Physics-Informed Online Estimation of Stiffness and Shape of Soft Robotic Manipulators

Abstract

Soft robots are designed to be highly compliant to reduce the risk of injury or damage to humans and the environment. This compliance can lead to large deformations when handling payloads, significantly altering the operation of the robot. The stiffness of a soft robot, actively tuned or passively influenced by inputs (e.g., pneumatic pressures), is an important state variable, yet difficult to measure directly for the control of a soft robot. In this paper we propose a novel physics-informed approach to online estimation of the stiffness and shape of a soft manipulator under a payload, based on measurements that are readily available (e.g., positions of five points on the manipulator, or the position and orientation of the tip). The same approach is also adapted for estimating the payload when the stiffness is known. The proposed method is illustrated and supported with experimental results on a soft pneumatic actuator. In particular, it is shown to produce more accurate shape estimate than a commonly adopted piecewise constant curvature (PCC) model (which cannot produce a stiffness estimate), with an average error 57% smaller than the PCC method. The stiffness values estimated are also shown to be consistent and fall within the expected physical range.