  Inspired by the octopus and other animals living in water, soft robots should
naturally lend themselves to underwater operations, as supported by encouraging
validations in deep water scenarios. This work deals with equipping soft arms
with the intelligence necessary to move precisely in wave-dominated
environments, such as shallow waters where marine renewable devices are
located. This scenario is substantially more challenging than calm deep water
since, at low operational depths, hydrodynamic wave disturbances can represent
a significant impediment. We propose a control strategy based on Nonlinear
Model Predictive Control that can account for wave disturbances explicitly,
optimising control actions by considering an estimate of oncoming hydrodynamic
loads. The proposed strategy is validated through a set of tasks covering
set-point regulation, trajectory tracking and mechanical failure compensation,
all under a broad range of varying significant wave heights and peak spectral
periods. The proposed control methodology displays positional error reductions
as large as 84% with respect to a baseline controller, proving the
effectiveness of the method. These initial findings present a first step in the
development and deployment of soft manipulators for performing tasks in
hazardous water environments.
