ArticleSensors (Basel, Switzerland)2020
Dynamic-Vision-Based Force Measurements Using Convolutional Recurrent Neural Networks.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Characterization of a Spiking Convolutional Processor for FPGA.Sensors (Basel, Switzerland) · 2026Article
- Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression.Sensors (Basel, Switzerland) · 2024Article
- A neuromorphic dataset for tabletop object segmentation in indoor cluttered environment.Scientific data · 2024Article
- Article
- Sim-to-Real for High-Resolution Optical Tactile Sensing: From Images to Three-Dimensional Contact Force Distributions.Soft robotics · 2022Article
- HiVTac: A High-Speed Vision-Based Tactile Sensor for Precise and Real-Time Force Reconstruction with Fewer Markers.Sensors (Basel, Switzerland) · 2022Article
- A Deep Learning Method for Vision Based Force Prediction of a Soft Fin Ray Gripper Using Simulation Data.Frontiers in robotics and AI · 2021Article
- Vision-Based Suture Tensile Force Estimation in Robotic Surgery.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this paper, a novel dynamic Vision-Based Measurement method is proposed to measure contact force independent of the object sizes. A neuromorphic camera (Dynamic Vision Sensor) is utilizused to observe intensity changes within the silicone membrane where the object is in contact. Three deep Long Short-Term Memory neural networks combined with convolutional layers are developed and implemented to estimate the contact force from intensity changes over time. Thirty-five experiments are conducted using three objects with different sizes to validate the proposed approach. We demonstrate that the networks with memory gates are robust against variable contact sizes as the networks learn object sizes in the early stage of a grasp. Moreover, spatial and temporal features enable the sensor to estimate the contact force every 10 ms accurately. The results are promising with Mean Squared Error of less than 0.1 N for grasping and holding contact force using leave-one-out cross-validation method.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.