Koopman-based Control of a Soft Continuum Manipulator Under Variable Loading Conditions
This addresses the challenge of reliable control for soft robotics in real-world manipulation tasks, representing a domain-specific incremental improvement.
The paper tackled the problem of controlling soft continuum manipulators under variable loading conditions by developing a data-driven approach that incorporates loads into a linear Koopman operator model and estimates them online, resulting in more accurate and precise trajectory following and successful pick-and-place of objects with unknown mass.
Controlling soft continuum manipulator arms is difficult due to their infinite degrees of freedom, nonlinear material properties, and large deflections under loading. This paper presents a data-driven approach to identifying soft manipulator models that enables consistent control under variable loading conditions. This is achieved by incorporating loads into a linear Koopman operator model as states and estimating their values online via an observer within the control loop. Using this approach, real-time, fully autonomous control of a pneumatically actuated soft continuum manipulator is achieved. In several trajectory following experiments, this controller is shown to be more accurate and precise than controllers based on models that are unable to explicitly account for loading. The manipulator also successfully performs pick and place of objects with unknown mass, demonstrating the efficacy of this approach in executing real-world manipulation tasks.