A motion planning approach to automatic obstacle avoidance during concentric tube robot teleoperation

Abstract

Concentric tube robots are thin, tentacle-like devices that can move along curved paths and can potentially enable new, less invasive surgical procedures. Safe and effective operation of this type of robot requires that the robot’s shaft avoid sensitive anatomical structures (e.g., critical vessels and organs) while the surgeon teleoperates the robot’s tip. However, the robot’s unintuitive kinematics makes it difficult for a human user to manually ensure obstacle avoidance along the entire tentacle-like shape of the robot’s shaft. We present a motion planning approach for concentric tube robot teleoperation that enables the robot to interactively maneuver its tip to points selected by a user while automatically avoiding obstacles along its shaft. We achieve automatic collision avoidance by precomputing a roadmap of collision-free robot configurations based on a description of the anatomical obstacles, which are attainable via volumetric medical imaging. We also mitigate the effects of kinematic modeling error in reaching the goal positions by adjusting motions based on robot tip position sensing. We evaluate our motion planner on a teleoperated concentric tube robot and demonstrate its obstacle avoidance and accuracy in environments with tubular obstacles.

Motion planning for a three-stage multilumen transoral lung access system

“…Kinematic modeling of concentric tube robots has rapidly developed to consider bending and torsional interactions among the tubes [12], [13], [14]. Prior work has also achieved position control [14], [13], [15] and obstacle avoidance [16] with these robots. In our work, we use a mechanics-based kinematic model [13] to compute the concentric tube robot's shape during planning.…”

Kinematic Control and Obstacle Avoidance for Soft Inflatable Manipulator

“…For soft robots in general, the majority of the navigation methods employed in the literature rely on either an optimization technique [10], [13] or samplingbased planners [19], [21]. While practically useful even in complex environments, these types of methods rely on the availability of a complete or at least near-complete knowledge of the environment.…”

Concentric Tube Robots: Rapid, Stable Path-Planning and Guidance for Surgical Use

“…Others have investigated Jacobianbased approximations [17] to speed-up computations. Precomputation of dense path-plans within the robot workspace has been proposed by [18], while sparse path-plans and random trees have been proposed in [14], [19].…”