Configuration Tracking Control of a Multi-Segment Soft Robotic Arm Using a Cosserat Rod Model

TL;DR

This paper presents the first closed-loop control of a multi-segment soft robotic arm's configuration using a Cosserat rod model, real-time sensing, and simulation-based inverse dynamics for precise manipulation.

Contribution

It introduces a novel control approach combining real-time sensing, simulation, and actuation for soft robotic arms with multiple segments, demonstrating effective configuration tracking.

Findings

Successful real-time control of multi-segment soft arm configurations

High accuracy in tip position and orientation tracking

Validation of Cosserat rod model for inverse dynamics estimation

Abstract

Controlling soft continuum robotic arms is challenging due to their hyper-redundancy and dexterity. In this paper we demonstrate, for the first time, closed-loop control of the configuration space variables of a soft robotic arm, composed of independently controllable segments, using a Cosserat rod model of the robot and the distributed sensing and actuation capabilities of the segments. Our controller solves the inverse dynamic problem by simulating the Cosserat rod model in MATLAB using a computationally efficient numerical solution scheme, and it applies the computed control output to the actual robot in real time. The position and orientation of the tip of each segment are measured in real time, while the remaining unknown variables that are needed to solve the inverse dynamics are estimated simultaneously in the simulation. We implement the controller on a multi-segment silicone robotic arm with pneumatic actuation, using a motion capture system to measure the segments' positions and orientations. The controller is used to reshape the arm into configurations that are achieved through different combinations of bending and extension deformations in 3D space. The resulting tracking performance indicates the effectiveness of the controller and the accuracy of the simulated Cosserat rod model that is used to estimate the unmeasured variables.