Knot-tying with Visual and Force Feedback for VR Laparoscopic Training

Real-time simulation of thread and knot-tying with visual and force feedback is an essential part of virtual reality laparoscopic training. This paper presents a physics-based thread simulator that enables realistic knot tying at haptic rendering rate. The virtual thread follows Newton's law and behaves naturally. The model considers main mechanical properties of the real thread such as stretching, compressing, bending and twisting, as well as contact forces due to self-collision and interaction with the environment, and the effect of gravity. The structure of the system has essential advantages over geometrically based approaches, as was illustrated in an implementation on the Xitact simulator

[1]  Jean-Claude Latombe,et al.  Efficient maintenance and self-collision testing for Kinematic Chains , 2002, SCG '02.

[2]  Mark H. Overmars,et al.  Spheres, molecules, and hidden surface removal , 1994, SCG '94.

[3]  Rieko Osu,et al.  Different mechanisms involved in adaptation to stable and unstable dynamics. , 2003, Journal of neurophysiology.

[4]  Jean-Claude Latombe,et al.  Real-time knot-tying simulation , 2004, The Visual Computer.

[5]  Etienne Burdet,et al.  Dynamic thread for real-time knot-tying , 2005, First Joint Eurohaptics Conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. World Haptics Conference.

[6]  H. Lönroth,et al.  The transfer of basic skills learned in a laparoscopic simulator to the operating room , 2002, Surgical Endoscopy And Other Interventional Techniques.

[7]  M. Ornstein,et al.  Virtual reality and laparoscopic surgery , 1995, The British journal of surgery.

[8]  Lydia E. Kavraki,et al.  Simulated knot tying , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[9]  Leonidas J. Guibas,et al.  Collision detection for deforming necklaces , 2002, SCG '02.

[10]  Mark H. Overmars,et al.  Spheres, molecules, and hidden surface removal , 1998, Comput. Geom..

[11]  T. Mori,et al.  Significance of ``hands-on training'' in laparoscopic surgery , 1998, Surgical Endoscopy.

[12]  Rieko Osu,et al.  The central nervous system stabilizes unstable dynamics by learning optimal impedance , 2001, Nature.

[13]  Carol O'Sullivan,et al.  Adaptive medial-axis approximation for sphere-tree construction , 2004, TOGS.

[14]  T. Grantcharov,et al.  Randomized clinical trial of virtual reality simulation for laparoscopic skills training , 2004, The British journal of surgery.

[15]  Steven C Cramer,et al.  Robotics, motor learning, and neurologic recovery. , 2004, Annual review of biomedical engineering.

[16]  R. Satava,et al.  Virtual Reality Training Improves Operating Room Performance: Results of a Randomized, Double-Blinded Study , 2002, Annals of surgery.