Self sensing robotic grippers reconstruct touch with few electrical measurements
Topology optimisation and simulated state reconstruction for self-sensing piezoresistive grippers
Computational Engineering, Finance, and Science
Summary
Robotic grippers often use external sensors to detect what they are holding, but these can be bulky or costly. This paper explores a way for grippers made from special piezoresistive materials to sense touch and movement internally by measuring electrical signals. The authors simulate how these grippers can reconstruct simple shapes and movements from just a few electrical readings. They also design the gripper shape to improve the accuracy and robustness of this sensing method, showing it works better against noise than designs focused only on mechanical strength.
What this means in practice
- •For robotic system integrators: Design robotic grippers that reduce external sensors by internally sensing contact and displacement from electrical signals within piezoresistive materials.$Commercial implications: Enables manufacturing and sale of self sensing grippers that simplify setups and reduce costs in automation and robotics industries.
- •For industrial automation engineers: Implement control systems for grippers with integrated sensing that can detect contact shapes and displacements for more responsive manipulation.
Tested on simulated data.
Authors
Connor N Mallon, Zachary J Wegert, Anthony P Roberts, Joshua Pinskier, Harry Bowman, Vivien J Challis
Abstract
We consider the computational design of a self-sensing robotic gripper that has the potential to reduce reliance on traditional external sensors. We computationally investigate the potential of a novel sensing method that exploits the coupled electromechanical properties of piezoresistive materials to enable mechanical perception using sparse electrical measurements. In this theoretical proof-of-concept, we show that under the idealised assumptions of linear constitutive relationships and quasi-static gripper operation, it is possible to reconstruct simple contact shapes and gripper displacement fields using a small number of simulated electrical measurements, suggesting the potential of the proposed sensing technique. We use topology optimisation to promote the quality of displacement state recovery by adding an electrical performance objective to the mechanical compliance requirement of the gripper. Using the proposed reconstruction technique, we showcase the improved reconstruction ability of an electrically optimised gripper design through an increased robustness to noisy synthetic measurements compared to a gripper designed with only a mechanical objective in the topology optimisation problem. In the future, detailed inclusion of non-linear effects and experimental realisation of this theoretical proof-of-concept may help remove the constraints of traditional sensors for robotic handling, potentially facilitating sensing in settings where current sensing systems are impractical.