Branching Vine Robots for Unmapped Environments

While exploring complex unmapped spaces is a persistent challenge for robots, plants are able to reliably accomplish this task. In this work we develop branching robots that deploy through an eversion process that mimics key features of plant growth (i.e., apical extension, branching). We show that by optimizing the design of these robots, we can successfully traverse complex terrain even in unseen instances of an environment. By simulating robot growth through a set of known training maps and evaluating performance with a reward heuristic specific to the intended application (i.e., exploration, anchoring), we optimized robot designs with a particle swarm algorithm. We show these optimization efforts transfer from training on known maps to performance on unseen maps in the same type of environment, and that the resulting designs are specialized to the environment used in training. Furthermore, we fabricated several optimized branching everting robot designs and demonstrated key aspects of their performance in hardware. Our branching designs replicated three properties found in nature: anchoring, coverage, and reachability. The branching designs were able to reach 25% more of a given space than non-branching robots, improved anchoring forces by 12.55x, and were able to hold greater than 100x their own mass (i.e., a device weighing 5 g held 575 g). We also demonstrated anchoring with a robot that held a load of over 66.7 N at an internal pressure of 50 kPa. These results show the promise of using branching vine robots for traversing complex and unmapped terrain.