ArXiv TLDR

Actuation space reduction to facilitate insightful shape matching in a novel reconfigurable tendon driven continuum manipulator

🐦 Tweet
2604.12792

Sabyasachi Dash, John Golden, Girish Krishnan

cs.RO

TLDR

A novel reconfigurable continuum manipulator simplifies shape matching by reducing actuation space through a sequential disk rotation strategy.

Key contributions

  • Introduces a reconfigurable tendon-driven continuum manipulator with actively rotating spacer disks.
  • Simplifies complex shape matching by projecting backbone shape into curvature-torsion space.
  • Proposes a sequential actuation strategy: proximal/intermediate disks for global shape, distal for fine-tuning.
  • Offers a model-free control approach, bypassing complex modeling of reconfigurable TDCMs.

Why it matters

This paper addresses the challenge of controlling reconfigurable continuum manipulators by offering a novel, model-free approach. It simplifies complex shape matching, making these versatile robots more practical and easier to control without intricate models.

Original Abstract

In tendon driven continuum manipulators (TDCMs), reconfiguring the tendon routing enables tailored spatial deformation of the backbone. This work presents a design in which tendons can be rerouted either prior to or after actuation by actively rotating the individual spacer disks. Each disk rotation thus adds a degree of freedom to the actuation space, complicating the mapping from a desired backbone curve to the corresponding actuator inputs. However, when the backbone shape is projected into an intermediate space defined by curvature and torsion (C-T), patterns emerge that highlight which disks are most influential in achieving a global shape. This insight enables a simplified, sequential shape-matching strategy: first, the proximal and intermediate disks are rotated to approximate the global shape; then, the distal disks are adjusted to fine-tune the end-effector position with minimal impact on the overall shape. The proposed actuation framework offers a model-free alternative to conventional control approaches, bypassing the complexities of modeling reconfigurable TDCMs.

📬 Weekly AI Paper Digest

Get the top 10 AI/ML arXiv papers from the week — summarized, scored, and delivered to your inbox every Monday.