A Unified and Modular Model Predictive Control Framework for Soft Continuum Manipulators under Internal and External Constraints

TL;DR

This paper presents a modular Model Predictive Control framework tailored for soft continuum robots, effectively managing nonlinear dynamics, constraints, and variable stiffness through a unified approach demonstrated via simulations and experiments.

Contribution

It introduces a task-space MPC framework for soft robots that integrates internal and external constraints with actuation dynamics in a modular, unified manner.

Findings

Successful implementation of task-space MPC for soft robotic control

Simulation and experimental validation of the proposed framework

Enhanced handling of nonlinearities and constraints in soft robot control

Abstract

Fluidically actuated soft robots have promising capabilities such as inherent compliance and user safety. The control of soft robots needs to properly handle nonlinear actuation dynamics, motion constraints, workspace limitations, and variable shape stiffness, so having a unique algorithm for all these issues would be extremely beneficial. In this work, we adapt Model Predictive Control (MPC), popular for rigid robots, to a soft robotic arm called SoPrA. We address the challenges that current control methods are facing, by proposing a framework that handles these in a modular manner. While previous work focused on Joint-Space formulations, we show through simulation and experimental results that Task-Space MPC can be successfully implemented for dynamic soft robotic control. We provide a way to couple the Piece-wise Constant Curvature and Augmented Rigid Body Model assumptions with internal and external constraints and actuation dynamics, delivering an algorithm that unites these aspects and optimizes over them. We believe that a MPC implementation based on our approach could be the way to address most of model-based soft robotics control issues within a unified and modular framework, while allowing to include improvements that usually belong to other control domains such as machine learning techniques.