A Non-Linear Model Predictive Task-Space Controller Satisfying Shape Constraints for Tendon-Driven Continuum Robots

TL;DR

This paper introduces a real-time non-linear model predictive control method for tendon-driven continuum robots that ensures shape constraints and collision avoidance, improving safety and performance in confined environments.

Contribution

It presents a novel MPC framework integrating a PCC model and local feedback for shape-constrained control of TDCRs, with real-time capabilities and hardware validation.

Findings

Outperforms Jacobian-based controllers in position tracking

Handles disturbances and shape constraints effectively

Validated on a physical prototype for teleoperation safety

Abstract

Tendon-Driven Continuum Robots (TDCRs) have the potential to be used in minimally invasive surgery and industrial inspection, where the robot must enter narrow and confined spaces. We propose a Model Predictive Control (MPC) approach to leverage the non-linear kinematics and redundancy of TDCRs for whole-body collision avoidance, with real-time capabilities for handling inputs at 30Hz. Key to our method's effectiveness is the integration of a nominal Piecewise Constant Curvature (PCC) model for efficient computation of feasible trajectories, with a local feedback controller to handle modeling uncertainty and disturbances. Our experiments in simulation show that our MPC outperforms conventional Jacobian-based controller in position tracking, particularly under disturbances and user-defined shape constraints, while also allowing the incorporation of control limits. We further validate our method on a hardware prototype, showcasing its potential for enhancing the safety of teleoperation tasks.