A Scalable, High-performance, Real-time Control Architecture with Application to Semi-autonomous Teleoperation

Zihan Chen, Johns Hopkins University

A scalable and real-time capable infrastructure is required to enable high degrees-of-freedom systems that need high-performance control and haptic rendering. The specific platform that motivates this thesis work is the open research platform da Vinci Research Kit (dVRK). For the system architecture, we propose a specialized IEEE-1394 (FireWire) broadcast protocol that takes advantage of broadcast and peer-to-peer transfers to minimize the number of transactions, and thus the software overhead, on the control PC, thereby enabling fast real-time control. It has also been extended to Ethernet via a novel Ethernet-to-FireWire bridge protocol. The software architecture consists of a distributed hardware interface layer, a real-time component-based software framework, and integration with the Robot Operating System (ROS). The architecture is scalable to support multiple active manipulators, reconfigurable to enable researchers to partition a full system into multiple independent subsystems, and extensible at all levels of control. This architecture has been applied to two semi-autonomous teleoperation applications. The first application is to a suturing task in Robotic Minimally Invasive Surgery (RMIS), that includes the development of virtual fixtures for the needle passing and knot tying sub-tasks, with a multi-user study to verify their effectiveness. The second application concerns time-delayed teleoperation of a robotic arm for satellite servicing. The research contribution includes the development of a line virtual fixture with augmented reality, a test for different time delay configurations and a multi-user study that evaluates the effectiveness of the system.

Speaker Biography

Zihan Chen received the Bachelors of Science degree in Control Science and Engineering and the Bachelors of Arts degree in English Language and Literature in 2010, and the Masters of Science and Engineering degree in Mechanical Engineering from Johns Hopkins University in 2012. He enrolled in the Computer Science Ph.D. program in 2012. His research focuses on scalable, high-performance control system and semi-autonomous teleoperation.