Sensor Fusion-based Parameterized Curve-driven Modeling for Digital Twin of Reconfigurable Soft Robot

Soft robotics, driven by material and design advancements, presents challenges for visualization and simulation due to its complex deformations. This paper proposes a digital twin (DT) system for reconfigurable soft robots in augmented reality (AR) environments. Leveraging parameterized curve-driven methods, the system enables dynamic modification of digital twins, accurately representing deformations. Three primary deformation patterns are identified, and sensor fusion captures real-time structural changes. Implemented within an AR environment, the system allows immersive inspection and simulation of soft robots. A case study validates its effectiveness.