Abstract: A common command and control architecture (alternatively termed herein as a “universal control architecture”) is disclosed that allows different unmanned systems, including different types of unmanned systems (e.g., air, ground, and/or maritime unmanned systems), to be controlled simultaneously through a common control device (e.g., a controller that can be an input and/or output device). The universal control architecture brings significant efficiency gains in engineering, deployment, training, maintenance, and future upgrades of unmanned systems. In addition, the disclosed common command and control architecture breaks the traditional stovepipe development involving deployment models and thus reducing hardware and software maintenance, creating a streamlined training/proficiency initiative, reducing physical space requirements for transport, and creating a scalable, more connected interoperable approach to control of unmanned systems over existing unmanned systems technology.

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.

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.

Abstract: Methods and systems are described herein for navigating a vehicle. A target location may be received, and based on the target location and vehicle parameters, an acceleration profile for the vehicle may be generated. The acceleration profile may include a first duration for accelerating the vehicle and a second duration for not accelerating the vehicle. When the acceleration profile is generated, a trajectory profile for travelling to the target location may be generated using the acceleration profile. The trajectory profile may include multiple instances of the acceleration profile. When the trajectory profile is being executed, a distance travelled by the vehicle after each instance of the acceleration profile is executed may be calculated. Based on the distance travelled after each instance of the acceleration profile is executed, an instance of when the target location is reached may be determined.

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.

Abstract: Methods and systems are described herein for determining three-dimensional locations of objects within identified portions of images. An image processing system may receive an image and an identification of location within an image. The image may be input into a machine learning model to detect one or more objects within the identified location. Multiple images may then be used to generate location estimations of those objects. Based on the location estimations, an accurate three-dimensional location may be calculated.

Abstract: Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.

Abstract: Methods and systems are described herein for generating composite data streams. A data stream processing system may receive multiple data streams from, for example, multiple unmanned vehicles and determine, based on the type of data within each data stream, a machine learning model for each data stream for processing the type of data. Each machine learning model may receive the frames of a corresponding data stream and output indications and locations of objects within those data streams. The data stream processing system may then generate a composite data stream with indications of the detected objects.

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.

Abstract: Methods and systems are described herein for enabling aerial vehicle navigation in GPS-denied areas. The system may use a camera to record images of terrain as the aerial vehicle is flying to a target location. The system may then detect (e.g., using a machine learning model) objects within those images and compare those objects with objects within an electronic map that was loaded onto the aerial vehicle. When the system finds one or more objects within the electronic map that match the objects detected within the recorded images, the system may retrieve locations (e.g., GPS coordinates) of the objects within the electronic map and calculate, based on the coordinates, the location of the aerial vehicle. Once the location of the aerial vehicle is determined, the system may navigate to a target location or otherwise adjust a flight path of the aerial vehicle.

Abstract: Methods and systems are described herein for navigating a vehicle. A target location may be received, and based on the target location and vehicle parameters, an acceleration profile for the vehicle may be generated. The acceleration profile may include a first duration for accelerating the vehicle and a second duration for not accelerating the vehicle. When the acceleration profile is generated, a trajectory profile for travelling to the target location may be generated using the acceleration profile. The trajectory profile may include multiple instances of the acceleration profile. When the trajectory profile is being executed, a distance travelled by the vehicle after each instance of the acceleration profile is executed may be calculated. Based on the distance travelled after each instance of the acceleration profile is executed, an instance of when the target location is reached may be determined.

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.

Abstract: Methods and systems are described herein for enabling aerial vehicle navigation in GPS-denied areas. The system may use a camera to record images of terrain as the aerial vehicle is flying to a target location. The system may then detect (e.g., using a machine learning model) objects within those images and compare those objects with objects within an electronic map that was loaded onto the aerial vehicle. When the system finds one or more objects within the electronic map that match the objects detected within the recorded images, the system may retrieve locations (e.g., GPS coordinates) of the objects within the electronic map and calculate, based on the coordinates, the location of the aerial vehicle. Once the location of the aerial vehicle is determined, the system may navigate to a target location or otherwise adjust a flight path of the aerial vehicle.

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.

Abstract: Methods and systems are described herein for determining three-dimensional locations of objects within identified portions of images. An image processing system may receive an image and an identification of location within an image. The image may be input into a machine learning model to detect one or more objects within the identified location. Multiple images may then be used to generate location estimations of those objects. Based on the location estimations, an accurate three-dimensional location may be calculated.

Abstract: Methods and systems are described herein for enabling aerial vehicle navigation in GPS-denied areas. The system may use a camera to record images of terrain as the aerial vehicle is flying to a target location. The system may then detect (e.g., using a machine learning model) objects within those images and compare those objects with objects within an electronic map that was loaded onto the aerial vehicle. When the system finds one or more objects within the electronic map that match the objects detected within the recorded images, the system may retrieve locations (e.g., GPS coordinates) of the objects within the electronic map and calculate, based on the coordinates, the location of the aerial vehicle. Once the location of the aerial vehicle is determined, the system may navigate to a target location or otherwise adjust a flight path of the aerial vehicle.

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.

Abstract: Methods and systems are described herein for a layered fail-safe redundancy system and architecture for privileged operation execution. The system may receive vehicle maneuvering commands from a controller over a first channel. When a user input is received to initiate a privileged mode for executing privileged commands, the system may receive a privileged command over a second channel. The system may identify, based on the privileged mode of operation and the privileged command, a privileged operation to be performed by a vehicle. The system may then transmit a request to the vehicle to perform the privileged operation.

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.

Abstract: Methods and systems are described herein for determining three-dimensional locations of objects within identified portions of images. An image processing system may receive an image and an identification of location within an image. The image may be input into a machine learning model to detect one or more objects within the identified location. Multiple images may then be used to generate location estimations of those objects. Based on the location estimations, an accurate three-dimensional location may be calculated.