Contact event detection for robotic oil drilling

Abstract

To ensure safe and reliable operation in a robotic oil drilling system, it is essential to detect contact events such as impacts and slips between end-effectors and workpieces. In this challenging application, where high forces are used to manipulate heavy metal pipes in noisy environments, acoustic emissions (AE) sensors offer a promising contact sensing solution. Real-time AE signal features are used to create a multinomial contact event classifier. The sensitivity of signal features to a variety of contact events including two types of slip is presented. Results indicate that the classifier is able to robustly and dynamically classify contact events with >90% accuracy using a small set of AE signal features.