Published In

Dronet'21: Proceedings of the 7th Workshop on Micro Aerial Vehicle Networks, Systems, and Applications

Document Type

Article

Publication Date

6-2021

Subjects

Drone aircraft -- Industrial applications, Drone aircraft

Abstract

Networked drones have the potential to transform various applications domains; yet their adoption particularly in indoor and forest environments has been stymied by the lack of accurate maps and autonomous navigation abilities in the absence of GPS, the lack of highly reliable, energy-efficient wireless communications, and the challenges of visually inferring and understanding an environment with resource-limited individual drones. We advocate a novel vision for the research community in the development of distributed, localized algorithms that enable the networked drones to dynamically coordinate to perform adaptive beam forming to achieve high capacity directional aerial communications, and collaborative machine learning to simultaneously localize, map and visually infer the challenging environment, even when individual drones are resource-limited in terms of computation and communication due to payload restrictions.

Rights

© 2021 Association for Computing Machinery.

Description

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

DOI

10.1145/3469259.3470488

Persistent Identifier

https://archives.pdx.edu/ds/psu/38681

Download

Share

COinS