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See detailReal-time Model Predictive Control for Aerial Manipulation
Dentler, Jan Eric UL

Doctoral thesis (2018)

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 ... [more ▼]

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. [less ▲]

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See detailImplementation and validation of an event-based real-time nonlinear model predictive control framework with ROS interface for single and multi-robot systems
Dentler, Jan Eric UL; Kannan, Somasundar UL; Olivares Mendez, Miguel Angel UL et al

in 2017 IEEE Conference on Control Technology and Applications (CCTA) (2017, August 30)

This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined ... [more ▼]

This paper presents the implementation and experimental validation of a central control framework. The presented framework addresses the need for a controller, which provides high performance combined with a low-computational load while being on-line adaptable to changes in the control scenario. Examples for such scenarios are cooperative control, task-based control and fault-tolerant control, where the system's topology, dynamics, objectives and constraints are changing. The framework combines a fast Nonlinear Model Predictive Control (NMPC), a communication interface with the Robot Operating System (ROS) [1] as well as a modularization that allows an event-based change of the NMPC scenario. To experimentally validate performance and event-based adaptability of the framework, this paper is using a cooperative control scenario of Unmanned Aerial Vehicles (UAVs). The source code of the proposed framework is available under [2]. [less ▲]

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See detaildenmpc
Dentler, Jan Eric UL

Software (2017)

This package is providing an object oriented real-time nonlinear model predictive control (NMPC) framework which developed at the Automation & Robotics Research Group http://wwwde.uni.lu/snt/research ... [more ▼]

This package is providing an object oriented real-time nonlinear model predictive control (NMPC) framework which developed at the Automation & Robotics Research Group http://wwwde.uni.lu/snt/research/automation_robotics_research_group at the University of Luxembourg. It features a modularization for multi-agent systems which allows the on-line change of agents, control objectives, constraints and couplings, triggered by ROS-messages. [less ▲]

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See detailA real-time model predictive position control with collision avoidance for commercial low-cost quadrotors
Dentler, Jan Eric UL; Kannan, Somasundar UL; Olivares Mendez, Miguel Angel UL et al

in IEEE Multi-Conference on Systems and Control (MSC 2016), Buenos Aires, Argentina, 2016 (2016, September 20)

Unmanned aerial vehicles (UAVs) are the future technology for autonomous fast transportation of individual goods. They have the advantage of being small, fast and not to be limited to the local ... [more ▼]

Unmanned aerial vehicles (UAVs) are the future technology for autonomous fast transportation of individual goods. They have the advantage of being small, fast and not to be limited to the local infrastructure. This is not only interesting for delivery of private consumption goods up to the doorstep, but also particularly for smart factories. One drawback of autonomous drone technology is the high development costs, that limit research and development to a small audience. This work is introducing a position control with collision avoidance as a first step to make low-cost drones more accessible to the execution of autonomous tasks. The paper introduces a semilinear state-space model for a commercial quadrotor and its adaptation to the commercially available AR.Drone 2 system. The position control introduced in this paper is a model predictive control (MPC) based on a condensed multiple-shooting continuation generalized minimal residual method (CMSCGMRES). The collision avoidance is implemented in the MPC based on a sigmoid function. The real-time applicability of the proposed methods is demonstrated in two experiments with a real AR.Drone quadrotor, adressing position tracking and collision avoidance. The experiments show the computational efficiency of the proposed control design with a measured maximum computation time of less than 2ms. [less ▲]

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