Abstract: Various embodiments of the teachings herein include a method for controlling a battery production system. An example includes: measuring production parameters with a plurality of sensors; determining a quality value of a battery cell with the measurement values; calculating a dependency of the quality value on the measurement values with a computing unit; calculating a dependency of the quality value on changed production parameters by performing a machine learning method; determining a controllable process parameter with an improved quality value using a parameter optimization for the machine learning method including a Bayesian optimization, wherein measurement values with associated quality values are included in the optimization as reference points; and using the controllable process parameter with an improved quality value in operation of the battery production system.

Abstract: Various embodiments include a method for producing a quality test system executing a quality test model with a filter mask and a quality model to determine a quality feature of a battery cell. The system has an electrochemical impedance spectroscopic unit for capturing test data relating to the battery within a frequency range. The method includes: creating the model; and producing the system. Creating the model includes: capturing spectroscopic learning data; creating the filter mask using a first machine learning method with analysis data from part of the frequency range by consulting the filter mask and creating the model using a second machine learning method. The first and the second learning method are coupled based on the learning data. The first machine learning method creates a filter mask determining the analysis data such that the second machine learning method creates a quality model optimized with respect to maximizing the quality.

Abstract: Various embodiments of the teachings herein include a method for producing an electrode layer of a battery store by a system using an electrode layer paste. An example method includes: acquiring system parameters associated with the production of the electrode layer; acquiring a measured value of a variable of the electrode layer; calculating a correction value from a comparison of the acquired measured value of the electrode layer with a defined target value range; and setting the system parameters as a function of the calculated correction value.

Abstract: A method, a system and a computer program product for changing an electrical state of charge of an electrical energy storage: a) stipulating at least one target value for a target state of charge of the electrical storage device, b) determining at least one actual value of an actual state of charge of the electrical storage device, c) extrapolating the actual value of the actual state of charge of the energy storage device to an extrapolation value of an extrapolation state of charge of the energy storage device, taking into account at least one charging parameter of the charging process, and d) comparing the target value of the target state of charge with the extrapolation value of the extrapolation state of charge and starting or interrupting the charging process if the extrapolation value deviates from the target value by more than 2%.

Abstract: Method and apparatus for industrial scale production of a suspension for a battery, wherein an input material is processed via ball milling in a rotating chamber of a device that is effected as a continuous process with a continuously controlled addition of the input material and with a continuously controlled delivery of the processed output material, where state parameters of the input material and process parameters of the manufacturing installation are acquired as first parameters during production of the suspension, results of laboratory analyses about the state or quality of the manufactured suspension are acquired as second parameters in a learning phase during production, the first and the second parameters are used in the learning phase for training a model for predicting the state or quality via machine learning, and where the device is open-loop or closed-loop controlled outside the learning phase via the first parameters and the trained model.

Abstract: The present invention relates to a novel robot drive train that is robust, and low cost. The drive train is capable of ascending obstacles greater than the height of its wheels, protects the robot against shocks/vibration, and is highly maneuverable, such as able to execute a zero-point turn. To control the bogie in a variety of scenarios, a novel mechanism is used to selectively limit the articulation range of the bogie and/or programmatically apply a preload to the bogie axle.

Abstract: The present invention relates to a novel robot drive train that is robust, and low cost. The drive train is capable of ascending obstacles greater than the height of its wheels, protects the robot against shocks/vibration, and is highly maneuverable, such as able to execute a zero-point turn. To control the bogie in a variety of scenarios, a novel mechanism is used to selectively limit the articulation range of the bogie and/or programmatically apply a preload to the bogie axle.

Abstract: Methods, systems, and apparatus for receiving a reference to an object located in an environment of a robot, accessing mapping data that indicates, for each of a plurality of object instances, respective probabilities of the object instance being located at one or more locations in the environment, wherein the respective probabilities are based at least on an amount of time that has passed since a prior observation of the object instance was made, identifying one or more particular object instances that correspond to the referenced object, determining, based at least on the mapping data, the respective probabilities of the one or more particular object instances being located at the one or more locations in the environment, selecting, based at least on the respective probabilities, a particular location in the environment where the referenced object is most likely located, and directing the robot to navigate to the particular location.

Abstract: Various embodiments include a method for producing a quality test system executing a quality test model with a filter mask and a quality model to determine a quality feature of a battery cell. The system has an electrochemical impedance spectroscopic unit for capturing test data relating to the battery within a frequency range. The method includes: creating the model; and producing the system. Creating the model includes: capturing spectroscopic learning data; creating the filter mask using a first machine learning method with analysis data from part of the frequency range by consulting the filter mask and creating the model using a second machine learning method. The first and the second learning method are coupled based on the learning data. The first machine learning method creates a filter mask determining the analysis data such that the second machine learning method creates a quality model optimized with respect to maximizing the quality.

Abstract: A method for analyzing image information using assigned scalar values includes a.) capturing a first image of an object or a situation and capturing at least one first scalar sensor value relating to the object or the situation; b.) capturing a second image of the object or the situation and capturing at least one second scalar sensor value relating to the object or the situation; c.) inserting the first image and the at least one first scalar sensor value into a first data structure as a consistent representation form, and inserting the second image and the at least one second scalar sensor value into a second data structure as a consistent representation form; d.) comparing the first and the second data structure and e.) outputting information if the comparison results in a difference that corresponds to a defined or definable criterion.

Abstract: Methods, systems, and apparatus for receiving a mapping of a property that includes a three-dimensional representation of the property, receiving observations of the property that each depict a portion of the property, providing the mapping and the observations to an object mapping engine, receiving an object mapping of the property, wherein the object mapping includes a plurality of object labels that each identify an object that was recognized from the observations and a location of the object within the three-dimensional representation that corresponds to a physical location of the object in the property, and obtaining a semantic mapping of the property that identifies semantic zones of the property with respect to the three-dimensional representation, wherein the semantic mapping is generated based on an output that results from a semantic mapping model processing the object mapping.

Abstract: Methods, systems, and apparatus for receiving a mapping of a property that includes a three-dimensional representation of the property, receiving observations of the property that each depict a portion of the property, providing the mapping and the observations to an object mapping engine, receiving an object mapping of the property, wherein the object mapping includes a plurality of object labels that each identify an object that was recognized from the observations and a location of the object within the three-dimensional representation that corresponds to a physical location of the object in the property, and obtaining a semantic mapping of the property that identifies semantic zones of the property with respect to the three-dimensional representation, wherein the semantic mapping is generated based on an output that results from a semantic mapping model processing the object mapping.

Abstract: The present invention relates to a novel robot drive train that is robust, and low cost. The drive train is capable of ascending obstacles greater than the height of its wheels, protects the robot against shocks/vibration, and is highly maneuverable, such as able to execute a zero-point turn. To control the bogie in a variety of scenarios, a novel mechanism is used to selectively limit the articulation range of the bogie and/or programmatically apply a preload to the bogie axle.

Abstract: Methods, systems, and apparatus for receiving data that represents a portion of a property that was obtained by a robot, identifying, based at least on the data, objects that the data indicates as being located within the portion of the property, determining, based on the objects, a semantic zone type corresponding to the portion of the property, accessing a mapping hierarchy for the property, wherein the mapping hierarchy for the property specifies semantic zones of the property that have corresponding semantic zone types and are associated with locations at the property, and specifies characteristics of the semantic zones, and selecting, from among the semantic zones and based at least on the semantic zone type and the data, a particular semantic zone, and setting, as a current location of the robot at the property, a particular location at the property associated with the particular semantic zone.

Abstract: Methods, systems, and apparatus for receiving a reference to an object located in an environment of a robot, accessing mapping data that indicates, for each of a plurality of object instances, respective probabilities of the object instance being located at one or more locations in the environment, wherein the respective probabilities are based at least on an amount of time that has passed since a prior observation of the object instance was made, identifying one or more particular object instances that correspond to the referenced object, determining, based at least on the mapping data, the respective probabilities of the one or more particular object instances being located at the one or more locations in the environment, selecting, based at least on the respective probabilities, a particular location in the environment where the referenced object is most likely located, and directing the robot to navigate to the particular location.

Abstract: Methods, systems, and apparatus for receiving a reference to an object located in an environment of a robot, accessing mapping data that indicates, for each of a plurality of object instances, respective probabilities of the object instance being located at one or more locations in the environment, wherein the respective probabilities are based at least on an amount of time that has passed since a prior observation of the object instance was made, identifying one or more particular object instances that correspond to the referenced object, determining, based at least on the mapping data, the respective probabilities of the one or more particular object instances being located at the one or more locations in the environment, selecting, based at least on the respective probabilities, a particular location in the environment where the referenced object is most likely located, and directing the robot to navigate to the particular location.

Abstract: Methods, systems, and apparatus for receiving data that represents a portion of a property that was obtained by a robot, identifying, based at least on the data, objects that the data indicates as being located within the portion of the property, determining, based on the objects, a semantic zone type corresponding to the portion of the property, accessing a mapping hierarchy for the property, wherein the mapping hierarchy for the property specifies semantic zones of the property that have corresponding semantic zone types and are associated with locations at the property, and specifies characteristics of the semantic zones, and selecting, from among the semantic zones and based at least on the semantic zone type and the data, a particular semantic zone, and setting, as a current location of the robot at the property, a particular location at the property associated with the particular semantic zone.

Abstract: Methods, systems, and apparatus for receiving data that represents a portion of a property that was obtained by a robot, identifying, based at least on the data, objects that the data indicates as being located within the portion of the property, determining, based on the objects, a semantic zone type corresponding to the portion of the property, accessing a mapping hierarchy for the property, wherein the mapping hierarchy for the property specifies semantic zones of the property that have corresponding semantic zone types and are associated with locations at the property, and specifies characteristics of the semantic zones, and selecting, from among the semantic zones and based at least on the semantic zone type and the data, a particular semantic zone, and setting, as a current location of the robot at the property, a particular location at the property associated with the particular semantic zone.

Abstract: Methods, systems, and apparatus for receiving a reference to an object located in an environment of a robot, accessing mapping data that indicates, for each of a plurality of object instances, respective probabilities of the object instance being located at one or more locations in the environment, wherein the respective probabilities are based at least on an amount of time that has passed since a prior observation of the object instance was made, identifying one or more particular object instances that correspond to the referenced object, determining, based at least on the mapping data, the respective probabilities of the one or more particular object instances being located at the one or more locations in the environment, selecting, based at least on the respective probabilities, a particular location in the environment where the referenced object is most likely located, and directing the robot to navigate to the particular location.

Abstract: Methods, systems, and apparatus for receiving a mapping of a property that includes a three-dimensional representation of the property, receiving observations of the property that each depict a portion of the property, providing the mapping and the observations to an object mapping engine, receiving an object mapping of the property, wherein the object mapping includes a plurality of object labels that each identify an object that was recognized from the observations and a location of the object within the three-dimensional representation that corresponds to a physical location of the object in the property, and obtaining a semantic mapping of the property that identifies semantic zones of the property with respect to the three-dimensional representation, wherein the semantic mapping is generated based on an output that results from a semantic mapping model processing the object mapping.