Abstract: A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Abstract: A control system and method for tensioning an active tether for a multirotor unmanned aerial system is provided. The control system includes a tensioning pulley and a tensioning spring. The tensioning spring is arranged to generate a variable tension force on the active tether. A transducer is connected adjacent to the tensioning spring to sense a linear displacement position of the tensioning pulley and to transmit a position signal to a controller. The position signal is proportional to the linear displacement position. A servomotor in communication with the controller receives a control signal from the controller in response to the position signal. The servomotor drives a cable reel. The cable reel is rotatably mounted in the frame for spooling the tether in response to rotation of the servomotor. The cable reel is rotated by the servomotor to maintain a predetermined tension on the tensioning spring.

Abstract: A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy. Near-bit depth-of-cut estimations are performed using a machine learning prediction deployed in an embedded processor to provide high-speed ROP estimates. The sensing capability is coupled with a near-bit clutching mechanism to support drilling dysfunction mitigation.

Abstract: A system or method for drilling includes autonomously controlling a rotary or percussive drilling process as it transitions through multiple materials with very different dynamics. The method determines a drilling medium based on real-time measurements and comparison to prior drilling data, and identifies the material type, drilling region, and approximately optimal setpoint based on data from at least one operating condition. The controller uses these setpoints initially to execute an optimal search to maximize performance by minimizing mechanical specific energy.