NDLI: Vision-Based Landing of Light Weight Unmanned Helicopters on a Smart Landing Platform

Content Provider	Springer Nature Link
Author	Mahoor, Mohammad H. Godzdanker, R. Dalamagkidis, K. Valavanis, K. P.
Copyright Year	2010
Abstract	This paper presents a simple and efficient solution to vision guided autonomous landing of a light-weight (<150 Kg) unmanned helicopter on a smart landing platform, called ISLANDS—Intelligent Self-Leveling and Nodal Docking System. The advantage of ISLANDS is that it may allow the helicopter upon docking to recharge its batteries or refuel, thus, indirectly increasing endurance and flight range. In order for the helicopter to dock with ISLANDS, an on-board ‘vision module’ coupled with the helicopter attitude controller is developed. This ‘vision module’ detects the location and orientation of ISLANDS and feeds back information to the helicopter attitude controller, which commands the helicopter to descent onto the landing platform at a desired orientation and speed. The Scale Invariant Feature Transform (SIFT) is used for automatic detection of the landing platform based on images captured by a single camera mounted on the helicopter. The detected SIFT features are used to estimate the 3-D orientation of the platform relative to the helicopter using Homography and RANSAC techniques. The focus of this paper is on the vision-guided landing technique in a predefined orientation and not on controller details, which may be found in Shim et al. (1998).
Starting Page	251
Ending Page	265
Page Count	15
File Format	PDF
ISSN	09210296
Journal	Journal of Intelligent & Robotic Systems
Volume Number	61
Issue Number	1-4
e-ISSN	15730409
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-11-06
Publisher Place	Dordrecht
Access Restriction	Subscribed
Subject Keyword	Unmanned helicopter Landing platform Vision-guided landing Mechanical Engineering Artificial Intelligence (incl. Robotics) Electrical Engineering Control , Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Artificial Intelligence Control and Systems Engineering Mechanical Engineering Electrical and Electronic Engineering Software

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