Control method of mobile robot and mobile robot

Abstract

This application discloses a control method for a mobile robot and a mobile robot. The control method optimizes the state vector of a visual inertial odometry or a laser inertial odometry by incorporating the instantaneous rotation center and the equivalent wheel track coefficient of the chassis into a tightly coupled factor graph, thereby reducing the error caused by the dynamic drift of the instantaneous rotation center and realizing precise speed control of the chassis, thus improving the control accuracy of the mobile robot.

Description

Technical Field

[0001] This application relates to the field of robotics, and in particular to a control method for a mobile robot and a mobile robot. Background Technology

[0002] In the field of robotics, multi-drive (such as four-wheel drive and six-wheel drive) differential chassis with more than two drives are widely used in inspection, logistics and distribution scenarios due to their strong passability. However, unlike the standard two-wheel drive differential model, the aforementioned multi-drive differential chassis rely on tire slip steering, and its ICR (Instantaneous Center of Rotation) will dynamically drift with changes in the ground friction coefficient, tire wear, load distribution and movement speed.

[0003] Traditional mobile robot control methods do not consider the dynamic drift of ICR (Interceptor Response), resulting in a large deviation between the output wheel speed command and the actual motion trajectory, leading to low control accuracy. For example, patent CN119018183A discloses a control method for four-wheeled chassis motion, including: analyzing the motion state of the chassis wheels and the chassis itself to establish a kinematic model of the chassis and wheels; obtaining the target position and the initial position of the chassis; using a PRM (Programmable Path Recognition) algorithm to plan the path for the chassis and obtaining the path planning result; acquiring the current position information of the chassis in real time; and adjusting the speed of the chassis wheels using a PID (Pulse Control Algorithm) based on the kinematic model, the path planning result, and the current position information. It is evident that this method does not consider the dynamic drift of ICR, resulting in low control accuracy for the mobile robot. Summary of the Invention

[0004] This application provides a control method and a mobile robot, which improves the control accuracy of the mobile robot by incorporating the instantaneous rotation center of the chassis and the equivalent wheel track coefficient into the state vector of the visual inertial odometry or laser inertial odometry for tight coupling factor graph optimization.

[0005] Firstly, a control method for a mobile robot is provided. The mobile robot's chassis is equipped with a left wheel set and a right wheel set, wherein the number of drive wheels in the left wheel set and the right wheel set is equal and greater than 1 in both. The mobile robot achieves steering by the speed difference between the left wheel set and the right wheel set. The control method includes: obtaining observation data of the mobile robot within a target sliding window, wherein the observation data includes at least sensor data provided by a target sensor of the mobile robot and inertial data provided by an inertial measurement unit of the mobile robot, wherein the target sensor is a visual inertial odometry or a laser inertial odometry; and constructing a factor graph model based on the observation data. The factors in the factor graph model include at least an instantaneous rotation center constraint factor, an equivalent wheelbase constraint factor, an inertial measurement constraint factor, and a reprojection constraint factor. The instantaneous rotation center constraint factor is a factor based on non-holonomic constraints. Based on the factor graph model, the state vector within the target sliding window is optimized to determine the real-time parameters corresponding to the chassis in the state vector. The real-time parameters include at least an instantaneous rotation center and an equivalent wheelbase coefficient, and the equivalent wheelbase coefficient characterizes the correspondence between the virtual wheelbase and the physical wheelbase. In response to receiving a speed command including a target speed, the left wheel set and the right wheel set are controlled according to the target speed and the real-time parameters.

[0006] In some embodiments, the observation data further includes the left measured rotational speed of the left wheel group provided by the left wheel encoder of the mobile robot, and the right measured rotational speed of the right wheel group provided by the right wheel encoder of the mobile robot. Controlling the left wheel group and the right wheel group based on the target speed and the real-time parameters includes: determining the left physical linear velocity of the grounding point of the left wheel group and the right physical linear velocity of the grounding point of the right wheel group based on the sensor data and the equivalent wheelbase coefficient; determining the left longitudinal slip coefficient based on the left physical linear velocity and the left measured rotational speed, and determining the right longitudinal slip coefficient based on the right physical linear velocity and the right measured rotational speed; determining the left target rotational speed and the right target rotational speed based on the target speed, the left longitudinal slip coefficient, and the right longitudinal slip coefficient; controlling the left wheel group based on the left target rotational speed, and controlling the right wheel group based on the right target rotational speed.

[0007] By determining the left and right longitudinal slip coefficients, the accuracy of the left and right target rotation speeds is improved, thus enabling more precise speed control of the mobile robot.

[0008] In some embodiments, the sensor data includes a first longitudinal linear velocity and a first angular velocity. Determining the left physical linear velocity of the contact point of the left wheel assembly and the right physical linear velocity of the contact point of the right wheel assembly based on the sensor data and the equivalent wheelbase coefficient includes: determining a first adjustment value based on the first angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase; determining the left physical linear velocity by subtracting the first adjustment value from the first longitudinal linear velocity; and determining the right physical linear velocity by adding the first adjustment value to the first longitudinal linear velocity. This improves the accuracy of the left and right physical linear velocities.

[0009] In some embodiments, determining the left longitudinal slip coefficient based on the left physical linear velocity and the left measured rotational speed, and determining the right longitudinal slip coefficient based on the right physical linear velocity and the right measured rotational speed, includes: determining the left longitudinal slip coefficient based on the wheel diameters of the left and right wheel sets, the left measured rotational speed, and the left physical linear velocity; and determining the right longitudinal slip coefficient based on the wheel diameter, the right measured rotational speed, and the right physical linear velocity. This improves the accuracy of the left and right longitudinal slip coefficients.

[0010] In some embodiments, determining the left target rotational speed and the right target rotational speed based on the target speed, the left longitudinal slip coefficient, and the right longitudinal slip coefficient includes: determining the left target geometrical speed and the right target geometrical speed at the instantaneous rotation center based on the target speed, the equivalent wheelbase coefficient, and the physical wheelbase; determining the left target rotational speed based on the wheel diameters of the left and right wheel sets, the left target geometrical speed, and the left longitudinal slip coefficient; and determining the right target rotational speed based on the wheel diameter, the right target geometrical speed, and the right longitudinal slip coefficient.

[0011] The accuracy of the left target rotational speed is improved by determining the left angular velocity by dividing the left target's geometric velocity by the wheel diameter, and then using the left longitudinal slip coefficient to compensate for longitudinal slip. Similarly, the accuracy of the right target rotational speed is improved by determining the right angular velocity by dividing the right target's geometric velocity by the wheel diameter, and then using the right longitudinal slip coefficient to compensate for longitudinal slip.

[0012] In some embodiments, the target velocity includes a target linear velocity and a target angular velocity. Determining the left and right target geometric velocities at the instantaneous rotation center based on the target velocity, the equivalent wheelbase coefficient, and the physical wheelbase includes: determining a second adjustment value based on the target angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase; determining the left target geometric velocity by subtracting the second adjustment value from the target linear velocity; and determining the right target geometric velocity by adding the second adjustment value to the target linear velocity. This improves the accuracy of the left and right target geometric velocities.

[0013] In some embodiments, the sensor data includes a first lateral linear velocity and a first angular velocity, and the first residual corresponding to the instantaneous rotation center constraint factor is determined by the first lateral linear velocity, the first angular velocity, and the predicted value of the instantaneous rotation center.

[0014] The first residual is determined by using the predicted values ​​of the first lateral linear velocity, the first angular velocity, and the instantaneous rotation center, thereby improving the accuracy of the first residual.

[0015] In some embodiments, the second residual corresponding to the equivalent wheelbase constraint factor is determined by the first angular velocity, the left measured rotational speed of the left wheel assembly, the right measured rotational speed of the right wheel assembly, the wheel diameters of the left and right wheel assemblies, the predicted value of the equivalent wheelbase coefficient, and the physical wheelbase. The left measured rotational speed is provided by the left wheel encoder of the mobile robot, and the right measured rotational speed is provided by the right wheel encoder of the mobile robot. This improves the accuracy of the second residual.

[0016] In some embodiments, the global sparse Jacobian matrix corresponding to the factor graph model includes a first matrix block and a second matrix block corresponding to the instantaneous rotation center constraint factor, and a third matrix block corresponding to the equivalent wheelbase constraint factor. The first matrix block is determined by the partial derivative of the first residual and the partial derivative of the predicted value of the instantaneous rotation center; the second matrix block is determined by the partial derivative of the first residual and the partial derivative of the zero bias of the inertial measurement unit; and the third matrix block is determined by the partial derivative of the second residual and the partial derivative of the predicted value of the equivalent wheelbase coefficient. This improves the accuracy of the global sparse Jacobian matrix, thereby improving control precision.

[0017] In a second aspect, a mobile robot is provided, including a chassis, a target sensor, an inertial measurement unit, and a controller. The chassis is provided with a left wheel set and a right wheel set, wherein the number of drive wheels in the left wheel set and the right wheel set is equal and greater than 1. The mobile robot achieves steering by means of the speed difference between the left wheel set and the right wheel set. The controller is configured to execute the control method of the mobile robot as described in the first aspect.

[0018] By applying the above technical solutions, the instantaneous rotation center and equivalent wheelbase coefficient of the chassis are incorporated into the state vector of the visual inertial odometry or laser inertial odometry for tight coupling factor graph optimization, which reduces the error caused by the dynamic drift of the instantaneous rotation center and achieves precise speed control of the chassis, thereby improving the control accuracy of the mobile robot. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating a control method for a mobile robot according to an embodiment of this application; Figure 2 This is a flowchart illustrating the control of the left and right wheel sets according to an embodiment of this application; Figure 3 This is a flowchart illustrating the determination of the left target rotation speed and the right target rotation speed according to an embodiment of this application; Figure 4 This is a system architecture diagram corresponding to a mobile robot control method according to an embodiment of this application; Figure 5 This is a schematic diagram of the factor graph model in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the principle of the four-wheel drive differential kinematic model according to an embodiment of this application. Figure 7 This is a flowchart illustrating a control method for a mobile robot according to another embodiment of this application; Figure 8 This is a structural block diagram of a mobile robot according to an embodiment of this application. Detailed Implementation

[0021] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0022] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0023] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0024] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0025] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application.

[0026] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0027] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0028] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0029] This application discloses a mobile robot control method that utilizes sensor data from a visual inertial odometry (VIO) or laser inertial odometry (LIO) and inertial data from an inertial measurement unit (LMU) to construct a factor graph model. Based on this model, the method optimizes the state vector within a target sliding window, determining real-time parameters corresponding to the chassis within the state vector. These real-time parameters include at least the instantaneous rotation center and the equivalent wheelbase coefficient. In response to a speed command including the target speed, the method controls the left and right wheel sets according to the target speed and the real-time parameters. By incorporating the chassis's instantaneous rotation center and equivalent wheelbase coefficient into the VIO or LIO state vector for tightly coupled factor graph optimization, the method reduces errors caused by ICR dynamic drift, achieving precise speed control of the chassis and thus improving the control accuracy of the mobile robot.

[0030] The mobile robot's chassis is equipped with a left wheel set and a right wheel set. The number of drive wheels in both the left and right wheel sets is equal and greater than one. The mobile robot achieves steering by utilizing the speed difference between the left and right wheel sets. Figure 1 As shown, the control method includes the following steps: Step S101: Obtain observation data of the mobile robot within the target sliding window. The observation data includes at least sensor data provided by the target sensor of the mobile robot and inertial data provided by the inertial measurement unit of the mobile robot. The target sensor is a visual inertial odometer or a laser inertial odometer.

[0031] In this embodiment, the mobile robot can be, for example, a lawnmower robot, a logistics delivery robot, or an inspection robot. The mobile robot includes a chassis, a target sensor, and an inertial measurement unit (IMU). The chassis has a left wheel set and a right wheel set, with the number of drive wheels in both sets being equal and greater than one. The mobile robot steers by the speed difference between the left and right wheel sets. For example, both the left and right wheel sets can each have two drive wheels, forming a four-wheel drive differential chassis. Alternatively, both sets can each have three drive wheels, forming a six-wheel drive differential chassis. The inertial measurement unit is the core sensor used to perceive the robot's own motion state. It primarily measures acceleration and angular velocity (some models also include magnetic field information) to provide crucial data for attitude estimation, balance control, navigation, and motion execution. The target sensor is either a VIO or LIO sensor. The VIO sensor fuses camera (vision) data with IMU data and can be used to estimate the mobile robot's pose (position and attitude). LIO integrates LiDAR and IMU data, enabling high-precision positioning and environmental mapping.

[0032] The target sliding window is a sliding window that conforms to the target duration. Observational data of the mobile robot within the target sliding window is obtained. This observational data includes at least sensor data provided by a visual inertial odometry or laser inertial odometry, and inertial data provided by an inertial measurement unit.

[0033] Step S102: Construct a factor graph model based on the observation data. The factors in the factor graph model include at least the instantaneous rotation center constraint factor, the equivalent wheel track constraint factor, the inertial measurement constraint factor, and the reprojection constraint factor. The instantaneous rotation center constraint factor is a factor based on non-integrity constraints.

[0034] In this embodiment, multiple factors are determined based on observation data, and a factor graph model is constructed based on each factor. Each factor includes at least an instantaneous rotation center constraint factor, an equivalent wheelbase constraint factor, an inertial measurement constraint factor, and a reprojection constraint factor. The instantaneous rotation center constraint factor is related to the chassis's ICR and is a factor based on NHC (Non-Holonomic Constraint). The equivalent wheelbase constraint factor is related to the chassis's equivalent wheelbase. The inertial measurement constraint factor is the IMU pre-integration factor. The reprojection constraint factor is used to describe the consistency of projection of 3D spatial points under different camera views.

[0035] Step S103: Optimize the state vector within the target sliding window based on the factor graph model, and determine the real-time parameters corresponding to the chassis in the state vector. The real-time parameters include at least the instantaneous rotation center and the equivalent wheel track coefficient, and the equivalent wheel track coefficient characterizes the correspondence between the virtual wheel track and the physical wheel track.

[0036] In this embodiment, probabilistic inference is performed based on a factor graph model to optimize the state vector of the mobile robot within the target sliding window. This state vector includes at least the instantaneous rotation center of the chassis and the equivalent wheelbase coefficient, and may also include pose, velocity, and the zero bias of the inertial measurement unit. The value of the instantaneous rotation center is its coordinate on the x-axis in the robot's body coordinate system. The equivalent wheelbase coefficient describes the differential slip characteristics during rotation; the product of the equivalent wheelbase coefficient and the physical wheelbase is the virtual wheelbase. For example, such as... Figure 6 As shown, the physical wheelbase is W, the virtual wheelbase is kW, and k is the equivalent wheelbase coefficient. The origin of the mobile robot's body coordinate system is the geometric center of the chassis. The x-axis of the body coordinate system is the axis along the chassis's forward direction, the y-axis is the horizontal axis to the right, and the z-axis is the vertical axis upward (satisfying the right-hand rule).

[0037] For example, the state vector X is defined as follows:

[0038] Where, … represents the pose. ,speed IMU zero bias . Let be the coordinates of the instantaneous rotation center on the x-axis in the local coordinate system, and k be the equivalent wheelbase coefficient.

[0039] Step S104: In response to receiving a speed command including a target speed, control the left wheel assembly and the right wheel assembly according to the target speed and the real-time parameters.

[0040] In this embodiment, the speed command can be issued by the user to the mobile robot, or it can be issued by other electronic devices or servers (such as the server corresponding to the navigation system) to the mobile robot. In response to receiving the speed command, the left and right wheel sets are controlled according to the target speed and real-time parameters to make the wheel speeds of the left and right wheel sets match the target speed.

[0041] The mobile robot control method of this application embodiment obtains observation data of the mobile robot within a target sliding window. The observation data includes at least sensor data provided by the target sensor of the mobile robot and inertial data provided by the inertial measurement unit of the mobile robot. The target sensor is a visual inertial odometry or a laser inertial odometry. A factor graph model is constructed based on the observation data. The factors in the factor graph model include at least an instantaneous rotation center constraint factor, an equivalent wheelbase constraint factor, an inertial measurement constraint factor, and a reprojection constraint factor. The instantaneous rotation center constraint factor is a factor based on non-holonomic constraints. The state vector within the target sliding window is optimized based on the factor graph model to determine the real-time parameters corresponding to the chassis in the state vector. The real-time parameters include at least the instantaneous rotation center and the equivalent wheelbase coefficient. The equivalent wheelbase coefficient represents the correspondence between the virtual wheelbase and the physical wheelbase. In response to receiving a speed command including the target speed, the left wheel set and the right wheel set are controlled according to the target speed and the real-time parameters. By incorporating the instantaneous rotation center and equivalent wheelbase coefficient of the chassis into the state vector of VIO or LIO for tight coupling factor graph optimization, the error caused by ICR dynamic drift is reduced, and precise speed control of the chassis is achieved, thereby improving the control accuracy of the mobile robot.

[0042] In some embodiments of this application, the observation data further includes the left measured rotational speed of the left wheel group provided by the left wheel encoder of the mobile robot, and the right measured rotational speed of the right wheel group provided by the right wheel encoder of the mobile robot. The left and right wheel groups are controlled according to the target speed and the real-time parameters, such as... Figure 2 As shown, it includes the following steps: Step S1041: Based on the sensor data and the equivalent wheel track coefficient, determine the left physical linear velocity of the grounding point of the left wheel assembly and the right physical linear velocity of the grounding point of the right wheel assembly.

[0043] In this embodiment, the mobile robot includes a left wheel encoder and a right wheel encoder. The left wheel encoder measures the rotational speed of the left wheel assembly, and the right wheel encoder measures the rotational speed of the right wheel assembly. The mobile robot's observation data also includes the left measured rotational speed provided by the left wheel encoder and the right measured rotational speed provided by the right wheel encoder.

[0044] Based on sensor data and the equivalent wheelbase coefficient, determine the physical linear velocities that the grounding points of the left and right wheelsets should have, i.e., the left physical linear velocity and the right physical linear velocity.

[0045] Step S1042: Determine the left longitudinal slip coefficient based on the left physical linear velocity and the left measured rotational speed, and determine the right longitudinal slip coefficient based on the right physical linear velocity and the right measured rotational speed.

[0046] In this embodiment, the left longitudinal slip coefficient can characterize the consistency between the left physical linear velocity and the left measured rotational speed, and the right longitudinal slip coefficient can characterize the consistency between the right physical linear velocity and the right measured rotational speed.

[0047] Step S1043: Determine the left target speed and the right target speed based on the target speed, the left longitudinal slip coefficient and the right longitudinal slip coefficient; control the left wheel assembly based on the left target speed and control the right wheel assembly based on the right target speed.

[0048] In this embodiment, inverse kinematics calculations are performed based on the target speed, left longitudinal slip coefficient, and right longitudinal slip coefficient to determine the left target speed and right target speed. The speed of the motor of the left wheel set or the PWM control signal is controlled based on the left target speed, and the speed of the motor of the right wheel set or the PWM control signal is controlled based on the right target speed.

[0049] By determining the left and right longitudinal slip coefficients, the accuracy of the left and right target rotation speeds is improved, thus enabling more precise speed control of the mobile robot.

[0050] In some embodiments of this application, the sensor data includes a first longitudinal linear velocity and a first angular velocity. Determining the left physical linear velocity of the contact point of the left wheel assembly and the right physical linear velocity of the contact point of the right wheel assembly based on the sensor data and the equivalent wheelbase coefficient includes: The first adjustment value is determined based on the first angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase. The left physical linear velocity is determined by subtracting the first adjustment value from the first longitudinal linear velocity; The right physical linear velocity is determined by adding the first longitudinal linear velocity to the first adjustment value.

[0051] In this embodiment, the sensor data includes a first longitudinal linear velocity and a first angular velocity estimated by the target sensor. The left physical linear velocity is determined by subtracting a first adjustment value from the first longitudinal linear velocity, and the right physical linear velocity is determined by adding the first adjustment value to the first longitudinal linear velocity. The first adjustment value is determined by the first angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase, thereby improving the accuracy of the left and right physical linear velocities.

[0052] In some embodiments of this application, the left physical linear velocity is determined by formula (2), and the right physical linear velocity is determined by formula (3).

[0053]

[0054]

[0055] in, The linear velocity of the left physical object. The first longitudinal linear velocity, Let ω be the first angular velocity, k be the equivalent wheelbase coefficient, and W be the physical wheelbase. The right physical linear velocity, The first adjustment value is formed.

[0056] In some embodiments of this application, determining the left longitudinal slip coefficient based on the left physical linear velocity and the left measured rotational speed, and determining the right longitudinal slip coefficient based on the right physical linear velocity and the right measured rotational speed, includes: The left longitudinal slip coefficient is determined based on the wheel diameters of the left and right wheel sets, the left measured rotational speed, and the left physical linear velocity. The right longitudinal slip coefficient is determined based on the wheel diameter, the right measured rotational speed, and the right physical linear velocity.

[0057] In this embodiment, all drive wheels in the left and right wheel sets use the same wheel diameter. The left longitudinal slip coefficient can be determined based on the wheel diameter, the left measured rotational speed, and the left physical linear velocity, and the right longitudinal slip coefficient can be determined based on the wheel diameter, the right measured rotational speed, and the right physical linear velocity, thereby improving the accuracy of the left and right longitudinal slip coefficients.

[0058] In some embodiments of this application, the longitudinal slip coefficient p = encoder measured speed / physical linear velocity of the grounding point. The left longitudinal slip coefficient is determined according to formula (4), and the right longitudinal slip coefficient is determined according to formula (5).

[0059]

[0060]

[0061] in, The left longitudinal slip coefficient, The right longitudinal slip coefficient, The left side measures the rotational speed, and R is the wheel diameter. The rotational speed is measured on the right.

[0062] In some embodiments of this application, the determination of the left target rotational speed and the right target rotational speed based on the target speed, the left longitudinal slip coefficient, and the right longitudinal slip coefficient is as follows: Figure 3 As shown, it includes the following steps: Step S10431: Determine the geometric velocity of the left target and the geometric velocity of the right target under the instantaneous rotation center based on the target velocity, the equivalent wheelbase coefficient, and the physical wheelbase.

[0063] In this embodiment, the left and right target geometric velocities are target geometric velocities that take into account ICR drift. ICR longitudinal offset mainly affects lateral velocity constraints, but in the differential control model, it is mainly reflected in the speed distribution of the left and right wheels after the rotation center changes. Here, the wheel track is corrected using an equivalent wheel track coefficient.

[0064] Step S10432: Determine the left target rotational speed based on the wheel diameters of the left and right wheel sets, the left target geometric velocity, and the left longitudinal slip coefficient.

[0065] The left angular velocity is determined by dividing the left target geometric velocity by the wheel diameter. The left longitudinal slip coefficient is used to compensate for the longitudinal slip by the left angular velocity, thereby determining the left target rotation speed and improving the accuracy of the left target rotation speed.

[0066] Step S10433: Determine the rotational speed of the right target based on the wheel diameter, the geometric velocity of the right target, and the right longitudinal slip coefficient.

[0067] The right angular velocity is determined by dividing the right target's geometric velocity by the wheel diameter. The right longitudinal slip coefficient is then used to compensate for the longitudinal slip by adjusting the right angular velocity, thereby determining the right target's rotational speed and improving the accuracy of the right target's rotational speed.

[0068] In some embodiments of this application, the left target rotational speed is determined by formula (6), and the right target rotational speed is determined by formula (7).

[0069]

[0070]

[0071] in, For the target rotational speed on the left, For the target rotational speed on the right, Given the geometric velocity of the left target, Let be the geometric velocity of the right target.

[0072] In some embodiments of this application, the target velocity includes a target linear velocity and a target angular velocity. Determining the left and right target geometric velocities at the instantaneous rotation center based on the target velocity, the equivalent wheelbase coefficient, and the physical wheelbase includes: The second adjustment value is determined based on the target angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase. The geometric velocity of the left target is determined by subtracting the second adjustment value from the target linear velocity; The geometric velocity of the right target is determined by adding the second adjustment value to the target linear velocity.

[0073] In this embodiment, the target velocity includes the target linear velocity and the target angular velocity. The left target geometric velocity is determined by subtracting a second adjustment value from the target linear velocity. The right target geometric velocity is determined by adding the second adjustment value to the target linear velocity. This second adjustment value is determined by the target angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase, thereby improving the accuracy of the left and right target geometric velocities.

[0074] In some embodiments of this application, the geometric velocity of the left target is determined by formula (8), and the geometric velocity of the right target is determined by formula (9).

[0075]

[0076]

[0077] in, For the target linear velocity, For the target angular velocity, This forms the second adjustment value.

[0078] In some embodiments of this application, the sensor data includes a first lateral linear velocity and a first angular velocity, and the first residual corresponding to the instantaneous rotation center constraint factor is determined by the first lateral linear velocity, the first angular velocity, and the predicted value of the instantaneous rotation center.

[0079] In this embodiment, based on the four-wheel drive differential kinematics, at the instantaneous center of rotation... At this point, the lateral velocity of the chassis should be 0. The first residual can be determined according to formula (10).

[0080]

[0081] in, For the first residual, The first lateral linear velocity in the body coordinate system. The first angular velocity in the body coordinate system. The predicted value of the instantaneous rotation center is used. The first residual is determined using the predicted values ​​of the first lateral linear velocity, the first angular velocity, and the instantaneous rotation center, thus improving the accuracy of the first residual.

[0082] In some embodiments of this application, the second residual corresponding to the equivalent wheelbase constraint factor is determined by the first angular velocity, the left measured rotational speed of the left wheel set, the right measured rotational speed of the right wheel set, the wheel diameters of the left and right wheel sets, the predicted value of the equivalent wheelbase coefficient, and the physical wheelbase. The left measured rotational speed is provided by the left wheel encoder of the mobile robot, and the right measured rotational speed is provided by the right wheel encoder of the mobile robot.

[0083] In this embodiment, the second residual can be determined by formula (11). This improves the accuracy of the second residual.

[0084]

[0085] In some embodiments of this application, the global sparse Jacobian matrix corresponding to the factor graph model includes a first matrix block and a second matrix block corresponding to the instantaneous rotation center constraint factor, and a third matrix block corresponding to the equivalent wheelbase constraint factor. The first matrix block is determined by the partial derivative of the first residual and the partial derivative of the predicted value of the instantaneous rotation center; the second matrix block is determined by the partial derivative of the first residual and the partial derivative of the zero bias of the inertial measurement unit; and the third matrix block is determined by the partial derivative of the second residual and the partial derivative of the predicted value of the equivalent wheelbase coefficient. This improves the accuracy of the global sparse Jacobian matrix, thereby improving control precision.

[0086] In this embodiment, during the optimization of state variables using the factor graph model, a global sparse Jacobian matrix is ​​formed using the Jacobian matrix blocks corresponding to each factor, and the optimization result is determined using the global sparse Jacobian matrix. The instantaneous rotation center constraint factor corresponds to the first and second matrix blocks, and the equivalent wheel track constraint factor corresponds to the third matrix block.

[0087] The first matrix block can be represented as:

[0088] From formula (12), it can be seen that when the chassis has rotational motion ( )hour, It becomes observable.

[0089] because Then, the second matrix block can be represented as:

[0090] in, This represents the zero bias of the inertial measurement unit. As can be seen from Equation 13, the estimation of ICR and the zero bias estimation of IMU are coupled and jointly optimized.

[0091] make The third matrix block can be represented as the tangential velocity difference of the wheel rim:

[0092] Simplifying, we get:

[0093] As can be seen from formula (15), when there is a difference in wheel speed (rotation) in the chassis, k can be observed; and the greater the difference in wheel speed, the more obvious the gradient and the faster the convergence.

[0094] The core function of inertial measurement constraint factors and reprojection constraint factors is to provide high accuracy. and "Truth value". Without these constraints, the above... and An internally consistent equation cannot yield an absolutely correct solution. and The target sensor essentially acts as a "virtual external observer," observing "how the chassis actually moves," thereby causing the kinematic parameters to converge to their true values.

[0095] To further illustrate the technical concept of this application, the technical solution will now be explained in conjunction with specific application scenarios.

[0096] This application provides a control method for a mobile robot, which uses a four-wheel drive differential chassis, meaning that both the left and right wheel sets have two drive wheels.

[0097] like Figure 4 As shown, this control method is executed based on a system architecture including an input terminal, a processing core, an output terminal, and an execution terminal. The input terminal is used to input sensor data from the target sensor (VIO or LIO), IMU data, and four-wheel motor encoder data. The processing core includes a state estimator, which internally displays a factor graph model under a sliding window of the target, containing pose nodes and parameter nodes. The output terminal outputs real-time pose and real-time parameters. The actuator receives navigation commands and real-time parameters through the motion controller, and outputs PWM control signals or target speeds for the motors of each drive wheel.

[0098] like Figure 5 As shown, one optimization includes The state nodes, and Nodes and Nodes. NHC constraint factor connects velocity / angular velocity nodes. Nodes, equivalent wheelbase constraint factors connect angular velocity nodes and Nodes. The NHC constraint factor is the instantaneous rotation center constraint factor based on NHC.

[0099] like Figure 6 As shown, the chassis is displayed in a top view, with labels. Point located at shaft The lateral velocity is shown at that location. and rotational components The offsetting relationship (i.e., the geometric meaning of NHC constraints), and the virtual wheelbase. Compared with physical wheelbase The comparison.

[0100] like Figure 7 As shown, sensor data acquisition and time synchronization are performed first, followed by factor graph construction and nonlinear optimization calculation to obtain... The result is obtained through post-processing calculations. Upon receiving and At that time, utilize , , Perform inverse kinematics calculations with parameters to determine... and and utilize and The speed of the motors of each drive wheel is controlled using the PID method.

[0101] By applying the above technical solutions, the following technical effects can be achieved: Tightly Coupled Optimized Architecture: A method is proposed to optimize the kinematic parameters of a four-wheel drive differential chassis. This method incorporates the VIO / LIO factor graph as a state variable for joint optimization. Unlike traditional Kalman filtering (EKF) or offline calibration, this method utilizes the iterative characteristics of graph optimization to handle nonlinear constraints more accurately.

[0102] Dynamic NHC Constraint Model: Innovatively constructs a constraint model where the point of action varies with... Changing NHC residuals Furthermore, its analytical Jacobian matrix was derived, thus resolving the model error caused by ICR longitudinal drift.

[0103] Decoupling parameter estimation strategy: A hierarchical observation strategy is proposed to solve the equivalent wheel track coefficient by the consistency of rotational motion (wheel speed difference vs. IMU angular velocity). Then, the longitudinal slip coefficient is solved by the consistency of linear velocity (wheel speed and vs. VIO linear velocity). This approach avoids the observability problem when multiple coupled parameters are estimated simultaneously.

[0104] Adaptive control based on online sensing: The VIO / LIO is upgraded from a simple "positioning module" to a "positioning + chassis sensing module". The motor control law is corrected in real time using the sensed parameters, realizing the chassis control system's adaptation to the ground environment.

[0105] This application also provides a mobile robot, such as... Figure 8 As shown, the mobile robot includes a chassis, a target sensor, an inertial measurement unit, and a controller. The chassis is provided with a left wheel set and a right wheel set. The number of drive wheels in the left wheel set and the right wheel set are equal and both are greater than 1. The mobile robot achieves steering by the speed difference between the left wheel set and the right wheel set. The controller is configured to execute the mobile robot control method as described in any embodiment of this application.

[0106] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0107] The above embodiments are merely exemplary embodiments of this application and are not intended to limit this application. The scope of protection of this application is defined by the claims. Those skilled in the art can make various modifications or equivalent substitutions to this application within its substance and scope of protection, and such modifications or equivalent substitutions should also be considered to fall within the scope of protection of this application.

Claims

1. A control method for a mobile robot, characterized in that, The mobile robot's chassis is equipped with a left wheel set and a right wheel set. The number of drive wheels in the left and right wheel sets is equal and greater than one in each set. The mobile robot steers by the speed difference between the left and right wheel sets. The control method includes: Obtain observation data of the mobile robot within the target sliding window. The observation data includes at least sensor data provided by the target sensor of the mobile robot and inertial data provided by the inertial measurement unit of the mobile robot. The target sensor is a visual inertial odometer or a laser inertial odometer. A factor graph model is constructed based on the observation data. The factors in the factor graph model include at least the instantaneous rotation center constraint factor, the equivalent wheel track constraint factor, the inertial measurement constraint factor, and the reprojection constraint factor. The instantaneous rotation center constraint factor is a factor based on non-integrity constraints. Based on the factor graph model, the state vector within the target sliding window is optimized to determine the real-time parameters corresponding to the chassis in the state vector. The real-time parameters include at least the instantaneous rotation center and the equivalent wheel track coefficient. The equivalent wheel track coefficient characterizes the correspondence between the virtual wheel track and the physical wheel track. In response to receiving a speed command including a target speed, the left wheel assembly and the right wheel assembly are controlled according to the target speed and the real-time parameters.

2. The control method for a mobile robot as described in claim 1, characterized in that, The observation data also includes the left measured rotational speed of the left wheel group provided by the left wheel encoder of the mobile robot, and the right measured rotational speed of the right wheel group provided by the right wheel encoder of the mobile robot. The control of the left and right wheel groups based on the target speed and the real-time parameters includes: Based on the sensor data and the equivalent wheelbase coefficient, determine the left physical linear velocity of the grounding point of the left wheel assembly and the right physical linear velocity of the grounding point of the right wheel assembly; The left longitudinal slip coefficient is determined based on the left physical linear velocity and the left measured rotational speed, and the right longitudinal slip coefficient is determined based on the right physical linear velocity and the right measured rotational speed. Based on the target speed, the left longitudinal slip coefficient, and the right longitudinal slip coefficient, the left target speed and the right target speed are determined. The left wheel assembly is controlled based on the left target speed, and the right wheel assembly is controlled based on the right target speed.

3. The control method for a mobile robot as described in claim 2, characterized in that, The sensor data includes a first longitudinal linear velocity and a first angular velocity. Determining the left physical linear velocity of the contact point of the left wheel assembly and the right physical linear velocity of the contact point of the right wheel assembly based on the sensor data and the equivalent wheelbase coefficient includes: The first adjustment value is determined based on the first angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase. The left physical linear velocity is determined by subtracting the first adjustment value from the first longitudinal linear velocity; The right physical linear velocity is determined by adding the first longitudinal linear velocity to the first adjustment value.

4. The control method for a mobile robot as described in claim 2, characterized in that, The step of determining the left longitudinal slip coefficient based on the left physical linear velocity and the left measured rotational speed, and determining the right longitudinal slip coefficient based on the right physical linear velocity and the right measured rotational speed, includes: The left longitudinal slip coefficient is determined based on the wheel diameters of the left and right wheel sets, the left measured rotational speed, and the left physical linear velocity. The right longitudinal slip coefficient is determined based on the wheel diameter, the right measured rotational speed, and the right physical linear velocity.

5. The control method for a mobile robot as described in claim 2, characterized in that, The step of determining the left target rotational speed and the right target rotational speed based on the target speed, the left longitudinal slip coefficient, and the right longitudinal slip coefficient includes: Based on the target speed, the equivalent wheelbase coefficient, and the physical wheelbase, determine the geometric speed of the left target and the geometric speed of the right target under the instantaneous rotation center; The rotational speed of the left target is determined based on the wheel diameters of the left and right wheel sets, the geometric velocity of the left target, and the left longitudinal slip coefficient. The rotational speed of the right target is determined based on the wheel diameter, the geometric velocity of the right target, and the right longitudinal slip coefficient.

6. The control method for a mobile robot as described in claim 5, characterized in that, The target velocity includes the target linear velocity and the target angular velocity. Determining the left and right target geometric velocities at the instantaneous rotation center based on the target velocity, the equivalent wheelbase coefficient, and the physical wheelbase includes: The second adjustment value is determined based on the target angular velocity, the equivalent wheelbase coefficient, and the physical wheelbase. The geometric velocity of the left target is determined by subtracting the second adjustment value from the target linear velocity; The geometric velocity of the right target is determined by adding the second adjustment value to the target linear velocity.

7. The control method for a mobile robot as described in claim 1, characterized in that, The sensor data includes a first lateral linear velocity and a first angular velocity, and the first residual corresponding to the instantaneous rotation center constraint factor is determined by the first lateral linear velocity, the first angular velocity, and the predicted value of the instantaneous rotation center.

8. The control method for a mobile robot as described in claim 7, characterized in that, The second residual corresponding to the equivalent wheelbase constraint factor is determined by the first angular velocity, the left measured rotational speed of the left wheel set, the right measured rotational speed of the right wheel set, the wheel diameters of the left and right wheel sets, the predicted value of the equivalent wheelbase coefficient, and the physical wheelbase. The left measured rotational speed is provided by the left wheel encoder of the mobile robot, and the right measured rotational speed is provided by the right wheel encoder of the mobile robot.

9. The control method for a mobile robot as described in claim 8, characterized in that, The global sparse Jacobian matrix corresponding to the factor graph model includes a first matrix block and a second matrix block corresponding to the instantaneous rotation center constraint factor, and a third matrix block corresponding to the equivalent wheelbase constraint factor. The first matrix block is determined by the partial derivative of the first residual and the partial derivative of the predicted value of the instantaneous rotation center. The second matrix block is determined by the partial derivative of the first residual and the partial derivative of the zero bias of the inertial measurement unit. The third matrix block is determined by the partial derivative of the second residual and the partial derivative of the predicted value of the equivalent wheelbase coefficient.

10. A mobile robot, characterized in that, The mobile robot includes a chassis, a target sensor, an inertial measurement unit, and a controller. The chassis is provided with a left wheel set and a right wheel set. The number of drive wheels in the left wheel set and the right wheel set are equal and greater than 1. The mobile robot achieves steering by the speed difference between the left wheel set and the right wheel set. The controller is configured to execute the control method of the mobile robot as described in any one of claims 1-9.

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