Magnetic resonance imaging (MRI) offers many benefits, including unsurpassed soft-tissue characterization and the ability to combine detection and biopsy into a single procedure. However, limited patient access in the narrow scanner bore requires tedious iterative positioning or use of robotic assistants that isolate the physician from the patient. As an alternative, we present a teleoperation technology for percutaneous procedures to meet the needs of interventional radiologists and overcome challenges imposed by the MR environment. The technology is demonstrated for a 1-DOF needle insertion procedure. The technology uses rolling diaphragms, a clutch, and a cable-capstan drive to propel the needle while relaying forces and motions to the operator. The system demonstrates excellent position tracking (< 0.7° error in the unloaded case) and reliably transmits changes in force. During needle teleoperation, users were able to detect light membrane punctures and differentiate spring stiffnesses nearly as accurately as by hand manipulation.

Grasping and manipulation of biological tissue are crucial processes during minimally invasive surgery (MIS). To enable atraumatic and reliable grasping, it would be useful to detect slip of the grasped object. Because tissue is moist, conformable, and delicate, and because the sensor must work in a surgical environment, this application requires a departure from conventional slip sensing methods. We present a technology and method based on hot-wire anemometry to detect slip while grasping wet, conformable materials and discuss how this approach may be extended to graspers used in robot-assisted surgery (RAS). We present our design and the results from characterization tests as well as experimental results that demonstrate its ability to detect planar direction of slip of wet, compliant objects.

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.