Mechanics of Compliant Multifunctional Robotic Structures

Abstract

In this investigation, we report on experiments and models we have developed for compliant multifunctional robotic structures using arrays of conducting polymer composites have been developed to form a “nervous system” to sense shape and force distributions. The objective of this research is to enable better training of robots by enabling them to physically communicate via human touch using new compliant multifunctional structures. To achieve this, arrays of conducting polymer composites have been developed to form a “nervous system” to sense shape and force distributions. This sensor array is integrated into compliant composite structures using a scalable additive manufacturing process. These sensor arrays are being developed for a variety of model robotic structures, for example flapping wing MAVs (i.e., bird-like robots) and stair-walking robots. Experimental details of the associated deformation response are quantified in real-time using Digital Image Correlation (DIC). Output from the sensor array is related to shape and force distributions by solving the nonlinear inverse problem using a novel Singular Value Decomposition (SVD) method. This research is leading to new compliant, scalable, sensing structures that simultaneously monitor in real-time both global and local shapes, as well as force distributions. Since compliant multifunctional sensing structures do not yet exist for robots, it is envisioned that it will enable realization of new bio-inspired control principles for training robots. This will significantly advance the ability to make safer interactions and decisions in co-robotics by differentiating robotic interactions with humans from other objects in their environment.