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High-precision Autonomous Flight in Constrained Shipboard Environments

Shichao Yang, Zheng Fang, Sezal Jain, Geetesh Dubey, Silvio Mano Maeta, Stephan Roth, Sebastian Scherer, Yu Zhang and Stephen T. Nuske
Tech. Report, CMU-RI-TR-15-06, Robotics Institute, Carnegie Mellon University, February, 2015

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Abstract

This paper addresses the problem of autonomous navigation of a micro aerial vehicle (MAV) inside of a constrained shipboard environment to aid in fire control, which might be perilous or inaccessible for humans. The environment is GPS-denied and visually degraded, containing narrow passageways, doorways and small objects protruding from the wall, which makes existing 2D LIDAR, vision or mechanical bumper-based autonomous navigation solutions fail. To realize autonomous navigation in such challenging environments, we first propose a fast and robust state estimation algorithm that fuses estimates from a direct depth odometry method and a Monte Carlo localization algorithm with other sensor information in a two-level fusion framework. Then, an online motion planning algorithm that combines trajectory optimization with receding horizon control is proposed for fast obstacle avoidance. All the computations are done in real-time onboard our customized MAV platform. We validate the system by running experiments in different environmental conditions. The results of over 10 runs show that our vehicle robustly navigates 20m long corridors only 1m wide and goes through a very narrow doorway (only 4cm clearance on each side) completely autonomously even when it is completely dark or full of light smoke.

BibTeX Reference
@techreport{Yang-2015-5909,
title = {High-precision Autonomous Flight in Constrained Shipboard Environments},
author = {Shichao Yang and Zheng Fang and Sezal Jain and Geetesh Dubey and Silvio Mano Maeta and Stephan Roth and Sebastian Scherer and Yu Zhang and Stephen T. Nuske},
school = {Robotics Institute , Carnegie Mellon University},
month = {February},
year = {2015},
number = {CMU-RI-TR-15-06},
address = {Pittsburgh, PA},
}
2017-09-13T10:38:48+00:00