VADER: a hardware and simulation platform for visually aware drone autonomy research

Andrew Buck; Matthew Deardorff; Derek T. Anderson; Timothy Wilkin; James M. Keller; Grant Scott; Robert H. Luke III; Raub Camaioni

doi:10.1117/12.2586360

12 April 2021 VADER: a hardware and simulation platform for visually aware drone autonomy research

Andrew Buck, Matthew Deardorff, Derek T. Anderson, Timothy Wilkin, James M. Keller, Grant Scott, Robert H. Luke III, Raub Camaioni

Author Affiliations +

Proceedings Volume 11758, Unmanned Systems Technology XXIII; 117580H (2021) https://doi.org/10.1117/12.2586360
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Low altitude Unmanned Aerial Systems (UASs) provide a highly flexible and capable platform for remote sensing and autonomous control. There are many applications that would benefit from an additional bird’s eye view, including mapping, environmental reconnaissance, and search and rescue to name a few. An autonomous (or partially autonomous) drone could assist in several of these scenarios, freeing the operator to focus on higherlevel strategic planning. While numerous commercial drones exist on the market, none truly provide a flexible foundation for vision guided autonomy research. Herein, we propose the design of a physical UAS platform, called VADER (Visually Aware Drone for Environmental Reconnaissance), and an accompanying simulation environment that addresses many of these tasks. In particular, we show how Commercial Off The Shelf (COTS) hardware and open source software can now be combined to realize powerful end-to-end UAS research solutions. The beauty of unifying these factors is accelerated prototyping and minimal time to migrate and test in the real world. This article outlines VADER and case studies are presented to demonstrate capabilities.

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Andrew Buck, Matthew Deardorff, Derek T. Anderson, Timothy Wilkin, James M. Keller, Grant Scott, Robert H. Luke III, and Raub Camaioni "VADER: a hardware and simulation platform for visually aware drone autonomy research", Proc. SPIE 11758, Unmanned Systems Technology XXIII, 117580H (12 April 2021); https://doi.org/10.1117/12.2586360

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