The recent development of Robot-Assisted Minimally Invasive Surgery (RAMIS) has brought much benefit to ease the performance of complex Minimally Invasive Surgery (MIS) tasks and lead to more clinical outcomes. Compared to direct master-slave manipulation, semi-autonomous control for the surgical robot can enhance the efficiency of the operation, particularly for repetitive tasks. However, operating in a highly dynamic in-vivo environment is complex. Supervisory control functions should be included to ensure flexibility and safety during the autonomous control phase. This paper presents a haptic rendering interface to enable supervised semi-autonomous control for a surgical robot. Bayesian optimization is used to tune user-specific parameters during the surgical training process. User studies were conducted on a customized simulator for validation. Detailed comparisons are made between with and without the supervised semi-autonomous control mode in terms of the number of clutching events, task completion time, master robot end-effector trajectory and average control speed of the slave robot. The effectiveness of the Bayesian optimization is also evaluated, demonstrating that the optimized parameters can significantly improve users' performance. Results indicate that the proposed control method can reduce the operator's workload and enhance operation efficiency.
Improving Control Precision and Motion Adaptiveness for Surgical Robot with Recurrent Neural Network
Surgical robot research is driven by the desire of
improving surgical outcomes. This paper proposed a Recurrent
Neural Network based controller to address two problems:
1) improving control precision, 2) increasing adaptiveness for
robot motion (explained in Section I). RNN was adopted in
this work mainly because 1) the problem formulation naturally
matches RNN structure, 2) RNN has advantages as an biologi-
cally inspired method. The proposed method was explained in
detail and analysis shows that the proposed method is able to
dynamically regulate outputs to increase the adaptiveness and
the control precision. This paper uses Raven II surgical robot
as an example to show the application of the proposed method,
and the numeral simulation results from the proposed method
and three other controllers show that the proposed method has
improved precision, improved high robustness against noise and
increased movement smoothness, and it keeps the manipulator
links as far away as possible from physical boundaries, which
potentially increases surgical safety and leads to improved
surgical outcomes.
- Award ID(s):
- 1637444
- Publication Date:
- NSF-PAR ID:
- 10037167
- Journal Name:
- Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems
- ISSN:
- 2153-0858
- Sponsoring Org:
- National Science Foundation
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