Texture and shape recognition implementing haptic sensors are widely explored in robotics.
There are various technologies haptic sensors use, such as vibrations, force feedback, air vortex rings, and ultrasound.
However, the implementation of this kind of sensor may be limited, especially in robotic hands and grippers, resulting in a reduced degree of freedom created to prevent damage to the haptic sensor.  
This project aims to address designing a 3D printer fingerprint pattern to enhance the body-borne vibration signal of an RH8D Adult size Robot Hand.
The amplified vibration signal, generated by the fabricated 3D printer texture pattern, is recorded by a contact microphone.
This strategy allows mounting sensors inside the robot's casing to reduce the mechanical constraint and noise background.
Strategically the RH8D has been designed with removable surface parts where the 3D printed texture pattern will be mounted.
Vibration analysis is carried out to optimize the surface pattern design by reducing the robot finder-hand to a damper, spring-mass system.
Three experimental iterations analyze the sensor response to kinesthetic and tactile exploration.
The result of the experimental iterations is a 3D printed surface capable of improving vibrations generated by an R8HD with a group of 100 different objects.
Finally, the data provided for the third experiment is collected as a haptic data set, which is used to train a machine-learning algorithm to identify the physical properties.