  Soft-growing robots are innovative devices that feature plant-inspired growth
to navigate environments. Thanks to their embodied intelligence of adapting to
their surroundings and the latest innovation in actuation and manufacturing, it
is possible to employ them for specific manipulation tasks. The applications of
these devices include exploration of delicate/dangerous environments,
manipulation of items, or assistance in domestic environments.
  This work presents a novel approach for design optimization of soft-growing
robots, which will be used prior to manufacturing to suggest engineers -- or
robot designer enthusiasts -- the optimal dimension of the robot to be built
for solving a specific task. I modeled the design process as a multi-objective
optimization problem, in which I optimize the kinematic chain of a soft
manipulator to reach targets and avoid unnecessary overuse of material and
resources. The method exploits the advantages of population-based optimization
algorithms, in particular evolutionary algorithms, to transform the problem
from multi-objective into a single-objective thanks to an efficient
mathematical formulation, the novel rank-partitioning algorithm, and obstacle
avoidance integrated within the optimizer operators.
  I tested the proposed method on different tasks to access its optimality,
which showed significant performance in solving the problem. Finally,
comparative experiments showed that the proposed method works better than the
one existing in the literature in terms of precision, resource consumption, and
run time.
