Robot motion control method, device, robot and storage medium

Abstract

Embodiments of the present invention disclose a robot motion control method, device, robot, and storage medium. The robot motion control method may specifically include: determining the current position and target position of the robot to be controlled; determining the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and target position; determining the target motion point of the robot to be controlled in the target motion trajectory based on the target motion trajectory, and determining the target motion state of the robot to be controlled at the target motion point; determining the target drive wheel motion parameters of the robot to be controlled based on the target motion state, and controlling the robot to be controlled to move based on the target drive wheel motion parameters. The technical solution of the embodiment of the present invention can accurately control the movement of the robot to be controlled, thereby improving the control accuracy of the robot to be controlled.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of control technology, and in particular to a robot motion control method, device, robot, and storage medium. Background Art

[0002] Power silos are used to store equipment, materials, instruments, meters, tools, and consumables required for power operations and maintenance. Silo handling robots can replace manual labor in silo storage and retrieval tasks. However, existing silo handling robots have low precision and are insufficient for silo storage and retrieval tasks. Summary of the Invention

[0003] Embodiments of the present invention provide a robot motion control method, device, robot, and storage medium, which can accurately control the movement of a robot to be controlled, thereby improving the control accuracy of the robot to be controlled.

[0004] According to one aspect of the present invention, there is provided a robot motion control method, comprising:

[0005] Determining a current position and a target position of a robot to be controlled; wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel;

[0006] Determining a target motion trajectory of the robot to be controlled in a pre-established robot map according to the current position and the target position;

[0007] Determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and determine a target motion state of the robot to be controlled at the target motion point;

[0008] The target driving wheel motion parameters of the robot to be controlled are determined according to the target motion state, and the robot to be controlled is controlled to move according to the target driving wheel motion parameters.

[0009] According to another aspect of the present invention, there is provided a robot motion control device, comprising:

[0010] a position determination module, configured to determine a current position and a target position of a robot to be controlled; wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel;

[0011] a motion trajectory determination module, configured to determine a target motion trajectory of the robot to be controlled in a pre-established robot map according to the current position and the target position;

[0012] a motion state determination module, configured to determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and determine a target motion state of the robot to be controlled at the target motion point;

[0013] The motion control module is used to determine the target driving wheel motion parameters of the robot to be controlled according to the target motion state, and control the robot to be controlled to move according to the target driving wheel motion parameters.

[0014] According to another aspect of the present invention, there is provided a power silo handling robot, the power silo handling robot comprising:

[0015] a first drive wheel, a second drive wheel, at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the robot motion control method described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot motion control method according to any embodiment of the present invention when executed.

[0019] The technical solution of the embodiment of the present invention determines the current position and target position of the robot to be controlled, and determines the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and target position, so as to determine the target motion point of the robot to be controlled in the target motion trajectory based on the target motion trajectory, and determines the target motion state of the robot to be controlled at the target motion point, thereby determining the target driving wheel motion parameters of the robot to be controlled based on the target motion state, and then controlling the robot to be controlled to move based on the target driving wheel motion parameters, thereby solving the problem of low motion accuracy of the electric silo handling robot in the prior art, and being able to accurately control the movement of the robot to be controlled, thereby improving the control accuracy of the robot to be controlled.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a robot motion control method provided by Example 1 of the present invention;

[0023] Figure 2 is a flow chart of a robot motion control method provided by the second embodiment of the present invention;

[0024] Figure 3 is a schematic diagram of a current position determination method provided by Embodiment 2 of the present invention;

[0025] Figure 4 This is a schematic diagram of the chassis structure of an electric silo robot provided in the third embodiment of the present invention;

[0026] Figure 5 This is an architectural diagram of a robot control module provided in Example 3 of the present invention;

[0027] Figure 6 This is an example flow chart of a robot motion control method provided by the third embodiment of the present invention;

[0028] Figure 7 is another example flow chart of a robot motion control method provided by the third embodiment of the present invention;

[0029] Figure 8 This is a motion relationship diagram for solving dual steering wheels of a robot provided by the third embodiment of the present invention;

[0030] Figure 9 is a schematic diagram of a robot motion control device provided by a fourth embodiment of the present invention;

[0031] Figure 10 It is a schematic structural diagram of an electric silo handling robot implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Example 1

[0035] Figure 1 This is a flow chart of a robot motion control method provided by the first embodiment of the present invention. This embodiment is applicable to the case of accurately controlling the motion of the robot to be controlled. The method can be executed by a robot motion control device, which can be implemented by software and / or hardware and can generally be directly integrated into the electric silo handling robot that executes this method. Specifically, Figure 1 As shown, the robot motion control method may specifically include the following steps:

[0036] S110, determining a current position and a target position of a robot to be controlled; wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel.

[0037] The robot to be controlled can be any electric silo handling robot awaiting control. Specifically, the robot to be controlled can include a first drive wheel and a second drive wheel. The first drive wheel can be one of the drive wheels of the robot to be controlled, and the second drive wheel can be another of the drive wheels of the robot to be controlled. It will be appreciated that motion control of the electric silo handling robot can be achieved by controlling the rotation of the first drive wheel and the second drive wheel. The current position can be the current location of the robot to be controlled. The target position can be the location of the cargo to be handled by the robot to be controlled.

[0038] In an embodiment of the present invention, when transporting goods in a power silo, an operator may send a cargo transporting instruction to the robot to be controlled. After receiving the cargo transporting instruction, the robot to be controlled may determine the current position and target position of the robot to be controlled.

[0039] S120 : Determine a target motion trajectory of the robot to be controlled in a pre-established robot map according to the current position and the target position.

[0040] The robot map may be a map of the power silo stored in the robot to be controlled. It is understood that the robot map may be created before the robot to be controlled performs its first handling task in the power silo. Repeated handling tasks in the same power silo do not require repeated map creation. The target motion trajectory may be the motion trajectory of the robot to be controlled when executing the cargo handling instruction.

[0041] In an embodiment of the present invention, after determining the current position and target position of the robot to be controlled, the target motion trajectory of the robot to be controlled can be further determined in a pre-established robot map based on the current position and target position. It is understood that the robot to be controlled may store maps corresponding to multiple different power silos.

[0042] S130. Determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and determine a target motion state of the robot to be controlled at the target motion point.

[0043] The motion point may be a point in the target motion trajectory at which the motion state of the robot to be controlled needs to be changed, for example, a straight line starting point, a straight line end point, a curve starting point, or a curve end point in the target motion trajectory, etc., and the embodiments of the present invention are not limited thereto. The target motion point may be one of a plurality of motion points in the target motion trajectory. Optionally, the target motion point may be a target point at which the robot to be controlled moves in the target motion trajectory. The target motion state may be the motion state of the robot to be controlled at the target motion point. Optionally, the motion state of the robot to be controlled may include the travel speed, angular velocity, and velocity offset angle of the robot to be controlled.

[0044] In an embodiment of the present invention, after determining the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and the target position, the target motion point of the robot to be controlled in the target motion trajectory can be further determined based on the target motion trajectory to determine the target motion state of the robot to be controlled at the target motion point.

[0045] S140: Determine target driving wheel motion parameters of the robot to be controlled according to the target motion state, and control the robot to be controlled to move according to the target driving wheel motion parameters.

[0046] The target driving wheel motion parameters may be motion parameters of the driving wheels of the robot to be controlled at the target motion point. Alternatively, the motion parameters of the driving wheels of the robot to be controlled may include the linear velocity and rotation angle of the driving wheels of the robot to be controlled.

[0047] In an embodiment of the present invention, after determining the target motion state of the robot to be controlled at the target motion point, the target driving wheel motion parameters of the robot to be controlled can be further determined according to the target motion state, so as to control the robot to be controlled to move according to the target driving wheel motion parameters.

[0048] The technical solution of this embodiment determines the current position and target position of the robot to be controlled, and determines the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and target position, so as to determine the target motion point of the robot to be controlled in the target motion trajectory based on the target motion trajectory, and determines the target motion state of the robot to be controlled at the target motion point, thereby determining the target driving wheel motion parameters of the robot to be controlled based on the target motion state, and then controlling the robot to be controlled to move based on the target driving wheel motion parameters, which solves the problem of low motion accuracy of the electric silo handling robot in the prior art, and can accurately control the robot to be controlled to move, thereby improving the control accuracy of the robot to be controlled.

[0049] Example 2

[0050] Figure 2 This is a flow chart of a robot motion control method provided by the second embodiment of the present invention. This embodiment is a further refinement of the above technical solutions, and provides multiple specific optional implementation methods for determining the current position of the robot to be controlled, determining the target motion state of the robot to be controlled at the target motion point, and determining the target driving wheel motion parameters of the robot to be controlled based on the target motion state. The technical solution in this embodiment can be combined with the various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method may include the following steps:

[0051] S210, determining a current position and a target position of a robot to be controlled; wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel.

[0052] Optionally, determining the current position of the robot to be controlled may include: obtaining the current positioning system information, current lidar system information and current inertial sensor information of the robot to be controlled; and determining the current position based on the current positioning system information, current lidar system information and current inertial sensor information.

[0053] Among them, the current positioning system information can be the information currently obtained by the positioning system in the robot to be controlled, for example, it can be the position information obtained by the positioning system, etc., and the embodiment of the present invention is not limited to this. Exemplarily, the positioning system can be GPS (Global Positioning System) or GNSS (Global Navigation Satellite System). The current lidar system information can be the information currently obtained by the lidar system in the robot to be controlled, for example, it can be the position information or speed information obtained by the lidar system, etc., and the embodiment of the present invention is not limited to this. The current inertial sensor information can be the information currently obtained by the inertial sensor system in the robot to be controlled, for example, it can be the acceleration, velocity or tilt angle information obtained by the inertial sensor system, etc., and the embodiment of the present invention is not limited to this. Exemplarily, the inertial sensor system can be an IMU (Inertial Measurement Unit) sensor.

[0054] Specifically, the current positioning system information, the current lidar system information and the current inertial sensor information of the robot to be controlled are obtained to determine the current position according to the current positioning system information, the current lidar system information and the current inertial sensor information.

[0055] In a specific example of an embodiment of the present invention, Figure 3 is a schematic diagram of a current position determination method provided by the second embodiment of the present invention, such as Figure 3 As shown, determining the current position can specifically include: after receiving accurate GPGGA (a GPS location information statement in the NMEA 0183 standard that contains information about the position, time, and accuracy of a global positioning system receiver) position information through the positioning system, taking the derivative of the measured position information with respect to time to obtain the forward speed and direction of the robot to be controlled, thereby determining the current positioning system information. Using a lidar system to scan a real-time point cloud within its field of view, and simultaneously matching the real-time point cloud features with a pre-built offline map, the position of the robot to be controlled within the robot map is determined, thereby determining the real-time position and speed of the robot to be controlled, thereby determining the current lidar system information. Using an inertial sensor system to measure the acceleration, angular velocity, angular acceleration, and tilt angle of the robot to be controlled in real time, thereby determining the current inertial sensor information. Time synchronization is performed on the current positioning system information, the current lidar system information, and the current inertial sensor information, and a Kalman filter algorithm is used to determine the position, velocity, and attitude information (such as angular displacement and direction of velocity) of the robot to be controlled based on the time-synchronized information.

[0056] For mobile robots that integrate multiple sensors, each sensor must achieve data synchronization and precise fusion. When arranging the spatial positions of the sensor modules, it is necessary to perform spatial registration between different sensors and unify the coordinate systems of each sensor. The result of the registration will directly affect the accuracy of data fusion. In order to reduce the workload and technical difficulty in engineering implementation, in the embodiment of the present invention, the laser radar system and the inertial sensor system of the robot to be controlled are installed in a stacked manner at the geometric center of the robot to be controlled, and the positioning system is placed adjacent to the laser radar system in front and behind. In this robot control platform, it is assumed that the laser radar system, inertial sensor system and positioning system are at the same point in space.

[0057] Time synchronization is the process of synchronizing the measurement data information generated by each sensor of the system to the same moment. Since the data measurements of different sensors are completely independent, the time for outputting the data to the robot controller for calculation and processing is also different, and due to the inconsistency of communication distance and communication time, the time of different sensors may be out of sync. The accuracy of the synchronization time is generally required to reach the millisecond level. The time synchronization method in the embodiment of the present invention uses the least squares method to unify the data of different sensors into a certain sensor output data with a longer period. Specifically, the positioning system receives GPGGA position data and GPRMc (recommended positioning information) time data, obtains the two data through the RS232 serial port, and then sends the synchronization time data to the inertial sensor system (such as IMU) through RS232, and at the same time sends the synchronization time to the laser radar system (such as LMS511 laser radar) through the 100M network port, thereby completing the time synchronization of the three.

[0058] Optionally, the target position of the robot to be controlled may be determined according to a cargo-carrying instruction sent by an operator to the robot to be controlled.

[0059] S220: Determine a target motion trajectory of the robot to be controlled in a pre-established robot map according to the current position and the target position.

[0060] Optionally, before determining the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and the target position, it may also include: generating movement instructions for the robot to be controlled, and controlling the robot to be controlled to move in the power silo according to the movement instructions; obtaining the mobile positioning system information, mobile lidar system information and mobile inertial sensor information of the robot to be controlled during the movement process; and establishing a robot map based on the mobile positioning system information, mobile lidar system information and mobile inertial sensor information.

[0061] The movement instruction may be an instruction to move the robot to be controlled. It is understood that the movement instruction may be an instruction manually generated by an operator when establishing a robot map. The mobile positioning system information may be information about the positioning system of the robot to be controlled during movement. The mobile lidar system information may be information about the lidar system of the robot to be controlled during movement. The mobile inertial sensor information may be information about the inertial sensor system of the robot to be controlled during movement.

[0062] Specifically, before the robot to be controlled performs the handling task in the power silo for the first time, a movement instruction for the robot to be controlled can be generated, and the robot to be controlled to move in the power silo according to the movement instruction, so as to obtain the mobile positioning system information, mobile lidar system information and mobile inertial sensor information of the robot to be controlled during the movement process, thereby establishing a robot map based on the mobile positioning system information, mobile lidar system information and mobile inertial sensor information.

[0063] Optionally, when determining the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and the target position, the robot lane identification and feature reference point information can also be obtained to optimize and update the target motion trajectory based on the robot lane identification and feature reference point information, thereby making the target motion trajectory more accurate.

[0064] Optionally, when determining the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and the target position, FRID (Radio Frequency Identification) tag information set at the handling action position can also be obtained to improve the walking accuracy of the robot to be controlled, and at the same time enable the robot to be controlled to accurately identify the goods to be handled.

[0065] S230: Determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory.

[0066] S240: Acquire current driving wheel motion parameters of the robot to be controlled at the current position, and calculate the calculated motion state of the robot to be controlled at the current position according to the current driving wheel motion parameters.

[0067] The current driving wheel motion parameters may be motion parameters of the driving wheel of the robot to be controlled at the current position, and the calculated motion state may be the motion state of the robot to be controlled calculated based on the current driving wheel motion parameters.

[0068] In an embodiment of the present invention, after determining the target motion point of the robot to be controlled within the target motion trajectory based on the target motion trajectory, current drive wheel motion parameters of the robot to be controlled at the current position may be further obtained to calculate the calculated motion state of the robot to be controlled at the current position based on the current drive wheel motion parameters. Optionally, obtaining the current drive wheel motion parameters of the robot to be controlled at the current position may be obtained by measuring with sensors in the robot to be controlled.

[0069] Optionally, the current driving wheel motion parameters may include the current driving wheel linear velocity and the current driving wheel rotation angle; accordingly, calculating the calculated motion state of the robot to be controlled at the current position based on the current driving wheel motion parameters may include: obtaining the driving wheel wheelbase between the first driving wheel and the second driving wheel of the robot to be controlled; calculating the calculated motion state of the robot to be controlled at the current position based on the current driving wheel motion parameters and the driving wheel wheelbase.

[0070] The current driving wheel linear velocity may be the linear velocity of the driving wheel of the robot to be controlled at the current position. The current driving wheel rotation angle may be the rotation angle of the driving wheel of the robot to be controlled at the current position. The driving wheel wheelbase may be the wheelbase between the first driving wheel and the second driving wheel.

[0071] Specifically, after obtaining the current driving wheel motion parameters of the robot to be controlled at the current position, the driving wheel wheelbase between the first driving wheel and the second driving wheel of the robot to be controlled can be further obtained to calculate the calculated motion state of the robot to be controlled at the current position based on the current driving wheel motion parameters and the driving wheel wheelbase.

[0072] Optionally, the calculated motion state of the robot to be controlled at the current position is calculated based on the current driving wheel motion parameters and the driving wheel wheelbase, which can be determined based on the following formula:

[0073]

[0074]

[0075]

[0076] Among them, ω1 represents the angular velocity in the calculated motion state; v1 represents the travel speed in the calculated motion state; α1 represents the velocity deviation angle in the calculated motion state; v 11 Indicates the current driving wheel linear speed of the first driving wheel; α 11 Indicates the current driving wheel rotation angle of the first driving wheel; α 21 Indicates the current driving wheel rotation angle of the second driving wheel; L indicates the driving wheel wheelbase.

[0077] S250: Determine a preset motion state of the robot to be controlled at the target motion point according to the target motion trajectory.

[0078] The preset motion state may be a motion state of the robot to be controlled at a target motion point that is preset according to a target motion trajectory.

[0079] In an embodiment of the present invention, after determining the target motion point of the robot to be controlled within the target motion trajectory according to the target motion trajectory, a preset motion state of the robot to be controlled at the target motion point can be further determined according to the target motion trajectory. It should be noted that the embodiment of the present invention does not limit the specific implementation method of determining the preset motion state of the robot to be controlled at the target motion point according to the target motion trajectory, as long as the preset motion state of the robot to be controlled at the target motion point can be determined according to the target motion trajectory.

[0080] It should be noted that the embodiment of the present invention does not limit the order of S240 and S250, that is, S240 and S250 can be performed simultaneously.

[0081] S260: Determine a first motion state deviation of the robot to be controlled according to the preset motion state and the calculated motion state.

[0082] S270. Determine a target motion state of the robot to be controlled at the target motion point according to the first motion state deviation and the preset motion state.

[0083] The first motion state deviation may be a deviation between a preset motion state and a calculated motion state. In an embodiment of the present invention, after determining the preset motion state and the calculated motion state, the first motion state deviation of the robot to be controlled may be further determined based on the preset motion state and the calculated motion state, so as to determine the target motion state of the robot to be controlled at the target motion point based on the first motion state deviation and the preset motion state.

[0084] Optionally, the target motion state of the robot to be controlled at the target motion point is determined according to the first motion state deviation and the preset motion state, which can be determined based on the following formula:

[0085] M0=M1+△1

[0086] Among them, M0 represents the target motion state; △1 represents the first motion state deviation; M1 represents the preset motion state.

[0087] Optionally, after determining the target motion state of the robot to be controlled at the target motion point, it can also include: determining the interval formed by the current position and the target motion point as the target motion interval, and determining at least one state correction point within the target motion interval; obtaining the motion parameters of the correction point driving wheels of the robot to be controlled at each state correction point, and controlling the robot to be controlled to move within the target motion interval according to the motion parameters of the driving wheels of each correction point; obtaining the motion parameters of the moving point driving wheels of the robot to be controlled at the target motion point, and calculating the moving point motion state of the robot to be controlled at the target motion point according to the motion parameters of the moving point driving wheels; determining the second motion state deviation of the robot to be controlled according to the motion state of the moving point and the preset motion state, and performing state correction on the target motion state according to the second motion state deviation and the preset motion state.

[0088] Among them, the target motion interval can be an interval in which the robot to be controlled can move, which is determined based on the current position and the target motion point. The state correction point can be a point within the target motion interval that can correct the motion of the robot to be controlled. It can be understood that the number of state correction points can be one or more, and the more state correction points there are, the more accurate the motion control of the robot to be controlled. The motion parameters of the driving wheels at the correction point can be the motion parameters of the driving wheels of the robot to be controlled when it moves to the state correction point. The motion parameters of the driving wheels at the moving point can be the motion parameters of the driving wheels of the robot to be controlled when it moves to the target motion point. The motion state of the moving point can be the motion state of the robot to be controlled when it moves to the target motion point. The second motion state deviation can be the deviation between the motion state of the moving point and the preset motion state.

[0089] Specifically, after determining the target motion state of the robot to be controlled at the target motion point, the interval formed by the current position and the target motion point can be further determined as the target motion interval, and at least one state correction point can be determined within the target motion interval to obtain the correction point driving wheel motion parameters of the robot to be controlled at each state correction point, so as to control the robot to be controlled to move within the target motion interval according to the motion parameters of the driving wheel at each correction point, and then obtain the motion parameters of the driving wheel at the target motion point of the robot to be controlled, and calculate the motion state of the robot to be controlled at the target motion point according to the motion parameters of the driving wheel at the motion point, so as to determine the second motion state deviation of the robot to be controlled according to the motion state of the motion point and the preset motion state, so as to perform state correction on the target motion state according to the second motion state deviation and the preset motion state.

[0090] Optionally, the motion state of the moving point of the robot to be controlled at the target moving point is calculated based on the motion parameters of the driving wheel of the moving point, which can be determined based on the following formula:

[0091]

[0092]

[0093]

[0094] Among them, ω2 represents the angular velocity of the moving point in the motion state; v2 represents the travel speed of the moving point in the motion state; α2 represents the velocity deviation angle of the moving point in the motion state; v 12 represents the linear velocity of the first driving wheel's motion parameters; α 12 represents the rotation angle of the first driving wheel's motion parameter; 22 Indicates the rotation angle of the second driving wheel in the motion parameter of the motion point driving wheel.

[0095] Optionally, controlling the robot to be controlled to move within the target motion range according to the motion parameters of the driving wheels at each correction point may include: calculating the correction point motion state of the robot to be controlled at the state correction point according to the motion parameters of the driving wheels at the correction point; determining the third motion state deviation of the robot to be controlled according to the correction point motion state and the preset motion state; determining the interval driving wheel motion parameters of the robot to be controlled according to the third motion state deviation and the preset motion state; and controlling the robot to be controlled to move within the target motion range according to the interval driving wheel motion parameters.

[0096] The correction point motion state may be the motion state of the robot to be controlled when it moves to the state correction point. The third motion state deviation may be the deviation between the correction point motion state and the preset motion state. The interval driving wheel motion parameters may be the motion parameters of the driving wheels of the robot to be controlled when it moves within the target motion interval.

[0097] Specifically, the correction point motion state of the robot to be controlled at the state correction point is calculated according to the motion parameters of the correction point driving wheel, and the third motion state deviation of the robot to be controlled is determined according to the correction point motion state and the preset motion state, so as to determine the interval driving wheel motion parameters of the robot to be controlled according to the third motion state deviation and the preset motion state, thereby controlling the robot to be controlled to move within the target motion interval according to the interval driving wheel motion parameters.

[0098] Optionally, the motion state of the correction point of the robot to be controlled at the state correction point is calculated according to the motion parameters of the driving wheel at the correction point, which can be determined based on the following formula:

[0099]

[0100]

[0101]

[0102] Among them, ω3 represents the angular velocity of the correction point in motion; v3 represents the travel speed of the correction point in motion; α3 represents the velocity deviation angle of the correction point in motion; v 13 Represents the linear velocity of the first driving wheel in the motion parameters of the driving wheel at the correction point; α 13 represents the rotation angle of the first driving wheel in the motion parameters of the correction point driving wheel; α 23 Indicates the rotation angle of the second driving wheel's correction point driving wheel motion parameters.

[0103] Optionally, determining the interval driving wheel motion parameters of the robot to be controlled based on the third motion state deviation and the preset motion state may include determining the interval motion state based on the third motion state deviation and the preset motion state, and determining the interval driving wheel motion parameters of the robot to be controlled based on the interval motion state.

[0104] Optionally, the interval driving wheel motion parameters of the robot to be controlled are determined according to the interval motion state, which can be determined based on the following formula:

[0105]

[0106]

[0107]

[0108]

[0109] Among them, v 14 represents the linear velocity of the first driving wheel in the interval driving wheel motion parameter; v 24 represents the linear velocity of the second driving wheel in the interval driving wheel motion parameter; α 14 represents the rotation angle of the first driving wheel in the interval driving wheel motion parameter; α 24 represents the rotation angle of the second driving wheel in the interval driving wheel motion parameter; ω4 represents the angular velocity in the interval motion state; v4 represents the travel speed in the interval motion state; α4 represents the speed deviation angle in the interval motion state.

[0110] S280: Determine target driving wheel motion parameters of the robot to be controlled according to the target motion state, and control the robot to be controlled to move according to the target driving wheel motion parameters.

[0111] Optionally, the target motion state includes a target travel speed, a target angular velocity, and a target velocity offset angle; accordingly, determining the target driving wheel motion parameters of the robot to be controlled based on the target motion state may include: obtaining the driving wheel wheelbase between the first driving wheel and the second driving wheel of the robot to be controlled; determining the target driving wheel motion parameters of the robot to be controlled based on the target motion state and the driving wheel wheelbase; wherein the target driving wheel motion parameters include the target driving wheel linear velocity and the target driving wheel rotation angle.

[0112] The target travel speed may be the travel speed of the robot to be controlled at the target movement point. The target angular velocity may be the angular velocity of the robot to be controlled at the target movement point. The target velocity offset angle may be the velocity offset angle of the robot to be controlled at the target movement point. The target drive wheel linear velocity may be the linear velocity of the drive wheels of the robot to be controlled at the target movement point. The target drive wheel rotation angle may be the rotation angle of the drive wheels of the robot to be controlled at the target movement point.

[0113] Specifically, after determining the target motion state of the robot to be controlled at the target motion point based on the first motion state deviation and the preset motion state, the driving wheel wheelbase between the first driving wheel and the second driving wheel of the robot to be controlled can be further obtained to determine the target driving wheel motion parameters of the robot to be controlled based on the target motion state and the driving wheel wheelbase.

[0114] Optionally, the target driving wheel motion parameters of the robot to be controlled are determined according to the target motion state and the driving wheel wheelbase, and can be determined based on the following formula:

[0115]

[0116]

[0117]

[0118]

[0119] Among them, v 15 represents the linear velocity of the target driving wheel motion parameter of the first driving wheel; v 25 represents the linear velocity of the target driving wheel motion parameter of the second driving wheel; α 15 represents the rotation angle of the target driving wheel motion parameter of the first driving wheel; α 25 represents the rotation angle in the target driving wheel motion parameter of the second driving wheel; ω5 represents the angular velocity in the target motion state; v5 represents the travel speed in the target motion state; α5 represents the speed deviation angle in the target motion state.

[0120] The technical solution of this embodiment determines the current position and target position of the robot to be controlled, determines the target motion trajectory in a pre-established robot map according to the current position and the target position, and determines the target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and then obtains the current driving wheel motion parameters of the robot to be controlled at the current position, so as to calculate the calculated motion state of the robot to be controlled at the current position according to the current driving wheel motion parameters, and determines the preset motion state of the robot to be controlled at the target motion point according to the target motion trajectory, determines the first motion state deviation of the robot to be controlled according to the preset motion state and the calculated motion state, thereby determining the target motion state of the robot to be controlled at the target motion point according to the first motion state deviation and the preset motion state, and determines the target driving wheel motion parameters of the robot to be controlled according to the target motion state, and then controls the robot to be controlled to move according to the target driving wheel motion parameters, thereby solving the problem of low motion accuracy of the electric silo handling robot in the prior art, and can accurately control the movement of the robot to be controlled, thereby improving the control accuracy of the robot to be controlled.

[0121] Example 3

[0122] In order to enable those skilled in the art to better understand the robot motion control method of this embodiment, a specific example is used below for illustration.

[0123] Figure 4 This is a schematic diagram of the chassis structure of an electric silo robot provided in the third embodiment of the present invention. Figure 4 As shown, the electric silo robot may include a robot chassis. Specifically, the robot chassis is composed of a robot structure body, a universal wheel, a steering wheel A, a steering wheel B, a navigation and positioning module (not shown in the figure), and a drive module (integrated in the steering wheel, not shown in the figure). The robot's motion control can be achieved by controlling the linear speed and rotation angle of the steering wheel A and the steering wheel B. The navigation and positioning module can be used to establish a robot map and real-time position control. It is understandable that the electric silo robot may also include a robot control module, Figure 5 This is an architecture diagram of a robot control module provided by the third embodiment of the present invention, such as Figure 5 As shown in the figure, the central control system (also known as the robot control module) includes an industrial computer, a switch, and a lower-level controller. The industrial computer is used to store the upper-level control program and perform sensor data calculations; the switch is used for data exchange between the control submodules and the central control system; the lower-level controller is mainly responsible for data interaction and control command transmission and reception for the motor drivers, navigation sensors, attitude sensors, ultrasonic sensors, and anti-fall sensors.

[0124] Figure 6 This is an example flow chart of a robot motion control method provided by the third embodiment of the present invention. Figure 6 As shown, the method may include: acquiring data information through IMU, lidar and GPS, and fusing the acquired data information to establish a robot map based on the fused data, and then performing path planning based on feature reference points, robot lane markings, FRID electronic tags and the robot map, thereby performing motion solving according to the planned path. Figure 7 This is another example flow chart of a robot motion control method provided by the third embodiment of the present invention. Figure 7 As shown, the method may include: obtaining the robot posture of the robot to be controlled, and determining the control direction of the robot to be controlled (such as right turn control, left turn control or straight control) according to the path planning, so as to perform forward control or inverse control according to the control direction, thereby performing motion control on the robot to be controlled.

[0125] Figure 8 This is a motion relationship diagram of a robot dual steering wheel solution provided by the third embodiment of the present invention, such as Figure 8 As shown in FIG, O1 and O2 respectively represent the first drive wheel (also known as the first steering wheel) and the second drive wheel (also known as the second steering wheel) of the robot to be controlled, O represents the geometric center point of the robot to be controlled, and L represents the wheelbase between the first drive wheel and the second drive wheel. The process of performing motion solution can include forward control and inverse control. Forward control can be used to calculate the motion state of the robot to be controlled based on the motion parameters of the drive wheels of the robot to be controlled. Inverse control can be used to calculate the motion parameters of the drive wheels of the robot to be controlled based on the motion state of the robot to be controlled.

[0126] Specifically, the calculation process of the positive control is as follows:

[0127] The input quantity (also known quantity) of the positive control is The quantity to be solved is

[0128] (1) Solve for ω0:

[0129] according to Figure 8 The angle relationship of the triangle △OO1O2 shown It can be seen that ∠O1OO2=α r +α f

[0130] According to the law of sine From this we can see According to the principle of instantaneous center of velocity, we can get

[0131] (2) Solve for v0:

[0132] In triangle △OO0O2, according to the cosine theorem It can be solved

[0133]

[0134] and then

[0135]

[0136] (2) Solve for α0:

[0137] In triangle △OO0O2, according to the sine theorem Right now

[0138]

[0139] Specifically, the calculation process of inverse solution control is as follows:

[0140] The input quantity (also known quantity) of the inverse control is The quantity to be solved is

[0141] (1) Solve for v r0

[0142] From the relationship between linear velocity and angular velocity, we can know that

[0143] In triangle △OO0O1, according to the cosine theorem, we can get

[0144] but

[0145] at this time

[0146] (2) Solving α r

[0147] In triangle △OO0O1, according to the sine theorem, we can get

[0148] but

[0149] (3) Solve for v f0

[0150] In triangle △OO0O2, according to the cosine theorem, we can get

[0151] but

[0152] at this time

[0153] (4) Solving α f

[0154] In triangle △OO0O2, according to the sine theorem, we can get

[0155] but

[0156] Among them, v r0 represents the linear velocity of the first driving wheel of the robot to be controlled; α r represents the rotation angle of the first driving wheel of the robot to be controlled; v f0 represents the linear velocity of the second driving wheel of the robot to be controlled; α f represents the rotation angle of the second driving wheel of the robot to be controlled; v0 represents the travel speed of the robot to be controlled; ω0 represents the angular velocity of the robot to be controlled; α0 represents the speed deviation angle of the robot to be controlled.

[0157] The above technical solution uses three sensors, IMU, lidar, and GPS, to complete robot mapping through data fusion, which can well adapt to the large-area working environment indoors and outdoors of power silos. The real-time control method based on dual steering wheel forward and inverse solutions greatly improves the real-time control accuracy of the robot's posture.

[0158] Example 4

[0159] Figure 9 Schematic diagram of a robot motion control device provided by the fourth embodiment of the present invention. Figure 9 As shown, the apparatus includes: a position determination module 910, a motion trajectory determination module 920, a motion state determination module 930, and a motion control module 940, wherein:

[0160] A position determination module 910 is configured to determine a current position and a target position of a robot to be controlled, wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel;

[0161] A motion trajectory determination module 920 is configured to determine a target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and the target position;

[0162] A motion state determination module 930 is configured to determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and determine a target motion state of the robot to be controlled at the target motion point;

[0163] The motion control module 940 is configured to determine target driving wheel motion parameters of the robot to be controlled according to the target motion state, and control the robot to be controlled to move according to the target driving wheel motion parameters.

[0164] The technical solution of this embodiment determines the current position and target position of the robot to be controlled, and determines the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and target position, so as to determine the target motion point of the robot to be controlled in the target motion trajectory based on the target motion trajectory, and determines the target motion state of the robot to be controlled at the target motion point, thereby determining the target driving wheel motion parameters of the robot to be controlled based on the target motion state, and then controlling the robot to be controlled to move based on the target driving wheel motion parameters, which solves the problem of low motion accuracy of the electric silo handling robot in the prior art, and can accurately control the robot to be controlled to move, thereby improving the control accuracy of the robot to be controlled.

[0165] Optionally, the position determination module 910 can be specifically used to: obtain the current positioning system information, current lidar system information and current inertial sensor information of the robot to be controlled; and determine the current position based on the current positioning system information, current lidar system information and current inertial sensor information.

[0166] Optionally, the motion trajectory determination module 920 can be specifically used to: generate movement instructions for the robot to be controlled, and control the robot to be controlled to move in the power silo according to the movement instructions; obtain the mobile positioning system information, mobile lidar system information and mobile inertial sensor information of the robot to be controlled during the movement process; and establish a robot map based on the mobile positioning system information, mobile lidar system information and mobile inertial sensor information.

[0167] Optionally, the motion state determination module 930 can be specifically used to: obtain the current driving wheel motion parameters of the robot to be controlled at the current position, and calculate the calculated motion state of the robot to be controlled at the current position based on the current driving wheel motion parameters; determine the preset motion state of the robot to be controlled at the target motion point based on the target motion trajectory; determine the first motion state deviation of the robot to be controlled based on the preset motion state and the calculated motion state; determine the target motion state of the robot to be controlled at the target motion point based on the first motion state deviation and the preset motion state.

[0168] Optionally, the current driving wheel motion parameters may include the current driving wheel linear velocity and the current driving wheel rotation angle; accordingly, the motion state determination module 930 can be further used to: obtain the driving wheel wheelbase between the first driving wheel and the second driving wheel of the robot to be controlled; and calculate the calculated motion state of the robot to be controlled at the current position based on the current driving wheel motion parameters and the driving wheel wheelbase.

[0169] Optionally, the motion state determination module 930 can also be specifically used to: determine the interval formed by the current position and the target motion point as the target motion interval, and determine at least one state correction point within the target motion interval; obtain the motion parameters of the correction point driving wheels of the robot to be controlled at each state correction point, and control the robot to be controlled to move within the target motion interval according to the motion parameters of the driving wheels at each correction point; obtain the motion parameters of the motion point driving wheels of the robot to be controlled at the target motion point, and calculate the motion state of the motion point of the robot to be controlled at the target motion point according to the motion parameters of the motion point driving wheels; determine the second motion state deviation of the robot to be controlled according to the motion state of the motion point and the preset motion state, and perform state correction on the target motion state according to the second motion state deviation and the preset motion state.

[0170] Optionally, the motion state determination module 930 can be further used to: calculate the correction point motion state of the robot to be controlled at the state correction point based on the correction point driving wheel motion parameters; determine the third motion state deviation of the robot to be controlled based on the correction point motion state and the preset motion state; determine the interval driving wheel motion parameters of the robot to be controlled based on the third motion state deviation and the preset motion state; and control the robot to be controlled to move within the target motion interval based on the interval driving wheel motion parameters.

[0171] Optionally, the target motion state may include a target travel speed, a target angular velocity, and a target velocity offset angle; accordingly, the motion control module 940 may be specifically used to: obtain the drive wheel wheelbase between the first drive wheel and the second drive wheel of the robot to be controlled; determine the target drive wheel motion parameters of the robot to be controlled based on the target motion state and the drive wheel wheelbase; wherein the target drive wheel motion parameters include the target drive wheel linear velocity and the target drive wheel rotation angle.

[0172] The robot motion control device provided in the embodiment of the present invention can execute the robot motion control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0173] Example 5

[0174] Figure 10A schematic diagram of a silo handling robot 10 is shown that can be used to implement an embodiment of the present invention. The silo handling robot is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The silo handling robot can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0175] like Figure 10 As shown, the electric silo handling robot 10 includes a first drive wheel, a second drive wheel, at least one processor 11, and a memory device, such as a read-only memory (ROM) 12 and a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory device stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from the storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electric silo handling robot 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0176] Multiple components in the electric silo handling robot 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, optical disk, etc.; and a communication unit 19, such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electric silo handling robot 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0177] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the robot motion control method.

[0178] In some embodiments, the robot motion control method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electric silo handling robot 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the robot motion control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the robot motion control method in any other suitable manner (e.g., via firmware).

[0179] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0181] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0182] To provide user interaction, the systems and techniques described herein can be implemented on an electric silo handling robot that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electric silo handling robot. Other types of devices can also be used to provide user interaction; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0183] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0184] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0185] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0186] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A robot motion control method, characterized in that: include: Determining a current position and a target position of a robot to be controlled; wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel; Determining a target motion trajectory of the robot to be controlled in a pre-established robot map according to the current position and the target position; Determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and determine a target motion state of the robot to be controlled at the target motion point; Determining target driving wheel motion parameters of the robot to be controlled according to the target motion state, and controlling the robot to be controlled to move according to the target driving wheel motion parameters; Determining the target motion state of the robot to be controlled at the target motion point includes: Acquiring current driving wheel motion parameters of the robot to be controlled at the current position, and calculating a calculated motion state of the robot to be controlled at the current position according to the current driving wheel motion parameters; Determining a preset motion state of the robot to be controlled at the target motion point according to the target motion trajectory; Determining a first motion state deviation of the robot to be controlled according to the preset motion state and the calculated motion state; Determining a target motion state of the robot to be controlled at the target motion point according to the first motion state deviation and the preset motion state; After determining the target motion state of the robot to be controlled at the target motion point, the method further includes: Determining an interval formed by the current position and the target motion point as a target motion interval, and determining at least one state correction point within the target motion interval; Acquiring motion parameters of the driving wheels of the correction points of the robot to be controlled at each state correction point, and controlling the robot to be controlled to move within the target motion range according to the motion parameters of the driving wheels of each correction point; Acquiring motion parameters of a driving wheel of a moving point of the robot to be controlled at the target moving point, and calculating a motion state of the moving point of the robot to be controlled at the target moving point according to the motion parameters of the driving wheel of the moving point; According to the motion state of the motion point and the preset motion state, a second motion state deviation of the robot to be controlled is determined, and the target motion state is corrected according to the second motion state deviation and the preset motion state.

2. The method according to claim 1, characterized in that Determining the current position of the robot to be controlled includes: Obtaining current positioning system information, current lidar system information, and current inertial sensor information of the robot to be controlled; The current position is determined based on the current positioning system information, the current lidar system information, and the current inertial sensor information.

3. The method according to claim 1, characterized in that Before determining the target motion trajectory of the robot to be controlled in a pre-established robot map based on the current position and the target position, the method further includes: generating a movement instruction for the robot to be controlled, and controlling the robot to be controlled to move in the power silo according to the movement instruction; Acquiring mobile positioning system information, mobile lidar system information, and mobile inertial sensor information of the robot to be controlled during movement; The robot map is established based on the mobile positioning system information, the mobile lidar system information and the mobile inertial sensor information.

4. The method according to claim 1, wherein The current driving wheel motion parameters include the current driving wheel linear velocity and the current driving wheel rotation angle; The calculating the motion state of the robot to be controlled at the current position according to the current driving wheel motion parameters includes: Obtaining a driving wheel wheelbase between a first driving wheel and a second driving wheel of the robot to be controlled; The calculated motion state of the robot to be controlled at the current position is calculated according to the current driving wheel motion parameters and the driving wheel wheelbase.

5. The method according to claim 1, wherein The controlling the robot to be controlled to move within the target motion range according to the motion parameters of the driving wheels at each correction point includes: Calculating the motion state of the robot to be controlled at the state correction point according to the motion parameters of the driving wheel at the correction point; Determining a third motion state deviation of the robot to be controlled according to the motion state of the correction point and the preset motion state; determining interval driving wheel motion parameters of the robot to be controlled according to the third motion state deviation and the preset motion state; The robot to be controlled is controlled to move within the target motion interval according to the interval driving wheel motion parameters.

6. The method according to claim 1, wherein The target motion state includes a target travel speed, a target angular speed and a target speed deviation angle; The step of determining target driving wheel motion parameters of the robot to be controlled according to the target motion state includes: Obtaining a driving wheel wheelbase between a first driving wheel and a second driving wheel of the robot to be controlled; Determining target driving wheel motion parameters of the robot to be controlled according to the target motion state and the driving wheel wheelbase; The target driving wheel motion parameters include a target driving wheel linear velocity and a target driving wheel rotation angle.

7. A robot motion control device, characterized in that: include: a position determination module, configured to determine a current position and a target position of a robot to be controlled; wherein the robot to be controlled is an electric silo handling robot having a first drive wheel and a second drive wheel; a motion trajectory determination module, configured to determine a target motion trajectory of the robot to be controlled in a pre-established robot map according to the current position and the target position; a motion state determination module, configured to determine a target motion point of the robot to be controlled in the target motion trajectory according to the target motion trajectory, and determine a target motion state of the robot to be controlled at the target motion point; a motion control module, configured to determine target drive wheel motion parameters of the robot to be controlled according to the target motion state, and control the robot to be controlled to move according to the target drive wheel motion parameters; The motion state determination module is specifically used to: obtain the current driving wheel motion parameters of the robot to be controlled at the current position, and calculate the calculated motion state of the robot to be controlled at the current position based on the current driving wheel motion parameters; determine the preset motion state of the robot to be controlled at the target motion point based on the target motion trajectory; determine the first motion state deviation of the robot to be controlled based on the preset motion state and the calculated motion state; determine the target motion state of the robot to be controlled at the target motion point based on the first motion state deviation and the preset motion state; The motion state determination module is also specifically used to: determine the interval formed by the current position and the target motion point as the target motion interval, and determine at least one state correction point within the target motion interval; obtain the motion parameters of the correction point driving wheels of the robot to be controlled at each state correction point, and control the robot to be controlled to move within the target motion interval according to the motion parameters of the driving wheels at each correction point; obtain the motion parameters of the motion point driving wheels of the robot to be controlled at the target motion point, and calculate the motion state of the motion point of the robot to be controlled at the target motion point according to the motion parameters of the motion point driving wheels; determine the second motion state deviation of the robot to be controlled according to the motion state of the motion point and the preset motion state, and perform state correction on the target motion state according to the second motion state deviation and the preset motion state.

8. An electric silo handling robot, characterized in that: The electric silo handling robot comprises: a first drive wheel, a second drive wheel, at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the robot motion control method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the robot motion control method according to any one of claims 1 to 6 when executed.

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