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Motion Planning for Concentric Tube Robots Using Mechanics-based Models.

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This study presents a new motion planner for concentric tube robots, enhancing minimally invasive surgery by navigating complex anatomy. The planner improves safety by accurately modeling robot shape and minimizing collision risks with obstacles.

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Area of Science:

  • Robotics
  • Minimally Invasive Surgery
  • Medical Device Technology

Background:

  • Concentric tube robots (CTRs) offer potential for novel minimally invasive surgical procedures.
  • Their complex kinematics, involving pre-curved, telescoping tubes, make motion planning challenging.
  • Reaching difficult anatomical sites requires precise control and obstacle avoidance.

Purpose of the Study:

  • To introduce a novel motion planner for CTRs.
  • To enhance maneuverability to clinical targets while minimizing collision probability.
  • To improve the accuracy of CTR shape modeling and account for actuation uncertainty.

Main Methods:

  • Developed a mechanics-based model for CTRs, incorporating torsional interactions between tubes.
  • Integrated this model with a sampling-based approach using the Rapidly-Exploring Roadmap (RRM).
  • Accounted for uncertainty in actuation and predicted device shape for robust planning.

Main Results:

  • The new motion planner accurately models CTR shape, considering inter-tube torsional effects.
  • The RRM-based approach guarantees optimal plans with increased computation time.
  • Demonstrated successful simulation in various scenarios, including a skull base neurosurgery case.

Conclusions:

  • The proposed motion planner significantly improves the ability to navigate CTRs to challenging surgical targets.
  • Accurate modeling and uncertainty consideration enhance safety and efficacy in minimally invasive procedures.
  • This approach has potential applications in complex surgeries like neurosurgery.