Visual Servoing and Visual Tracking

Chaumette, François; Hutchinson, Seth

doi:10.1007/978-3-540-30301-5_25

Cited by 145 publications

(182 citation statements)

References 65 publications

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“…The generalization abilities of the CDS framework ensure coordinated behavior of the visuomotor controller, even when the motion is abruptly perturbed outside the region of the provided human demonstrations. Similar to visual servoing (Espiau et al 1992;Mansard et al 2006;Natale et al 2007;Chaumette and Hutchinson 2008), it performs a closed-loop control, and hence, it ensures that the target can be reached under perturbations. Our approach inherently combines learned visuomotor transformations and visual servoing characteristics, thus it eliminates the need to rely on an external ad hoc mechanism for switching between the two modes (Natale et al 2007).…”

Section: Discussion On Controller Architecturementioning

confidence: 99%

“…Not being able to rapidly and synchronously react to perturbations can cause fatal consequences for both the robot and its environment. Solutions to robotic visual-based reaching follow either of two well-established approaches: techniques that learn visuomotor transformations (Hoffmann et al 2005;Natale et al 2005Natale et al , 2007Hulse et al 2009;Jamone et al 2012), which operate in an open-loop manner, or visual servoing techniques (Espiau et al 1992;Mansard et al 2006;Natale et al 2007;Chaumette and Hutchinson 2008;Jamone et al 2012), which are closed-loop methods. Techniques that learn the visuomotor maps are very appealing because of their simplicity and practical applications.…”

Section: Robotic Visually Aided Manipulation and Obstacle Avoidancementioning

confidence: 99%

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Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Lukic

Santos-Victor²,

Billard

2014

Biol Cybern

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We investigate the role of obstacle avoidance in visually guided reaching and grasping movements. We report on a human study in which subjects performed prehensile motion with obstacle avoidance where the position of the obstacle was systematically varied across trials. These experiments suggest that reaching with obstacle avoidance is organized in a sequential manner, where the obstacle acts as an intermediary target. Furthermore, we demonstrate that the notion of workspace travelled by the hand is embedded explicitly in a forward planning scheme, which is actively involved in detecting obstacles on the way when performing reaching. We find that the gaze proactively coordinates the pattern of eye-arm motion during obstacle avoidance. This study provides also a quantitative assessment of the coupling between the eye-arm-hand motion. We show that the coupling follows regular phase dependencies and is unaltered during obstacle avoidance. These observations provide a basis for the design of a computational model. Our controller extends the coupled dynamical systems framework and provides fast and synchronous control of the eyes, the arm and the hand within a single and compact framework, mimicking similar control system found in humans. We validate our model for visuomotor control of a humanoid robot.

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Section: Discussion On Controller Architecturementioning

confidence: 99%

Section: Robotic Visually Aided Manipulation and Obstacle Avoidancementioning

confidence: 99%

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Lukic

Santos-Victor²,

Billard

2014

Biol Cybern

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“…This is one of the reasons why the closed-control loop for rigid robots and the state estimation are necessary. Vision based technique for rigid robots (visual servoing [ Agin (1979); Chaumette (1998); Chaumette & Hutchinson (2008); Corke & Hutchinson (2001) ;Fung & Chen (2010); Malis at al. (1999);Marchand at al.…”

Section: Fig 1 Rigid Actuatormentioning

confidence: 99%

Estimation of Position and Orientation for Non-Rigid Robots Control Using Motion Capture Techniques

Mazurek¹

2012

Serial and Parallel Robot Manipulators - Kinematics, Dynamics, Control and Optimization

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“…[12]. The recent Springer Handbook of Robotics [13] includes a chapter describing the assumptions and methods behind visual servoing.…”

Section: Visual Servoingmentioning

confidence: 99%

“…If this process is repeated until convergence is achieved, the result would be termed a "position-based visual servo control" (PBVS) structure [12] [13]. However, it is important to note that EPEC has been developed with the constraints of planetary robotics in mind, which leads to an emphasis on the amount of correction in the first iteration of the PBVS loop.…”

Section: On-line Epecmentioning

confidence: 99%

Vision guided manipulation for planetary robotics — position control

Nickels

DiCicco

Bajracharya

et al. 2010

Robotics and Autonomous Systems

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Manipulation systems for planetary exploration operate under severe restrictions. They need to integrate vision and manipulation to achieve the reliability, safety, and predictability required of expensive systems operating on remote planets. They also must operate on very modest hardware that is shared with many other systems, and must operate without human intervention.Typically such systems employ calibrated stereo cameras and calibrated manipulators to achieve precision of the order of one centimeter with respect to instrument placement activities. This paper presents three complementary approaches to vision guided manipulation designed to robustly achieve high precision in manipulation. These approaches are described and compared, both in simulation and on hardware.In-situ estimation and adaptation of the manipulator and/or camera models in these methods account for changes in the system configuration, thus ensuring consistent precision for the life of the mission. All three methods provide severalfold increases in accuracy of manipulator positioning over the standard flight approach.

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Visual Servoing and Visual Tracking

Cited by 145 publications

References 65 publications

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Estimation of Position and Orientation for Non-Rigid Robots Control Using Motion Capture Techniques

Vision guided manipulation for planetary robotics — position control

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