A multi-level control architecture for the bionic handling assistant

Abstract

The bionic handling assistant is one of the largest soft continuum robots and very special in being a pneumatically operated platform that is able to bend, stretch, and grasp in all directions. It nevertheless shares many challenges with smaller continuum and other soft robots such as parallel actuation, complex movement dynamics, slow pneumatic actuation, non-stationary behavior, and a lack of analytic models. To master the control of this challenging robot, we argue for a tight integration of standard analytic tools, simulation, control, and state-of-the-art machine learning into an overall architecture that can serve as blueprint for control design also beyond the BHA. To this aim, we show how to integrate specific modes of operation and different levels of control in a synergistic manner, which is enabled by using modern paradigms of software architecture and middleware. We thereby achieve an architecture with unique overall control abilities for a soft continuum robot that allow for flexible experimentation toward compliant user-interaction, grasping, and online learning of internal models.