尔曼滤波，将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，能够增强对于外部信号的鲁棒性，从而准确地确定对应当前时刻的实际状态信息。
在一些公开实施例中，在基于参考信息，确定机器人的状态噪声之后，方法还包括：若状态噪声不满足预设噪声条件，则进行预设提示。
因此，在状态噪声不满足预设噪声条件时，进行预设提示，能够使用户感知异常的状态噪声，提高用户体验。
在一些公开实施例中，状态噪声包括：利用当前时刻及其之前若干时刻的测量状态信息得到的测量干扰噪声；对应地，预设噪声条件包括：测量干扰噪声小于第一噪声阈值；若状态噪声不满足预设噪声条件，则进行预设提示包括：若测量干扰噪声不满足预设噪声条件，则输出第一预警消息，所述第一预警消息用于提示状态测量受到干扰；和/或，状态噪声包括：利用当前时刻的实际行驶信息得到的状态转移噪声；对应地，预设噪声条件包括：状态转移噪声小于第二噪声阈值；若状态噪声不满足预设噪声条件，则进行预设提示包括：若状态转移噪声不满足预设噪声条件，则输出第二预警消息，所述第二预警消息用于提示机器人存在车体打滑风险。
因此，在测量干扰噪声不满足预设条件时，输出第一预警消息，以提示状态测量受到干扰，从而能够在状态测量受到干扰时，使用户及时感知，提高用户体验；在状态转移噪声不满足预设条件时，输出第二预警消息，以提示机器人存在车体打滑风险，从而能够在机器人出现车体打滑风险时，使用户及时感知，提高用户体验。
在一些公开实施例中，获取机器人的参考信息，包括：对机器人的周边环境进行图像采集，得到对应当前时刻的环境图像数据；基于当前时刻的环境图像数据，确定机器人对应当前时刻的测量状态信息；测量状态信息和实际状态信息均包括以下至少一者：机器人的位置、机器人的姿态、机器人的速度。
因此，通过对机器人周边环境进行图像采集，得到对应当前时刻的环境图像户数，并基于当前时刻的环境图像数据，确定机器人对应当前时刻的测量状态信息，且将测量状态信息和实际状态信息设置为均包含机器人的位置、机器人的姿态和机器人的速度中的至少一者，从而能够快速获取到机器人对应当前时刻的测量状态信息，进而能够有利于提高机器人状态确定的速度。
本申请实施例提供了一种机器人的状态确定装置，包括：测量状态获取模块、状态噪声确定模块和实际状态获取模块，测量状态获取模块配置为获取机器人的参考信息；其中，参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息，机器人对应当前时刻的实际行驶信息；状态噪声确定模块配置为基于参考信息，确定机器人的状态噪声；实际状态获取模块配置为利用所述状态噪声，得到机器人对应当前时刻的实际状态信息。
本申请实施例提供了一种机器人，包括机器人本体以及设置在机器人本体上的存储器和处理器，存储器和处理器相互耦接，处理器用于执行存储器中存 储的程序指令，以实现上述的状态确定方法。
本申请实施例提供了一种计算机可读存储介质，其上存储有程序指令，程序指令被处理器执行时实现上述的状态确定方法。
本申请实施例提供了一种计算机程序，包括计算机可读代码，当所述计算机可读代码在机器人中运行时，所述机器人中的处理器执行用于实现上述的状态确定方法。
本申请实施例提供一种状态确定方法及装置、机器人、存储介质和计算机程序，通过获取机器人的参考信息，且参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息，并基于参考信息，确定机器人的状态噪声，从而利用状态噪声，得到机器人对应当前时刻的实际状态信息，进而在确定状态过程中，能够无需利用大量粒子进行模拟，有利于提高状态确定的速度。另外，由于该状态噪声是根据已获得的若干测量状态信息和/或当前实际行驶信息确定，因此能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从而使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。
附图说明
图1是本申请实施例机器人的状态确定方法一实施例的流程示意图；
图2是本申请实施例利用卡尔曼滤波确定机器人的实际状态信息的流程示意图；
图3是本申请实施例机器人的状态确定装置一实施例的框架示意图；
图4是本申请实施例机器人一实施例的框架示意图；
图5是本申请实施例计算机可读存储介质一实施例的框架示意图。
具体实施方式
下面结合说明书附图，对本申请实施例的方案进行详细说明。
以下描述中，为了说明而不是为了限定，提出了诸如特定系统结构、接口、技术之类的具体细节，以便透彻理解本申请。
本文中术语“系统”和“网络”在本文中常被可互换使用。本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中字符“/”，一般表示前后关联对象是一种“或”的关系。此外，本文中的“多”表示两个或者多于两个。
近年来AI教育逐渐变得火热，大多数AI教育课程都以在线课程平台为基础进行延伸，辅以相应的硬件设备，如智能小车、智能机器人等。除了教学作用，学校往往会组织一些基于机器人的比赛，如电子设计大赛、自动驾驶比赛等。在这些比赛中，选手们需要自己设计电路与算法，在比赛中与其他选手的机器人对抗。
在这种比赛中，机器人通常需要从上位机接收到比赛的全局信息，其中包 含机器人自身位置、速度及姿态等这些对机器人决策十分重要的信息。由于机器人和上位机的沟通基本都使用串口通讯，而通讯规则所有选手都事先知道。因此，难免会出现有的选手在机器人上放置干扰器以向其他选手发送错误的通讯信号误导对手。其中，最为常见的是发送错误的位置、速度及姿态信息，干扰对手的判断。如果被干扰的机器人算法不够鲁棒，就会出现失控现象。
除了在比赛中，在一些人工智能展厅中也会有利用通讯收集信息进行运转的机器人，如果有人发送干扰信号去干扰通讯，展示就会出现问题，造成难以估量的损失。例如，当车辆发生打滑等现象时，车辆采用的融合定位系统也可能出现定位漂移等，这也是一种典型的定位劫持问题。目前已有的方案中多使用粒子滤波的方法进行抗劫持，这种方法需要对每个智能体进行数十万的粒子模拟，且随着空间扩大计算量也会进一步增加，当场上存在多个机器人时难以满足实时计算的需求。
同时，在其他各种的应用场景下，机器人在行驶过程中也难免会受到干扰，例如，自由空间广泛存在的白噪声，甚至干扰信号，从而影响机器人的正常行驶，严重时，机器人甚至会出现失控、打滑等现象。
基于此，本申请实施例提供一种机器人的状态确定方法，能够提高机器人状态确定的准确性。请参阅图1，图1是本申请实施例机器人的状态确定方法一实施例的流程示意图。本申请实施例提供的方法步骤可以通过机器人等硬件设备执行，或者通过处理器运行计算机可执行代码的方式执行。所述状态确定方法可以包括如下步骤：
步骤S11：获取机器人的参考信息。
本申请实施例中，机器人的参考信息可以包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息。
需要说明的是，机器人的状态在不同时刻可能发生变化，例如，机器人在当前时刻相对于前一时刻发生了移动，当然，在其他应用场景中，机器人的状态也可能未发生变化，本领域技术人员可以根据机器人的实际操作情况进行确定。针对于此，机器人需确定其在不同时刻下的实际状态，以便于进行后续操作。
本申请实施例中，为了确定机器人对应当前时刻的实际状态信息，可先获取对应当前时刻的测量状态信息，从而基于本申请实施例中的步骤，利用对应当前时刻的测量状态信息，得到对应当前时刻的实际状态信息。可以理解的是，本文所述的对应某个时刻的信息，并不一定是在该时刻获得，有可能是该时刻附近所获得的。例如，对应当前时刻的测量状态信息可以是在当前时刻获取到的；当考虑通信时延时，对应当前时刻的测量状态信息也可以是在当前时刻的前若干时刻(例如，前0.5秒、前1秒等)获取到的，在此不做限定。
在一些公开实施例中，测量状态信息是对机器人进行状态测量所得到的，在一个公开实施场景中，可以对机器人的周边环境进行采集，得到对应当前时刻的环境图像数据，基于当前时刻的环境图像数据，确定机器人对应当前时刻的测量状态信息。例如，可以通过机器人行驶环境中所安装的摄像器件对机器人的周边环境进行图像采集；或者，也可以通过机器人上安装的摄像器件对周 边环境进行图像采集，在此不做限定。在一些公开实施例中，为了准确地描述机器人的状态，机器人的测量状态信息和实际状态信息可以包括：机器人的位置、机器人的状态、机器人的速度中的至少一者。例如，机器人的位置可以包括机器人所在的位置坐标(如，经纬度)、机器人的状态可以包括机器人的行驶状态(如，加速度)。以测量状态信息包括机器人的位置和机器人的速度为例，为了便于描述，可以将对应当前时刻的测量状态信息用公式(1)表示：
Figure PCTCN2021088224-appb-000001
上式中，z k表示机器人对应当前时刻k的测量状态信息，p表示测量状态信息中机器人的位置，v表示测量状态信息中机器人的速度。
类似地，以实际状态信息包括机器人的位置和机器人的速度为例，为了便于描述，可以将对应当前时刻的实际状态信息用公式(2)表示：
Figure PCTCN2021088224-appb-000002
上式中，
Figure PCTCN2021088224-appb-000003
表示机器人对应当前时刻k的实际状态信息，p′表示实际状态信息中机器人的位置，v′表示实际状态信息中机器人的速度。
此外，机器人对应若干时刻的测量状态信息可以包括机器人对应当前时刻及其之前若干时刻的测量状态信息。以当前时刻为k时刻为例，当前时刻之前若干时刻可以表示为k时刻之前n个时刻，n的取值可以根据实际应用需要进行设置。例如，n可以为5、10、15等等，在此不做限定。
在另一个公开实施场景中，实际行驶信息可以包括机器人的行驶角度信息、电机驱动信息和行驶速度信息。其中，行驶角度信息可以从机器人的舵机控制记录中获取，机器人可以包括转向轮，机器人的舵机用于驱动机器人的转向轮以一定的角度转向。电机驱动信息可以从机器人的电机控制记录中获取，机器人还可以包括驱动轮，机器人的电机用于驱动机器人的驱动轮以一定的速度移动。行驶速度信息可以从机器人的编码器中获取。
步骤S12：基于参考信息，确定机器人的状态噪声。
机器人的状态噪声表示机器人在行驶过程中对其状态产生影响的噪声，例如，机器人在从某一状态转移到另一状态时所产生的状态转移噪声；或者，机器人接收测量状态信息过程中所产生的测量干扰噪声，在此不做限定。
在一个公开实施场景中，可以利用对应当前时刻及其之前若干时刻的测量状态信息，确定机器人的测量干扰噪声。其中，当前时刻之前若干时刻可以参考前述描述。故此，能够从外部测量角度确定机器人的噪声，从而能够衡量机器人在行驶过程中外部的干扰。
在一个公开实施场景中，可以获取当前时刻及其之前若干时刻的测量状态信息的离散程度，利用离散程度，确定测量干扰噪声。例如，当前时刻及其之前若干时刻的测量状态信息的离散程度可以为当前时刻及其之前若干时刻的测量状态信息的标准差。又如，当前时刻及其之前若干时刻的测量状态信息的离散程度也可以为当前时刻及其之前若干时刻的测量状态信息的方差，在此不做限定。故此，能够有利于降低确定离散程度的复杂度和计算量，有利于提高状 态确定的速度。
在另一个公开实施场景中，还可以将离散程度与预设增益参数之间的积作为测量干扰噪声。预设增益参数可以实际情况进行设置，在此不做限定。在一些公开实施例中，测量干扰噪声可以用公式(3)表示：
R＝K Rσ(z k-n:k)         (3)；
上式中，R表示测量干扰噪声，z k-n:k表示对应当前时刻k及其之前n个时刻的测量状态信息，σ(z k-n:k)表示对应当前时刻k及其之前n个时刻的测量状态信息的标准差，K R表示预设增益参数，其中，预设增益参数可以为大于0的数值，如，0.5、1、1.5等等，在此不做限定。
在另一个公开实施场景中，可以利用当前时刻的实际行驶信息，确定机器人的状态转移噪声。以当前时刻为k时刻为例，可以利用k时刻的实际行驶信息，确定机器人的状态转移噪声，故此，能够从机器人自身状态的角度确定机器人的噪声，从而能够衡量机器人在行驶过程中内部的干扰。在一些公开实施例中，可以根据第一状态噪声、第二状态噪声中的至少一者，得到机器人的状态转移噪声，且第一状态噪声是利用行驶角度信息和加速度信息得到的，第二状态噪声是利用电机驱动信息和行驶速度信息确定得到的。
在一个实施场景中，可以从舵机的角度进行考虑，利用行驶角度信息和行驶速度信息，确定机器人的第一状态噪声，从而利用第一状态噪声，确定机器人的状态转移噪声。例如，可以利用行驶角度信息和行驶速度信息，确定机器人的第一状态噪声，并将第一状态噪声作为机器人的状态转移噪声。
在另一个实施场景中，还可以从电机的角度考虑，利用电机驱动信息和行驶速度信息，确定机器人的第二状态噪声，从而利用第二状态，确定机器人的状态转移噪声。例如，也可以利用电机驱动信息和行驶速度信息，确定机器人的第二状态噪声，并将第二状态噪声作为机器人的状态转移噪声。
在又一个实施场景中，还可以利用行驶角度信息和行驶速度信息，确定机器人的第一状态噪声，并利用电机驱动信息和行驶速度信息，确定机器人的第二状态噪声，从而可以利用第一状态噪声和第二状态噪声，得到机器人的状态转移噪声，进而能够同时考虑舵机的角度和电机的角度，有利于提高状态转移噪声的准确性。
在一些公开实施例中，在利用第一状态噪声和第二状态噪声，得到状态转移噪声的情况下，可以对第一状态噪声和第二状态噪声进行加权处理，得到状态转移噪声。此外，第一状态噪声和第二状态噪声对应的权值可以根据实际情况进行设置。例如，第一状态噪声相对于第二状态噪声重要时，可以设置第一状态噪声对应的权值大于第二状态噪声的权值；又如，第二状态噪声相对于第一状态噪声重要时，可以设置第二状态噪声对应的权值大于第一状态噪声的权值。此外，也可以将第一状态噪声对应的权值设置为等于第二状态噪声对应的权值，例如，将第一状态噪声对应的权值设置为0.5，第二状态噪声对应的权值也设置为0.5。
在一些公开实施例中，机器人可以包括驱动轮和转向轮，驱动轮用于驱动 机器人行驶，转向轮用于改变机器人的行驶方向，则行驶速度信息可以包括机器人的驱动轮间的实际速度差。例如，机器人包括两个驱动轮，则两个驱动轮的速度差即为实际速度差。为了便于描述，可以将实际速度差表示为e w，行驶角度信息可以包括机器人的转向轮的实际转向角度，为了便于描述，可以将实际转向角度表示为α，则可以利用速度差与转向角度之间的第一映射关系(为了便于描述，可以将第一映射关系表示为f 1)对实际转向角度α进行映射处理，得到与实际转向角度α对应的理论速度差(为了便于描述，可以将理论速度差表示为f 1(α))，从而可以利用实际速度差e w和理论速度差f 1(α)之间的差异，确定第一状态噪声，例如，可以将实际速度差e w和理论速度差f 1(α)之间的差的平方作为第一状态噪声。第一映射关系可以对预先采集得到多对速度差与转向角度进行统计分析而得到，例如，在机器人正常行驶过程中，采集M对速度差与转向角度，并对采集得到的M对速度差与转向角度进行拟合，得到速度差与转向角度之间的第一映射关系，M的数值可以根据实际情况进行设置，在此不做限定。
在一些公开实施例中，行驶速度信息还可以包括驱动轮的实际平均速度，即机器人各驱动轮的速度均值。例如，机器人包括两个驱动轮，则两个驱动轮的速度均值即为实际平均速度，为了便于描述，可以将实际平均速度表示为v w，电机驱动信息可以包括机器人电机的实际平均驱动信号值，即机器人各驱动轮对应的电机的信号均值。例如，机器人包括两个驱动轮，当驱动信号为脉宽调制信号时，实际平均驱动信号值可以为两个驱动轮对应的电机的脉宽调制信号均值，为了便于描述，可以将平均驱动信号值表示为p w，则可以利用平均速度与平均驱动信号值之间的第二映射关系(为了便于描述，可以将第二映射关系表示为f 2)对实际平均驱动信号值进行映射处理，得到与实际平均驱动信号值对应的理论平均速度(为了便于描述，可以将理论平均速度表示为f 2(p w))，从而可以利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声。例如，可以将实际平均速度v w和理论平均速度f 2(p w)之间的差的平方作为第二状态噪声。第二映射关系可以对预先采集得到的多对平均速度与平均驱动信号值进行统计分析而得到。例如，在机器人正常行驶过程中，采集N对平均速度与平均驱动信号值，并对N对平均速度与平均驱动信号值进行拟合，得到平均速度与平均驱动信号值之间的第二映射关系，N的数值可以根据实际情况进行设置，在此不做限定。
通过上述步骤，可以得到机器人的状态转移噪声，其中，所述状态转移噪声可以用公式(4)表示：
Q＝k 1(f 1(α)-e w) 2+k 2(f 2(p w)-v w) 2          (4)；
上式中，Q表示机器人的状态转移噪声，k 1表示第一状态噪声对应的权值，k 2表示第二状态噪声对应的权值，(f 1(α)-e w) 2表示第一状态噪声，(f 2(p w)-v w) 2表示第二状态噪声，e w表示实际速度差，f 1表示第一映射关系，α表示实际转向角度，v w表示实际平均速度，f 2表示第二映射关系，p w表示平均驱动信号值。
在一个公开实施场景中，可以通过上述步骤获得状态转移噪声和测量干扰噪声。当然，在实际应用时，也可以根据实际情况，通过上述步骤获得状态转移噪声，并将测量干扰噪声设置为一固定值，如考虑理想情况，可以将测量干扰噪声设置为0，即直接将状态转移噪声作为机器人的状态噪声。如也可以将测量干扰噪声设置为1、2、3等非零值，如还可以将测量干扰噪声设置为白噪声，在此不做限定。当然，还可以根据实际情况，通过上述步骤获得测量干扰噪声，并将状态转移噪声设置为一固定值，如考虑理想情况，可以将状态转移噪声设置为0，即直接将测量干扰噪声作为机器人的状态噪声，如也可以将状态转移噪声设置为1、2、3等非零值，如还可以将状态转移噪声设置为白噪声，在此不做限定。
步骤S13：利用状态噪声，得到机器人对应当前时刻的实际状态信息。
在一些公开实施例中，可以利用状态噪声对机器人对应之前时刻的实际状态信息和当前时刻的测量状态信息进行处理，得到机器人对应当前时刻的实际状态信息。例如，可以利用卡尔曼滤波结合状态噪声对机器人对应之前时刻的实际状态信息和当前时刻的测量状态信息进行处理，得到机器人对应当前时刻的实际状态信息。
在一个公开实施场景中，可以基于状态噪声确定滤波增益，并对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，并利用滤波增益的卡尔曼滤波，将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，得到机器人对应当前时刻的实际状态信息。
在一个公开实施场景中，请结合参阅图2，图2是本申请实施例利用卡尔曼滤波确定机器人的实际状态信息的流程示意图，本申请实施例提供的方法步骤可以通过机器人等硬件设备执行，或者通过处理器运行计算机可执行代码的方式执行。其中，可以通过如下步骤利用卡尔曼滤波确定机器人的实际状态信息：
步骤S21：利用机器人的状态转移参数和状态转移噪声对对应之前时刻的后验估计协方差进行处理，得到对应当前时刻的先验估计协方差。
在一些公开实施例中，以当前时刻是k时刻，当前时刻的前一时刻为k-1时刻为例，所述对应当前时刻的先验估计协方差可以用公式(5)表示：
P k-＝AP k-1A T+Q            (5)；
上式中，P k-表示对应当前时刻的先验估计协方差，P k-1表示对应前一时刻的后验估计协方差，后验估计协方差表示前一时刻的实际状态信息
Figure PCTCN2021088224-appb-000004
的协方差，即前一时刻的实际状态信息
Figure PCTCN2021088224-appb-000005
的不确定度。需要说明的是，本申请实施场景所描述的是获取对应当前时刻的实际状态信息
Figure PCTCN2021088224-appb-000006
的步骤，故前一时刻的实际状态信息
Figure PCTCN2021088224-appb-000007
可以参照公开实施场景所公开的步骤得到。后验估计协方差的获取方式可以参阅本申请实施例中的后文描述。此外，A表示矩阵形式的机器人的状态转移参数，状态转移参数A用于表示机器人的运动模型。例如，可以用状态转移参数A表示机器人以一定的加速度加速运动，或机器人以一定的速度匀速运 动，可以由用户设置，A T表示状态转移参数的转置，Q表示状态转移噪声，计算方式可以参阅前文相关描述。
步骤S22：利用实际状态信息到测量状态信息的变换参数和测量干扰噪声对对应当前时刻的先验估计协方差进行处理，得到对应当前时刻的滤波增益。
在一些公开实施例中，仍以当前时刻是k时刻，当前时刻的前一时刻为k-1时刻为例，所述对应当前时刻的滤波增益可以用公式(6)表示：
Figure PCTCN2021088224-appb-000008
上式中，K k表示对应当前时刻的滤波增益，H表示矩阵形式的变换参数，变换参数H用于描述实际状态信息和测量状态信息之间的转换关系，如可以用于描述实际状态信息和测量状态信息为线性关系，例如，变换参数H可以由用户进行设置，如可以将变换参数H设置为单位矩阵，在此不做限定，H T表示变换参数的转置，R表示测量干扰噪声，计算方式可以参阅前文相关描述。P k-表示对应当前时刻的先验估计协方差，先验估计协方差P k-表示对应当前时刻的预测状态信息
Figure PCTCN2021088224-appb-000009
的协方差，即对应当前时刻的预测状态信息
Figure PCTCN2021088224-appb-000010
的不确定度，计算方式可以参阅前文相关描述。
故此，通过测量干扰噪声和状态转移噪声可以确定对应当前时刻的滤波增益。在一些公开实施例中，测量干扰噪声和状态转移噪声中的至少一者是通过前述步骤计算得到的，例如，测量干扰噪声是利用前述步骤计算得到的，或者，状态转移噪声是利用前述步骤计算得到的，或者，测量干扰噪声和状态转移噪声均是利用前述步骤计算得到的，在此不做限定。
步骤S23：利用机器人的状态转移参数和输入状态转移参数分别对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行处理，得到对应当前时刻的预测状态信息。
在一些公开实施例中，仍以当前时刻是k时刻，当前时刻的前一时刻为k-1时刻为例，所述对应当前时刻的预测状态信息可以用公式(7)表示：
Figure PCTCN2021088224-appb-000011
上式中，
Figure PCTCN2021088224-appb-000012
表示对应当前时刻的预测状态信息，
Figure PCTCN2021088224-appb-000013
表示对应前一时刻的实际状态信息，如前文所述，本申请实施场景所描述的是获取对应当前时刻的实际状态信息
Figure PCTCN2021088224-appb-000014
的步骤，故前一时刻的实际状态信息
Figure PCTCN2021088224-appb-000015
可以参照本申请实施场景所公开的步骤得到。特别地，当k等于0时，实际状态信息
Figure PCTCN2021088224-appb-000016
可以初始化设置为0，u k-1表示对应前一时刻的实际行驶信息，实际行驶信息可以包括机器人的行驶角度信息、电机驱动信息和行驶速度信息，获取方式可以参阅前述公开实施例中的相关描述。A表示机器人的状态转移参数，可以参阅前文描述，B表示输入状态转移参数，输入状态转移参数B用于描述输入的实际行驶信息和状态信息之间的转换关系，从而可以通过输入状态转移参数B将输入的实际行驶信息转换为状态信息，再与机器人对应前一时刻的实际状态信息相结合，得到机器人对应当前时刻的预测状态信息，即理论上，机器人对应当前时刻的状态信息。
步骤S24：将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，得到机器人对应当前时刻的实际状态信息。
在一些公开实施例中，仍以当前时刻是k时刻，当前时刻的前一时刻为k-1时刻为例，所述对应当前时刻的实际状态信息可以用公式(8)表示：
Figure PCTCN2021088224-appb-000017
上式中，
Figure PCTCN2021088224-appb-000018
表示对应当前时刻的实际状态信息，
Figure PCTCN2021088224-appb-000019
表示对应当前时刻的预测状态信息，K k表示对应当前时刻的滤波增益，z k对应当前时刻的测量状态信息，H表示实际状态信息到测量状态信息的变换参数，可以参阅前文相关描述。即
Figure PCTCN2021088224-appb-000020
表示测量状态信息与预测状态信息之间的残差，利用滤波增益K k和预测状态信息对该残差进行修正，即得到机器人对应当前时刻的实际状态信息
Figure PCTCN2021088224-appb-000021
步骤S25：利用滤波增益和变换参数对对应当前时刻的先验估计协方差进行更新，得到对应当前时刻的后验估计协方差。
在一些公开实施例中，仍以当前时刻是k时刻，当前时刻的前一时刻为k-1时刻为例，所述对应当前时刻的后验估计协方差可以用公式(9)表示：
P k＝(I-K kH)P k-          (9)；
上式中，P k表示对应当前时刻的后验估计协方差，I表示单位矩阵，K k表示矩阵形式的滤波增益，H表示矩阵形式的变换参数，P k-表示矩阵形式的对应当前时刻的先验估计协方差。特别地，当k等于0时，后验估计协方差P k可以初始化设置为全零的矩阵。
通过对对应当前时刻的先验估计协方差进行更新，得到对应当前时刻的后验估计协方差，故重复本申请实施例中的步骤，能够确定对应下一时刻(即k+1时刻)的实际状态信息，如此循环，可以在机器人行驶过程中，确定机器人对应各个时刻的实际状态信息。
上述方案，通过获取机器人的参考信息，且参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息，并基于参考信息，确定机器人的状态噪声，从而利用状态噪声，得到机器人对应当前时刻的实际状态信息。进而在确定状态的过程中，能够无需利用大量粒子进行模拟，有利于提高状态确定的速度。另外，由于该状态噪声是根据若干时刻的测量状态信息和/或当前时刻的实际行驶信息确定的，因此能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从而使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。
其中，在一些公开实施例中，为了能够使用户及时感知异常的状态噪声，提高用户体验，还可以在状态噪声不满足预设噪声条件时，进行预设提示。预设提示可以以声、光、文字中的至少一种形式实现。例如，播放提示语音，或者，点亮提示灯，或者输出提示文字等等，在此不做限定。
在一个公开实施场景中，状态噪声可以包括利用若干测量状态信息得到的测量干扰噪声，获取方式可以参阅前述公开实施例中的相关步骤。此外，预设噪声条件可以包括测量干扰噪声小于第一噪声阈值，第一噪声阈值的数值可以根据实际情况进行设置。若测量干扰噪声不满足预设噪声条件，则可以输出第 一预警消息，以提示状态测量受到干扰，从而能够在状态测量受到干扰时，使用户及时感知，提高用户体验。
在另一个公开实施场景中，状态噪声可以包括利用当前时刻的实际行驶信息得到的状态转移噪声，获取方式可以参阅前述公开实施例中的相关步骤。此外，预设噪声条件可以包括状态转移噪声小于第二噪声阈值，第二噪声阈值的数值可以根据实际情况进行设置，在此不做限定。若状态转移噪声不满足预设噪声条件，则输出第二预警消息，以提示机器人存在车体打滑风险，从而能够在机器人出现车体打滑风险时，使用户及时感知，提高用户体验。
上述第一预警消息、第二预警消息可以以声、光、文字中的至少一种形式实现。例如，播放提示语音，或者，点亮提示灯，或者输出提示文字等等，在此不做限定。
本申请实施例中，利用卡尔曼滤波系统实现了内置编码器与外部输入的融合定位，并根据左右轮速度状态给出状态转移噪声估计，再利用所述噪声估计判断外部输入变化是否合理，最后根据判断结果进行融合决策，实现了避免定位劫持，并给出故障信号，如此，能够(1)提高信号劫持的鲁棒性，减少受到的干扰。(2)发生信号劫持时，能够给出警告。(3)相比于现有技术中通常使用的大量粒子模型实施粒子滤波的方法，本方法的计算量小、收敛速度快，能够满足机器人定位系统对于精度度和速度的要求。
请参阅图3，图3是本申请实施例机器人的状态确定装置30一实施例的框架示意图。机器人的状态确定装置30包括测量状态获取模块31、状态噪声确定模块32和实际状态获取模块33。测量状态获取模块31配置为获取机器人的参考信息，其中，参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息；状态噪声确定模块32配置为基于参考信息，确定机器人的状态噪声；实际状态获取模块33配置为利用所述状态噪声，得到机器人对应当前时刻的实际状态信息。
上述方案，通过获取机器人的参考信息，且参考信息包括以下至少一者：机器人对应若干时刻的测量状态信息、机器人对应当前时刻的实际行驶信息，并基于参考信息，确定机器人的状态噪声，从而利用状态噪声，得到机器人对应当前时刻的实际状态信息，进而在确定状态的过程中，能够无需利用大量粒子进行模拟，有利于提高状态确定的速度。另外，由于该状态噪声是根据若干时刻的测量状态信息和/或当前时刻的实际行驶信息确定，因此能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从而使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。
在一些公开实施例中，状态噪声确定模块32包括测量干扰确定子模块，配置为利用对应当前时刻及其之前若干时刻的测量状态信息，确定机器人的测量干扰噪声；状态噪声确定模块32包括状态转移确定子模块，配置为利用当前时刻的实际行驶信息，确定机器人的状态转移噪声。
区别于前述公开实施例，利用对应当前时刻及其之前若干时刻的测量状态信息，确定机器人的测量干扰噪声，故此，能够从外部测量角度确定机器人的噪声，从而能够衡量机器人在行驶过程中外部的干扰；利用当前时刻的实际行 驶信息，确定机器人的状态转移噪声，故此，能够从机器人自身状态的角度确定机器人的噪声，从而能够衡量机器人在行驶过程中内部的干扰。
在一些公开实施例中，测量干扰确定子模块包括离散获取单元，配置为获取当前时刻及其之前若干时刻的测量状态信息的离散程度；测量干扰确定子模块包括噪声确定单元，配置为利用离散程度，确定测量干扰噪声。
区别于前述公开实施例，利用当前时刻及其之前若干时刻的测量状态信息的离散程度，并利用离散程度确定测量干扰噪声，能够准确地衡量机器人在行驶过程中外部的干扰。
在一些公开实施例中，当前时刻及其之前若干时刻的测量状态信息的离散程度为当前时刻及其之前若干时刻的测量状态信息的标准差。
区别于前述公开实施例，通过将当前时刻及其之前若干时刻的测量状态信息的离散程度设置为当前时刻及其之前若干时刻的测量状态信息的标准差，能够有利于降低确定离散程度的复杂度和计算量，有利于提高状态确定的速度。
在一些公开实施例中，噪声确定单元配置为将离散程度与预设增益参数之间的积作为测量干扰噪声。
区别于前述公开实施例，将离散程度与预设增益参数之间的积作为测量干扰噪声，能够有利于提高测量干扰噪声的准确性，有利于提高状态确定的精度。
在一些公开实施例中，实际行驶信息包括机器人的行驶角度信息、电机驱动信息和行驶速度信息；状态转移确定子模块配置为利用第一状态噪声和第二状态噪声中的至少一者，得到机器人的状态转移噪声；其中，第一状态噪声是利用行驶角度信息和行驶速度信息确定得到的，第二状态噪声是利用电机驱动信息和行驶速度信息确定得到的。
区别于前述公开实施例，将实际行驶信息设置为包括机器人的行驶角度信息、电机驱动信息和行驶速度信息，从而利用第一状态噪声和第二状态噪声中的至少一者，得到机器人的状态转移噪声，且第一状态噪声是利用行驶角度信息和行驶速度信息确定得到的，第二状态噪声是利用电机驱动信息和行驶速度信息确定得到的，能够有利于提高状态转移噪声的准确性。
在一些公开实施例中，机器人包括驱动轮和转向轮，驱动轮用于驱动机器人行驶，转向轮用于改变机器人的行驶方向；行驶速度信息包括机器人的驱动轮间的实际速度差，行驶角度信息包括机器人的转向轮的实际转向角度；第一状态噪声确定单元包括第一映射子单元，配置为利用速度差与转向角度之间的第一映射关系对实际转向角度进行映射处理，得到与实际转向角度对应的理论速度差；第一状态噪声确定单元包括第一状态噪声确定子单元，配置为利用实际速度差与理论速度差之间的差异，确定第一状态噪声。
区别于前述公开实施例，将行驶速度信息设置为包括机器人的驱动轮间的实际速度差，行驶角度信息设置为包括机器人的转向轮的实际转向角度，从而利用速度差与转向角度之间的第一映射关系对实际转向角度进行映射处理，得到与实际转向角度对应的理论速度差，并利用实际速度差与理论速度差之间的差异，确定第一状态噪声，故能够从机器人的转向轮的角度，确定机器人的第一状态噪声。
在一些公开实施例中，机器人包括驱动轮，驱动轮用于驱动机器人行驶；行驶速度信息包括机器人的驱动轮的实际平均速度，电机驱动信息包括机器人的电机的实际平均驱动信号值；第二状态噪声确定单元包括第二映射子单元，配置为利用平均速度与平均驱动信号值之间的第二映射关系对实际平均驱动信号值进行映射处理，得到与实际平均驱动信号值对应的理论平均速度；第二状态噪声确定单元包括第二状态噪声确定子单元，配置为利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声。
区别于前述公开实施例，将行驶速度信息设置为包括机器人的驱动轮的实际平均速度，电机驱动信息设置为包括机器人的电机的实际平均驱动信号值，从而利用平均速度与平均驱动信号值之间的第二映射关系对实际平均驱动信号值进行映射处理，得到与实际平均驱动信号值对应的理论平均速度，并利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声，故能够从机器人的驱动轮的角度，确定机器人的第二状态噪声。
在一些公开实施例中，第一状态噪声确定子单元配置为将实际速度差与理论速度差之间的差的平方作为第一状态噪声；第二状态噪声确定子单元配置为将实际平均速度和理论平均速度之间的差的平方作为第二状态噪声。
区别于前述公开实施例，将将实际速度差与理论速度差之间的差的平方作为第一状态噪声，将实际平均速度和理论平均速度之间的差的平方作为第二状态噪声，能够降低第一状态噪声和第二状态噪声计算的复杂度和计算量，有利于提高状态确定的速度。
在一些公开实施例中，实际状态获取模块33配置为利用状态噪声对机器人对应之前时刻的实际状态信息和当前时刻的测量状态信息进行处理，得到机器人对应当前时刻的实际状态信息。
区别于前述实施例，通过利用状态噪声处理机器人对应当前时刻的测量状态信息和对应之前时刻的实际状态信息，有利于使机器人在当前时刻的测量状态信息和之前时刻的实际状态信息之间取得平衡，使得确定得到的实际状态信息相对于测量状态信息得到修正，进而能够有利于提高机器人状态确定的精度。
在一些公开实施例中，实际状态获取模块33配置为基于状态噪声确定滤波增益，并对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，并利用滤波增益的卡尔曼滤波，将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，得到机器人对应当前时刻的实际状态信息。
区别于前述公开实施例，基于状态噪声确定滤波增益，并对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，并利用滤波增益的卡尔曼滤波，将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，能够增强对于外部信号的鲁棒性，从而准确地确定对应当前时刻的实际状态信息。
在一些公开实施例中，机器人的状态确定装置30还包括提示模块，配置为在状态噪声不满足预设噪声条件时，进行预设提示。
区别于前述公开实施例，在状态噪声不满足预设噪声条件时，进行预设提 示，能够使用户感知异常的状态噪声，提高用户体验。
在一些公开实施例中，状态噪声包括：利用若干测量状态信息得到的测量干扰噪声；对应地，预设噪声条件包括：测量干扰噪声小于第一噪声阈值；提示模块包括第一预警子模块，配置为在测量干扰噪声不满足预设噪声条件时，输出第一预警消息，以提示状态测量受到干扰；和/或，状态噪声包括：利用当前时刻的实际行驶信息得到的状态转移噪声；对应地，预设噪声条件包括：状态转移噪声小于第二噪声阈值；提示模块包括第二预警子模块，配置为在状态转移噪声不满足预设噪声条件时，输出第二预警消息，以提示机器人存在车体打滑风险。
区别于前述公开实施例，在测量干扰噪声不满足预设条件时，输出第一预警消息，以提示状态测量受到干扰，从而能够在状态测量受到干扰时，使用户及时感知，提高用户体验；在状态转移噪声不满足预设条件时，输出第二预警消息，以提示机器人存在车体打滑风险，从而能够在机器人出现车体打滑风险时，使用户及时感知，提高用户体验。
在一些公开实施例中，测量状态获取模块31包括数据采集子模块，配置为对机器人的周边环境进行图像采集，得到对应当前时刻的环境图像数据；测量状态获取模块31包括测量状态确定子模块，配置为基于当前时刻的环境图像数据，确定机器人对应当前时刻的测量状态信息；其中，测量状态信息和实际状态信息均包括以下至少一者：机器人的位置、机器人的姿态、机器人的速度。
区别于前述公开实施例，通过对机器人的周边环境进行图像采集，得到对应当前时刻的环境图像户数，并基于当前时刻的环境图像数据，确定机器人对应当前时刻的测量状态信息，且将测量状态信息和实际状态信息设置为均包含机器人的位置、机器人的姿态和机器人的速度中的至少一者，从而能够快速获取到机器人对应当前时刻的测量状态信息，进而能够有利于提高机器人状态确定的速度。
请参阅图4，图4是本申请实施例机器人40一实施例的框架示意图。机器人40包括机器人本体41以及设置在机器人本体41上的存储器42和处理器43，存储器42和处理器43相互耦接，处理器43用于执行存储器42中存储的程序指令，以实现上述任一状态确定方法实施例的步骤。
在一些公开实施例中，处理器43用于控制其自身以及存储器42以实现上述任一状态确定方法实施例的步骤。处理器43还可以称为CPU(Central Processing Unit，中央处理单元)。处理器43可能是一种集成电路芯片，具有信号的处理能力。处理器43还可以是通用处理器、DSP(Digital Signal Processor，数字信号处理器)、ASIC(Application Specific Integrated Circuit，专用集成电路)、FPGA(Field-Programmable Gate Array，现场可编程门阵列)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。另外，处理器43可以由集成电路芯片共同实现。
上述方案，状态噪声是根据已获得的若干测量状态信息和/或当前实际行驶信息确定，能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从 而能够使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。
在一些公开实施例中，该机器人40还包括设置在机器人本体41上的若干个轮子以及用于驱动轮子行走的电机和用于驱动轮子转向的舵机。例如，该机器人包括第一轮组和第二轮组，第一轮组与电机连接，以作为驱动轮，第二轮组与舵机连接，以作为转向轮。另外，为了获得机器人的行驶信息，机器人40还可包括速度测量组件，该速度测量组件可设置在驱动轮上，以用于获得驱动轮的速度。在一应用场景中，机器人40包括4个轮子，其中，两个前轮作为转向轮，两个后轮作为驱动轮，且每个后轮设置有编码器，以获得对应后轮的速度。由此，机器人可通过读取编码器获得行驶速度，并通过读取舵机控制记录可获得行驶角度。此外，可以根据不同的实际应用需求，将机器人本体41设置为不同的外形。例如，对于快件配送应用，可以将机器人本体41设置为具有小汽车、面包车等车型的外形；或者，对于服务引导应用，可以将机器人本体41设置为具有一般人形、卡通动物等外形，可以根据实际应用需求进行设置，在此不再一一举例。
在一些公开实施例中，为实现获得测量状态信息，机器人40还可设置有摄像器件，由此通过摄像器件拍摄得到的环境图像，确定机器人40的测量状态信息。
请参阅图5，图5为本申请实施例计算机可读存储介质50一实施例的框架示意图。计算机可读存储介质50存储有能够被处理器运行的程序指令501，程序指令501用于实现上述任一状态确定方法实施例的步骤。
上述方案，状态噪声是根据已获得的若干测量状态信息和/或当前实际行驶信息确定，能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从而能够使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。
在一些公开实施例中，本申请实施例提供一种计算机程序，包括计算机可读代码，当所述计算机可读代码在机器人中运行时，所述机器人中的处理器执行实现上述方法。
在本申请所提供的几个实施例中，应该理解到，所揭露的方法和装置，可以通过其它的方式实现。例如，以上所描述的装置实施方式仅仅是示意性的，例如，模块或单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性、机械或其它的形式。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施方式方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实施例的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(Processor)执行本申请实施例各个实施方式方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、ROM(Read-Only Memory，只读存储器)、RAM(Random Access Memory，随机存取存储器)、磁碟或者光盘等各种可以存储程序代码的介质。
工业实用性
本申请实施例提供了一种状态确定方法及装置、机器人、存储介质和计算机程序，所述方法包括：获取所述机器人的参考信息；其中，所述参考信息包括以下至少一者：所述机器人对应若干时刻的测量状态信息、所述机器人对应当前时刻的实际行驶信息；基于所述参考信息，确定所述机器人的状态噪声；利用所述状态噪声，得到所述机器人对应所述当前时刻的实际状态信息。根据本申请实施例提供的机器人的状态确定方法，在确定状态的过程中，能够无需利用大量粒子进行模拟，有利于提高状态确定的速度。另外，由于该状态噪声是根据已获得的若干测量状态信息和/或当前实际行驶信息确定的，因此能够从机器人外部测量角度和/或机器人自身状态角度衡量噪声，从而使得状态噪声与实际情况更加贴近，进而提高后续确定的实际状态信息的准确性。

Claims (28)

  1. 一种机器人的状态确定方法，其中，所述方法包括：
    获取所述机器人的参考信息；其中，所述参考信息包括以下至少一者：所述机器人对应若干时刻的测量状态信息、所述机器人对应当前时刻的实际行驶信息；
    基于所述参考信息，确定所述机器人的状态噪声；
    利用所述状态噪声，得到所述机器人对应所述当前时刻的实际状态信息。
  2. 根据权利要求1所述的方法，其中，所述基于所述参考信息，确定所述机器人的状态噪声，包括：
    利用对应所述当前时刻及其之前若干时刻的测量状态信息，确定所述机器人的测量干扰噪声；
    和/或，利用所述当前时刻的实际行驶信息，确定所述机器人的状态转移噪声。
  3. 根据权利要求2所述的方法，其中，所述利用对应所述当前时刻及其之前若干时刻的测量状态信息，确定所述机器人的测量干扰噪声包括：
    获取所述当前时刻及其之前若干时刻的测量状态信息的离散程度；
    利用所述离散程度，确定所述测量干扰噪声。
  4. 根据权利要求3所述的方法，其中，所述当前时刻及其之前若干时刻的测量状态信息的离散程度为所述当前时刻及其之前若干时刻的测量状态信息的标准差；对应地，
    所述利用所述离散程度，确定所述测量干扰噪声，包括：
    将所述离散程度与预设增益参数之间的积作为所述测量干扰噪声。
  5. 根据权利要求2至4任一项所述的方法，其中，所述实际行驶信息包括所述机器人的行驶角度信息、电机驱动信息和行驶速度信息；
    所述利用所述当前时刻的实际行驶信息，确定所述机器人的状态转移噪声包括：
    利用第一状态噪声、第二状态噪声中的至少一者，得到所述机器人的所述状态转移噪声；
    其中，所述第一状态噪声是利用所述行驶角度信息和所述行驶速度信息确定得到的，所述第二状态噪声是利用所述电机驱动信息和所述行驶速度信息确定得到的。
  6. 根据权利要求5所述的方法，其中，所述机器人包括驱动轮和转向轮，所述驱动轮用于驱动所述机器人行驶，所述转向轮用于改变所述机器人的行驶方向；所述行驶速度信息包括所述机器人的驱动轮间的实际速度差，所述行驶角度信息包括所述机器人的转向轮的实际转向角度；
    对应地，在所述利用第一状态噪声、第二状态噪声中的至少一者，得到所述机器人的所述状态转移噪声之前，所述方法还包括：
    利用速度差与转向角度之间的第一映射关系对所述实际转向角度进行映射处理，得到与所述实际转向角度对应的理论速度差；
    利用所述实际速度差与所述理论速度差之间的差异，确定所述第一状态噪声；和/或，
    所述机器人包括驱动轮，所述驱动轮用于驱动所述机器人行驶；所述行驶速度信息包括所述机器人的驱动轮的实际平均速度，所述电机驱动信息包括所述机器人的电机的实际平均驱动信号值；
    对应地，在所述利用第一状态噪声、第二状态噪声中的至少一者，得到所述机器人的所述状态转移噪声之前，所述方法还包括：
    利用平均速度与平均驱动信号值之间的第二映射关系对所述实际平均驱动信号值进行映射处理，得到与所述实际平均驱动信号值对应的理论平均速度；
    利用所述实际平均速度和所述理论平均速度之间的差异，确定所述第二状态噪声。
  7. 根据权利要求6所述的方法，其中，所述利用所述实际速度差与所述理论速度差之间的差异，确定所述第一状态噪声，包括：
    将所述实际速度差与所述理论速度差之间的差的平方作为所述第一状态噪声；
    所述利用所述实际平均速度和所述理论平均速度之间的差异，确定所述第二状态噪声，包括：
    将所述实际平均速度和所述理论平均速度之间的差的平方作为所述第二状态噪声。
  8. 根据权利要求1至7任一项所述的方法，其中，所述利用所述状态噪声，得到所述机器人对应所述当前时刻的实际状态信息包括：
    利用所述状态噪声对所述机器人对应之前时刻的实际状态信息和所述当前时刻的测量状态信息进行处理，得到所述机器人对应所述当前时刻的实际状态信息。
  9. 根据权利要求8所述的方法，其中，所述利用所述状态噪声对所述机器人对应之前时刻的实际状态信息和所述当前时刻的测量状态信息进行处理，得到所述机器人对应所述当前时刻的实际状态信息，包括：
    基于所述状态噪声确定滤波增益；
    对所述机器人对应前一时刻的实际状态信息和所述前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息；
    利用所述滤波增益的卡尔曼滤波，将所述当前时刻的预测状态信息与所述当前时刻的测量状态信息进行融合，得到所述机器人对应所述当前时刻的实际状态信息。
  10. 根据权利要求9所述的方法，其中，所述基于所述状态噪声确定滤波增益，包括：
    利用所述机器人的状态转移参数和状态转移噪声对对应之前时刻的后验估计协方差进行处理，得到对应当前时刻的先验估计协方差，并利用实际状态信息到测量状态信息的变换参数和测量干扰噪声对所述对应当前时刻的先验估计协方差进行处理，得到对应当前时刻的滤波增益；
    所述对所述机器人对应前一时刻的实际状态信息和所述前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，包括：利用所述机器人的状态转移参数和输入状态转移参数分别对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行处理，得到对应当前时刻的预测状态信息。
  11. 根据权利要求1至10任一项所述的方法，其中，在所述基于所述参考信息，确定所述机器人的状态噪声之后，所述方法还包括：
    若所述状态噪声不满足预设噪声条件，则进行预设提示。
  12. 根据权利要求11所述的方法，其中，所述状态噪声包括：利用所述当前时刻及其之前若干时刻的测量状态信息得到的测量干扰噪声；对应地，所述预设噪声条件包括：所述测量干扰噪声小于第一噪声阈值；所述若所述状态噪声不满足预设噪声条件，则进行预设提示包括：
    若所述测量干扰噪声不满足所述预设噪声条件，则输出第一预警消息，所述第一预警消息用于提示状态测量受到干扰；
    和/或，所述状态噪声包括：利用所述当前时刻的实际行驶信息得到的状态转移噪声；对应地，所述预设噪声条件包括：所述状态转移噪声小于第二噪声阈值；所述若所述状态噪声不满足预设噪声条件，则进行预设提示包括：
    若所述状态转移噪声不满足所述预设噪声条件，则输出第二预警消息，所述第二预警消息用于提示所述机器人存在车体打滑风险。
  13. 根据权利要求1至12任一项所述的方法，其中，所述获取所述机器人的参考信息，包括：
    对所述机器人的周边环境进行图像采集，得到对应当前时刻的环境图像数据；
    基于所述当前时刻的环境图像数据，确定所述机器人对应当前时刻的测量状态信息；
    所述测量状态信息和实际状态信息均包括以下至少一者：所述机器人的位置、所述机器人的姿态、所述机器人的速度。
  14. 一种机器人的状态确定装置，其中，包括：
    测量状态获取模块，配置为获取所述机器人的参考信息；其中，所述参考信息包括以下至少一者：所述机器人对应若干时刻的测量状态信息、所述机器人对应当前时刻的实际行驶信息；
    状态噪声确定模块，配置为基于所述参考信息，确定所述机器人的状态噪声；
    实际状态获取模块，配置为利用所述状态噪声，得到所述机器人对应所述当前时刻的实际状态信息。
  15. 根据权利要求14所述的装置，其中，所述状态噪声确定模块包括：
    测量干扰确定子模块，配置为利用对应当前时刻及其之前若干时刻的测量状态信息，确定所述机器人的测量干扰噪声；
    状态转移确定子模块，配置为利用当前时刻的实际行驶信息，确定所述机器人的状态转移噪声。
  16. 根据权利要求15所述的装置，其中，测量干扰确定子模块包括：
    离散获取单元，配置为获取当前时刻及其之前若干时刻的测量状态信息的离散程度；
    噪声确定单元，配置为利用所述离散程度，确定所述测量干扰噪声。
  17. 根据权利要求16所述的装置，其中，所述当前时刻及其之前若干时刻的测量状态信息的离散程度为所述当前时刻及其之前若干时刻的测量状态信息的标准差；对应地，
    所述噪声确定单元，配置为将离散程度与预设增益参数之间的积作为所述测量干扰噪声。
  18. 根据权利要求15至17任一项所述的装置，其中，所述实际行驶信息包括所述机器人的行驶角度信息、电机驱动信息和行驶速度信息；
    所述状态转移确定子模块，配置为利用第一状态噪声、第二状态噪声中的至少一者，得到所述机器人的状态转移噪声；
    其中，所述第一状态噪声是利用所述行驶角度信息和所述行驶速度信息确定得到的，所述第二状态噪声是利用所述电机驱动信息和所述行驶速度信息确定得到的。
  19. 根据权利要求18所述的装置，其中，所述机器人包括驱动轮和转向轮，所述驱动轮用于驱动所述机器人行驶，所述转向轮用于改变所述机器人的行驶方向；所述行驶速度信息包括所述机器人的驱动轮间的实际速度差，所述行驶角度信息包括所述机器人的转向轮的实际转向角度；
    对应地，所述装置还包括第一状态噪声确定单元，所述第一状态噪声确定单元，包括：
    第一映射子单元，配置为利用速度差与转向角度之间的第一映射关系对实际转向角度进行映射处理，得到与实际转向角度对应的理论速度差；
    第一状态噪声确定子单元，配置为利用实际速度差与理论速度差之间的差异，确定第一状态噪声；
    和/或，所述机器人包括驱动轮，所述驱动轮用于驱动所述机器人行驶；所述行驶速度信息包括所述机器人的驱动轮的实际平均速度，所述电机驱动信息包括机器人的电机的实际平均驱动信号值；
    对应地，所述装置还包括第二状态噪声确定单元，所述第二状态噪声确定单元包括：
    第二映射子单元，配置为利用平均速度与平均驱动信号值之间的第二映射关系对实际平均驱动信号值进行映射处理，得到与实际平均驱动信号值对应的理论平均速度；
    第二状态噪声确定子单元，配置为利用实际平均速度和理论平均速度之间的差异，确定第二状态噪声。
  20. 根据权利要求19所述的装置，其中，
    所述第一状态噪声确定子单元，配置为将实际速度差与理论速度差之间的差的平方作为第一状态噪声；
    所述第二状态噪声确定子单元，配置为将实际平均速度和理论平均速度之间的差的平方作为第二状态噪声。
  21. 根据权利要求14至20任一项所述的装置，其中，所述实际状态获取模块，配置为利用状态噪声对机器人对应之前时刻的实际状态信息和当前时刻的测量状态信息进行处理，得到机器人对应当前时刻的实际状态信息。
  22. 根据权利要求21所述的装置，其中，所述实际状态获取模块，配置为基于状态噪声确定滤波增益，并对机器人对应前一时刻的实际状态信息和前一时刻的实际行驶信息进行预测，得到对应当前时刻的预测状态信息，并利用滤波增益的卡尔曼滤波，将当前时刻的预测状态信息与当前时刻的测量状态信息进行融合，得到机器人对应当前时刻的实际状态信息。
  23. 根据权利要求14至22任一项所述的装置，其中，所述装置还包括：
    提示模块，配置为在状态噪声不满足预设噪声条件时，进行预设提示。
  24. 根据权利要求23所述的装置，其中，所述状态噪声包括：利用若干测量状态信息得到的测量干扰噪声；对应地，所述预设噪声条件包括：测量干扰噪声小于第一噪声阈值；所述提示模块包括第一预警子模块，配置为在测量干扰噪声不满足预设噪声条件时，输出第一预警消息，所述第一预警消息用于提示状态测量受到干扰；
    和/或，所述状态噪声包括：利用当前时刻的实际行驶信息得到的状态转移噪声；对应地，所述预设噪声条件包括：状态转移噪声小于第二噪声阈值；所述提示模块包括第二预警子模块，配置为在状态转移噪声不满足预设噪声条件时，输出第二预警消息，所述第二预警消息用于提示机器人存在车体打滑风险。
  25. 根据权利要求14至24任一项所述的装置，其中，所述测量状态获取模块包括：
    数据采集子模块，配置为对机器人的周边环境进行图像采集，得到对应当前时刻的环境图像数据；
    测量状态确定子模块，配置为基于当前时刻的环境图像数据，确定机器人对应当前时刻的测量状态信息；
    所述测量状态信息和实际状态信息均包括以下至少一者：所述机器人的位置、所述机器人的姿态、所述机器人的速度。
  26. 一种机器人，其中，包括机器人本体以及设置在所述机器人本体上的存储器和处理器，所述处理器和存储器相互耦接，所述处理器用于执行所述存储器中存储的程序指令，以实现权利要求1至13任一项所述的状态确定方法。
  27. 一种计算机可读存储介质，其上存储有程序指令，其中，所述程序指令被处理器执行时实现权利要求1至13任一项所述的状态确定方法。
  28. 一种计算机程序，包括计算机可读代码，当所述计算机可读代码在机器人中运行时，所述机器人中的处理器执行用于实现权利要求1至13任一项所述的状态确定方法。
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Families Citing this family (1)

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Publication number Priority date Publication date Assignee Title
CN114260890B (zh) * 2020-08-26 2023-11-03 北京市商汤科技开发有限公司 机器人的状态确定方法及装置、机器人及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090028274A (ko) * 2007-09-14 2009-03-18 삼성전자주식회사 로봇의 위치를 계산하기 위한 장치 및 방법
CN106156790A (zh) * 2016-06-08 2016-11-23 北京工业大学 一种应用于传感器网络的分布式协作算法和数据融合机制
CN110422175A (zh) * 2019-07-31 2019-11-08 上海智驾汽车科技有限公司 车辆状态估计方法以及装置、电子设备、存储介质、车辆
CN110861123A (zh) * 2019-11-14 2020-03-06 华南智能机器人创新研究院 机器人运行状态的可视化监控和评估的方法及装置
CN112025706A (zh) * 2020-08-26 2020-12-04 北京市商汤科技开发有限公司 机器人的状态确定方法及装置、机器人及存储介质

Family Cites Families (29)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0663930B2 (ja) * 1989-10-04 1994-08-22 日産自動車株式会社 車両状態量推定装置
JP2002331478A (ja) * 2001-05-02 2002-11-19 Yaskawa Electric Corp ロボットの動作速度決定方法
KR101234797B1 (ko) * 2006-04-04 2013-02-20 삼성전자주식회사 로봇 및 산출된 공분산을 이용한 로봇의 위치 추정 방법
KR100877071B1 (ko) * 2007-07-18 2009-01-07 삼성전자주식회사 파티클 필터 기반의 이동 로봇의 자세 추정 방법 및 장치
KR101038581B1 (ko) * 2008-10-31 2011-06-03 한국전력공사 트랙형 이동 로봇을 이용하여 발전설비 시설물의 감시점검을 제공하기 위한 방법, 이동 로봇 시스템 및 운영방법
KR101086364B1 (ko) * 2009-03-20 2011-11-23 삼성중공업 주식회사 칼만 필터를 이용한 로봇 기구학 변수 추정 방법 및 시스템
JP5803155B2 (ja) * 2011-03-04 2015-11-04 セイコーエプソン株式会社 ロボット位置検出装置及びロボットシステム
CN102862666B (zh) * 2011-07-08 2014-12-10 中国科学院沈阳自动化研究所 一种基于自适应ukf的水下机器人状态和参数联合估计方法
KR101390776B1 (ko) * 2013-03-14 2014-04-30 인하대학교 산학협력단 퍼지 확장 칼만 필터를 이용한 위치인식 장치, 방법 및 이동로봇
KR102009481B1 (ko) * 2013-12-26 2019-08-09 한화디펜스 주식회사 이동체 주행 제어 장치 및 방법
US9517561B2 (en) * 2014-08-25 2016-12-13 Google Inc. Natural pitch and roll
JP6541026B2 (ja) * 2015-05-13 2019-07-10 株式会社Ihi 状態データ更新装置と方法
KR101789776B1 (ko) * 2015-12-09 2017-10-25 세종대학교산학협력단 주행로봇의 진행각 추정을 위한 바이어스 보정 장치 및 방법
JP6770393B2 (ja) * 2016-10-04 2020-10-14 株式会社豊田中央研究所 トラッキング装置及びプログラム
KR20180068102A (ko) * 2016-12-13 2018-06-21 주식회사 큐엔티 로봇 고장 모니터링 서비스 제공 방법 및 서버
CN106956282B (zh) * 2017-05-18 2019-09-13 广州视源电子科技股份有限公司 角加速度确定方法、装置、机器人及存储介质
CN107644441A (zh) * 2017-08-30 2018-01-30 南京大学 基于三维成像的多足机器人复杂路况离散成落脚点方法
CN107748562A (zh) * 2017-09-30 2018-03-02 湖南应用技术学院 一种综合性服务机器人
CN109959381B (zh) * 2017-12-22 2021-06-04 深圳市优必选科技有限公司 一种定位方法、装置、机器人及计算机可读存储介质
CN108128308B (zh) * 2017-12-27 2020-04-14 长沙理工大学 一种分布式驱动电动汽车的车辆状态估计系统及方法
CN110361003B (zh) * 2018-04-09 2023-06-30 中南大学 信息融合方法、装置、计算机设备和计算机可读存储介质
CN108710295B (zh) * 2018-04-20 2021-06-18 浙江工业大学 一种基于渐进容积信息滤波的机器人跟随方法
CN108621161B (zh) * 2018-05-08 2021-03-02 中国人民解放军国防科技大学 基于多传感器信息融合的足式机器人本体状态估计方法
CN108896049A (zh) * 2018-06-01 2018-11-27 重庆锐纳达自动化技术有限公司 一种机器人室内运动定位方法
CN108645415A (zh) * 2018-08-03 2018-10-12 上海海事大学 一种船舶航迹预测方法
CN109443356A (zh) * 2019-01-07 2019-03-08 大连海事大学 一种含测量噪声的无人船位置与速度估计结构及设计方法
CN109813307A (zh) * 2019-02-26 2019-05-28 大连海事大学 一种无人船多传感器数据融合的导航系统及其设计方法
CN111044053B (zh) * 2019-12-31 2022-04-01 三一重工股份有限公司 单舵轮无人车的导航方法、装置和单舵轮无人车
CN111136660B (zh) * 2020-02-19 2021-08-03 清华大学深圳国际研究生院 机器人位姿定位方法及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20090028274A (ko) * 2007-09-14 2009-03-18 삼성전자주식회사 로봇의 위치를 계산하기 위한 장치 및 방법
CN106156790A (zh) * 2016-06-08 2016-11-23 北京工业大学 一种应用于传感器网络的分布式协作算法和数据融合机制
CN110422175A (zh) * 2019-07-31 2019-11-08 上海智驾汽车科技有限公司 车辆状态估计方法以及装置、电子设备、存储介质、车辆
CN110861123A (zh) * 2019-11-14 2020-03-06 华南智能机器人创新研究院 机器人运行状态的可视化监控和评估的方法及装置
CN112025706A (zh) * 2020-08-26 2020-12-04 北京市商汤科技开发有限公司 机器人的状态确定方法及装置、机器人及存储介质

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