A Multi-Layer Control Scheme for a centralized UAV formation

Brandão, Alexandre Santos; Barbosa, Joao P. A.; Mendoza, Valentín; Sarcinelli-Filho, Mário; Carelli, Ricardo

doi:10.1109/icuas.2014.6842373

Cited by 31 publications

(9 citation statements)

References 18 publications

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“…We will prove that under all the above uncertainties, we are able to solve the integrated optimization control problem (6) for formation of multiple cooperative UAVs (3) using the proposed LBMPC optimization cost function (9).…”

Section: Endformentioning

confidence: 96%

Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control

Hafez

Givigi

Yousefi

2018

Asian Journal of Control

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This paper presents a solution for the formation flight problem for multiple unmanned aerial vehicles (UAVs) cooperating to execute a required mission. Learning Based Model Predictive Control (LBMPC) is implemented on the team of UAVs in order to accomplish the required formation. All flight simulations respect Reynold's rules of flocking to avoid UAV collisions with nearby flockmates, match the team members velocity and stay close to each other during the formation. The main contribution of this paper lies in the application of LBMPC to solve the problem of formation for an autonomous team of UAVs. The proposed solution is theoretically, by the application of analysis to the problem, demonstrated to be stable. Moreover, simulations support the findings of the paper. The main contributions of this paper are the proposed LBMPC formulation for formation of vehicles with uncertainty in their models, and the theoretical analysis of the solution.

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Section: Endformentioning

confidence: 96%

Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control

Hafez

Givigi

Yousefi

2018

Asian Journal of Control

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“…An aircraft model can be represented by four interconnected dynamic subsystems: Actuator Dynamics (responsible for transforming the joystick or computer generated commands in servomotor actuation), Rotary-Wing Dynamics (which embeds the aerodynamic parameters and the thrust generation associated to the four independent motors), Forces and Torques Generation (where the thrust force decomposition based on the attitude of the vehicle takes place) and Rigid-Body Dynamics (which defines the displacement and inclination of the rotorcraft in the free Cartesian space, caused by the forces and torques acting on it) [13], [14], [15]. Control signals u represent the real input controls (those effectively applied to the UAV), where • uż represents a linear velocity command, which causes displacements over the z w axis; • uψ represents an angular velocity command, which causes rotations around the z w axis; • u φ represents indirectly a linear velocity command, which causes displacements over the y b axis, so that u φ = uẏ ; • u θ represents indirectly a linear velocity command, which causes displacements over the x b axis, so that u φ = uẋ.…”

Section: The Uav Modelmentioning

confidence: 99%

UAV obstacle avoidance using RGB-D system

Santos

Santana

Brandão

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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This work proposes an obstacle avoidance strategy for UAV navigation in indoor environments. The proposal is able to compute the distance among the UAV and the obstacles (which change their position dynamically), and then to select the closest one. When a collision risk is pointed out, the algorithm establish some escape points, whose orientation is aligned tangentially to the obstacle edge or to the UAV normal displacement. Considering that only the desired point is change during the avoidance maneuver, the stability of the whole nonlinear system is demonstrated in the sense of Lyapunov. Information Filter is used to track the 3D positioning of the UAV and the obstacles. Moreover, UAV state variables are given by a Decentralized Information Filter, which fuses information from the Inertial Measurement Unit onboard the aircraft and the depth-camera sensor (RGB-D). The effectiveness of the proposal is demonstrated by simulation results, which take into account the AR.drone rotorcraft dynamic model.

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“…Since aerial manipulators present limitations in terms of payload, team of UAVMs can be adopted to carry out complex missions such as, e.g., cooperative transportation of large and/or heavy payloads [5] and cooperative assembly of structures in remote or hazardous environments. Several frameworks for multi-robot aerial systems have been proposed in literature: [6] proposes a decisional architecture for multi-UAVs systems, that uses different control schemes depending on the status of the current task, in [7] coordination of swarms of UAVs is achieved via a fuzzy control methodology, and in [8] a multi-layer control scheme is proposed to guide a formation of three UAVs in trajectory tracking missions. Multiple robots allow to achieve complex missions by assigning the single sub-tasks to different vehicles.…”

Section: Introductionmentioning

confidence: 99%

Coordinated Control of Aerial Robotic Manipulators: Theory and Experiments

Muscio

Pierri

Trujillo

et al. 2018

IEEE Trans. Contr. Syst. Technol.

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This paper presents a three-layer control architecture for coordinated control of multiple aerial manipulators (UAVMs): the centralized top layer plans the end-effector desired trajectories of each UAVM; the middle layer, local to each vehicle, computes the corresponding motion references; the bottom layer is a low level dynamic motion controller, which tracks the motion references. At second layer, the overall mission is hierarchically decomposed in a set of elementary behaviors, which are combined together, via the Null Space-based Behavioral approach, into more complex compound behaviors. For each UAVM, the suitable compound behavior to be executed is selected by a supervisor. The proposed framework has been tested through an experimental campaign.

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A Multi-Layer Control Scheme for a centralized UAV formation

Cited by 31 publications

References 18 publications

Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control

Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control

UAV obstacle avoidance using RGB-D system

Coordinated Control of Aerial Robotic Manipulators: Theory and Experiments

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