Model Predictive Control for Trajectory Tracking of Unmanned Aerial Vehicles Using Robot Operating System

2017 ◽  
pp. 3-39 ◽  
Author(s):  
Mina Kamel ◽  
Thomas Stastny ◽  
Kostas Alexis ◽  
Roland Siegwart
Keyword(s):  
Operating System ◽  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Robot Operating System ◽  
Aerial Vehicles

scholarly journals A Robot Operating System Framework for Secure UAV Communications

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1369
Author(s):  
Hyojun Lee ◽  
Jiyoung Yoon ◽  
Min-Seong Jang ◽  
Kyung-Joon Park
Keyword(s):  
Operating System ◽  
Unmanned Aerial Vehicles ◽  
Ground Control ◽  
Unmanned Aerial System ◽  
Security Framework ◽  
Security Vulnerabilities ◽  
Robot Operating System ◽  
Security Issues ◽  
Aerial Vehicles ◽  
Ground Control Station

To perform advanced operations with unmanned aerial vehicles (UAVs), it is crucial that components other than the existing ones such as flight controller, network devices, and ground control station (GCS) are also used. The inevitable addition of hardware and software to accomplish UAV operations may lead to security vulnerabilities through various vectors. Hence, we propose a security framework in this study to improve the security of an unmanned aerial system (UAS). The proposed framework operates in the robot operating system (ROS) and is designed to focus on several perspectives, such as overhead arising from additional security elements and security issues essential for flight missions. The UAS is operated in a nonnative and native ROS environment. The performance of the proposed framework in both environments is verified through experiments.

Model Predictive Control Based Dynamic Geofence System for Unmanned Aerial Vehicles

2017 ◽  
Author(s):  
Songyuan Zhang ◽  
Dong Wei ◽  
Minh Quan Huynh ◽  
Jian Xing Quek ◽  
Xiao Ma ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Predictive Control ◽  
Aerial Vehicles

scholarly journals Autonomous Unmanned Aerial Vehicles in Search and Rescue Missions Using Real-Time Cooperative Model Predictive Control

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4067 ◽  
Author(s):  
Fabio A. A. Andrade ◽  
Anthony Hovenburg ◽  
Luciano Netto de de Lima ◽  
Christopher Dahlin Rodin ◽  
Tor Arne Johansen ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Real Time ◽  
Predictive Control ◽  
Kinematic Model ◽  
Search And Rescue ◽  
International Standards ◽  
Area Of Interest ◽  
Aerial Vehicles ◽  
Human Operators

Unmanned Aerial Vehicles (UAVs) have recently been used in a wide variety of applications due to their versatility, reduced cost, rapid deployment, among other advantages. Search and Rescue (SAR) is one of the most prominent areas for the employment of UAVs in place of a manned mission, especially because of its limitations on the costs, human resources, and mental and perception of the human operators. In this work, a real-time path-planning solution using multiple cooperative UAVs for SAR missions is proposed. The technique of Particle Swarm Optimization is used to solve a Model Predictive Control (MPC) problem that aims to perform search in a given area of interest, following the directive of international standards of SAR. A coordinated turn kinematic model for level flight in the presence of wind is included in the MPC. The solution is fully implemented to be embedded in the UAV on-board computer with DUNE, an on-board navigation software. The performance is evaluated using Ardupilot’s Software-In-The-Loop with JSBSim flight dynamics model simulations. Results show that, when employing three UAVs, the group reaches 50% Probability of Success 2.35 times faster than when a single UAV is employed.

scholarly journals Decentralized Mesh-Based Model Predictive Control for Swarms of UAVs

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4324
Author(s):  
Salvatore Rosario Bassolillo ◽  
Egidio D’Amato ◽  
Immacolata Notaro ◽  
Luciano Blasi ◽  
Massimiliano Mattei
Keyword(s):  
Model Predictive Control ◽  
Unmanned Aerial Vehicles ◽  
Predictive Control ◽  
Control Strategy ◽  
Target Area ◽  
Guidance And Control ◽  
Aerial Vehicles ◽  
Control Scheme ◽  
And Control ◽  
Numerical Simulator

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

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