Real-time friction sensing, feedback and control for dexterous prosthetic and robotic manipulation. Prosthetic and robotic hands demonstrate poor dexterity during object manipulation, often dropping objects. Humans rarely allow objects to slip because we can sense when an object is slippery and adjust our grip. Exceptionally little research has been directed at replicating this ability to sense friction. This project aims to enable artificial hands to estimate frictional properties while graspin ....Real-time friction sensing, feedback and control for dexterous prosthetic and robotic manipulation. Prosthetic and robotic hands demonstrate poor dexterity during object manipulation, often dropping objects. Humans rarely allow objects to slip because we can sense when an object is slippery and adjust our grip. Exceptionally little research has been directed at replicating this ability to sense friction. This project aims to enable artificial hands to estimate frictional properties while grasping an object. Non-invasive methods to feed back this frictional information to an amputee will also be investigated. Finally, the friction-sensing system will be used to improve robotic gripper control. The outcomes of this research will significantly advance the fields of prosthetics, telesurgery, and service and manufacturing robotics.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE150100548
Funder
Australian Research Council
Funding Amount
$359,000.00
Summary
Neural and robotic correlates of predictive coding and selective attention. Whether a human catching a ball, a dog leaping at a frisbee or a dragonfly hunting prey amidst a swarm, brains both large and small have evolved the ability to focus attention on one moving target, even in the presence of distracters. This project aims to investigate how brains solve this challenging problem by recording the activity of dragonfly neurons that selectively attend to one target whilst ignoring others. The p ....Neural and robotic correlates of predictive coding and selective attention. Whether a human catching a ball, a dog leaping at a frisbee or a dragonfly hunting prey amidst a swarm, brains both large and small have evolved the ability to focus attention on one moving target, even in the presence of distracters. This project aims to investigate how brains solve this challenging problem by recording the activity of dragonfly neurons that selectively attend to one target whilst ignoring others. The project aims to examine how expectation and attention are encoded in the brain and will build an autonomous robot using computational models bio-inspired from this neuronal processing. Robots capable of visually perceiving and interacting with targets in natural environments have applications in health, surveillance and defence.Read moreRead less
Visual guidance of flight in birds. Birds flying rapidly amidst the branches of trees engage continually in a three-dimensional slalom. This project will study birds flying through tunnels and gaps, to understand how they use their eyes and wings to achieve this agility. The results could suggest better designs for unmanned aerial vehicles operating in dense urban environments.