BROWSE

Related Researcher

Author's Photo

Oh, Hyondong
Autonomous Systems Laboratory
Research Interests
  • Autonomy and decision making for unmanned vehicles
  • Cooperative control and path planning for unmanned vehicles
  • Nonlinear guidance and control
  • Estimation and sensor/information fusion
  • Vision-based navigation and control
  • Bio-inspired self-organising multi-vehicle system

ITEM VIEW & DOWNLOAD

Autonomous UAV Trail Navigation with Obstacle Avoidance Using Deep Neural Networks

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
Autonomous UAV Trail Navigation with Obstacle Avoidance Using Deep Neural Networks
Author
Back, SeunghoCho, GangikOh, JinwooTran, Xuan-ToaOh, Hyondong
Issue Date
2020-12
Publisher
SPRINGER
Citation
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS , v.100, no.3-4, pp.1195 - 1211
Abstract
This paper proposes a vision-based bike trail following approach with obstacle avoidance using CNN (Convolutional Neural Network) for the UAV (Unmanned Aerial Vehicle). The UAV is controlled to follow a given trail while keeping its position near the center of the trail using the CNN. Also, to return to the original path when the UAV goes out of the path or the camera misses the trail due to disturbances such as wind, the control commands from the CNN are stored for a certain duration of time and used for recovering from such disturbances. To avoid obstacles during the trail navigation, the optical flow computed with another CNN is used to determine the safe maneuver. By combining these methods of i) trail following, ii) disturbance recovery, and iii) obstacle avoidance, the UAV deals with various situations encountered when traveling on the trail. The feasibility and performance of the proposed approach are verified through realistic simulations and flight experiments in real-world environments.
URI
https://scholarworks.unist.ac.kr/handle/201301/48295
URL
https://link.springer.com/article/10.1007/s10846-020-01254-5
DOI
10.1007/s10846-020-01254-5
ISSN
0921-0296
Appears in Collections:
MEN_Journal Papers
Files in This Item:
There are no files associated with this item.

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU