A novel event-based incipient slip detection using Dynamic Active-Pixel Vision Sensor (DAVIS)

In this paper, a novel approach to detect incipient slip based on the contact area between a transparent silicone medium and different objects using a neuromorphic event-based vision sensor (DAVIS) is proposed. Event-based algorithms are developed to detect incipient slip, slip, stress distribution and object vibration. Thirty-seven experiments were performed on five objects with different sizes, shapes, materials and weights to compare precision and response time of the proposed approach. The proposed approach is validated by using a high speed constitutional camera (1000 FPS). The results indicate that the sensor can detect incipient slippage with an average of 44.1 ms latency in unstructured environment for various objects. It is worth mentioning that the experiments were conducted in an uncontrolled experimental environment, therefore adding high noise levels that affected results significantly. However, eleven of the experiments had a detection latency below 10 ms which shows the capability of this method. The results are very promising and show a high potential of the sensor being used for manipulation applications especially in dynamic environments

JJHK dataset

This photograph originally appeared in the 2018 Research student photography and image competition held to celebrate National Science Week (11-19 August 2018). Blurb: The coloured parts in the images are the reduced graphene oxide while others part is graphene oxide. And there is a significant crack in the centre of the reduced graphene oxide that is why we call it broken bridge structure. Based on this unique structure produced by us, we can achieve ultrahigh sensitivity during the test, which means a wide potential application (such as piezoresistive pressure sensor and so on) in future