Soft Synergies: Model Order Reduction of Hybrid Soft-Rigid Robots via Optimal Strain Parameterization

2024-05-21

IEEE Transactions on robotics

AI Summary

Soft and rigid-soft hybrid robots exhibit strong nonlinearity and high-dimensional strain coupling, making it challenging to simultaneously achieve modeling accuracy and computational efficiency. To address this, we propose a strain-parameterized reduced-order modeling (ROM) framework: leveraging simulation data, an enhanced Proper Orthogonal Decomposition (POD) extracts optimal coupled strain modes, enabling construction of a structure-preserving low-dimensional strain-space model. We introduce the novel concept of “mechanical synergy” to uniformly characterize strain coupling across soft, hyper-redundant rigid, and closed-loop rigid-soft hybrid systems. The method enables real-time shape estimation of a six-pneumatic-chamber soft arm using only two marker points. It reduces configuration-space dimensionality by over 90%, while matching the static and dynamic accuracy of high-fidelity models. Computational speed improves by two to three orders of magnitude, enabling real-time simulation and closed-loop control.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Soft robots offer remarkable adaptability and safety advantages over rigid robots, but modeling their complex, nonlinear dynamics remains challenging. Strain-based models have recently emerged as a promising candidate to describe such systems, however, they tend to be high-dimensional and time-consuming. This article presents a novel model order reduction approach for soft and hybrid robots by combining strain-based modeling with proper orthogonal decomposition (POD). The method identifies optimal coupled strain basis functions—or mechanical synergies—from simulation data, enabling the description of soft robot configurations with a minimal number of generalized coordinates. The reduced order model (ROM) achieves substantial dimensionality reduction in the configuration space while preserving accuracy. Rigorous testing demonstrates the interpolation and extrapolation capabilities of the ROM for soft manipulators under static and dynamic conditions. The approach is further validated on a snake-like hyper-redundant rigid manipulator and a closed-chain system with soft and rigid components, illustrating its broad applicability. Moreover, the approach is leveraged for shape estimation of a real six-actuator soft manipulator using only two position markers, showcasing its practical utility. Finally, the ROM's dynamic and static behavior is validated experimentally against a parallel hybrid soft-rigid system, highlighting its effectiveness in representing the high-order model and the real system. This POD-based ROM offers significant computational speed-ups, paving the way for real-time simulation and control of complex soft and hybrid robots.

Problem

• Reduces high-dimensional soft robot models
• Combines strain modeling with Proper Orthogonal Decomposition
• Enables real-time simulation of hybrid robots

Innovation

• Combines strain modeling with Proper Orthogonal Decomposition.
• Identifies optimal strain basis for minimal coordinates.
• Validates model on hybrid soft-rigid systems effectively.