Motion planning for steerable needles in 3D environments with obstacles using rapidly-exploring Random Trees and backchaining

Steerable needles composed of a highly flexible material and with a bevel tip offer greater mobility compared to rigid needles for minimally invasive medical procedures. In this paper, we apply sampling-based motion planning technique to explore motion planning for the steerable bevel-tip needle in 3D environments with obstacles. Based on the Rapidly-exploring Random Trees (RRTs) method, we develop a motion planner to quickly build a tree to search the configuration space using a new exploring strategy, which generates new states using randomly sampled control space instead of the deterministically sampled one used in classic RRTs. Notice the fact that feasible paths might not be found for any given entry point and target configuration, we also address the feasible entry point planning problem to find feasible entry points in a specified entry zone for any given target configuration. To solve this problem, we developed a motion planning algorithm based on RRTs with backchaining, which grow backward from the target to explore the configuration space. Finally, simulation results with a approximated realistic prostate needle insertion environment demonstrate the performance of the proposed motion planner.

[1]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[2]  S. LaValle.  Rapidly-exploring random trees : a new tool for path planning , 1998 .

[3]  Steven M. LaValle,et al.  RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[4]  Steven M. LaValle,et al.  Robot Motion Planning: A Game-Theoretic Foundation , 2000, Algorithmica.

[5]  S. LaValle,et al.  Randomized Kinodynamic Planning , 2001 .

[6]  Michael M. Curtiss,et al.  RRTs for nonlinear, discrete, and hybrid planning and control , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[7]  Allison M. Okamura,et al.  Robotic needle insertion: effects of friction and needle geometry , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[8]  James J. Kuffner,et al.  Effective sampling and distance metrics for 3D rigid body path planning , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[9]  Gregory S. Chirikjian,et al.  Steering flexible needles under Markov motion uncertainty , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Jin Seob Kim,et al.  Diffusion-Based Motion Planning for a Nonholonomic Flexible Needle Model , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[11]  Robert J. Webster,et al.  Design Considerations for Robotic Needle Steering , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[12]  Allison M. Okamura,et al.  Planning for Steerable Bevel-tip Needle Insertion Through 2D Soft Tissue with Obstacles , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[13]  Jin Seob Kim,et al.  Nonholonomic Modeling of Needle Steering , 2006, Int. J. Robotics Res..

[14]  Nancy M. Amato,et al.  An obstacle-based rapidly-exploring random tree , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[15]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[16]  Kenneth Y. Goldberg,et al.  Constant-Curvature Motion Planning Under Uncertainty with Applications in Image-Guided Medical Needle Steering , 2006, WAFR.

[17]  Rajni V. Patel,et al.  Minimization of needle deflection in robot‐assisted percutaneous therapy , 2007, The international journal of medical robotics + computer assisted surgery : MRCAS.

[18]  Vinutha Kallem,et al.  Image-guided Control of Flexible Bevel-Tip Needles , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[19]  Thierry Siméon,et al.  The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty , 2007, Robotics: Science and Systems.

[20]  S. Shankar Sastry,et al.  Screw-based motion planning for bevel-tip flexible needles in 3D environments with obstacles , 2008, 2008 IEEE International Conference on Robotics and Automation.

[21]  Ross A. Knepper,et al.  Empirical Sampling of Path Sets for Local Area Motion Planning , 2008, ISER.