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Online Aerial 2.5D Terrain Mapping and Active Aerial Vehicle Exploration for Ground Robot Navigation

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Abstract

This paper discusses a collaborative air-ground team of autonomous vehicles for exploring and navigating outdoors within an unknown environment. A custom multi-rotor equipped with onboard downward facing stereo cameras flies over an unknown environment capturing imagery and reconstructing a height map of the ground surface online. Together with a simple terrain classifier, this height map is used to detect obstacles and estimate navigation costs for a Clearpath Robotics Jackal. Given a user defined goal, an online exploration algorithm automatically directs the aerial vehicle to expand the frontiers of the map until a path for the ground vehicle from the current position to the goal is discovered. Simultaneously, this algorithm directs the ground vehicle to execute partial paths to make progress towards the goal before a complete path has been discovered. Aside from the trajectory execution and the state estimation for the unmanned ground vehicle and the graphical interface for the operator, the online exploration algorithm and reconstruction all run onboard the UAV by taking advantage of the graphics processing unit (GPU) computing capabilities of the Nvidia TX2 for both stereo calculations and map construction. This paper presents the improvements made to an existing multi-robot system, the mapping and exploration algorithms. Finally, this paper discusses both simulation and hardware experiments conducted to validate the behavior of the exploration algorithm.

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Data Availability

Data collected and used in this publication is available upon written request to Kevin Kochersberger at Virginia Tech

Code Availability

The software referenced in this publication is available upon written request to Kevin Kochersberger at Virginia Tech

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Funding

This work was funded through the National Science Foundation I/UCRC Center for Unmanned Aircraft Systems Phase II Site Addition, Award No. 1650465.

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Authors and Affiliations

  1. Virginia Tech, Blacksburg, VA, USA

    Anthony Wagner, John Peterson, James Donnelly, Shivam Chourey & Kevin Kochersberger

Authors
  1. Anthony Wagner
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  2. John Peterson
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  3. James Donnelly
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  4. Shivam Chourey
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  5. Kevin Kochersberger
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Contributions

KK, AW, JP, and JD conceived this research; AW, JP, JD, and SC contributed software to this research; AW, JP, JD, and SC performed experiments and analysis; AW, JP, and KK wrote the paper and participated in the revisions of it.

Corresponding author

Correspondence to Anthony Wagner.

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The authors declare that they have no conflict of interest.

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Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was funded through the National Science Foundation I/UCRC Center for Unmanned Aircraft Systems Phase II Site Addition, Award No. 1650465. The Jackal ground robot was graciously loaned by the Army Research Laboratory for these experiments.

Appendix A: Simulation Environment Diagrams

Appendix A: Simulation Environment Diagrams

Fig. 23
figure 23

Demo 2 environment. This environment features long obstacles with a much shorter path through the center. Designed to be a worst case for the RRT explore algorithm

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Fig. 24
figure 24

Demo 7 environment. This environment features two C shaped obstacles one facing the start and one facing the goal.

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Fig. 25
figure 25

Demo 8 environment. This environment features a large number of square obstacles to simulate an urban environment

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Fig. 26
figure 26

Demo 9 environment. This environment features a simple dead end near the start that is approximately 8 meters deep

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Wagner, A., Peterson, J., Donnelly, J. et al. Online Aerial 2.5D Terrain Mapping and Active Aerial Vehicle Exploration for Ground Robot Navigation. J Intell Robot Syst 106, 58 (2022). https://doi.org/10.1007/s10846-022-01751-9

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