Vision-based Autonomous Landing Control for Unmanned Helicopters

Panos Marantos, George C. Karras, Panagiotis Vlantis, Kostas J. Kyriakopoulos

Research output: Contribution to journalArticlepeer-review


This paper presents the design of a complete control system for the autonomous landing of unmanned flybarless helicopters on known stationary visual landmarks. A state estimator based on the complementary filters notion, estimates the position, translational velocity and attitude vectors of the vehicle by fusing data acquired from the on–board camera and an Inertial Measurement Unit. A vision-aided nonlinear model predictive controller is designed for the landing motion of the helicopter, assuming that the on–board camera is rigidly (i.e., no additional Degrees of Freedom (DOF)) attached on the vehicle. Although the under–actuated character of the helicopter dynamics introduces counter–goals for minimizing the error between the vehicle and the landmark, the proposed control scheme guarantees, via hard nonlinear constraints, that the landmark will always be kept inside the camera field of view during the landing procedure. In order to simplify the derived algorithm without violating the robustness of the proposed controller, we reformulate the translational helicopter dynamics in order to reduce the number of the unknown model parameters to a minimum. Moreover, a parameter/disturbance observer is designed for estimating simultaneously the vehicle’s unknown dynamic parameters as well as the induced disturbances. The efficacy of the proposed landing scheme is evaluated via a set of experimental and simulation results, using a small–scale flybarless helicopter.

Original languageEnglish (US)
Pages (from-to)145-158
Number of pages14
JournalJournal of Intelligent and Robotic Systems: Theory and Applications
Issue number1
StatePublished - Sep 1 2018


  • Autonomous landing
  • Model predective control
  • Parameter identification
  • State estimation
  • Unnmaned helicopters

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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