Publications
Autonomous control of pneumatically-powered percussive drilling through highly layered formations
The ability to rapidly drill through diverse, layered materials can greatly enhance future mine-rescue operations, energy exploration, and underground operations. Pneumatic-percussive drilling holds great promise in this area due to its ability to penetrate very hard materials and potential for portability. Currently such systems require expert operators who require extensive training. We envision future applications where first responders who lack such training can still respond rapidly and safely perform operations. Automated techniques can reduce the dependence on expert operators while increasing efficiency and safety. However, current progress in this area is restricted by the difficulty controlling such systems and the complexity of modeling percussive rock-bit interactions. In this work we develop and experimentally validate a novel intelligent percussive drilling architecture that is tailored to autonomously operate in diverse, layered materials. Our approach combines low-level feedback control, machine learning-based material classification, and on-line optimization. Our experimental results demonstrate the effectiveness of this approach and illustrate the performance benefits over conventional methods.