WO2018223776A1

WO2018223776A1 - Control method, apparatus and system for robot, and computer-readable storage medium

Info

Publication number: WO2018223776A1
Application number: PCT/CN2018/083351
Authority: WO
Inventors: 霍峰
Original assignee: 北京京东尚科信息技术有限公司; 北京京东世纪贸易有限公司
Priority date: 2017-06-07
Filing date: 2018-04-17
Publication date: 2018-12-13
Also published as: CN109002035A

Abstract

A control method and apparatus for a robot. The method comprises: in the universe of discourse of a proceeding angular deviation and a positioning deviation of a robot at a current moment, respectively performing fuzzification on the proceeding angular deviation and the positioning deviation to obtain a fuzzy value of the proceeding angular deviation and a fuzzy value of the positional deviation (102); performing fuzzy reasoning on the fuzzy value of the proceeding angular deviation and the fuzzy value of the positional deviation to obtain a fuzzy value of a motion correction amount of the robot (103); and in the universe of discourse of the motion correction amount, performing defuzzification processing on the fuzzy value of the motion correction amount to obtain the motion correction amount (104); and further comprises adjusting the universe of discourse of the proceeding angular deviation and the universe of discourse of the positional deviation in real time according to the proceeding angular deviation and the positional deviation at the current moment (101). The method and apparatus can improve the self-adaptation capability and response speed of robot motion control.

Description

Robot control method, device, system and computer readable storage medium

Cross-reference to related applications

The present application is based on the application of the US Application No. 201710420589.4, filed on June 7, 2017, and the priority of which is hereby incorporated by reference.

Technical field

The present disclosure relates to the field of control technologies, and in particular, to a control method of a robot, a control device of the robot, a control system of the robot, and a computer readable storage medium.

Background technique

The development of logistics technology has been fully integrated into information and unmanned, relying on efficient and accurate control of robots can improve logistics efficiency and quality. Therefore, how to quickly and smoothly correct and correct the robot's motion trajectory by the robot motion control method is the main technical problem that needs to be faced.

The related technology generally controls each driving wheel of the robot according to the calculation result of the path planning method, thereby achieving the purpose of correcting the motion track.

Summary of the invention

The inventors of the present disclosure have found that the above related art has the following problems: the traditional path planning method needs to perform a complex solution according to the current position and speed of the robot to pre-plan the correcting path, resulting in a slow control response, and for the environment, the road surface, and the like. The adaptive ability of the influence caused by external factors is poor. In view of at least one of the above problems, the present disclosure proposes a control technology scheme for a robot, which can improve the adaptive ability and response speed of the robot motion control.

According to some embodiments of the present disclosure, there is provided a method of controlling a robot, comprising: blurring the traveling angle deviation and the position deviation in a range of a traveling angle deviation and a position deviation of a current moment of the robot, respectively Obtaining a fuzzy value of the deviation of the traveling angle and a fuzzy value of the positional deviation; performing fuzzy inference on the fuzzy value of the traveling angle deviation and the fuzzy value of the positional deviation to obtain a fuzzy value of the motion correction amount of the robot; In the domain of the motion correction amount, the blur correction value of the motion correction amount is subjected to deblurring processing to obtain the motion correction amount; wherein the control method further includes the travel angle deviation and the position according to the current time Deviation, real-time adjustment of the domain of the travel angle deviation and the domain of the positional deviation.

Optionally, the domain of the motion correction amount is adjusted in real time according to the traveling speed of the current moment of the robot.

Optionally, the domain of the deviation of the angle of travel of the previous moment and the domain of the positional deviation are [-P, P] and [-E, E], respectively, and the deviation of the traveling angle and the positional deviation of the current time are θ and y, respectively. Determining, according to a ratio of |y| and |E| and a ratio of |θ| and |P|, a first domain scaling factor α; a domain of the deviation of the traveling angle at the current time and a domain of the positional deviation Adjusted to [-αP, αP] and [-αE, αE], respectively.

Optionally, the first domain scaling factor α=max{(|y|/|E|) ^τ , (|θ|/|P|) ^τ }+ε, where τ is (0, 1] The constant between ε is a constant between [0.08, 0.12].

Optionally, determining a second universe scaling factor β according to a traveling speed v of the current moment of the robot, wherein β determined in a case where v is greater than or equal to a threshold is greater than β determined in a case where v is less than a threshold; The domain of the motion correction amount at the current time is adjusted from the field of motion correction amount [-T, T] of the previous moment to [-βT, βT].

Optionally, the second domain scaling factor:

Where β _t is a constant between (0, 1) and v _t is a preset crawling speed.

Optionally, the predetermined creep speed v _t is a constant between [32, 48] mm/s.

Alternatively, the blur value is at least one of a large deviation in the positive direction, a deviation in the positive direction, a small deviation in the positive direction, no deviation, a large deviation in the reverse direction, a deviation in the reverse direction, or a small deviation in the reverse direction.

According to still other embodiments of the present disclosure, there is provided a control apparatus for a robot, comprising: a blurring processing component, for the deviation of the traveling angle and the position of the traveling angle deviation and the position deviation of the current moment of the robot respectively Obscuring the positional deviation to obtain a fuzzy value of the deviation of the traveling angle and a fuzzy value of the positional deviation; the fuzzy inference component is configured to perform fuzzy inference on the fuzzy value of the traveling angle deviation and the fuzzy value of the positional deviation, a fuzzy value of the motion correction amount of the robot; the defuzzification processing component is configured to perform a blurring process on the blur value of the motion correction amount in the domain of the motion correction amount to obtain the motion correction amount; And the fuzzification processing component is further configured to perform real-time adjustment on the domain of the traveling angle deviation and the domain of the position deviation according to the traveling angle deviation and the position deviation of the current time.

Optionally, the deblurring processing component is further configured to perform real-time adjustment of the domain of the motion correction amount according to a traveling speed of the current moment of the robot.

Optionally, the fuzzification processing component is configured to perform the following steps: the domain of the deviation of the traveling angle deviation and the domain of the position deviation at the previous moment are [-P, P] and [-E, E], respectively The traveling angle deviation and the positional deviation of the time are θ and y, respectively, and the first domain scaling factor α is determined according to the ratio of |y| and |E| and the ratio of |θ| and |P|; The domain of the angular deviation and the domain of the positional deviation are adjusted to [-αP, αP] and [-αE, αE], respectively.

Optionally, the deblurring processing component is configured to perform the step of: determining a second universe scaling factor β according to a traveling speed v of the current moment of the robot, wherein β determined in a case where v is greater than or equal to a threshold, It is larger than β determined when v is smaller than the threshold value; the domain of the motion correction amount at the current time is adjusted from the domain [-T, T] of the motion correction amount at the previous time to [-βT, βT].

Optionally, the second domain scaling factor:

Where β _t is a constant between (0, 1) and v _t is a preset crawling speed.

Optionally, the blur value is at least one of a large deviation in the positive direction, a deviation in the positive direction, a small deviation in the positive direction, no deviation, a large deviation in the reverse direction, a deviation in the reverse direction, and a small deviation in the reverse direction.

According to still further embodiments of the present disclosure, there is provided a control apparatus for a robot, comprising: a memory and a processor coupled to the memory, the processor being configured to execute based on an instruction stored in the memory device One or several steps in the control method of the robot described in any of the above embodiments.

According to still further embodiments of the present disclosure, a control system for a robot includes: a position sensor for acquiring a real-time position and a traveling direction of the robot; and a processor for performing the method described in any of the above embodiments One or several steps in the robot's control method.

According to still further embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program, the program being executed by a processor to implement one or more of the control methods of the robot described in any of the above embodiments Steps.

In the above embodiment, according to the fuzzy control theory, only a relatively simple solution process is needed to correct the motion trajectory of the robot, and the control response speed is improved; and the control input amount is adjusted in real time according to the current position and speed of the robot. And the fuzzy set theory domain of output, improve the adaptability of robot motion control.

Other features of the present disclosure and its advantages will be apparent from the following detailed description of exemplary embodiments.

DRAWINGS

The drawings described herein are provided to provide a further understanding of the present disclosure, which is a part of the present disclosure, and the description of the present disclosure and the description thereof are not intended to limit the disclosure. In the drawing:

FIG. 1 illustrates an exemplary flowchart of a method of controlling a robot in accordance with some embodiments of the present disclosure;

2 illustrates a schematic diagram of a method of controlling a robot in accordance with some embodiments of the present disclosure;

FIG. 3 illustrates an exemplary block diagram of a control device of a robot, in accordance with some embodiments of the present disclosure;

FIG. 4 illustrates an exemplary block diagram of a control device of a robot according to further embodiments of the present disclosure;

FIG. 5 illustrates an exemplary block diagram of a control device of a robot in accordance with further embodiments of the present disclosure;

FIG. 6 illustrates an exemplary block diagram of a control system of a robot, in accordance with some embodiments of the present disclosure.

detailed description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the drawings. It should be noted that the relative arrangement of the components and steps, numerical expressions and numerical values set forth in the embodiments are not intended to limit the scope of the disclosure.

In the meantime, it should be understood that the dimensions of the various parts shown in the drawings are not drawn in the actual scale relationship for the convenience of the description.

The following description of the at least one exemplary embodiment is merely illustrative and is in no way

Techniques, methods and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods and apparatus should be considered as part of the authorization specification.

In all of the examples shown and discussed herein, any specific values are to be construed as illustrative only and not as a limitation. Accordingly, other examples of the exemplary embodiments may have different values.

It should be noted that similar reference numerals and letters indicate similar items in the following figures, and therefore, once an item is defined in one figure, it is not required to be further discussed in the subsequent figures.

FIG. 1 illustrates an exemplary flow chart of a method of controlling a robot in accordance with some embodiments of the present disclosure.

As shown in FIG. 1, the method includes: step 101, adjusting a field of angular deviation and position deviation in real time; step 102, determining a fuzzy value of the angular deviation and the position deviation; and step 103, determining a fuzzy value of the motion correction amount; and the step 104. Determine a motion correction amount.

In step 101, the domain of the deviation of the traveling angle and the field of the positional deviation are adjusted in real time based on the deviation of the traveling angle and the positional deviation at the current time. In some embodiments, robot motion can be controlled according to the schematic in FIG.

2 shows a schematic diagram of a method of controlling a robot in accordance with some embodiments of the present disclosure.

As shown in FIG. 2, the ideal traveling direction of the robot 20 is the X axis, and the position coordinate of the robot 20 in the vertical direction of the X axis is the Y axis. The actual position of the robot 20 at the moment is the point M(x, y), and the ideal position should be the coordinate system origin O(0, 0), and the angle v between the speed v of the robot 20 and the X axis is θ, where the ideal moment is The position can be determined by scanning the markings laid on the ground by a scanning device on the robot 20, which can be a two-dimensional code with a spacing of 1 meter. Therefore, at the current time, the displacement deviation of the robot 20 is y, and the deviation of the traveling angle is θ, and the angular deviation can be set clockwise to be positive and counterclockwise to be negative.

In some embodiments, the domain of the deviation of the angle of travel of the previous moment and the domain of the positional deviation are [-P, P] and [-E, E], respectively, and the deviation of the traveling angle and the positional deviation at the current time are respectively θ. And y. The first domain scaling factor α can be determined from the ratio of |y| to |E| and the ratio of |θ| to |P|.

For example, the first domain scaling factor may be determined according to the traveling angle deviation θ and the position deviation y of the current time as α=max{(|y|/|E|) ^τ , (|θ|/|P|) ^τ }+ ε, where τ is a constant between (0, 1). Here ε is to ensure that the adjusted domain does not infinitely close to 0, can be set to a constant between [0.08, 0.12], or according to the actual The situation is set to a relatively small number such as 0.15, 0.2, etc. that is not zero.

The domains of the travel angle deviation and positional deviation at the previous moment [-P, P] and [-E, E] are respectively adjusted to [-αP, αP] and [-αE, αE]. For example, in the case of a robot with a no-load speed of 2 m/s, in order to ensure that the robot does not derail, the initial domain of the deviation of the travel angle can be set to [-1°, 1°], and the initial domain of positional deviation is [- 20mm, 20mm], at the first moment when the robot starts moving, the initial domain can be adjusted according to the scaling factor calculated at the first moment.

In step 102, the travel angle deviation and the position deviation are blurred in the field of the travel angle deviation and the position deviation of the current time of the robot, respectively, and the blur value of the travel angle deviation and the blur value of the position deviation are obtained.

In some embodiments, the fuzzy value may be set to a positive direction large deviation PB, a positive direction deviation PM, a positive direction small deviation PS, an unbiased ZE, a reverse large deviation NB, a reverse direction deviation NM, or a reverse small deviation NS. The membership function can be set for each fuzzy value according to the actual situation. The membership function is preferably a triangle membership function, and a trapezoid membership function can also be used. Taking θ and y into each of the above membership functions, respectively, calculating the membership degree corresponding to each fuzzy value, and then determining which fuzzy θ and y should be blurred by comparing the degree of membership of θ and y for each fuzzy value. value.

In step 103, fuzzy inference is performed on the blur value of the travel angle deviation and the blur value of the position deviation to obtain a blur value of the motion correction amount of the robot.

In some embodiments, as shown in FIG. 2, the robot 20 has two

drive wheels

21 and 22, and the motion correction amounts of the drive wheel 21 and the drive wheel 22 can be set to be opposite to each other, for example, the motion correction of the drive wheel 21. The amount is z, and the motion correction amount of the drive wheel 22 is -z. When the correction is performed, the two

drive wheels

21 and 22 are simultaneously controlled in accordance with the respective correction amounts. The correspondence between the motion correction amount z of the drive wheel 21 and the fuzzy inference of θ and y can be as shown in the following table:

Fuzzy reasoning table of driving wheel 21

The fuzzy reasoning correspondence can be adjusted according to the actual situation.

In step 104, in the domain of the motion correction amount, the blur value of the motion correction amount is subjected to deblurring processing to obtain a motion correction amount.

In some embodiments, the domain of the motion correction amount is adjusted in real time based on the travel speed v of the current time of the robot. For example, the second universe scaling factor β may be determined according to the traveling speed v of the current moment of the robot, and it is ensured that β determined in the case where v is greater than or equal to the threshold is greater than β determined in the case where v is less than the threshold.

For example, determining the second domain scaling factor according to the traveling speed v of the current moment of the robot is

Where β _t is a constant between (0, 1), v _t is the preset crawling speed; the domain of the motion correction amount of the current time is from the field of motion correction of the previous moment [-T, T] Adjust to [-βT, βT]. The crawling speed v _t is a preset slower robot traveling speed when the robot approaches the target position, so as to prevent the robot from coming over the brake or adjusting to exceed the target position. For example, depending on the type of robot and the current road conditions, the creep speed can be a constant between [32, 48] mm/s. For example, in the case where the two-dimensional code spacing is 1 m and the robot's no-load speed is 2 m/s, the initial domain of the motion correction amount can be set to [-30 mm/s, 30 mm/s], which is operated by the robot. At the first moment, the initial domain can be adjusted by scaling factor β.

In the above embodiment, the response speed to the robot control is improved by the fuzzy control; the fuzzy set theory field of the fuzzy control input and the output quantity is adjusted in real time, and the correcting ability for the robot trajectory is improved; the opposite of the same control amount is adopted. The number of the two driving wheels of the robot is controlled at the same time, which overcomes the control error caused by the uncoordinated control of each driving wheel.

FIG. 3 illustrates an exemplary block diagram of a control device of a robot, in accordance with some embodiments of the present disclosure.

As shown in FIG. 3, the apparatus includes: a fuzzification processing component 31, a fuzzy inference component 32, and a defuzzification processing component 33.

The blurring processing unit 31 blurs the traveling angle deviation and the position deviation in the field of the traveling angle deviation and the position deviation of the current time of the robot, respectively, and obtains the blur value of the traveling angle deviation and the blur value of the position deviation. The fuzzification processing component 31 can also adjust the domain of the deviation of the traveling angle deviation and the position deviation in real time according to the deviation of the traveling angle and the position deviation at the current time.

For example, the fuzzification processing component 31 can acquire real-time position information of the robot through a position sensor mounted on the robot. The position sensor may be a motor optical code disc, and the blurring processing component 31 calculates the real-time position and the traveling direction of the robot by the motor optical code discs of the two driving wheels of the robot, thereby determining the traveling angle deviation and the position deviation.

In some embodiments, the fuzzification processing component 31 is configured to perform the steps of: the domain of the deviation of the angle of travel of the previous moment and the domain of the positional deviation are [-P, P] and [-E, E], respectively. The traveling angle deviation and the position deviation of the current time are θ and y, respectively, and the first domain expansion factor α is determined according to the ratio of |y| and |E| and the ratio of |θ| and |P|; The domain of the deviation and the field of positional deviation are adjusted to [-αP, αP] and [-αE, αE], respectively.

For example, the blurring processing component 31 determines that the first domain expansion factor is α=max{(|y|/|E|) ^τ , (|θ|/|P| according to the traveling angle deviation θ and the position deviation y of the current time. ) ^τ } + ε, where τ is a constant between (0, 1) and ε is a constant between [0.08, 0.12].

The fuzzy inference component 32 performs fuzzy inference on the blur value of the travel angle deviation and the blur value of the position deviation to obtain a blur value of the motion correction amount of the robot.

The deblurring processing component 33 deblurs the blurring value of the motion correction amount in the domain of the motion correction amount to obtain a motion correction amount.

For example, as shown in FIG. 2, the robot 20 has two

drive wheels

21 and 22 that are respectively driven by two motors. The driving

wheels

21 and 22 can be driven by differential driving, that is, when the two motors rotate at the same speed in the same direction, the robot advances and retreats in a straight line, and when the two motors rotate in the same direction at the same speed, the robot turns in place. In this driving mode, the motion correction amounts of the

drive wheels

21 and 22 are opposite to each other.

In some embodiments, the deblurring processing component 33 adjusts the universe of motion corrections in real time based on the speed of travel of the robot at the current time. For example, the deblurring processing component 33 is configured to perform the steps of: determining the second universe scaling factor β according to the traveling speed v of the current moment of the robot, and being able to ensure that β determined in the case where v is greater than or equal to the threshold, is greater than less than v. β determined in the case of the threshold; the domain of the motion correction amount at the current time is adjusted from the field of motion correction [-T, T] of the previous moment to [-βT, βT].

For example, the deblurring processing component 33 determines that the second domain scaling factor is based on the traveling speed v of the current moment of the robot.

β _t is a constant between (0, 1), v _t is the preset crawling speed, and the domain of the motion correction amount at the current time is from the field of motion correction of the previous moment [-T, T] Adjust to [-βT, βT].

In the above embodiment, the fuzzification processing component and the defuzzification processing component can adjust the fuzzy set theory domain in real time according to actual conditions, and can correct the robot under different working conditions such as load change and road surface condition change, thereby enhancing the adaptive capability. Increases the accuracy of the control and reduces the control response time.

FIG. 4 illustrates an exemplary block diagram of a control device of a robot in accordance with further embodiments of the present disclosure.

As shown in FIG. 4, the apparatus 40 of this embodiment includes a memory 41 and a processor 42 coupled to the memory 41, the processor 42 being configured to perform any of the implementations of the present disclosure based on instructions stored in the memory 41. One or more steps in the control method of the robot in the example.

The memory 41 may include, for example, a system memory, a fixed non-volatile storage medium, or the like. The system memory stores, for example, an operating system, an application, a boot loader, a database, and other programs.

FIG. 5 illustrates an exemplary block diagram of a control device of a robot in accordance with further embodiments of the present disclosure.

As shown in FIG. 5, in the control device 50, the processor 520 is coupled to the memory 510 via the BUS bus 530. Display device 50 may also be coupled to external storage device 550 via storage interface 560 for invoking external data, and may also be coupled to a network or another computer system (not shown) via network interface 560. It will not be described in detail here.

In some embodiments, one or more of the control methods of any of the foregoing embodiments can be implemented by storing data instructions through memory 510 and processing the instructions by processor 520.

As shown in FIG. 6, the control system 6 includes a position sensor 61 and a processor 62. The position sensor 61 is used to acquire the real-time position and traveling direction of the robot. The processor 62 is operative to perform one or more of the robotic control methods of any of the above embodiments.

In some embodiments, a computer readable storage medium is provided having stored thereon a computer program that, when executed by a processor, implements one or more of the steps of controlling a robot in any of the above embodiments. For example, the computer readable storage medium is a non-transitory computer readable storage medium.

Heretofore, the control method of the robot, the control device of the robot, the control system of the robot, and the computer readable storage medium according to the present disclosure have been described in detail. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein according to the above description.

The methods and systems of the present disclosure may be implemented in a number of ways. For example, the methods and systems of the present disclosure may be implemented in software, hardware, firmware, or any combination of software, hardware, or firmware. The above-described sequence of steps for the method is for illustrative purposes only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless otherwise specifically stated. Moreover, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine readable instructions for implementing a method in accordance with the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

While some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood that It will be appreciated by those skilled in the art that the above embodiments may be modified without departing from the scope and spirit of the disclosure. The scope of the disclosure is defined by the appended claims.

Claims

A robot control method includes:

Deviating the travel angle deviation and the position deviation in a field of the travel angle deviation and the position deviation of the current moment of the robot, respectively, to obtain a blur value of the travel angle deviation and a blur value of the position deviation;

Performing fuzzy inference on the fuzzy value of the traveling angle deviation and the fuzzy value of the position deviation to obtain a fuzzy value of the motion correction amount of the robot;

And in the domain of the motion correction amount, performing a blurring process on the blur value of the motion correction amount to obtain the motion correction amount;

The control method further includes: adjusting the domain of the traveling angle deviation and the domain of the position deviation in real time according to the traveling angle deviation and the position deviation of the current time.
The control method according to claim 1, further comprising:

The domain of the motion correction amount is adjusted in real time according to the traveling speed of the robot at the current time.
The control method according to claim 1, wherein said real time adjustment comprises:

The domain of the deviation and the positional deviation of the travel angle deviation at the previous moment are [-P, P] and [-E, E], respectively, and the travel angle deviation and position deviation at the current time are θ and y, respectively, according to |y The ratio of |E| and the ratio of |θ| to |P| determine the first domain scaling factor α;

The domain of the traveling angle deviation at the current time and the domain of the positional deviation are respectively adjusted to [-αP, αP] and [-αE, αE].
The control method according to claim 3, wherein said determining the first domain expansion factor α comprises:

The first domain scaling factor α=max{(|y|/|E|) τ , (|θ|/|P|) τ }+ε, where τ is a constant between (0, 1), ε is a constant between [0.08, 0.12].
The control method according to claim 2, wherein said real-time adjustment of said domain of said motion correction amount comprises:

Determining a second universe scaling factor β according to a traveling speed v of the current moment of the robot,

Wherein, β determined in the case where v is greater than or equal to the threshold is greater than β determined in the case where v is less than the threshold;

The domain of the motion correction amount at the current time is adjusted from the field of motion correction amount [-T, T] of the previous time to [-βT, βT].
The control method according to claim 5, wherein said determining the second domain expansion factor β comprises:

Second domain expansion factor

Where β t is a constant between (0, 1) and v t is a preset crawling speed.
The control method according to claim 6, wherein the predetermined creep speed v t is a constant between [32, 48] mm/s.
The control method according to any one of claims 1 to 7, wherein the blur value is: a large deviation in a positive direction, a deviation in a positive direction, a small deviation in a positive direction, no deviation, a large deviation in a reverse direction, a deviation in a reverse direction, At least one of the small deviations in the opposite direction.
A robot control device comprising:

a fuzzification processing component, configured to blur the traveling angle deviation and the position deviation in a field of the traveling angle deviation and the position deviation of the current moment of the robot, respectively, to obtain a fuzzy value and a position deviation of the traveling angle deviation Fuzzy value

a fuzzy inference component, configured to perform fuzzy inference on the fuzzy value of the traveling angle deviation and the fuzzy value of the position deviation to obtain a fuzzy value of the motion correction amount of the robot;

a deblurring processing component, configured to perform a blurring process on the fuzzy value of the motion correction amount in the domain of the motion correction amount to obtain the motion correction amount;

The fuzzification processing component is further configured to adjust the domain of the traveling angle deviation and the domain of the position deviation in real time according to the traveling angle deviation and the position deviation of the current time.
The control device according to claim 9, wherein

The deblurring processing component is further configured to perform real-time adjustment of the domain of the motion correction amount according to a traveling speed of the current moment of the robot.
The control device of claim 9, wherein the fuzzification processing component is configured to perform the following steps:

The domain of the deviation and the positional deviation of the travel angle deviation at the previous moment are [-P, P] and [-E, E], respectively, and the travel angle deviation and position deviation at the current time are θ and y, respectively, according to |y The ratio of |E| and the ratio of |θ| to |P| determine the first domain scaling factor α;

The domain of the traveling angle deviation at the current time and the domain of the positional deviation are respectively adjusted to [-αP, αP] and [-αE, αE].
The control device according to claim 11, wherein

The first domain scaling factor α=max{(|y|/|E|) τ , (|θ|/|P|) τ }+ε, where τ is a constant between (0, 1), ε is a constant between [0.08, 0.12].
The control device according to claim 10, wherein said deblurring processing component is configured to perform the following steps:

Determining a second universe scaling factor β according to a traveling speed v of the current moment of the robot,

Wherein, β determined in the case where v is greater than or equal to the threshold is greater than β determined in the case where v is less than the threshold;

The domain of the motion correction amount at the current time is adjusted from the field of motion correction amount [-T, T] of the previous time to [-βT, βT].
The control device according to claim 13, wherein

Second domain expansion factor

Where β t is a constant between (0, 1) and v t is a preset crawling speed.
The control device according to claim 14, wherein the predetermined creep speed v t is a constant between [32, 48] mm/s.
The control device according to any one of claims 9 to 15, wherein the blur value is: a large deviation in a positive direction, a deviation in a positive direction, a small deviation in a positive direction, no deviation, a large deviation in a reverse direction, a deviation in a reverse direction, At least one of the small deviations in the opposite direction.
A robot control device comprising:

Memory;

a processor coupled to the memory, the processor configured to perform one of the control methods of the robot of any one of claims 1-8 based on an instruction stored in the memory device A few steps.
A robot control system comprising:

a position sensor for acquiring a real-time position and a traveling direction of the robot; and

A processor for performing one or more of the steps of controlling a robot according to any one of claims 1-8.
A computer readable storage medium having stored thereon a computer program that, when executed by a processor, implements one or more of the methods of controlling a robot of any of claims 1-8.