Robust navigation of a soft growing robot by exploiting contact with the environment

Abstract

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 article, 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.

Document Details

Document Type
Pub Defense Publication
Publication Date
Mar 20, 2020
Source ID
10.1177/0278364920903774

Entities

People

  • Allison M. Okamura
  • Elliot W Hawkes
  • Joseph D Greer
  • Laura H Blumenschein
  • Ron Alterovitz

Organizations

  • Air Force Office of Scientific Research
  • National Science Foundation
  • Stanford University
  • University of California, Santa Barbara
  • University of North Carolina at Chapel Hill

Tags

Fields of Study

  • Computer science

Readers

  • Robotics and Automation.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • Autonomy