Adaptive Control and Optimal Trajectory Generation for Highly Dynamic Tasks on a Soft Robot

Abstract

Large-scale soft robots have the capability and potential to perform highly dynamic tasks such as hammering a nail into a board, throwing items long distances, or manipulating objects in cluttered environments. This is due to their joints being underdamped and their ability to store potential energy. The soft robots presented in this article are pneumatically actuated and thus have the ability to perform these tasks without the need for large motors or gear trains. However, getting soft robots to perform highly dynamic tasks requires controllers that can track highly dynamic trajectories to complete those tasks. For soft robots, this is a difficult problem to solve due to the uncertainty in their shape and their complicated dynamics and kinematics. This article presents a formulation of a model reference adaptive controller (MRAC) that causes a three-link soft robot arm to behave like a highly dynamic 2nd-order critically damped system. Using the dynamics of a 2nd-order system, we also present a method to generate joint trajectories for throwing a ball to a desired point in Cartesian space. We demonstrate the viability of our joint-level controller in simulation and on hardware with a reported maximum root mean square error of 0.0872 radians between a reference and executed trajectory. We also demonstrate that our combined MRAC controller and trajectory generator can, on average, throw a ball to within 25–28% of a desired landing location for a throwing distance of between 1.5 and 2 m on real hardware.