Sensorless force and displacement estimation in soft actuators

Abstract

Sensing forms an integral part of soft matter based robots due to their compliance, dependence on loading conditions, and virtually infinite degrees of freedom. Previous studies have developed several extrinsic sensors and embedded them into soft actuators for displacement and force estimation. What has not been investigated is whether soft robots themselves possess intrinsic sensing capabilities, especially in the case of pneumatically powered soft robots. Such an approach, that exploits the inherent properties of a system toward sensing is called sensorless estimation. Here, we introduce sensorless estimation for the first time in pneumatically powered soft actuators. Specifically, we show that the intrinsic properties of pressure and volume can be used to estimate the output force and displacement of soft actuators. On testing this approach with a bending actuator, we observed errors under 10% and 15% for force and displacement estimation respectively, with randomized and previously unseen test conditions. We also show that combining this approach with a conventional embedded sensor improves estimation accuracy due to sensing redundancy. By modelling soft actuators additionally as sensors, this work presents a new, readily implementable sensing modality that helps us better understand the highly complex behaviour of soft matter based robots.