Estimating Point of Contact , Force and Torque in a Biomimetic Tactile Sensor with Deformable Skin

The human fingertip is exquisitely sensitive to touch. Tactile information sensed by the mechanoreceptors of the fingertip make it possible to deduce information such as the location of contact and the normal forces, shear forces and torques being applied to the skin. Humans use this information to grasp and handle objects with great dexterity. Robotic systems seeking to obtain similar performance would benefit from these sensory capabilities. We have previously developed a multimodal tactile sensor – the BioTac®. Normal and shear forces are encoded by changes in the impedance of electrodes distributed over the core of the BioTac when the overlying elastomeric skin and conductive liquid deform. Previous studies have employed machine learning to quantify the relationship between sensory values and tri-axial force. In this study we present analytical methods that successfully estimate point of contact, tri-axial force and torque during contact with the BioTac. Estimating shear force and torque separately when they occur simultaneously remains challenging.