  Soft growing robots, are a type of robots that are designed to move and adapt
to their environment in a similar way to how plants grow and move with
potential applications where they could be used to navigate through tight
spaces, dangerous terrain, and hard-to-reach areas. This research explores the
application of deep reinforcement Q-learning algorithm for facilitating the
navigation of the soft growing robots in cluttered environments. The proposed
algorithm utilizes the flexibility of the soft robot to adapt and incorporate
the interaction between the robot and the environment into the decision-making
process. Results from simulations show that the proposed algorithm improves the
soft robot's ability to navigate effectively and efficiently in confined
spaces. This study presents a promising approach to addressing the challenges
faced by growing robots in particular and soft robots general in planning
obstacle-aware paths in real-world scenarios.
