Journal
SENSORS
Volume 20, Issue 16, Pages -Publisher
MDPI
DOI: 10.3390/s20164469
Keywords
vision-based measurements; dynamic force estimation; dynamic vision sensor; LSTM; neuromorphic sensor; even-based sensor
Funding
- Kingston University London
- Khalifa University of Science and Technology [CIRA-2018-55, RC1-2018-KUCARS]
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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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