Article
Grasp Planning and Visual Servoing for an Outdoors Aerial Dual Manipulator
Author/s | Ramón Soria, Pablo
Arrue Ullés, Begoña C. Ollero Baturone, Aníbal |
Department | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Publication Date | 2020-02 |
Deposit Date | 2023-05-25 |
Published in |
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Abstract | This paper describes a system for grasping known objects with unmanned aerial vehicles (UAVs) provided with dual manipulators using an RGB-D camera. Aerial manipulation remains a very challenging task. This paper covers ... This paper describes a system for grasping known objects with unmanned aerial vehicles (UAVs) provided with dual manipulators using an RGB-D camera. Aerial manipulation remains a very challenging task. This paper covers three principal aspects for this task: object detection and pose estimation, grasp planning, and in-flight grasp execution. First, an artificial neural network (ANN) is used to obtain clues regarding the object's position. Next, an alignment algorithm is used to obtain the object's six-dimensional (6D) pose, which is filtered with an extended Kalman filter. A three-dimensional (3D) model of the object is then used to estimate an arranged list of good grasps for the aerial manipulator. The results from the detection algorithm—that is, the object's pose—are used to update the trajectories of the arms toward the object. If the target poses are not reachable due to the UAV's oscillations, the algorithm switches to the next feasible grasp. This paper introduces the overall methodology, and provides the experimental results of both simulation and real experiments for each module, in addition to a video showing the results. |
Funding agencies | Unión Europea Ministerio de Economía, Industria y Competitividad (MINECO). España |
Project ID. | SI-1439/2015
DPI2017-89790-R |
Citation | Ramón Soria, P., Arrue Ullés, B.C. y Ollero Baturone, A. (2020). Grasp Planning and Visual Servoing for an Outdoors Aerial Dual Manipulator. Engineering, 6 (1), 77-88. https://doi.org/10.1016/j.eng.2019.11.003. |
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Engineering_2020_Arrue_Grasp_OA.pdf | 3.181Mb | [PDF] | View/ | |