A Versatile Neural Network Configuration Space Planning and Control Strategy for Modular Soft Robot Arms

TL;DR

This paper introduces a versatile planning and control strategy for modular soft robot arms that effectively manages nonlinearities and errors, enabling complex tasks and online interactions using biLSTM-based models.

Contribution

The paper presents S2C2A, a novel biLSTM-based framework for configuration space planning and control of MSRAs, addressing modeling inaccuracies and enabling diverse tasks.

Findings

Successfully performs position and orientation control

Enables obstacle avoidance capabilities

Supports online interaction with targets and obstacles

Abstract

Modular soft robot arms (MSRAs) are composed of multiple modules connected in a sequence, and they can bend at different angles in various directions. This capability allows MSRAs to perform more intricate tasks than single-module robots. However, the modular structure also induces challenges in accurate planning and control. Nonlinearity and hysteresis complicate the physical model, while the modular structure and increased DOFs further lead to cumulative errors along the sequence. To address these challenges, we propose a versatile configuration space planning and control strategy for MSRAs, named S2C2A (State to Configuration to Action). Our approach formulates an optimization problem, S2C (State to Configuration planning), which integrates various loss functions and a forward model based on biLSTM to generate configuration trajectories based on target states. A configuration controller C2A (Configuration to Action control) based on biLSTM is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. We validate our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position and orientation control and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work focuses on addressing MSRA challenges, such as more accurate physical models.