Method for determining wheel slip of at least one wheel of a robot assembly by means of a position determining apparatus, computer program product, computer-readable storage medium, and position determining apparatus

Abstract

The invention relates to a method for determining wheel slip in a robot assembly (10), comprising the steps of: detecting a first wheel angle of a first wheel (22) of a first robot part (12) of the robot assembly (10) by means of a first wheel angle sensor (26); detecting a second wheel angle of a second wheel (24) of the first robot part (12) by means of a second wheel angle sensor (28); detecting a third wheel angle of a third wheel (30) of a second robot part (16) of the robot assembly (10) by means of a third wheel angle sensor (32), wherein the first robot part (12) and the second robot part (16) are interconnected via a joint (14); detecting a fourth wheel angle of a fourth wheel (34) of the second robot part (16) by means of a fourth wheel angle sensor (36); transmitting the detected wheel angles to an electronic computing device (38) of the position determining apparatus (42); and determining wheel slip at one of the four wheels (22, 24, 30, 34) by verifying the detected wheel angles. The invention also relates to a computer program product, to a computer-readable storage medium and to a position determining apparatus (42).

Description

[0001] Description

[0002] Method for determining a wheel slip of at least one wheel of a robot arrangement by means of a position-determining device, computer program product, computer-readable storage medium and position-determining device

[0003] The following invention relates to a method for determining wheel slip of at least one wheel of a robot assembly using a position-determining device. Furthermore, the invention relates to a corresponding computer program product, a corresponding computer-readable storage medium, and a corresponding position-determining device.

[0004] The basis for the spatial navigation of mobile robots, for example, is the robot's current position. This is typically determined from a combination of the distance traveled based on wheel revolutions and an absolute reference in the world coordinate system, for example, using magnetic, inductive, or optical markers.

[0005] Determining the position from wheel revolutions with a known vehicle geometry is called odometry. The measurement rate of odometry is quite high compared to determining the absolute reference, but it has the problem that errors accumulate over the course of operation because it is an incremental measurement. Possible causes of the error include, for example, inaccurate wheel diameters and geometry of the vehicle, the known friction between the tire and the ground, and wheel slip.

[0006] The better and more reliably the odometry functions, the better / longer an autonomous vehicle can operate even without an absolute reference.

[0007] In particular, autonomous mobile robots, so-called AGVs, require extensive odometry calibration during commissioning. Any wear on the wheels can only be compensated for by regular recalibration.

[0008] The object of the present invention is to provide a method, a computer program product, a computer-readable storage medium, and a position-determining device by means of which reliable position determination for a robot assembly can be realized. This object is achieved by a method, a computer program product, a computer-readable storage medium, and a position-determining device according to the independent patent claims. Advantageous embodiments are specified in the subclaims.

[0009] One aspect of the invention relates to a method for determining wheel slip of at least one wheel of a robot assembly using a position-determining device. A first wheel angle of a first wheel of a first robot part of the robot assembly is detected using a first wheel angle sensor of the position-determining device. A second wheel angle of a second wheel of the first robot part is detected using a second wheel angle sensor of the position-determining device. A third wheel angle of a third wheel of a second robot part of the robot assembly is detected using a third wheel angle sensor of the position-determining device, wherein the first robot part and the second robot part are connected to one another via a joint. A fourth wheel angle of a fourth wheel of the second robot part of the robot assembly is detected using a fourth wheel angle sensor of the position-determining device.The detected wheel angles are transmitted to an electronic computing device of the position determining device and a wheel slip on one of the four wheels is determined by mutual verification of the detected wheel angles relative to one another by means of the electronic computing device.

[0010] Thus, based on the various wheel angle measurements, the wheel slip can be reliably determined, and in particular, the results of the individual wheel angle sensors can be verified. If, for example, there is a discrepancy between the expected wheel angle and the actually determined wheel angle, a corresponding wheel slip can be determined. Based on the wheel slip, a corresponding calibration or change signal can be generated to take the wheel slip into account during the evaluation. This allows the position of the robot assembly to be reliably determined.

[0011] In the robot arrangement presented, the first robot part and the second robot part are connected via a corresponding joint, for example, as in a wheel loader. In particular, a type of articulated steering can thus be formed between the first robot part and the second robot part. In principle, however, the method is also applicable to similar geometries, such as an Ackermann steering system or a turntable steering system.

[0012] For such geometries, redundant information about the vehicle state is available if the articulation angle gamma and wheel rotation speeds are known. For each of the two vehicle parts, i.e., for each robot part, the independent position x, y, and theta, which corresponds in particular to the steering angle (i.e., a relative angle of the robot part to its surroundings), can be calculated by integrating the wheel speeds without considering wheel slip. Since the two robot parts are mechanically coupled via the joint with a degree of freedom gamma, in particular with at least one rotational degree of freedom, the position of one vehicle part and the articulation angle are sufficient. The other information is redundant. Alternatively, the articulation angle could also be determined without a sensor for the articulation angle using the odometry of the two robot parts.

[0013] In all typical applications, however, wheel slip occurs, which varies depending on the friction coefficient pairing / current torque and rotational speed of the wheels. The idea is to utilize this redundant information to at least partially determine the slip values ​​that are unpredictable when considering the individual wheels alone.

[0014] Thus, the redundant information regarding the different robot parts is used to verify the wheel rotation speed and thus also to determine the wheel slip.

[0015] According to an advantageous embodiment, a bending angle between the first robot part and the second robot part is additionally determined based on the detected wheel angles. In particular, the bending angle can occur at the joint. Based on the different wheel angles, the bending angle itself can then also be determined. Thus, the position of the robot assembly can be reliably determined.

[0016] It is further advantageous if a bending angle between the first robot part and the second robot part is additionally determined using a bending angle sensor of the position-determining device. In particular, when the bending angle sensor is present, the bending angle can thus be reliably determined, and the wheel slip can thus be reliably determined using the bending angle. This enables reliable position determination of the robot arrangement. In a further advantageous embodiment, it is provided that a position of the robot arrangement is determined based on the detected wheel angles and / or on the basis of the determined wheel slip. In particular, a position relative in a space can be determined. In particular, the position of the robot arrangement can thus be determined based on odometry.This eliminates the need for complicated positioning devices, as the position of the robot arrangement can be determined incrementally based solely on the odometry data.

[0017] It is also advantageous if the respective rotational speed of a respective wheel is determined based on a particular wheel angle. For example, the corresponding rotational speed of the wheel can then be determined based on the wheel angle, whereby a linear movement of the robot or robot arrangement can be determined based on the rotational speed, which in turn allows a position in space to be determined.

[0018] It has also proven advantageous to consider the respective radius r of each wheel to determine the rotational speed. In particular, the radius of the wheel is also taken into account, since the linear speed V of the robot assembly is also dependent on the radius itself. This can be achieved, in particular, using the formula:

[0019] V RL = r*w RL * (1+slippage RL ), where V is the linear velocity, r is the radius, w corresponds to the wheel angle and slip corresponds to the coefficient of friction and this is determined here, for example, for the rear (R rear) left (L left) wheel.

[0020] It is also advantageous to use a Kalman filter or a Lüneberg observer to determine wheel slip. This allows, in particular, an observer model with a state estimate to be used. These are simple observers within the control system, which can thus easily calculate the slip.

[0021] It is also advantageous to use a machine learning model to determine wheel slip. Due to the strong nonlinearity, it is an alternative to determine the corresponding wheel slip using a machine learning algorithm, particularly a deep learning method, such as a neural network. This allows the nonlinearity to be advantageously taken into account.

[0022] It has also proven advantageous to use simulation data and / or measurement data to train the machine learning model. This allows both measurement data actively generated in the field to be used. If, for example, there is an insufficient amount of measurement data available, simulation data can also be used. This allows for reliable training of the machine learning model.

[0023] It is also advantageous if the robot assembly is provided as an autonomously operated mobile robot. In particular, the robot assembly is thus provided as an AGV (Automated Guided Vehicle). In other words, the robot assembly can move independently within a given environment based on a position determination and travel from a first location to a second location. Based on the improved position determination, improved operation of the AGV can now also be realized.

[0024] It has also proven advantageous to additionally determine the position of the robot assembly based on optical detection of a marker in the vicinity of the robot assembly. This allows the position determination to be verified accordingly, and the position determination via odometry can be improved based on the optical position determination. Alternatively or additionally, a corresponding improvement in the position determination can also be achieved based on satellite-based navigation, radar-based navigation, or even LIDAR-based positioning.

[0025] It is also advantageous if a wheel slip control system is adjusted based on the determined wheel slip. If, for example, a corresponding wheel slip is detected, a wheel slip control system can be initiated, preventing wheel slip in the future. In particular, this allows appropriate control signals for the wheel slip control system to be generated, thereby enabling improved operation of the robot assembly.

[0026] The method presented is, in particular, a computer-implemented method. Therefore, a further aspect of the invention relates to a computer program product with program code means that, when the program code means are processed by the electronic computing device, cause an electronic computing device to perform a method according to the preceding aspect.

[0027] Furthermore, the invention therefore also relates to a computer program product with program code means which cause an electronic computing device to carry out a method according to the preceding aspect when the program code means are processed by the electronic computing device.

[0028] Furthermore, the invention also relates to a position-determining device for determining wheel slip of at least one wheel of a robot assembly, comprising at least a first wheel angle sensor, a second wheel angle sensor, a third wheel angle sensor, a fourth wheel angle sensor, and an electronic computing device. The position-determining device is configured to perform a method according to the preceding aspect. In particular, the method is performed by means of the position-determining device.

[0029] Furthermore, the invention also relates to a robot arrangement with at least a first robot part, a second robot part and a position determining device according to the preceding aspect.

[0030] Advantageous embodiments of the method are to be regarded as advantageous embodiments of the computer program product, the computer-readable storage medium, the position-determining device, and the robot arrangement. The position-determining device and the robot arrangement, in particular, have specific features for carrying out corresponding method steps.

[0031] Here and in the following, an artificial neural network can be understood as software code that is stored on a computer-readable storage medium and represents one or more networked artificial neurons or can emulate their function. The software code can also contain multiple software code components that can, for example, have different functions. In particular, an artificial neural network can implement a nonlinear model or a nonlinear algorithm that maps an input to an output, where the input is given by an input feature vector or an input sequence, and the output can, for example, contain an output category for a classification task, one or more predicted values, or a predicted sequence.

[0032] A computing unit / electronic computing device can be understood, in particular, as a data processing device that contains a processing circuit. The computing unit can therefore, in particular, process data to perform computing operations. This may also include operations for performing indexed access to a data structure, for example, a look-up table (LUT).

[0033] The computing unit may, in particular, contain one or more computers, one or more microcontrollers, and / or one or more integrated circuits, for example one or more application-specific integrated circuits (ASICs), one or more field-programmable gate arrays (FPGAs), and / or one or more single-chip systems (SoCs). The computing unit may also contain one or more processors, for example one or more microprocessors, one or more central processing units (CPUs), one or more graphics processing units (GPUs), and / or one or more signal processors, in particular one or more digital signal processors (DSPs). The computing unit may also include a physical or virtual network of computers or other of the aforementioned units.

[0034] In various embodiments, the computing unit includes one or more hardware and / or software interfaces and / or one or more memory units.

[0035] A memory unit can be a volatile data memory, for example a dynamic random access memory (DRAM) or a static random access memory (SRAM), or a non-volatile data memory, for example a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory or flash EEPROM, a ferroelectric random access memory (FRAM), a magnetoresistive random access memory,MRAM (magnetoresistive random access memory) or phase-change random access memory (PCRAM).

[0036] For applications or application situations that may arise in a method according to the invention and which are not explicitly described herein, it may be provided that, according to the method, an error message and / or a request to enter user feedback is output and / or a standard setting and / or a predetermined initial state is set.

[0037] Regardless of the grammatical gender of a particular term, persons with male, female or other gender identities are included.

[0038] Further features and combinations of features of the invention will become apparent from the figures and their description, as well as from the claims. In particular, further embodiments of the invention do not necessarily have to contain all features of one of the claims. Further embodiments of the invention may have features or combinations of features not mentioned in the claims.

[0039] Showing:

[0040] FIG 1 shows a schematic plan view of an embodiment of a robot arrangement with an embodiment of a position determining device; and

[0041] FIG 2 shows a schematic block diagram according to an embodiment of a position determining device.

[0042] The invention is explained in more detail below using specific embodiments and associated schematic drawings. In the figures, identical or functionally equivalent elements may be provided with the same reference numerals. The description of identical or functionally equivalent elements may not necessarily be repeated for different figures.

[0043] FIG. 1 shows a schematic plan view of an embodiment of a robot assembly 10. In the following exemplary embodiment, the robot assembly 10 comprises a first robot part 12 and a second robot part 16 coupled to the first robot part 12 via a joint 14. For this purpose, it can be provided, in particular, that a bending angle sensor 18 for determining a bending angle 20 is provided between the first robot part 12 and the second robot part 16.

[0044] Furthermore, it is particularly provided that the first robot part 12 has at least a first wheel 22 and a second wheel 24. The wheels 22, 24 can, in particular, be operated separately from one another. A first wheel angle sensor 26 is formed on the first wheel 22. A second wheel angle sensor 28 is formed on the second wheel 24.

[0045] The second robot part 16 has a third wheel 30 with an associated third wheel angle sensor 32. Furthermore, the second robot part 16 has a fourth wheel 34 with an associated fourth wheel angle sensor 36. The wheels 30, 34 can, in particular, also be operated separately from one another.

[0046] In other words, the first robot part 12 and the second robot part 16 have separate drive options.

[0047] Furthermore, it is shown in particular that a position-determining device 42 can have at least one electronic computing device 38. In the present exemplary embodiment, this is formed in the first robot part 12. However, this is purely exemplary and can also be formed, for example, in the second robot part 16.

[0048] FIG 1 thus shows, in particular, that a method for determining wheel slip of at least one wheel 22, 24, 30, 34 can be presented here. This method involves, in particular, wheel slip determination using artificial intelligence (K1). In particular, a first wheel angle of the first wheel 22 of the first robot part 12 is detected using the first wheel angle sensor 26, and a second wheel angle of the second wheel 24 is detected using the second wheel angle sensor 28. Furthermore, the third wheel angle of the third wheel 30 is detected using the third wheel angle sensor 32, and a fourth wheel angle of the fourth wheel 34 is detected using the fourth wheel angle sensor 36.The detected wheel angles are then transmitted to the electronic computing device 38 of the position-determining device 42, and wheel slip on one of the four wheels 22, 24, 30, 34 is determined by mutually verifying the detected wheel angles relative to one another using the electronic computing device 38. Furthermore, FIG. 1 shows that, in particular, the articulation angle 20 between the first robot part 12 and the second robot part 16 can additionally be determined based on the detected wheel angles. Alternatively or additionally, the articulation angle sensor 18 can be provided to determine the corresponding articulation angle 20.

[0049] Furthermore, FIG 1 shows in particular that a position P1, P2 can be determined on the basis of the detected wheel angle and / or on the basis of the determined wheel slip. The present first position P1 is assigned to the first robot part 12, wherein this can be described in particular in x and y coordinates and in a corresponding steering angle Theta2. A second position P2 is assigned to the second robot part 16, which likewise has corresponding x and y coordinates and a corresponding steering angle Theta2. For example, in the present exemplary embodiment, the first steering angle Thetal can be different from the second steering angle Theta2 and thus a corresponding articulation angle 20 can be determined. For example, the articulation angle 20 can then also be considered via the difference between the first steering angle Thetal and the second steering angle Theta2.

[0050] Furthermore, FIG. 1 shows in particular that the robot assembly is provided as an autonomously operated mobile robot. It can also be provided that a position of the robot assembly 10 can be determined based on an optical detection of a marker in an environment 40 of the robot assembly 10. Furthermore, a wheel slip control system can also be adjusted based on the determined wheel slip.

[0051] In particular, a neural network can then be trained based on the determined wheel slip, which can carry out a reliable position determination.

[0052] In particular, the invention provides a solution for the presented vehicle geometry with articulated steering. In principle, however, the method can also be applied in the same way to other geometries, such as Ackermann steering or turntable steering.

[0053] For these geometries, the known steering angles Theta and the wheel rotational speed are available as redundant information about the vehicle state. For each of the two vehicle parts, the position x, y, Theta can be calculated independently by integrating the wheel speeds, particularly without considering wheel slip. Since the two parts are mechanically coupled via joint 14 and thus the articulation angle 20 in the degree of freedom gamma, the position of one vehicle part and the corresponding steering angle and articulation angle 20 are sufficient. The other information is redundant. Alternatively, the articulation angle 20 could also be calculated using the odometry of the two robot parts 12, 16 without an articulation angle sensor 18 for the articulation angle 20.

[0054] In all typical applications, however, slip occurs at wheels 22, 24, 30, and 34, which varies depending on the friction coefficient pairing / current torque and rotational speed at wheels 22, 24, 30, and 34. The idea is to exploit the redundant information to at least partially determine the slip values ​​that are unpredictable when considering the individual wheels 22, 24, 30, and 34 alone.

[0055] For this purpose, an observer model with a state estimation can be used, for example a Lüneberg observer or a Kalman filter.

[0056] FIG 2 shows a schematic block diagram according to an embodiment of the electronic computing device 38. In this case, a torque 44 of a respective wheel 22, 24, 30, 34 can serve as an input signal, for example. The robot parts 12, 16 then actually measure the wheel angle and, from this, determine the articulation angle 20. The torque 44 is also input into a mathematical model 46 for the robot arrangement 10. The output signal of the mathematical model 46 can in turn be an estimated wheel angle and an estimated articulation angle. In a corresponding plus / minus module, the information from the mathematical model 46 and the robot parts 12, 16 is compared with one another. This information is then additionally fed back to the mathematical model 46. The corresponding position P1, P2 and their steering angles Thetal and Theta2 can then be output as a further output signal.The mathematical model 46 can in particular be provided as a machine learning model.

[0057] Particularly due to the strong nonlinearity, it is also advisable to determine the mathematical model 46 using a machine algorithm, especially using deep learning methods. The training data can be both simulation data and measurement data.

[0058] The problem is the nonlinear behavior due to wheel slip. The formula for the rear left wheel, for example, especially the third wheel 30 in this embodiment, illustrates the problem: V RL = r*w RL * (1+slippage RL )

[0059] The relationship between wheel speed and linear motion is determined by the wheel radius r and the slip. The result is then incorporated into the wheel odometry calculation and can also be used for wheel slip control, which can prevent wheels 22, 24, 30, and 34 from spinning.

[0060] List of reference symbols

[0061] 10 Robot arrangement

[0062] 12 first robot part

[0063] 14 joint

[0064] 16 second robot part

[0065] 18 Articulation angle sensor

[0066] 20 bend angles

[0067] 22 first wheel

[0068] 24 second wheel

[0069] 26 first wheel angle sensor

[0070] 28 second wheel angle sensor

[0071] 30 third wheel

[0072] 32 third wheel angle sensor

[0073] 34 fourth wheel

[0074] 36 fourth wheel angle sensor

[0075] 38 electronic computing device

[0076] 40 Surroundings

[0077] 42 Positioning device

[0078] 44 torque

[0079] 46 mathematical model

Claims

Patent claims 1. A method for determining a wheel slip of at least one wheel (22, 24, 30, 34) of a robot arrangement (10) by means of a position determining device (42), comprising the steps: - detecting a first wheel angle of a first wheel (22) of a first robot part (12) of the robot arrangement (10) by means of a first wheel angle sensor (26) of the position determining device (42); - detecting a second wheel angle of a second wheel (24) of the first robot part (12) by means of a second wheel angle sensor (28) of the position determining device (42); - detecting a third wheel angle of a third wheel (30) of a second robot part (16) of the robot arrangement (10) by means of a third wheel angle sensor (32) of the position determining device (42), wherein the first robot part (12) and the second robot part (16) are connected to one another via a joint (14); - detecting a fourth wheel angle of a fourth wheel (34) of the second robot part (16) by means of a fourth wheel angle sensor (36) of the position determining device (42); - transmitting the detected wheel angles to an electronic computing device (38) of the position determining device (42); and - Determining a wheel slip on one of the four wheels (22, 24, 30, 34) by mutually verifying the detected wheel angles relative to each other by means of the electronic Computing device (38).

2. Method according to claim 1, characterized in that in addition a bending angle (20) between the first robot part (12) and the second robot part (16) is determined on the basis of the detected wheel angles.

3. Method according to claim 1 or 2, characterized in that in addition a bending angle (20) between the first robot part (12) and the second robot part (16) is determined by means of a bending angle sensor (18) of the position determining device (42).

4. Method according to one of the preceding claims, characterized in that a position of the robot arrangement (10) is determined on the basis of the detected wheel angles and / or on the basis of the determined wheel slip.

5. Method according to claim 4, characterized in that a respective rotational speed of a respective wheel (22, 24, 30, 34) is determined on the basis of a respective wheel angle.

6. Method according to claim 5, characterized in that a respective radius of a respective wheel (22, 24, 30, 34) is taken into account for determining the rotational speed.

7. Method according to one of the preceding claims, characterized in that a Kalman filter or a Lünberger observer is used to determine the wheel slip.

8. Method according to one of claims 1 to 6, characterized in that a machine learning model is used to determine the wheel slip.

9. The method according to claim 8, characterized in that simulation data and / or measurement data are used to train the machine learning model.

10. Method according to one of the preceding claims, characterized in that the robot arrangement (10) is provided as an autonomously operated mobile robot.

11. Method according to one of the preceding claims, characterized in that a position of the robot arrangement (10) is additionally determined on the basis of an optical detection of a marker in an environment (40) of the robot arrangement (10).

12. Method according to one of the preceding claims, characterized in that a wheel slip control is adapted on the basis of the determined wheel slip.

13. A computer program product comprising program code means which cause an electronic computing device (38) to carry out a method according to one of claims 1 to 12 when the program code means are processed by the electronic computing device (38).

14. A computer-readable storage medium comprising at least one computer program product according to claim 13.

15. Position determining device (42) for determining a wheel slip of at least one wheel (22, 24, 30, 34) of a robot arrangement (10), with at least a first wheel angle sensor (26), a second wheel angle sensor (28), a third wheel angle sensor (32), a fourth wheel angle sensor (26) and an electronic computing device (38), wherein the position determining device (42) is designed to carry out a method according to one of claims 1 to 12.

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