R‐ADVANCE: Rapid Adaptive Prediction for Vision‐based Autonomous Navigation, Control, and Evasion

Abstract

In this article, we present a monocular visual reactive navigation system capable of navigating at high speeds, without GPS, in unknown complex cluttered environments. The system, called R‐ADVANCE (Rapid Adaptive Prediction for Vision‐based Autonomous Navigation, Control, and Evasion), consists of a set of biologically inspired visual perception and reactive control algorithms that provide low‐computation reactive obstacle avoidance while navigating at high speeds in search of a goal object. These algorithms, along with basic planning, and augmented with low‐precision visual odometry, were implemented on a micro unmanned aerial vehicle and tested in a number of challenging environments. While each of the individual algorithmic and hardware elements has been previously studied in limited environments, this work is the first time that these novel components have been integrated and flight‐tested. To achieve fast flight, an NVIDIA Tegra TK1 was used as the main processor, allowing us to parallelize the system to process 1280 × 720 video streams at 40 fps, reaching flight speeds up to 19 m/s (≈68 km/h) or 42 mph.

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 21, 2017
Source ID
10.1002/rob.21744

Entities

People

  • Hector Escobar-Alvarez
  • Jacob Regenstein
  • Karl Klingebiel
  • N. Andrew Browning
  • Neil F. Johnson
  • Steven A. P. Quintero
  • Tom Hebble

Organizations

  • Defense Advanced Research Projects Agency

Tags

Readers

  • Computer Vision.
  • Distributed Systems and Data Platform Development
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • Autonomy
  • Autonomy - Autonomous System Control
  • Space
  • Space - Spacecraft Maneuvers