Reference : Real-time Model Predictive Control for Aerial Manipulation
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
Real-time Model Predictive Control for Aerial Manipulation
Dentler, Jan Eric mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
University of Luxembourg, ​Luxembourg, ​​Luxembourg
Docteur en Sciences de l'Ingénieur
Voos, Holger mailto
Hadji-Minaglou, Jean-Régis mailto
Kannan, Somasundar mailto
Antonelli, Gianluca mailto
Kayacan, Erdal mailto
[en] Nonlinear Model Predictive Control ; Aerial Manipulation ; Cooperative Control ; Unmanned Aerial Vehicle ; Distributed Systems ; Task-based Control
[en] The rapid development in the field of Unmanned Aerial Vehicles (UAVs) is driven by new applications in agriculture, logistics, inspection and smart manufacturing. The future keys in these domains are the abilities to autonomously interact with the environment and with other robotic systems. This thesis is providing control engineering solutions to contribute to these key capabilities.
The first step of this thesis is to develop an understanding of the dynamic behavior of UAVs. For this purpose, dynamic and kinematic models are presented to describe a UAV's motion. This includes a kinematic model which is suitable for off-the-shelf UAVs and combines full 360° heading operation with a low computational complexity. The presented models are subsequently used to develop a nonlinear model predictive control NMPC strategy. In this context, the performance of several NMPC solvers and inequality constraint handling techniques is evaluated. The real-time capability and NMPC performance are validated with real AR.Drone 2.0 and DJI M100 quadrotors. This includes collision avoidance and advanced tracking scenarios. The design work-flow for the related control objectives and constraints is presented accordingly. As a next step, this UAV NMPC strategy is extended for a UAV with attached robotic arm. For this purpose, the forward kinematics of the robotic arm are developed and combined with the kinematic model of the UAV. The resulting NMPC strategy is validated in a grasping scenario with a real aerial manipulator. The final step of this thesis is the NMPC of cooperating UAVs. The computational complexity of such scenarios conflicts directly with the fast UAV dynamics. In addition, control objectives and system topologies can dynamically change. To address these challenges, this thesis presents the DENMPC software framework. DENMPC provides a computationally efficient central NMPC strategy that allows changing the control scenario at runtime. This is finally stated in the control of a real cooperative aerial manipulation scenario.
Researchers ; Professionals ; Students ; General public ; Others
FnR ; FNR9312118 > Jan Eric Dentler > FLYMAN > Controller design for cooperative flying manipulation using small quadrotor UAVs > 15/11/2014 > 14/11/2018 > 2014

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20181017_dentler_thesis.pdfModel Predictive Control of Cooperative Aerial ManipulationAuthor postprint22.98 MBView/Open

Additional material(s):

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Open access
20180618_dentler_aerialmanipulation.mp4Model predictive control of aerial manipulation using DENMPC (DJI M100 with robotic arm) - Manipulating a bottle36 MBView/Open
Open access
20180618_dentler_cooperativeaerialmanipulation.mp4Nonlinear model predictive control of cooperative aerial manipulation using DENMPC (AR.Drone 2.0 and DJI M100)- Manipulation in formation20.5 MBView/Open
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20180618_dentler_runtimeocpchange.mp4Model predictive control of multiple AR.Drone 2.0 using DENMPC - Adding and removing drones from formation14.07 MBView/Open
Open access
20180618_dentler_uavnmpc.mp4Model predictive control of AR.Drone 2.0 and DJI M100 using DENMPC - Trajectory tracking23.39 MBView/Open
Open access
20180618_dentler_directionvector.mp4Model predictive control of AR.Drone 2.0 using DENMPC - Comparison of standard model with direction vector model9.93 MBView/Open
Open access
20180618_dentler_coneconstraintaroundstaticpointwithobstacle.mp4Model predictive control of AR.Drone 2.0 using DENMPC - Collision avoidance and advanced sensor tracking9.18 MBView/Open

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