Abstract: Examples are disclosed that relate to methods and systems for classifying a surface type. One example provides a system comprising a wearable device comprising at least one force sensor, and a computing device having a processor and associated memory storing instructions executable by the processor. The instructions are executable by the processor to, during a training phase, receive training data including a plurality of training data pairs. Each training data pair includes force sensor training data received from the at least one force sensor, or from a simulation or observation, and a label indicating at least one of a plurality of defined surface types. An AI model is trained to predict a classified surface type based on run-time force sensor data. The run-time force sensor data is input into the trained AI model to thereby cause the AI model to output a predicted classification of a run-time surface type.

Abstract: Examples are disclosed that relate to methods and systems for classifying the possession or control of a target asset. One example provides a system comprising one or more computing devices having processors and associated memories storing instructions executable by the processors. The instructions are executable to conduct a simulation or observation of an in-field event comprising a plurality of actors controlling a plurality of in-field assets. The system is further configured to monitor telemetry data from each of the in-field assets. In addition, tagging data is received from an in-field asset under the control of a member of the friendly group. A training data set is generated including the telemetry data and the tagging data, and an artificial intelligence model is trained to predict whether a run-time target asset is in the possession or control of the friendly or the unfriendly actor, or lost, based on run-time telemetry data.

Abstract: Examples are disclosed that relate to target classification systems, weapons, and methods for classifying a target. One example provides a target classification system comprising a user device, a user input device, a pose sensor fixed to the user device, a processor, and a memory storing instructions executable by the processor. A visual alignment aid indicates a line of sight to one or more of a plurality of targets. The user input device is configured to receive at least a first input type and a second input type. The instructions are executable to receive a user input and determine the pose of the line of sight. The one or more targets are tagged with a first target classification when the first input type is received and tagged with a second target classification when the second input type is received. The instructions are further executable to output targeting data to another device.

Abstract: One example provides a target classification system comprising a display subsystem configured to display an image captured by a camera of an in-field device. The image includes one or more targets. The target classification system is configured to receive a user input indicating a location of the one or more targets in a screen space coordinate system of the display subsystem. Location information in a world space coordinate system is determined by receiving a pose of the camera; using the pose of the camera and the location in the screen space to trace a ray; and using at least a position of the camera and an orientation of the ray to generate coordinates in the world space. Target classification information is determined, and targeting data is output comprising the coordinates in the world space and the target classification information.

Abstract: Examples are disclosed that relate to methods, computing devices, and systems for determining a user's readiness for an operational task. One example provides a method comprising, during a training phase, receiving training input data including, for each user training session, a training data pair including, as input, a distance of travel, a mode of travel, and one or more environmental conditions, and as ground truth output, a time elapsed for performance of the training task. An artificial intelligence (AI) performance model is trained to model user performance of the training task based on the training data pairs. During a run-time phase, operational input data is received that is associated with an operational task performed by the user. The performance model is used to infer a predicted time elapsed for performance of the operational task.

Abstract: A computing device is provided that includes a processor having a plurality of pins that are electrically coupled to a plurality of pins of a connector, and a memory device storing a state table that maps the plurality of pins of the connector to a plurality of connection types. The processor is configured to perform an authentication process for at least one connection type to determine whether an authenticated device configured for the at least one connection type is coupled to the connector. The authentication process is performed at least in part by sending an authentication signal to one or more of the plurality of pins of the connector mapped to the at least one connection type, and receiving an expected authentication signal response on one or more of the plurality of pins of the connector mapped to the at least one connection type.

Abstract: A computing device, including a processor configured to receive sensor data from a control device. The control device may include a control processor configured to execute control instructions to control an actuator of a target electromechanical system and may further include one or more sensors. The processor may identify a first subset of the sensor data and a second subset of the sensor data. The processor may generate first control instructions based on the first subset and transmit the first control instructions to the control processor of the control device. The processor may transmit the second subset to a remote computing device. In response to transmitting the second subset to the remote computing device, the processor may receive a remote processing result from the remote computing device. The processor may generate second control instructions from the remote processing result and transmit the second control instructions to the control processor.

Abstract: An edge computing device is provided that includes a plurality of input electrodes that are communicatively coupled to one or more communication channels of a programmable logic controller that implements control logic to control a controlled device. The edge computing device furthers include a processor configured to, at a training time, receive signals via the plurality of input electrodes, detect inputs to the one or more communication channels and outputs from the one or more communication channels of the programmable logic controller, generate a set of training data based on the detected inputs and outputs of the programmable logic controller, and train an artificial intelligence model using the generated set of training data. The processor is further configured to, at a run-time, emulate the control logic of the programmable logic controller using the trained artificial intelligence model.

Abstract: Examples are disclosed that relate to methods and systems for classifying the possession or control of a target asset. One example provides a system comprising one or more computing devices having processors and associated memories storing instructions executable by the processors. The instructions are executable to conduct a simulation or observation of an in-field event comprising a plurality of actors controlling a plurality of in-field assets. The system is further configured to monitor telemetry data from each of the in-field assets. In addition, tagging data is received from an in-field asset under the control of a member of the friendly group. A training data set is generated including the telemetry data and the tagging data, and an artificial intelligence model is trained to predict whether a run-time target asset is in the possession or control of the friendly or the unfriendly actor, or lost, based on run-time telemetry data.

Abstract: A ruggedized edge computing assembly is provided, which includes an edge computing device having a processor configured to control a controlled device. The ruggedized edge computing assembly includes a field connector configured to connect to the edge computing device via a plurality of pins and to the controlled device via a coupling. The ruggedized edge computing assembly further includes a housing overmolded around each of the field connector and the edge computing device. The housing includes two portions which are a field connector portion configured to accommodate the field connector and an edge computing device portion configured to accommodate the edge computing device. The two portions are configured to interlockingly engage together at an interface.

Abstract: A computing device is provided, including a processor having a plurality of pins that are electrically coupled to a connector via respective traces. The computing device may further include a memory device storing a state table that maps the plurality of pins to a respective plurality of connection protocols. The processor may be configured to implement control logic for the plurality of pins at least in part by receiving a selection of a pin of the plurality of pins. Implementing the control logic may further include receiving an updated connection protocol for the selected pin. Implementing the control logic may further include updating the state table such that the selected pin is mapped to the updated connection protocol. Implementing the control logic may further include, via the connector, establishing a connection to an external device using the updated connection protocol implemented at the selected pin.

Abstract: A computing device, including a processor configured to receive sensor data from a control device. The control device may include a control processor configured to execute control instructions to control an actuator of a target electromechanical system and may further include one or more sensors. The processor may identify a first subset of the sensor data and a second subset of the sensor data. The processor may generate first control instructions based on the first subset and transmit the first control instructions to the control processor of the control device. The processor may transmit the second subset to a remote computing device. In response to transmitting the second subset to the remote computing device, the processor may receive a remote processing result from the remote computing device. The processor may generate second control instructions from the remote processing result and transmit the second control instructions to the control processor.