Robust Navigation of a Soft Growing Robot by Exploiting Contact with the Environment
This addresses navigation challenges for soft robots, offering a novel approach that leverages their compliance for improved robustness, though it is incremental in applying contact exploitation specifically to soft growing robots.
The paper tackled the problem of robot navigation by developing a method that exploits environmental contact for a soft growing robot, rather than avoiding it, resulting in paths that are more robust to uncertainty compared to contact-avoiding planners.
Navigation and motion control of a robot to a destination are tasks that have historically been performed with the assumption that contact with the environment is harmful. This makes sense for rigid-bodied robots where obstacle collisions are fundamentally dangerous. However, because many soft robots have bodies that are low-inertia and compliant, obstacle contact is inherently safe. As a result, constraining paths of the robot to not interact with the environment is not necessary and may be limiting. In this paper, we mathematically formalize interactions of a soft growing robot with a planar environment in an empirical kinematic model. Using this interaction model, we develop a method to plan paths for the robot to a destination. Rather than avoiding contact with the environment, the planner exploits obstacle contact when beneficial for navigation. We find that a planner that takes into account and capitalizes on environmental contact produces paths that are more robust to uncertainty than a planner that avoids all obstacle contact.