Multirotor nonlinear model predictive control based on visual servoing of evolving features

This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to ma...

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Bibliographic Details
Published in:Autonomous robots 2024-12, Vol.48 (8), p.30
Main Authors: Aspragkathos, Sotirios N., Rousseas, Panagiotis, Karras, George C., Kyriakopoulos, Kostas J.
Format: Article
Language:English
Subjects:
Altitude
Artificial Intelligence
Cameras
Computer Imaging
Control
Control algorithms
Dynamic models
Engineering
Mechatronics
Moving targets
Nonlinear control
Optimization techniques
Pattern Recognition and Graphics
Predictive control
Real variables
Robotics
Robotics and Automation
Robots
Safety management
Servocontrol
Surveillance
Systems stability
Tracking
Unmanned aerial vehicles
Vision
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Description
Summary:This article presents a Visual Servoing Nonlinear Model Predictive Control (NMPC) scheme for autonomously tracking a moving target using multirotor Unmanned Aerial Vehicles (UAVs). The scheme is developed for surveillance and tracking of contour-based areas with evolving features. NMPC is used to manage input and state constraints, while additional barrier functions are incorporated in order to ensure system safety and optimal performance. The proposed control scheme is designed based on the extraction and implementation of the full dynamic model of the features describing the target and the state variables. Real-time simulations and experiments using a quadrotor UAV equipped with a camera demonstrate the effectiveness of the proposed strategy.
ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-024-10183-3