Field-hardened Robotic Autonomy for Subterranean Exploration

EasyChair Preprint 1452

Abstract

In this paper a comprehensive approach to enable resilient robotic autonomy in subterranean environments is presented. Emphasizing on the use of aerial robots to explore underground settings such as mines and tunnels, the presented methods address critical challenges related to extreme sensor degradation, path planning in large-scale, multi-branched and geometrically-constrained environments, and reliable operation subject to lack of communications. To facilitate resilience in such conditions, novel methods in multi-modal localization and mapping, as well as graph-based exploration path planning are proposed and combined with custom system design. Through a set of field evaluation activities in real-life subterranean environments we present a "field-hardened" solution that demonstrably enables reliable robotic operation in the hard to access but often crucial underground settings.

Keyphrases: Localization, Robotics, path planning, subterranean

@booklet{EasyChair:1452,
  author    = {Tung Dang and Frank Mascarich and Shehryar Khattak and Huan Nguyen and Nikhil Khedekar and Christos Papachristos and Kostas Alexis},
  title     = {Field-hardened Robotic Autonomy for Subterranean Exploration},
  howpublished = {EasyChair Preprint 1452},
  year      = {EasyChair, 2019}}