AttributesValues
type
value
  • Drone swarms are becoming a new tool for many tasks including surveillance, search, rescue, construction, and defense related activities. As their usage increases, so does the possibility of adversarial attacks on their contribution to these use cases. One possible avenue, whether deliberate or not, is to deny access to the position feedback offered by the Global Positioning System (GPS). Operating in these ‘GPS denied’ environments poses a new challenge; both in navigation, and in collision avoidance. This study proposes two novel concepts; a structural model of environmental deviance to aid in autonomous navigation, and a method to use the output of said model to implement a collision avoidance system. Both of these concepts are developed and tested in the framework of a simulated environment that mimics a GPS-denied scenario. Using data from hundreds of simulated swarm flights, this work shows structured learning can improve navigational accuracy without the need for externally provided position feedback.
subject
  • Global Positioning System
  • Emerging technologies
  • Robots
  • Scientific modeling
  • 20th-century inventions
  • Vehicle safety technologies
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software