🤖 AI Summary
研究通过拓扑优化和模拟状态重建，利用压阻材料的机电耦合特性，设计自感知机器人夹爪，减少对外部传感器依赖。
📝 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.