Task-Driven PCA-Based Design Optimization of Wearable Cutaneous Devices

Claudio Pacchierotti1, Eric Young2, Katherine J. Kuchenbecker3

  • 1Centre national de la recherche scientifique (CNRS)
  • 2University of Pennsylvania
  • 3Max Planck Institute for Intelligent Systems

Details

10:30 - 13:00 | Tue 22 May | podI | [email protected]

Session: Haptics and Interfaces

Abstract

Small size and low weight are critical requirements for wearable haptic interfaces, making it essential to work toward the optimization of their sensing and actuation systems. We present a new approach for task-driven design optimization of fingertip cutaneous haptic devices. Given one (or more) target tactile interactions to render and a cutaneous device to optimize, we evaluate the minimum number and best configuration of the device’s actuators to minimize the estimated haptic rendering error. First, we calculate the motion needed for the original cutaneous device to render the considered target interaction. Then, we run a principal component analysis (PCA) to search for possible couplings between the original motor inputs, looking also for the best way to reconfigure them. If some couplings exist, we can re-design our cutaneous device with fewer motors, optimally configured to render the target tactile sensation. The proposed approach is quite general and can be applied to different tactile sensors and cutaneous devices. We validated it using a BioTac tactile sensor and custom plate-based 3-DoF and 6-DoF fingertip cutaneous devices, considering six representative target tactile interactions. The algorithm was able to find couplings between each device's motor inputs, proving it to be a viable approach to optimize the design of wearable and portable cutaneous devices. Finally, we present two examples of optimized designs for our 3-DoF fingertip cutaneous device.