AirDrop: Towards Collaborative, Multi-Resolution Air-Ground Teaming for Terrain-Aware Navigation
Loading...
Links to Files
Author/Creator
Author/Creator ORCID
Date
2023-02-22
Type of Work
Department
Program
Citation of Original Publication
Kasthuri Jayarajah, Sean Gart, and Aryya Gangopadhyay. 2023. AirDrop: Towards Collaborative, Multi-Resolution Air-Ground Teaming for Terrain-Aware Navigation. In Proceedings of the 24th International Workshop on Mobile Computing Systems and Applications (HotMobile '23). Association for Computing Machinery, New York, NY, USA, 55–60. https://doi.org/10.1145/3572864.3580335
Rights
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
Public Domain Mark 1.0
Subjects
Abstract
Driven by advances in deep neural network models that fuse multimodal input such as RGB and depth representations to accurately
understand the semantics of the environments (e.g., objects of different classes, obstacles, etc.), ground robots have gone through
dramatic improvements in navigating unknown environments. Relying on their singular, limited perspective, however, can lead to
suboptimal paths that are wasteful and quickly drain out their batteries, especially in the case of long-horizon navigation. We consider
a special class of ground robots, that are air-deployed, and pose the
central question: can we leverage aerial perspectives of differing
resolutions and fields of view from air–to–ground robots to achieve
superior terrain-aware navigation? We posit that a key enabler
of this direction of research is collaboration between such robots,
to collectively update their route plans, leveraging advances in
long-range communication and on-board computing. Whilst each
robot can capture a sequence of high resolution images during their
descent, intelligent, lightweight pre-processing on-board can dramatically reduce the size of the data that needs to be shared among
its peers over severely bandwidth-limited long range communication channels (e.g., over sub gigahertz frequencies). In this paper,
we discuss use cases and key technical challenges that must be
resolved to realize our vision for collaborative, multi-resolution
terrain-awareness for air–to–ground robots.