Fish and amphibians can sense their hydrodynamic environment via fluid flow sensing organs, called lateral lines. Using this lateral line they are able to detect disturbances in the hydrodynamic near field which enables hydrodynamic imaging, i.e. obstacle detection. Via two experiments we demonstrate a novel artificial lateral line of four bio-inspired 2D fluid flow sensors and show that the measurements of the enacted sensors agree with an established hydrodynamic model. These measurements from the array are then used to localize both vibrating and unidirectionally moving objects using an artificial neural network in a bounded area of 36 by 11 cm which extends beyond the area directly in front of the sensor array. In this area, the average Euclidean localization error is 1.3 cm for a vibrating object, while for moving a object it is on average 3.3 cm.
|Title of host publication||2019 IEEE Sensors Applications Symposium (SAS)|
|Publication status||Published - 6-May-2019|
|Event|| 2019 IEEE Sensors Applications Symposium (SAS) - Sophia Antipolis, France|
Duration: 11-Mar-2019 → 13-Mar-2019
|Conference||2019 IEEE Sensors Applications Symposium (SAS)|
|Period||11/03/2019 → 13/03/2019|