4.6 Article

Large-Scale Surface Shape Sensing with Learning-Based Computational Mechanics

Journal

ADVANCED INTELLIGENT SYSTEMS
Volume 3, Issue 11, Pages -

Publisher

WILEY
DOI: 10.1002/aisy.202100089

Keywords

computational mechanics; ensemble learning; flexible sensors; robotic proprioception; surface shape sensing

Funding

  1. Research Grants Council (RGC) of Hong Kong [17207020, 17206818, 17205919, T42-409/18-R]
  2. Innovation and Technology Commission (ITC) [UIM/353]

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Proprioception enables organisms to interact safely and accurately with their environment and each other by perceiving their own configuration and movement in space. A novel flexible sensor framework incorporating computational mechanics and machine learning is proposed to achieve robots with similar perceptive capability. This framework utilizes finite element analysis and ensemble learning to enable real-time, robust, and high-order surface reconstruction, demonstrating enhanced sensing performance in accuracy, repeatability, and feasibility on a large-scale sensor.
Proprioception, the ability to perceive one's own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin, and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, a flexible sensor framework that incorporates a novel modeling strategy, taking advantage of computational mechanics and machine learning, is proposed. The sensor framework on a large flexible sensor that transforms sparsely distributed strains into continuous surface is implemented. Finite element (FE) analysis is utilized to determine design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real time, robust, and high-order surface reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated on such a large-scale sensor before.

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