Title :
Contact event detection for robotic oil drilling
Author :
Wu, X. Alice ; Burkhard, Natalie ; Heyneman, Barrett ; Valen, Roald ; Cutkosky, Mark
Author_Institution :
Center for Design Res., Stanford Univ., Stanford, CA, USA
fDate :
May 31 2014-June 7 2014
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.
Keywords :
acoustic emission; end effectors; feature extraction; industrial robots; oil drilling; pipes; reliability; safety; sensors; signal classification; AE sensors; acoustic emissions sensors; contact event detection; contact sensing solution; end-effectors; heavy metal pipes; impacts; multinomial contact event classifier; noisy environments; real-time AE signal features; reliable operation; robotic oil drilling system; safe operation; signal features sensitivity; slips; workpieces; Accuracy; Grippers; Noise; Robot sensing systems; Steel; acoustic emissions; contact sensing; manipulation; slip;
Conference_Titel :
Robotics and Automation (ICRA), 2014 IEEE International Conference on
Conference_Location :
Hong Kong
DOI :
10.1109/ICRA.2014.6907171