Accounting for Hysteresis in the Forward Kinematics of Nonlinearly-Routed Tendon-Driven Continuum Robots via a Learned Deep Decoder Network

TL;DR

This paper introduces a deep decoder neural network that accurately predicts the shape of tendon-driven continuum robots by accounting for hysteresis effects, outperforming existing physics-based and learning-based models.

Contribution

The paper presents a novel deep decoder architecture that incorporates hysteresis modeling for improved shape prediction of tendon-driven robots.

Findings

The proposed model outperforms physics-based models in shape accuracy.

It significantly reduces prediction errors caused by hysteresis.

The method is validated on a physical robot with promising results.

Abstract

Tendon-driven continuum robots have been gaining popularity in medical applications due to their ability to curve around complex anatomical structures, potentially reducing the invasiveness of surgery. However, accurate modeling is required to plan and control the movements of these flexible robots. Physics-based models have limitations due to unmodeled effects, leading to mismatches between model prediction and actual robot shape. Recently proposed learning-based methods have been shown to overcome some of these limitations but do not account for hysteresis, a significant source of error for these robots. To overcome these challenges, we propose a novel deep decoder neural network that predicts the complete shape of tendon-driven robots using point clouds as the shape representation, conditioned on prior configurations to account for hysteresis. We evaluate our method on a physical tendon-driven robot and show that our network model accurately predicts the robot's shape, significantly outperforming a state-of-the-art physics-based model and a learning-based model that does not account for hysteresis.