Towards Autonomous Aerial Scouting Using Multi-Rotors in Subterranean Tunnel Navigation

Christoforos Kanellakis,Petros S Karvelis,Sina Sharif Mansouri,Ali-Akbar Agha-Mohammadi,George Nikolakopoulos

doi:10.1109/access.2021.3076578

Christoforos Kanellakis, Petros S Karvelis + Show 3 more

Open Access

https://doi.org/10.1109/access.2021.3076578

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Abstract

This work establishes a robocentric framework around a non-linear Model Predictive Control (NMPC) for autonomous navigation of quadrotors in tunnel-like environments. The proposed framework enables obstacle free navigation capabilities for resource constraint platforms in areas with critical challenges including darkness, textureless surfaces as well as areas with self-similar geometries, without any prior knowledge. The core contribution of the proposed framework stems from the merging of perception dynamics in a model-based optimization approach, aligning the vehicles heading to the tunnels’ open space expressed in the $x$ axis coordinate in the image frame of the most distant area. Moreover, the aerial vehicle is considered as a free-flying object that plans its actions using egocentric onboard sensors. The proposed method can be deployed in both fully illuminated indoor corridors or featureless dark tunnels, leveraging visual processing from either RGB-D or monocular sensors for generating direction commands to keep flying in the proper direction. Multiple experimental field trials demonstrate the effectiveness of the proposed method in challenging environments.

Highlights

The quest for autonomous MAVs that can reliably navigate in partially-known or unknown areas brings these platforms in the forefront of research and technological breakthroughs, while introducing novel approaches for several application areas
This article approaches the problem of autonomous navigation of low-cost aerial robots, referred with the term ‘‘aerial scouts’’, which is a subcomponent of the NeBula autonomy framework [4], related to multi-robot exploration missions in complex environments [5]–[7]
The proposed architecture couples a Nonlinear Model Predictive Control (NMPC) with a visual processing scheme in the local frame of the robot for maintaining proper obstacle free direction along the tunnel axis, following the open space area described with the furthest distance in depth images

Summary

INTRODUCTION

The quest for autonomous MAVs that can reliably navigate in partially-known or unknown areas brings these platforms in the forefront of research and technological breakthroughs, while introducing novel approaches for several application areas. The proposed architecture couples a Nonlinear Model Predictive Control (NMPC) with a visual processing scheme in the local frame of the robot for maintaining proper obstacle free direction along the tunnel axis, following the open space area described with the furthest distance in depth images. Compared to the State-of-the-art, this work proposes an alternative architecture on the high level model based control for MAVs, which integrates visual perception state in the command generation, to enable reactive fast exploration of unknown subterranean/urban tunnel environments. B. CONTRIBUTIONS Based on the aforementioned state of the art, the major contribution of this article stems from the establishment of an aerial scout robot, capable to navigate along tunnels, leveraging visual data for keeping the proper direction along the tunnel, following open spaces identified through depth image processing.

AERIAL SCOUT FRAMEWORK

MAV DYNAMICS

PERCEPTION STATE

VISUAL PROCESSING

CONTROL DESIGN

CONCLUSION