Learning to Detect Collisions for Continuum Manipulators without a Prior Model

Due to their flexibility, dexterity, and compact size, Continuum Manipulators (CMs) can enhance minimally invasive interventions. In these procedures, the CM may be operated in proximity of sensitive organs; therefore, requiring accurate and appropriate feedback when colliding with their surroundings. Conventional CM collision detection algorithms rely on a combination of exact CM constrained kinematics model, geometrical assumptions such as constant curvature behavior, a priori knowledge of the environmental constraint geometry, and/or additional sensors to scan the environment or sense contacts. In this paper, we propose a data-driven machine learning approach using only the available sensory information, without requiring any prior geometrical assumptions, model of the CM or the surrounding environment. The proposed algorithm is implemented and evaluated on a non-constant curvature CM, equipped with Fiber Bragg Grating (FBG) optical sensors for shape sensing purposes. Results demonstrate successful detection of collisions in constrained environments with soft and hard obstacles with unknown stiffness and location.Comment: Accepted for publication in International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 201

Exact and Efficient Collision Detection for a Multi-section Continuum Manipulator

Abstract—Continuum manipulators, featuring “continuous backbone structures”, are promising for deft manipulation of a wide range of objects under uncertain conditions in less-structured and cluttered environments. A multi-section trunk/tentacle robot is such a continuum manipulator. With a continuum robot, manipulation means a continuous whole arm motion, where the arm is often bent into a continuously deforming concave shape. To approximate such an arm with a polygonal mesh for collision detection is expensive not only because a fine mesh is required to approximate concavity but also because each time the manipulator deforms, a new mesh has to be built for the new configuration. However, most generic collision detection algorithms apply to only polygonal meshes or objects of convex primitives. In this paper, we propose an efficient algorithm for Collision Detection between an Exact Continuum Manipulator (CD-ECoM) and its environments, which is applicable to any continuum manipulator featuring multiple constant-curvature sections. Our test results show that using this algorithm is both accurate and more efficient in both time and space to detect collisions than approximating the continuum manipulator as polygonal meshes and applying an existing generic collision detection algorithm. The algorithm is essential for path/trajectory planning of continuum manipulators. I