Method for dynamic detection of ratcheting effects and dynamic compensation of trajectories in collaborative robots by means of artificial intelligence

Abstract

The invention relates to a method for detecting a ratcheting effect associated with at least one joint of a robot, in particular a collaborative robot, during operation of the robot by means of artificial intelligence on the basis of operating data of the robot, in particular of the collaborative robot, the method comprises the steps of: providing operational data, in particular current flow data, relating to the temporal behavior of a motor, in particular a servomotor, of the robot; detecting a ratchet effect of a joint of the robot, in particular in the case of a strain wave gear mechanism, a harmonic drive and / or an elliptical sun gear mechanism, by evaluating operational data as indirect input data or direct input data of the artificial intelligence system, thereby producing output data; wherein the output data of the artificial intelligence system comprises an indicator indicating whether a ratcheting effect associated with the joint, in particular the gear mechanism, has occurred.

Description

Technical Field

[0001] The present invention generally relates to the technical field of robots, autonomous mobile robots (AMRs) and in particular collaborative robots (cobots) as well as safety and precision engineering in workplaces and assembly systems. Background Art

[0002] In recent years, collaborative robot systems have been used in more and more facilities. This has led to human workers increasingly sharing the same space with collaborative robot systems. Such robots are designed in particular with collaborative properties. For example, if an object or a worker is on the route, the movement should first be stopped to prevent damage and injuries. Strain wave gear mechanisms (harmonic drives / harmonic gearboxes) are often used as gear mechanisms. In this case, in particular during the movement of collision stops, a process that is generally referred to as ratcheting can occur. The internal teeth of the gear mechanism can slide or shift relative to each other by, for example, one tooth, but also by a plurality of teeth sliding or shifting relative to each other.

[0003] It is always difficult to determine this ratcheting effect correctly, both quantitatively and qualitatively. For example, if the ratcheting effect is not taken into account, or if the ratcheting effect that has occurred remains unobserved, etc., the robot - and therefore the end effector - may stop in the wrong end position, etc.

[0004] It is therefore an object of the present invention to further develop a collaborative robotic system in such a way that it can perform its tasks very reliably and robustly, in particular in a working environment shared with humans.

[0005] US7439693B2 discloses a method and a motor control device for detecting abnormality. The method for detecting abnormality in a linear motion device has the following steps, wherein a motor control device of a motor drives a movable body of the linear motion device: detecting the position or speed of the movable body; performing feedback control to supply current or voltage to the motor based on the detection result; and detecting abnormality of the linear motion device based on an abnormal signal component included in a waveform of the supply current or supply voltage.

[0006] Here, the control unit of the motor driver performs feedback control based on the detection value of the linear encoder to supply power to the linear motor. The abnormality detection unit monitors the supply current of the linear motor, and the abnormality detection unit detects the abnormality of the linear motion device based on the waveform of the supply current. When the abnormality is detected, the user is alerted by means of a lamp, a buzzer, an email, or the like. Therefore, the abnormality of the linear motion device can be detected early and accurately. Summary of the invention

[0007] The invention provides a method according to claim 1. Therefore, a method for detecting ratcheting effects associated with at least one joint of a robot, in particular a collaborative robot (cobot), during operation of the robot by means of artificial intelligence based on operating data of the robot, the method comprising the following steps: providing operating data, in particular current flow data, related to the temporal behavior of a motor, in particular a servomotor, of the robot; detecting ratcheting effects of the joints of the robot, in particular ratcheting effects in the case of strain wave gear mechanisms, harmonic drives and / or elliptical center gear mechanisms, by evaluating the operating data as indirect input data or direct input data of an artificial intelligence system, thereby generating output data, wherein the output data of the artificial intelligence system comprises an indicator indicating whether ratcheting effects associated with a joint, in particular a gear mechanism, have occurred.

[0008] The AI ​​system determines with exceptional precision whether a ratcheting effect has occurred, with greater accuracy than using conventional methods.

[0009] For example, the temporal behavior of the flow of electric current is described by data that can be used as input data for artificial intelligence systems.

[0010] This enables the AI ​​system to classify whether ratcheting occurred during the time period in question. Ratcheting can also be quantified, for example. In a strain wave gear mechanism, for example, it can be determined how many teeth were skipped during ratcheting.

[0011] The AI ​​system also performs the task with greater accuracy than would be possible using other or known methods.

[0012] The invention also provides a collaborative robot according to claim 5. Therefore, a collaborative robot is provided, comprising: a robotic device, in particular a robotic arm and / or an assembly system, wherein the robotic device has one or more joints that can be moved by motors, in particular servomotors; one or more motor control units for controlling the motors, wherein the collaborative robot is further configured to use an artificial intelligence system to detect ratcheting of the joints, in particular ratcheting in strain wave gear mechanisms, harmonic drives and / or elliptical center gear mechanisms, by evaluating operational data as indirect input data or direct input data to the artificial intelligence system, wherein the operational data comprises data related to the temporal behavior of the motors of the robot, in particular current flow data.

[0013] In addition, the present invention provides an artificial intelligence system and a suitable training method. Therefore, according to the present invention, an artificial intelligence system, in particular linear regression and / or nonlinear regression, fast Fourier transform and / or support vector machine and / or artificial neural network, in particular recursive neural network, is provided, which artificial intelligence system is configured to determine whether there is a ratcheting effect of a gear mechanism, in particular a gear mechanism in a collaborative robot, and / or to determine the number of teeth skipped in the gear mechanism during the ratcheting effect, the artificial intelligence system comprising: one or more input neurons for receiving input data, the input data comprising data based on: operating data related to the time behavior of a motor, in particular a servomotor, in particular current flow data.

[0014] According to a further development, the artificial intelligence system is designed to provide output data comprising an indicator indicating whether a ratcheting effect has occurred in connection with the joint, in particular the gear mechanism.

[0015] Thus, a properly trained AI system can immediately and reliably generate such a label describing whether a ratcheting effect exists. For example, such a label can be represented by "0" and "1", where "1" describes that a ratcheting effect has occurred. Appropriate technical corrective measures can be reliably enabled.

[0016] According to a further development, if ratcheting has occurred, the output data also indicates the extent of ratcheting that has occurred, in particular the number of teeth in the gear mechanism skipped during ratcheting and / or the angle skipped due to ratcheting.

[0017] Thus, a properly trained AI system can immediately and reliably generate such a label that quantifies the ratcheting effect. For example, such a label can be represented as "0", "1", "2", "3", etc., where the number represents how many teeth were skipped. Such a labeling by the AI ​​system is a more accurate determination than labeling based on conventional methods. Appropriate technical corrective measures are reliably enabled.

[0018] According to a further development, the collaborative robot is further configured to calculate a corrected trajectory and / or a corrected control command, in particular based on the number of teeth skipped during the ratcheting effect and / or based on the angle skipped due to the ratcheting effect.

[0019] For example, the value of the current angle is corrected in the control unit so that after the ratcheting effect has occurred, it again corresponds to the angle actually present on the robot.

[0020] Therefore, the determinations of the AI ​​system can be used to improve the dynamic behavior of the robotic device.

[0021] According to a further development, the collaborative robot comprises a central control unit and at least one motor control unit, wherein the central control unit and the motor control units can communicate with each other via a bus, in particular an Ethernet bus, wherein, in particular, a determination as to whether a ratcheting effect has occurred in connection with a joint is performed in the associated motor control unit, wherein, in particular, a determination of the number of teeth skipped in the gear mechanism during the ratcheting effect is performed in the associated motor control unit, wherein, in particular, a calculation of a corrected trajectory and / or a corrected control command is performed in the central control unit. This division of labor and communication between the central control unit and the motor control units is particularly resource-efficient, thereby enabling efficient ratcheting effect detection and dynamic correction in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The preferred embodiments of the present disclosure are described below with reference to the following drawings:

[0023] Figure 1 : shows a flow chart (in the form of a repetitive loop) of a central control unit for detecting ratcheting effects (including compensation) according to an embodiment of the present invention.

[0024] Figure 2 : A flow chart (in the form of a repeating loop) is shown for detecting ratcheting (including quantitative determination of skipped teeth) by a motor control unit according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Figure 1 A flow chart (in the form of a repeating loop) is shown for detecting ratcheting (including compensation) by a central control unit according to an embodiment of the present invention.

[0026] In step 101 , it is checked whether a ratcheting error has been received from the joint (or its motor control unit) (ratcheting occurs).

[0027] If this is the case, the value of the currently occupied angle is corrected in step 102. It is corrected by the angle caused by the ratcheting effect. For example, it depends on the number of skipped teeth and / or one or more total numbers of teeth present in the corresponding gear mechanism.

[0028] For example, the central control unit assumes a special mode which is only assumed when a ratcheting effect is observed.

[0029] In step 103, the corresponding variable of the current end effector position of the collaborative robot is updated. In the example, this is a calculation based on the result from step 102. In step 104, the trajectory to be traversed by the joint in the future is recalculated or updated. For example, this is calculated in the following way: the robot can achieve the configuration that will occur if no ratcheting effect occurs. However, other characteristics are also conceivable. For example, the trajectory is calculated using the LSPB method (linear segments mixed with parabolas).

[0030] In step 105 the central control unit returns to normal mode. This is the mode normally used unless a ratcheting effect occurs. This is the mode the central control unit is in at the start 101 of the flow chart.

[0031] The flowchart terminates with end 110 .

[0032] Figure 2 A flow chart (in the form of a repeating loop) is shown for detecting ratcheting (including quantitative determination of skipped teeth) by a motor control unit according to an embodiment of the present invention.

[0033] In step 201, time behavior data, such as the motor current of the joint motor, are loaded into a calculation buffer. In step 202, the AI ​​system according to the invention is used to determine whether a ratchet effect has occurred at the joint (and, if necessary, to quantify it). If a ratchet effect is clearly detected at branch 203, the corresponding ratchet effect error code is output to the communication bus. This enables the central control unit to be informed of the error / ratchet effect. For example, the error code contains information about how many teeth have been skipped.

[0034] The flowchart terminates with end 210 .

[0035] The embodiments schematically described here can be further developed by numerous details of the invention, in particular by details already described above.

[0036] Although some aspects have been described in the context of an apparatus, it is apparent that these aspects also constitute a description of a corresponding method, wherein a block or apparatus corresponds to a method step or a function of a method step. Similarly, aspects described in the context of a method step also represent a description of a corresponding block or element or a property of a corresponding apparatus.

[0037] Exemplary embodiments of the present invention can be implemented in a computer system. The computer system can be a local computer device (e.g., a personal computer, a laptop computer, a tablet computer, or a cellular phone) with one or more processors and one or more storage devices, or can be a distributed computer system (e.g., a cloud computing system with one or more processors or one or more storage devices distributed in various locations, such as distributed in a local client and / or one or more remote server farms and / or data centers). The computer system may include any circuit or combination of circuits. In an exemplary embodiment, the computer system may include one or more processors, and the one or more processors may be of any type. As used herein, a processor may mean any type of computing circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computer (CISC), a reduced instruction set computer (RISC), a very long instruction word (VLIW) microprocessor, a graphics processor, a digital signal processor (DSP), a multi-core processor, a field programmable gate array (FPGA), or any other type of processor or processing circuit. Other types of circuitry that a computer system may include may be custom circuits, application specific integrated circuits (ASICs), and the like, such as one or more circuits (e.g., communications circuits) for use with wireless devices such as cellular phones, tablet computers, laptop computers, two-way radios, and similar electronic systems. A computer system may include one or more storage devices, which may include one or more storage elements suitable for a particular application, such as a main memory in the form of a random access memory (RAM), one or more hard disks, and / or one or more drives for handling removable media such as CDs, flash memory cards, DVDs, and the like. A computer system may also include a display device, one or more speakers, and a keyboard and / or controller, which may include a mouse, trackball, touch screen, voice recognition device, or any other device that allows a system user to input information into and receive information from a computer system.

[0038] Some or all of the method steps may be performed by (or using) hardware devices such as processors, microprocessors, programmable computers or electronic circuits. In some exemplary embodiments, one or more of the key method steps may be performed by such devices.

[0039] Depending on the specific implementation requirements, exemplary embodiments of the present invention may be implemented using hardware or software. The implementation may be implemented using a non-volatile storage medium, such as a digital storage medium, such as a floppy disk, DVD, Blu-ray disc, CD, ROM, PROM and EPROM, EEPROM or flash memory, on which electronically readable control signals are stored, which interact (or can interact) with a programmable computer system to perform the corresponding method. Therefore, the digital storage medium may be computer readable.

[0040] Some exemplary embodiments according to the invention comprise a data carrier having electronically readable control signals, which are capable of interacting with a programmable computer system, such that the system performs one of the methods described herein.

[0041] Generally, the exemplary embodiments of the present invention can be implemented as a computer program product with a program code, wherein when the computer program product is running on a computer, the program code effectively performs one of the methods described.For example, the program code can be stored on a machine-readable medium.

[0042] Further exemplary embodiments comprise the computer program for performing one of the methods described herein, stored on a machine-readable medium.

[0043] In other words, exemplary embodiments of the present invention are, therefore, a computer program having a program code for performing one of the methods described herein, when the computer program runs on a computer.

[0044] A further exemplary embodiment of the present invention is therefore a storage medium (or a data carrier or a computer-readable medium), which storage medium comprises: a computer program stored thereon for performing one of the methods described herein when the computer program is executed by a processor. The data carrier, the digital storage medium or the recorded medium is typically tangible and / or non-transitory. A further exemplary embodiment of the present invention is an apparatus as described herein, comprising a processor and a storage medium.

[0045] Therefore, a further exemplary embodiment of the present invention is a data stream or a sequence of signals representing the computer program for performing one of the methods described herein.For example, the data stream or the sequence of signals may be configured to be transmitted via a data communication connection, for example via the Internet.

[0046] Another exemplary embodiment comprises a processing means, for example a computer or a programmable logic device, configured to or adapted to perform any of the methods described herein.

[0047] A further exemplary embodiment comprises a computer on which the computer program for performing one of the methods described herein is installed.

[0048] Another exemplary embodiment according to the invention comprises an apparatus or system configured to send (e.g., electronically or optically) a computer program for performing one of the methods described herein to a receiver. The receiver may be, for example, a computer, a mobile device, a storage device, etc. The apparatus or system may, for example, comprise a file server for sending the computer program to the receiver.

[0049] In some exemplary embodiments, a programmable logic device (e.g., a field programmable gate array, FPGA) may be used to perform some or all of the functions of the methods described herein. In some exemplary embodiments, a field programmable gate array may collaborate with a microprocessor to perform any of the methods described herein. Typically, the method is preferably performed by each hardware device.

[0050] Exemplary embodiments may be based on the use of machine learning models and / or machine learning algorithms (i.e., artificial intelligence). Machine learning may refer to algorithms and statistical models that a computer system can use to perform specific tasks without explicit instructions, rather than relying on models and reasoning. For example, in machine learning, instead of rule-based data transformations, data transformations that can be derived from analysis of historical and / or training data can be used. For example, machine learning can be used or a machine learning algorithm can be used to analyze the content of an image. In order for a machine learning model to analyze the content of an image, a training image can be used as input and training content information can be used as output to train the machine learning model. By training a machine learning model with a large number of training images and / or training sequences (e.g., words or sentences) and associated training content information (e.g., labels or annotations), the machine learning model "learns" to recognize the content of the image, so that the machine learning model can be used to recognize the content of the image that is not included in the training data. The same principle can also be used for other types of sensor data: by training a machine learning model using training sensor data and a desired output, the machine learning model "learns" the transformation between the sensor data and the output, which can be used to provide an output based on non-training sensor data provided to the machine learning model. The provided data (e.g., sensor data, metadata, and / or image data) may be pre-processed to obtain a feature vector, which is used as input to a machine learning model.

[0051] The machine learning model can be trained using training input data. The above example uses a training method known as supervised learning. In supervised learning, a machine learning model is trained using multiple training sample values, wherein each sample value may include multiple input data values ​​and multiple expected output values, i.e., each training sample value is associated with an expected output value. By specifying both the training sample values ​​and the expected output values, the machine learning model "learns" which output value to provide based on input sample values ​​similar to the sample values ​​provided as part of the training. In addition to supervised learning, semi-supervised learning can also be used. In semi-supervised learning, some of the training sample values ​​lack the expected output value. Supervised learning can be based on supervised learning algorithms (e.g., classification algorithms, regression algorithms, or similarity learning algorithms). Classification algorithms can be used when the output is limited to a limited set of values ​​(classification variables), i.e., when the input is classified as one of a limited set of values. Regression algorithms can be used when the output shows any numerical value (within a certain range). Similarity learning algorithms can be similar to both classification algorithms and regression algorithms, but are based on learning from examples using similarity functions that measure the similarity or correlation between two objects. In addition to supervised learning or semi-supervised learning, unsupervised learning can also be used to train machine learning models. In unsupervised learning, (only) input data can be provided, and an unsupervised learning algorithm can be used to find structure in the input data (e.g., by grouping or clustering the input data to find commonalities in the data). Clustering is the distribution of input data including multiple input values ​​into subsets (clusters) so that input values ​​within the same cluster are similar according to one or more (predefined) similarity criteria, while these input values ​​are dissimilar to input values ​​included in other clusters.

[0052] Reinforcement learning is a third group of machine learning algorithms. In other words, reinforcement learning can be used to train machine learning models. In reinforcement learning, one or more software actors (i.e., "software agents") are trained to perform actions in an environment. Rewards are calculated based on the actions performed. Reinforcement learning is based on training one or more software agents to select actions such that the cumulative reward increases, thereby causing the software agent to become better at the task given to it (as evidenced by increasing rewards).

[0053] In addition, some techniques can be applied to some of the machine learning algorithms in the machine learning algorithm. For example, feature learning can be used. In other words, the machine learning model can be trained at least in part using feature learning, and / or the machine learning algorithm can include a feature learning component. Feature learning algorithms, known as representation learning algorithms, can retain the information in their input but transform the information in a way that makes it useful, usually as a preprocessing stage before performing classification or prediction. Feature learning can be based on principal component analysis or cluster analysis, for example.

[0054] In some examples, anomaly detection (i.e., outlier detection) can be used, which is intended to provide identification of input values ​​that are suspicious because they are significantly different from the majority of input data and training data. In other words, the machine learning model can be trained at least in part using anomaly detection, and / or the machine learning algorithm can include an anomaly detection component.

[0055] In some examples, a machine learning algorithm may use a decision tree as a prediction model. In other words, a machine learning model may be based on a decision tree. In a decision tree, observations about an object (e.g., a set of input values) may be represented by branches of the decision tree, and output values ​​corresponding to the object may be represented by leaves of the decision tree. A decision tree may support both discrete and continuous values ​​as output values. If discrete values ​​are used, the decision tree may be referred to as a classification tree; if continuous values ​​are used, the decision tree may be referred to as a regression tree.

[0056] Association rules are another technique that can be used in machine learning algorithms. In other words, a machine learning model can be based on one or more association rules. Association rules are created by identifying relationships between variables in a large data set. A machine learning algorithm can identify and / or use one or more ratio rules that represent knowledge derived from the data. These rules can be used, for example, to store, manipulate, or apply knowledge.

[0057] Machine learning algorithms are typically based on machine learning models. In other words, the term "machine learning algorithm" may refer to a set of instructions that can be used to create, train, or use a machine learning model. The term "machine learning model" may refer to a data structure and / or rule set that represents learned knowledge (e.g., based on training performed by a machine learning algorithm). In an exemplary embodiment, using a machine learning algorithm may imply using an underlying machine learning model (or multiple underlying machine learning models). Using a machine learning model may imply that the machine learning model and / or the data structures / rule sets that make up the machine learning model are trained by a machine learning algorithm.

[0058] For example, a machine learning model can be an artificial neural network (ANN). ANN is a system inspired by biological neural networks, such as those found in the retina or brain. ANN includes multiple interconnected nodes and multiple connections between nodes - so-called edges. There are usually three types of nodes: input nodes that receive input values, hidden nodes that are (only) connected to other nodes, and output nodes that provide output values. Each node can represent an artificial neuron. Each edge can send information from one node to another node. The output of a node can be defined as a (non-linear) function of the input (e.g., the sum of its inputs). The input of a node can be used in a function based on the "weight" of the edge or the node that provides the input. As part of the learning process, the weights of the nodes and / or edges can be adjusted. In other words, training an artificial neural network can include adjusting the weights of the nodes and / or edges of the artificial neural network, that is, to achieve the desired output for a specific input.

[0059] Alternatively, the machine learning model can be a support vector machine, a random forest model or a gradient enhancement model. A support vector machine (i.e., a support vector network) is a supervised learning model with an associated learning algorithm that can be used to analyze data (e.g., in classification or regression analysis). A support vector machine can be trained by providing an input with multiple training input values ​​belonging to one of two categories. A support vector machine can be trained so that new input values ​​are assigned to one of two categories. Alternatively, the machine learning model can be a Bayesian network, which is a probabilistic directed acyclic graph model. A Bayesian network can use a directed acyclic graph to represent a set of random variables and their conditional dependencies. Alternatively, the machine learning model can be based on a genetic algorithm, which is a search algorithm and heuristic technique that simulates a natural selection process.

Claims

1. A method for detecting a ratcheting effect associated with at least one joint of a robot, in particular a collaborative robot (cobot), during operation of the robot by means of artificial intelligence based on operating data of the robot, the method comprising the following steps: - providing operating data, in particular current flow data, relating to the temporal behavior of motors, in particular servomotors, of the robot, - detecting ratcheting of the joints of the robot, in particular in the case of strain wave gear mechanisms, harmonic drives and / or elliptical center gear mechanisms, by evaluating the operating data as indirect input data or direct input data of an artificial intelligence system, thereby generating output data, Therein, the output data of the artificial intelligence system comprises an indicator indicating whether a ratcheting effect associated with the joint, in particular the gear mechanism, has occurred.

2. The method according to claim 1, wherein: The artificial intelligence uses supervised learning or semi-supervised learning techniques, in particular linear regression and / or nonlinear regression, fast Fourier transform and / or support vector machine and / or artificial neural network, in particular recursive neural network.

3. The method according to claim 1 or 2, wherein: If ratcheting has occurred, the output data further indicates the extent of ratcheting that has occurred, in particular the number of teeth in the gear mechanism skipped during ratcheting and / or the angle skipped due to the ratcheting.

4. The method according to claim 3, further comprising the step of calculating a corrected trajectory and / or a corrected control command, in particular based on the number of teeth skipped during ratcheting and / or based on the angle skipped due to the ratcheting.

5. A collaborative robot comprising: - robotic devices, in particular robotic arms and / or assembly systems, wherein the robotic device has one or more joints that can be moved by a motor, in particular a servomotor, - one or more motor control units for controlling said motors, wherein the collaborative robot is further configured to use the artificial intelligence system to detect ratcheting of a joint, in particular a ratcheting effect in a strain wave gear mechanism, a harmonic drive and / or an elliptical center gear mechanism, by evaluating operating data as indirect input data or direct input data to the artificial intelligence system, Therein, the operating data include data relating to the temporal behavior of motors of the robot, in particular current flow data.

6. The collaborative robot according to claim 5, wherein: The artificial intelligence system is designed to provide output data including an indicator indicating whether ratcheting associated with the joint, in particular the gear mechanism, has occurred.

7. The collaborative robot according to claim 5 or 6, wherein: If ratcheting has occurred, the output data further indicates the extent of ratcheting that has occurred, in particular the number of teeth in the gear mechanism skipped during ratcheting and / or the angle skipped due to the ratcheting.

8. The collaborative robot according to any one of claims 5 to 7 is further configured to calculate a corrected trajectory and / or a corrected control instruction, in particular based on the number of teeth skipped during the ratchet effect and / or based on the angle skipped due to the ratchet effect.

9. The collaborative robot according to any one of claims 5 to 8, comprising a central control unit and at least one motor control unit, in, The central control unit and the motor control unit can communicate with each other via a bus, in particular an Ethernet bus. wherein, in particular, the determination as to whether ratcheting has occurred in connection with the joint is performed in an associated motor control unit, wherein, in particular, the determination of the number of teeth skipped in the gear mechanism during the ratcheting effect is performed in an associated motor control unit, In particular, the calculation of the corrected trajectory and / or the corrected control commands takes place in the central control unit.

10. A computer, a computer system or a computer network configured to perform locally or non-locally a method according to any one of claims 1 to 4.

11. An artificial intelligence system, in particular a machine learning model, in particular a linear regression and / or a non-linear regression, a fast Fourier transform and / or a support vector machine and / or an artificial neural network, in particular a recurrent neural network, configured to determine whether there is a ratcheting effect of a gear mechanism, in particular a gear mechanism in a collaborative robot, and / or to determine the number of teeth skipped in the gear mechanism during the ratcheting effect, the artificial intelligence system comprising: One or more input neurons for receiving input data, the input data comprising data based on: o Operating data relating to the temporal behavior of a motor, in particular a servomotor, in particular current flow data.

12. A method for training an artificial intelligence system according to claim 11, in particular by supervised learning or semi-supervised learning using the following dataset as input data: A data set comprising data relating to the temporal behavior of the motor, in particular current flow data, said data set being provided with a classification label as to whether said data corresponds to a ratcheting effect having occurred.

13. The method according to claim 12, wherein: The label also indicates the number of teeth skipped in the ratcheting effect corresponding to the data.

14. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to perform the method according to any one of claims 1 to 4, 12 or 13.

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