Postdoc: Tactile Sensor Design and Friction-Based Robotic Gripping (2 years, temporary).
Background: Prosthetic and robotic hands demonstrate poor dexterity during object manipulation, often dropping objects. Humans rarely allow objects to slip because we can sense if an object is slippery and adjust our grip. In recent years, while we have learned more about the biomechanics and neuroscience underpinning our ability to sense friction, there is still much to learn. Perhaps unsurprisingly, given how poorly we understand human friction sensing, very little research has been directed at replicating this ability to sense friction or slipperiness in artificial sensors. This research program has three work packages: (WP1) To advance our understanding of how humans sense friction; (WP2) To demonstrate, using a number of friction-based tactile sensor prototypes currently under development by our research groups in Dublin and Sydney, that friction sensing leads to improved dexterity in robotic manipulation; (WP3) To use advanced design, manufacturing, and instrumentation methods to miniaturise the proposed sensors to a scale similar to a human finger pad. The outcomes of this research, which would endow artificial hands with the ability to feel the slipperiness and/or impending loss of grip of a grasped object, could significantly advance the fields of prosthetics, telesurgery, and service, agricultural, and manufacturing robotics.
Position description: The employee will contribute to WP2 & WP3 of the program, developing state-of-the-art friction-sensing tactile sensors for robotic gripping applications. See prototype demo: https://youtu.be/uKQE_NObHJo. We have developed a novel optical technique to instrument silicone protrusions which resemble the papillae found in the skin of the human finger pad. We can measure forces and displacements with excellent accuracy (millinewtons and microns, respectively). Creating an array of such protrusion allows us to approximate the biomechanics of the human pad, and possibly exceed its sensitivity; in addition to measuring contact forces, torques, vibration, and slip, this ultimately allows us to sense the friction of the contact without ever completely losing the grasp.
- Contribute to the development of state-of-the-art next-generation robotic fingers which can sense friction/slipperiness in real-time. Prototype demo: See Above
- Increase the density (decrease the size and increase the sensing element count) of existing finger prototypes (9 elements with 1 element / 8.5 mm2) to approach the resolution of human touch sensation (100+ elements with ~1 element/mm2) using state-of-the-art micro/milli-manufacturing methods;
- Contribute to the installation and programming of research equipment, including: 3D printers; six-axis robotic arms; six-axis hexapod robotic stages; robotic grippers;
- Use deep learning approaches to develop algorithms that can interpret tactile sensor signals to estimate physical properties of an object, such as texture, shape and friction.
- Perform machining/moulding/assembly of bespoke small-scale mechanical and electrical equipment, including robotic grippers and test apparatus;
- Contribute to the development of real-time control software for bespoke robotic stage control strategies;
- Provide technical assistance to ongoing human neurological experiments;
- Contribute to the collection, analysis and dissemination of experimental data and results.
Supervision and research environment: The postdoc will report to Stephen Redmond (School of Electrical and Electronic Engineering, UCD).
Salary: €37,874 per annum. The duration of the appointment is two years (temporary). The project is funded by Science Foundation Ireland’s President of Ireland Future Research Leaders Award, held by A/Prof Redmond. Both tactile sensor design work packages are also partly supported by US Office of Naval Research Global funding held by A/Prof Redmond and Dr Heba Khamis at UNSW (Sydney, Australia).
How to apply: Visit https://www.ucd.ie/workatucd/jobs/ -> “External Applicants” -> “Search by Reference Number”… Use job reference number: 012378