Abstract: Disclosed is a system and method for reducing the total latency for transferring a frame from the low latency camera system mounted on an aerial vehicle to the display of the remote controller. The method includes reducing the latency through each of the modules of the system, i.e. through a camera module, an encoder module, a wireless interface transmission, wireless interface receiver module, a decoder module and a display module. To reduce the latency across the modules, methods such as overclocking the image processor, pipelining the frame, squashing the processed frame, using a fast hardware encoder that can perform slice based encoding, tuning the wireless medium using queue sizing, queue flushing, bitrate feedback, physical medium rate feedback, dynamic encoder parameter tuning and wireless radio parameter adjustment, using a fast hardware decoder that can perform slice based decoding and overclocking the display module are used.

Abstract: A pipeline in a controller may be configured to interface between sensors and actuators. The pipeline may elements such as drivers, filters, a combine, estimators, controllers, a mixer, and actuator controllers. The drivers may receive sensor data and pre-process the received sensor data. The filters may filter the pre-processed sensor data to generate filtered sensor data. The combine may package the filtered sensor data to generate packaged sensor data. The estimators may determine estimates of a position of a vehicle based on the packaged sensor data. The controllers may generate control signals based on the determined estimates. The mixer may modify the generated control signals based on limitations of the vehicle. The actuator controllers may generate actuator control signals based on the modified control signals to drive the actuators.

Abstract: Disclosed is a configuration to control automatic return of an aerial vehicle. The configuration stores a return location in a storage device of the aerial vehicle. The return location may correspond to a location where the aerial vehicle is to return. One or more sensors of the aerial vehicle are monitored during flight for detection of a predefined condition. When a predetermined condition is met a return path program may be loaded for execution to provide a return flight path for the aerial vehicle to automatically navigate to the return location.

Abstract: A controller system of an aerial vehicle may receive environmental data from one or more sensors of the aerial vehicle and adjusts limits of the aerial vehicle given the environmental conditions. When the aerial vehicle receives an input, such as a flight input from a remote controller or an environmental input such as a gust of wind, the controller system calculates appropriate motor inputs that are provided to the thrust motors of the aerial vehicle such that the adjusted limits of the aerial vehicle are not exceeded. In calculating the appropriate input to the thrust motors, the controller system performs an iterative process. For example, for a given maximum torque that can be applied to the thrust motors, the controller system iteratively allocates the torque such that torque components that are important for the stability of the aerial are first fulfilled, whereas subsequent torque components may be fulfilled or scaled back.

Abstract: Described herein are systems for the production, communication, routing, service, authentication, and consumption of cryptographically authenticable contextual content produced by cryptographically authenticable devices; example implementations of the architecture for a Trusted Contextual Content Device which produces Trusted Contextual Content; and example implementations of the architecture for a Trusted Drone Device which produces Trusted Contextual Content. For example, some of the methods used may include accessing a first set of sensor data from one or more sensors; receiving, a first trusted contextual content that includes a first digital signature; generating a data structure including the first trusted contextual content and data based on the first set of sensor data; signing the data structure using a signing key to generate a second trusted contextual content including a second digital signature; and storing or transmitting the second trusted contextual content.

Abstract: Ultrasonic ranging state management for a UAV is described. A transducer transmits an ultrasonic signal and receives an ultrasonic response thereto using a gain value. A noise floor estimation mechanism determines a noise floor estimate. A state mechanism sets an ultrasonic ranging state used by the transducer to a first ultrasonic ranging state. The transducer transmits an ultrasonic signal and responsively receive an ultrasonic response to the ultrasonic signal using a gain value according to the noise floor estimate. The state mechanism processes the ultrasonic response to determine whether to determine a new noise floor estimate, adjust the gain value used by the transducer, or change the ultrasonic ranging state of the UAV to a second ultrasonic ranging state. The configurations of the first and second ultrasonic ranging states differ as to, for example, power and gain levels used by the transducer to receive ultrasonic responses.

Abstract: A mapping system receives sensor data from an unmanned aerial vehicle. The mapping system further receives images from a camera of the unmanned aerial vehicle. The mapping system determines an altitude of the camera based on the sensor data. The mapping system calculates a footprint of the camera based on the altitude of the camera and a field of view of the camera. The mapping system constructs a localized map based on the images and the footprint of the camera.

Abstract: A pipeline in a controller may be configured to interface between sensors and actuators. The pipeline may include elements such as drivers, filters, a combine, estimators, controllers, a mixer, and actuator controllers. The drivers may receive sensor data and pre-process the received sensor data. The filters may filter the pre-processed sensor data to generate filtered sensor data. The combine may package the filtered sensor data to generate packaged sensor data. The estimators may determine estimates of a position of a vehicle based on the packaged sensor data. The controllers may generate control signals based on the determined estimates. The mixer may modify the generated control signals based on limitations of the vehicle. The actuator controllers may generate actuator control signals based on the modified control signals to drive the actuators.

Abstract: Disclosed is a configuration to control automatic return of an aerial vehicle. The configuration stores a return location in a storage device of the aerial vehicle. The return location may correspond to a location where the aerial vehicle is to return. One or more sensors of the aerial vehicle are monitored during flight for detection of a predefined condition. When a predetermined condition is met a return path program may be loaded for execution to provide a return flight path for the aerial vehicle to automatically navigate to the return location.

Abstract: Ultrasonic ranging state management for a UAV is described. A transducer transmits an ultrasonic signal and receives an ultrasonic response thereto using a gain value. A noise floor estimation mechanism determines a noise floor estimate. A state mechanism sets an ultrasonic ranging state used by the transducer to a first ultrasonic ranging state. The transducer transmits an ultrasonic signal and responsively receive an ultrasonic response to the ultrasonic signal using a gain value according to the noise floor estimate. The state mechanism processes the ultrasonic response to determine whether to determine a new noise floor estimate, adjust the gain value used by the transducer, or change the ultrasonic ranging state of the UAV to a second ultrasonic ranging state. The configurations of the first and second ultrasonic ranging states differ as to, for example, power and gain levels used by the transducer to receive ultrasonic responses.

Abstract: Disclosed is a system and method for reducing the total latency for transferring a frame from the low latency camera system mounted on an aerial vehicle to the display of the remote controller. The method includes reducing the latency through each of the modules of the system, i.e. through a camera module, an encoder module, a wireless interface transmission, wireless interface receiver module, a decoder module and a display module. To reduce the latency across the modules, methods such as overclocking the image processor, pipelining the frame, squashing the processed frame, using a fast hardware encoder that can perform slice based encoding, tuning the wireless medium using queue sizing, queue flushing, bitrate feedback, physical medium rate feedback, dynamic encoder parameter tuning and wireless radio parameter adjustment, using a fast hardware decoder that can perform slice based decoding and overclocking the display module are used.

Abstract: Target value detection for an unmanned aerial vehicle is described. The unmanned aerial vehicle includes a first transducer that transmits a first ultrasonic signal and receives a first ultrasonic response and a second transducer that transmits a second ultrasonic signal and receives a second ultrasonic response. The second transducer has a wider beam pattern than the first transducer. Determinations are made as to whether either or both of the first or second ultrasonic responses includes a target value within range areas associated with the respective beam patterns of the first and second transducers. A confidence value is generated based on the determinations. The target value is reflected from an object and the confidence value indicates a likelihood of a position of the unmanned aerial vehicle with respect to the object.

Abstract: Disclosed is a system and method for reducing the total latency for transferring a frame from the low latency camera system mounted on an aerial vehicle to the display of the remote controller. The method includes reducing the latency through each of the modules of the system, i.e. through a camera module, an encoder module, a wireless interface transmission, wireless interface receiver module, a decoder module and a display module. To reduce the latency across the modules, methods such as overclocking the image processor, pipelining the frame, squashing the processed frame, using a fast hardware encoder that can perform slice based encoding, tuning the wireless medium using queue sizing, queue flushing, bitrate feedback, physical medium rate feedback, dynamic encoder parameter tuning and wireless radio parameter adjustment, using a fast hardware decoder that can perform slice based decoding and overclocking the display module are used.

Abstract: A controller system of an aerial vehicle may receive environmental data from one or more sensors of the aerial vehicle and adjusts limits of the aerial vehicle given the environmental conditions. When the aerial vehicle receives an input, such as a flight input from a remote controller or an environmental input such as a gust of wind, the controller system calculates appropriate motor inputs that are provided to the thrust motors of the aerial vehicle such that the adjusted limits of the aerial vehicle are not exceeded. In calculating the appropriate input to the thrust motors, the controller system performs an iterative process. For example, for a given maximum torque that can be applied to the thrust motors, the controller system iteratively allocates the torque such that torque components that are important for the stability of the aerial are first fulfilled, whereas subsequent torque components may be fulfilled or scaled back.

Abstract: Target value detection for an unmanned aerial vehicle is described. The unmanned aerial vehicle includes a first transducer that transmits a first ultrasonic signal and receives a first ultrasonic response and a second transducer that transmits a second ultrasonic signal and receives a second ultrasonic response. The second transducer has a wider beam pattern than the first transducer. Determinations are made as to whether either or both of the first or second ultrasonic responses includes a target value within range areas associated with the respective beam patterns of the first and second transducers. A confidence value is generated based on the determinations. The target value is reflected from an object and the confidence value indicates a likelihood of a position of the unmanned aerial vehicle with respect to the object.

Abstract: Described herein are systems for the production, communication, routing, service, authentication, and consumption of cryptographically authenticable contextual content produced by cryptographically authenticable devices; example implementations of the architecture for a Trusted Contextual Content Device which produces Trusted Contextual Content; and example implementations of the architecture for a Trusted Drone Device which produces Trusted Contextual Content. For example, some of the methods used may include accessing a first set of sensor data from one or more sensors; receiving, a first trusted contextual content that includes a first digital signature; generating a data structure including the first trusted contextual content and data based on the first set of sensor data; signing the data structure using a signing key to generate a second trusted contextual content including a second digital signature; and storing or transmitting the second trusted contextual content.

Abstract: A controller system of an aerial vehicle may receive environmental data from one or more sensors of the aerial vehicle and adjusts limits of the aerial vehicle given the environmental conditions. When the aerial vehicle receives an input, such as a flight input from a remote controller or an environmental input such as a gust of wind, the controller system calculates appropriate motor inputs that are provided to the thrust motors of the aerial vehicle such that the adjusted limits of the aerial vehicle are not exceeded. In calculating the appropriate input to the thrust motors, the controller system performs an iterative process. For example, for a given maximum torque that can be applied to the thrust motors, the controller system iteratively allocates the torque such that torque components that are important for the stability of the aerial are first fulfilled, whereas subsequent torque components may be fulfilled or scaled back.

Abstract: A pipeline in a controller may be configured to interface between sensors and actuators. The pipeline may elements such as drivers, filters, a combine, estimators, controllers, a mixer, and actuator controllers. The drivers may receive sensor data and pre-process the received sensor data. The filters may filter the pre-processed sensor data to generate filtered sensor data. The combine may package the filtered sensor data to generate packaged sensor data. The estimators may determine estimates of a position of a vehicle based on the packaged sensor data. The controllers may generate control signals based on the determined estimates. The mixer may modify the generated control signals based on limitations of the vehicle. The actuator controllers may generate actuator control signals based on the modified control signals to drive the actuators.

Abstract: Disclosed is a configuration to control automatic return of an aerial vehicle. The configuration stores a return location in a storage device of the aerial vehicle. The return location may correspond to a location where the aerial vehicle is to return. One or more sensors of the aerial vehicle are monitored during flight for detection of a predefined condition. When a predetermined condition is met a return path program may be loaded for execution to provide a return flight path for the aerial vehicle to automatically navigate to the return location.

Abstract: A mapping system receives sensor data from an unmanned aerial vehicle. The mapping system further receives images from a camera of the unmanned aerial vehicle. The mapping system determines an altitude of the camera based on the sensor data. The mapping system calculates a footprint of the camera based on the altitude of the camera and a field of view of the camera. The mapping system constructs a localized map based on the images and the footprint of the camera.