Systems and Methods for Determining Path Confidence for Unmanned Vehicles

    公开(公告)号:US20190042859A1

    公开(公告)日:2019-02-07

    申请号:US15667391

    申请日:2017-08-02

    Abstract: Examples implementations relate to determining path confidence for a vehicle. An example method includes receiving a request for a vehicle to navigate a target location. The method further includes determining a navigation path for the vehicle to traverse a first segment of the target location based on a plurality of prior navigation paths previously determined for traversal of segments similar to the first segment of the target location. The method also includes determining a confidence level associated with the navigation path. Based on the determined confidence level, the method additionally includes selecting a navigation mode for the vehicle from a plurality of navigation modes corresponding to a plurality of levels of remote assistance. The method further includes causing the vehicle to traverse the first segment of the target location using a level of remote assistance corresponding to the selected navigation mode for the vehicle.

    Model for Determining Drop-off Spot at Delivery Location

    公开(公告)号:US20190041852A1

    公开(公告)日:2019-02-07

    申请号:US15667180

    申请日:2017-08-02

    Abstract: An example system includes a delivery vehicle, a sensor connected to the delivery vehicle, and a control system that determines a delivery destination for an object. The control system receives sensor data representing a physical environment of at least a portion of the delivery destination and determines a drop-off spot for the object within the delivery destination by way of an artificial neural network (ANN). The ANN is trained to determine the drop-off spot based on previously-designated drop-off spots within corresponding delivery destinations and includes an input node that receives the sensor data, hidden nodes connected to the input node, and an output node connected to the hidden nodes that provides data indicative of a location of the drop-off spot. The control system additionally causes the delivery vehicle to move to and place the object at the drop-off spot.

    Electroencephalogram bioamplifier
    16.
    发明授权

    公开(公告)号:US10952680B2

    公开(公告)日:2021-03-23

    申请号:US15855870

    申请日:2017-12-27

    Abstract: A bioamplifier for analyzing electroencephalogram (EEG) signals is disclosed. The bioamplifier includes an input terminal for receiving an EEG signal from a plurality of sensors coupled to a user. The bioamplifier also includes an analogue-to-digital converter arranged to receive the EEG signal from the input terminal and convert the EEG signal to a digital EEG signal. A data processing apparatus within the bioamplifier is arranged to receive the digital EEG signal from the analogue-to-digital converter and programmed to process, in real time the digital EEG signal using a first machine learning model to generate a cleaned EEG signal having a higher signal-to-noise ratio than the digital EEG signal. The bioamplifier further includes a power source to provide electrical power to the analogue-to-digital converter and the data processing apparatus. The bioamplifier includes a housing that contains the analogue-to-digital converter, the data processing apparatus, the power source, and the sensor input.

    INTERFACE FOR ELECTROENCEPHALOGRAM FOR COMPUTER CONTROL

    公开(公告)号:US20200225749A1

    公开(公告)日:2020-07-16

    申请号:US16832645

    申请日:2020-03-27

    Abstract: A method for analyzing electroencephalogram (EEG) signals is disclosed. Information associated with two or more options is presented to a user. EEG signals from a sensor coupled to the user are received contemporaneously to the user receiving information associated with the two or more options. The EEG signals are processed in real time to determine which one of the options was selected by the user. In response to determining which one of the options was selected by the user, an action from one or more possible actions associated with the information presented to the user is selected. An output associated with the selected action is then generated.

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