Abstract: A method of simulating a quadcopter includes testing a plurality of quadcopter components at a plurality of operating conditions to generate one or more lookup tables for characteristics of the quadcopter components. The lookup tables are stored for quadcopter component simulation in a quadcopter simulator. When a simulated input value for a simulated quadcopter component is received in the simulator lookup table, entries corresponding to the simulated input value are read from the lookup table and simulated quadcopter component output is generated. Simulated quadcopter output to a flight controller is generated according to the simulated quadcopter component output from one or more entries of one or more lookup tables.

Abstract: A method of simulating a quadcopter includes recording camera output for one or more video cameras under constant conditions and subtracting a constant signal from the recorded camera output to obtain a camera noise recording. Simulated camera noise is generated from the camera noise recording and is added to a plurality of simulated camera outputs of a quadcopter simulator to generate noise-added simulated camera outputs. The noise-added simulated camera outputs are sent to an Artificial Intelligence (AI) controller coupled to the quadcopter simulator for the AI controller to use to pilot a simulated quadcopter of the quadcopter simulator.

Abstract: A method of debugging quadcopter piloting code includes coupling an Artificial Intelligence (AI) controller configured with AI piloting code to a workstation having a quadcopter simulator and initiating piloting of a simulated quadcopter of the quadcopter simulator by the AI piloting code of the AI controller. Operations of the quadcopter simulator are logged, and communications timestamped. Subsequently, in response to an AI piloting code event at an event time, the event time is determined from a timestamped communication and a logged operation of the quadcopter simulator having a timestamp corresponding to the event time is found. The quadcopter simulator is rewound to at least the logged operation and one or more operations of the quadcopter simulator and the AI piloting code are stepped through to identify AI piloting code errors relating to the AI piloting code event.

Abstract: An apparatus includes a quadcopter simulator coupled to an Artificial Intelligence (AI) controller. The quadcopter controller is configured to receive quadcopter flight control commands and to generate simulated sensor output and simulated camera output for a plurality of stereoscopic cameras of a simulated quadcopter. The AI controller is configured to receive the simulated sensor and camera output from the quadcopter simulator, determine a flight path for the simulated quadcopter according to the simulated sensor and camera output, generate the quadcopter flight control commands according to the flight path, and provide the quadcopter flight control commands to the quadcopter simulator.

Abstract: An autonomous quadcopter has four motors, each motor coupled to a corresponding propeller and a flight controller coupled to the four motors to provide input to the four motors to control flight. The autonomous quadcopter also has a plurality of cameras and an Artificial Intelligence (AI) controller coupled to the plurality of cameras to receive input from the plurality of cameras, determine a flightpath for the autonomous quadcopter according to the input from the plurality of cameras, and provide commands to the flight controller to direct the flight controller to follow the flightpath.

Abstract: Techniques are described for tracking and determining a three dimensional path travelled by controlled unmanned aircraft (i.e. drones) or other moving objects. By monitoring the strength of communication signals transmitted by an object, the strength of control signals received by the object, and altitude data generated by the object, its three dimensional path is determined. For example, these techniques can be applied to racing drones to determine their positions on a course. An end gate structure for such a course that can automatically transmit disable signals to the drones upon completing the course is also described.

Abstract: A diversity receiver synchronizes and mixes multiple input signals. In one embodiment, the receiver demodulates the multiple input signals prior to synchronizing, converts the demodulated multiple input signals from analog signals to digital signals, synchronizes the demodulated digital signals, converts the synchronized demodulated digital signals to analog signals and mixes the synchronized demodulated analog signals based on a characteristic of the input signals existing prior to the demodulating.

Abstract: An airframe health monitoring system uses a controller to automatically determine a status of the airframe of an aircraft in real time, at power up and periodically during flight of the aircraft, based on the output of pressure sensors such as ribbon sensors and flex sensors mounted on the airframe arms and body. The controller can determine in real time whether the airframe has a fault or is damaged based on continuity measured in ribbon sensors located along the outside surfaces of the airframe. The controller can also determine a metric on the status of the airframe in real time based on arm impulses measured in flex sensors located along inside surfaces of arms of the airframe. This will lead to less expensive, more accurate, faster, automated detection of airframe faults, airframe damage and/or metrics on the health of the airframe.

Abstract: Techniques are described for the exchange of control signals between a controlled unmanned aircraft (i.e. drone) and a ground control station and for the transmission of communication signals, such as video, from the drone to the ground control station so that the signals are more difficult to intercept or jam. The video signal transmitted from the drone can be an analog RF signal employing one or more of video “scrambling”, RF signal inversion, hopping, usage of a wide frequency range and other techniques. To secure the control signals between the drone and the ground control station, techniques can include hopping, encryption and use of a wide frequency range.

Abstract: An airframe health monitoring system uses a controller to automatically determine a status of the airframe of an aircraft in real time, at power up and periodically during flight of the aircraft, based on the output of pressure sensors such as ribbon sensors and flex sensors mounted on the airframe arms and body. The controller can determine in real time whether the airframe has a fault or is damaged based on continuity measured in ribbon sensors located along the outside surfaces of the airframe. The controller can also determine a metric on the status of the airframe in real time based on arm impulses measured in flex sensors located along inside surfaces of arms of the airframe. This will lead to less expensive, more accurate, faster, automated detection of airframe faults, airframe damage and/or metrics on the health of the airframe.

Abstract: A wireless power initiated test system can use an aircraft controller to automatically initiate a test sequence to determine status of systems of the aircraft, based on the detection of receipt of the wireless power at the aircraft. The detection of receipt of the wireless power at the aircraft can be based on the detection at a voltage regulator of the aircraft which is receiving the wireless power from a wireless power receiver of the aircraft. The system may also power the aircraft controller during the test sequence using the wireless power received. Such a system may quickly and efficiently initiate, power and perform a pre-flight test sequence to determine statuses of systems of the aircraft, such as during a competition or race of the aircraft. The system may apply to drones, as well as of other types of aircraft.

Abstract: Techniques are described for tracking and determining a three dimensional path travelled by controlled unmanned aircraft (i.e. drones) or other moving objects. By monitoring the strength of communication signals transmitted by an object, the strength of control signals received by the object, and altitude data generated by the object, its three dimensional path is determined. For example, these techniques can be applied to racing drones to determine their positions on a course. An end gate structure for such a course that can automatically transmit disable signals to the drones upon completing the course is also described.

Abstract: Techniques are described for tracking and determining a three dimensional path traveled by controlled unmanned aircraft (i.e. drones) or other moving objects. By monitoring the strength of communication signals transmitted by an object, the strength of control signals received by the object, and altitude data generated by the object, its three dimensional path is determined. For example, these techniques can be applied to racing drones to determine their positions on a course. An end gate structure for such a course that can automatically transmit disable signals to the drones upon completing the course is also described.