Heuristic vision-based computation of planar antipodal grasps on unknown objects

Morales, Antonio; Recatala, G.; Sanz, Pedro J.; Pobil, Ángel P. del

doi:10.1109/robot.2001.932613

Cited by 27 publications

(13 citation statements)

References 12 publications

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“…The current trend is to incorporate sensor information for grasp planning and synthesis, such as vision [2,3,4,5,6] or range sensors [7]. In this line, several approaches have also adopted machine learning techniques to determine the relevant features that indicate a successful grasp [8,4,9,10].…”

Section: State Of the Artmentioning

confidence: 99%

Visually-guided manipulation techniques for robotic autonomous underwater panel interventions

Peñalver

Pérez

Fernández

et al. 2015

Annual Reviews in Control

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The long term of this ongoing research has to do with increasing the autonomy levels for underwater intervention missions. Bearing in mind that the specific mission to face has been the intervention on a panel, in this paper some results in different development stages are presented by using the real mechatronics and the panel mockup. Furthermore, some details are highlighted describing two methodologies implemented for the required visually-guided manipulation algorithms, and also a roadmap explaining the different testbeds used for experimental validation, in increasing complexity order, are presented. It is worth mentioning that the aforementioned results would be impossible without previous generated know-how for both, the complete developed mechatronics for the autonomous underwater vehicle for intervention, and the required 3D simulation tool. In summary, thanks to the implemented approach, the intervention system is able to control the way in which the gripper approximates and manipulates the two panel devices (i.e. a valve and a connector) in autonomous manner and, results in different scenarios demonstrate the reliability and feasibility of this autonomous intervention system in water tank and pool conditions.

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Section: State Of the Artmentioning

confidence: 99%

Visually-guided manipulation techniques for robotic autonomous underwater panel interventions

Peñalver

Pérez

Fernández

et al. 2015

Annual Reviews in Control

Self Cite

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“…The research is then focused on grasp analysis, the study of the physical properties of a given grasp; and grasp synthesis, the computation of grasps that meet certain desirable properties, (Bicchi and Kumar, 2000;Coelho Jr. et al, 1998;Namiki et al, 2003;Platt Jr. et al, 2002;Shimoga, 1996). More recently, it has been proposed to use vision as a solution to obtain the lacking information about object shapes or to use contact information to explore the object Morales et al, 2001;Platt Jr. et al, 2002). Another trend has focused on the use of machine learning approaches to determine the relevant features that indicate a successful grasp (Coelho et al, 2001;Kamon et al, 1998;Morales et al, 2004).…”

Section: Object Manipulationmentioning

confidence: 99%

An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World

Rasolzadeh¹,

Björkman²,

Huebner³

et al. 2009

The International Journal of Robotics Research

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The ability to autonomously acquire new knowledge through interaction with the environment is an important research topic in the field of robotics. The knowledge can be acquired only if suitable perception-action capabilities are present: a robotic system has to be able to detect, attend to and manipulate objects in its surrounding. In this paper, we present the results of our longterm work in the area of vision based sensing and control. The work on finding, attending, recognizing and manipulating objects in domestic environments is studied. We present a stereo based vision system framework where aspects of Top-down and Bottom-up attention as well as foveated attention are put into focus and demonstrate how the system can be utilized for robotic object grasping.

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“…This problem is important and difficult mainly because of the high number of DOFs involved in grasping arbitrary objects with complex hands. Another important research area is grasp planning without detailed object models where sensor information such as computational vision is used to extract relevant features in order to compute suitable grasps, [4], [5]. Some ideas of how to learn or refine grasping strategies have been presented in [6], [7].…”

Section: A Related Workmentioning

confidence: 99%

Early Reactive Grasping with Second Order 3D Feature Relations

Aarno

Sommerfeld

Kragić

et al. 2007

Recent Progress in Robotics: Viable Robotic Service to Human

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Heuristic vision-based computation of planar antipodal grasps on unknown objects

Cited by 27 publications

References 12 publications

Visually-guided manipulation techniques for robotic autonomous underwater panel interventions

Visually-guided manipulation techniques for robotic autonomous underwater panel interventions

An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World

Early Reactive Grasping with Second Order 3D Feature Relations

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