S2C2A: A Flexible Task Space Planning and Control Strategy for Modular Soft Robot Arms

2024-10-04

arXiv.org

AI Summary

Modular soft robotic arms (MSRAs) suffer from high dimensionality, strong nonlinearity, modeling uncertainty, and error accumulation across modules, leading to significant challenges in motion planning and control. Method: This paper proposes S2C2A, an end-to-end task-space framework enabling hierarchical closed-loop mapping from “state → configuration → action.” It introduces a novel decoupled two-stage architecture—S2C (state-to-configuration planning) and C2A (configuration-to-actuation control)—and integrates a biLSTM-based forward model with first-principles physics for robust configuration tracking under imprecise proprioceptive feedback. The method encompasses optimization-driven configuration trajectory generation, hybrid physics-informed learning-based configuration control, cable-driven soft actuator modeling, and feedback control. Results: Evaluated on a cable-driven MSRA platform, S2C2A achieves offline pose control and obstacle avoidance, and—critically—demonstrates, for the first time, online target interaction and dynamic obstacle avoidance while effectively suppressing cumulative errors.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modular soft robot arms (MSRAs) are composed of multiple independent modules connected in a sequence. Due to their modular structure and high degrees of freedom (DOFs), these modules can simultaneously bend at different angles in various directions, enabling complex deformation. This capability allows MSRAs to perform more intricate tasks than single module robots. However, the modular structure also induces challenges in accurate planning, modeling, and control. Nonlinearity, hysteresis, and gravity complicate the physical model, while the modular structure and increased DOFs further lead to accumulative errors along the sequence. To address these challenges, we propose a flexible task 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 MSRA model to generate configuration trajectories based on target MSRA states. Given the model complexity, we leverage a biLSTM network as the forward model. Subsequently, a configuration controller C2A (Configuration to Action control) is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. Both a biLSTM network and a physical model are utilized for configuration control. We validated our strategy using a cable-driven MSRA, demonstrating its ability to perform diverse offline tasks such as position control, orientation control, and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work will focus on addressing MSRA challenges, such as developing more accurate physical models and reducing configuration estimation errors along the module sequence.

Problem

• Addresses nonlinearity and hysteresis in modular soft robot arms
• Reduces cumulative errors from increased DOFs in MSRAs
• Enables accurate planning and control without precise sensing

Innovation

• biLSTM-based forward model for trajectory planning
• Configuration controller using only internal sensing
• Optimization integrating multiple loss functions