Vine robots grow safely by finding paths with minimum pressure
An Efficient Algorithm for Minimum-Pressure Growth Planning of Vine Robots
Robotics
Summary
Vine robots are soft robots that move by growing at their tips, which makes navigating through cluttered spaces tricky because too much pressure can cause them to burst. The authors developed a new method to calculate the safest path for these robots by minimizing the pressure needed to grow along a route. Their approach finds the best paths in two dimensions and good approximations in three dimensions, and it works quickly by focusing on certain key points near obstacles. They tested their method both in computer simulations and real experiments, and they also shared their software tool so others can use it.
What this means in practice
- •For soft robot developers: Plan growth paths that avoid excessive pressure to prevent failure during obstacle navigation in cluttered environments.
- •For search and rescue teams: Use vine robots with optimized growth paths to explore confined and obstructed areas safely and effectively.
Authors
Andres C. Torres, Tobia Marcucci, Elliot W. Hawkes
Abstract
Vine robots navigate cluttered environments by extending from their tip. Although their ability to operate in such environments has been extensively demonstrated, little work has addressed growth planning, i.e., finding optimal growth paths. Moreover, existing planners do not account for the growth pressure necessary to follow a given path, which can cause the robot to burst when it is too high. In this paper, we address the problem of finding minimum-pressure paths for vine robots growing around polytopic obstacles. We propose an efficient algorithm that is guaranteed to find globally optimal solutions in 2D and approximate solutions in 3D, with an error that vanishes as a discretization parameter approaches zero. First, we derive a growth pressure equation for vine robots of arbitrary shape, which we use to show that there always exists a minimum-pressure path that is piecewise-linear and can bend only at specific points on the obstacles. We then leverage this observation to reduce the growth-planning problem to a shortest-path problem with time-dependent weights, which we efficiently solve using a modified Dijkstra's algorithm. We demonstrate the speed and scalability of our approach through numerical simulations. We also validate our algorithm with hardware experiments and provide an open-source and high-performance implementation in the Python package, VinePlanner: https://github.com/Ahsoka/VinePlanner.