Inertially isolated spatial control

    公开(公告)号:US12223124B2

    公开(公告)日:2025-02-11

    申请号:US18311370

    申请日:2023-05-03

    Abstract: Methods and systems are described herein for detecting motion-induced errors received from inertial-type input devices and for generating accurate vehicle control commands that account for operator movement. These methods and systems may determine, using motion data from inertial sensors, whether the hand/arm of the operator is moving in the same motion as the body of the operator, and if both are moving in the same way, these systems and methods may determine that the motion is not intended to be a motion-induced command. However, if the hand/arm of the operator is moving in a different motion from the body of the operator, these methods and systems may determine that the operator intended the motion to be a motion-induced command to a vehicle.

    SYSTEMS AND METHODS OF REMOTE TELEOPERATION OF ROBOTIC VEHICLES

    公开(公告)号:US20220083054A1

    公开(公告)日:2022-03-17

    申请号:US17417194

    申请日:2019-12-31

    Abstract: Systems and methods of manipulating/controlling robots. In many scenarios, data collected by a sensor (connected to a robot) may not have very high precision (e.g., a regular commercial/inexpensive sensor) or may be subjected to dynamic environmental changes. Thus, the data collected by the sensor may not indicate the parameter captured by the sensor with high accuracy. The present robotic control system is directed at such scenarios. In some embodiments, the disclosed embodiments can be used for computing a sliding velocity limit boundary for a spatial controller. In some embodiments, the disclosed embodiments can be used for teleoperation of a vehicle located in the field of view of a camera.

    Classification parallelization architecture

    公开(公告)号:US12266164B2

    公开(公告)日:2025-04-01

    申请号:US18772099

    申请日:2024-07-12

    Abstract: Methods and systems are described herein for hosting and arbitrating algorithms for the generation of structured frames of data from one or more sources of unstructured input frames. A plurality of frames may be received from a recording device and a plurality of object types to be recognized in the plurality of frames may be determined. A determination may be made of multiple machine learning models for recognizing the object types. The frames may be sequentially input into the machine learning models to obtain a plurality of sets of objects from the plurality of machine learning models and object indicators may be received from those machine learning models. A set of composite frames with the plurality of indicators corresponding to the plurality of objects may be generated, and an output stream may be generated including the set of composite frames to be played back in chronological order.

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