Robotic grippers aim to be increasingly precise, being effective in object manipulation and gentle on fragile objects. In this scenario, endowing a gripper with a distributed sensing system sensitive to a set of dynamic touch patterns seems crucial to improve the gripper as a manipulation device. In humans, the velocity of touch influences haptic perception with impact on object manipulation and exploration. Therefore, the current study has also potential to drive sensory feedback for intuitive human-in-the-loop control of haptic devices. In this study, we embed an electronic skin (e-skin) into the thumb fingertip of a robotic gripper to investigate tactile sensor response during an easy task of object grasp-release, thus focusing on normal forces only. The reference electronic skin is a tactile sensing system based on piezoelectric polymers, coupled to a rigid substrate and covered by an elastomer layer. The cover layer has been chosen because it is easily disposable and for its ‘human skin’-like softness characteristics. Main scope is to investigate whether piezoelectric sensors are sensitive to the grasping speed. The parameter we set is the speed of the servomotors for the proximal and distal flexion of the fingers. Increasing the speed of the servomotors results in an increase of the speed of the phalanges, i.e. the grasping speed. Two objects of different stiffness have been tested and three different features of the sensor signal have been analyzed. These preliminary results show an increasing linear behavior of the amplitudes of the maximum and minimum peaks in the sensor signal with the servomotor speed. The size of the processing window seems not to impact the relation between the signal energy and the speed, thus suggesting that the smallest window could be effectively used to extract such feature in embedded devices in real-time. These pilot studies are hints at the fact that coupling information from such features could enable inferring the grasping force and speed.
A pilot study: electronic skin sensitive to the grasping speed
Chiara Micheli;Yahya Abbass;Christian Gianoglio;Lucia Seminara
2023-01-01
Abstract
Robotic grippers aim to be increasingly precise, being effective in object manipulation and gentle on fragile objects. In this scenario, endowing a gripper with a distributed sensing system sensitive to a set of dynamic touch patterns seems crucial to improve the gripper as a manipulation device. In humans, the velocity of touch influences haptic perception with impact on object manipulation and exploration. Therefore, the current study has also potential to drive sensory feedback for intuitive human-in-the-loop control of haptic devices. In this study, we embed an electronic skin (e-skin) into the thumb fingertip of a robotic gripper to investigate tactile sensor response during an easy task of object grasp-release, thus focusing on normal forces only. The reference electronic skin is a tactile sensing system based on piezoelectric polymers, coupled to a rigid substrate and covered by an elastomer layer. The cover layer has been chosen because it is easily disposable and for its ‘human skin’-like softness characteristics. Main scope is to investigate whether piezoelectric sensors are sensitive to the grasping speed. The parameter we set is the speed of the servomotors for the proximal and distal flexion of the fingers. Increasing the speed of the servomotors results in an increase of the speed of the phalanges, i.e. the grasping speed. Two objects of different stiffness have been tested and three different features of the sensor signal have been analyzed. These preliminary results show an increasing linear behavior of the amplitudes of the maximum and minimum peaks in the sensor signal with the servomotor speed. The size of the processing window seems not to impact the relation between the signal energy and the speed, thus suggesting that the smallest window could be effectively used to extract such feature in embedded devices in real-time. These pilot studies are hints at the fact that coupling information from such features could enable inferring the grasping force and speed.File | Dimensione | Formato | |
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