High speed grasping using visual and force feedback

Namiki, Akio; Nakabo, Yoshihiro; Ishii, Idaku; Ishikawa, Masatoshi

doi:10.1109/robot.1999.774085

Cited by 51 publications

(26 citation statements)

References 8 publications

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“…In [17], an integral image is used to speed up feature detection. Vision chips, FPGAs (Field-programmable Gate Arrays), and Graphics Cards with parallel computation capability are favored to implement image processing algorithms [18]- [20]. However, the image processing delay still largely exceeds the cycle time of a robot joint position control loop.…”

Section: Introductionmentioning

confidence: 99%

Cloud-Based Networked Visual Servo Control

Lou

Chen

et al. 2013

IEEE Trans. Ind. Electron.

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Abstract-The performance of vision-based control systems, in particular of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control, which integrates networked computational resources for cloud image processing, is considered in this article. The main contributions of this article are i) a real-time transport protocol for transmitting large volume image data on a cloud computing platform, which enables high sampling rate visual feedback, ii) a stabilizing control law for the networked visual servo control system with time-varying feedback time delay, and iii) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the networked visual servo control system with sending rate scheduling is validated in an object tracking scenario on a 14 degree-of-freedom dual-arm robot. Experimental results show the superior performance of our approach. In particular the communication network load is substantially reduced by means of the scheduling strategy without performance degradation.

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Section: Introductionmentioning

confidence: 99%

Cloud-Based Networked Visual Servo Control

Lou

Chen

et al. 2013

IEEE Trans. Ind. Electron.

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“…Concerning hybrid visual-force systems, we should mention studies like ) which extend the "task frame" formalism (Bruyninckx and De Schutter, 1996). In (Namiki et al, 1999) a system for grasping objects in real time, which employs information from an external camera and that obtained from the force sensors of a robotic hand, is described. Another strategy used for the combination of both sensorial systems is the use of impedance control.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Visual-Force Control in Unknown Workspaces

Pomares¹,

Torres²,

Pérez

2005

Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics

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Abstract:This paper proposes the definition of a new adaptive system that combines visual and force information. At each moment, the proportion of information used from each sensor is variable depending on the adequacy of each sensor to control the task. The sensorial information obtained is processed to allow the use of both sensors for controlling the robot and avoiding situations in which the control actions are contradictory. Although the visual servoing systems have certain robustness with respect to calibration errors, when the image-based control systems are combined with force control we must accurately know the intrinsic parameters. For this purpose an adaptive approach is proposed which updates the intrinsic parameters during the task.

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“…(12). Parameters of this polynomial expression were optimized using all possible target trajectories in the neighborhood of the catching point.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…In our vision chip architecture the photo detectors and the parallel processing elements are integrated in a single system without the I/O bottleneck which results in a sampling rate higher than 1 KHz. This technique was applied to a sensory-motor fusion system [13] used to produce high-speed grasping and manipulation [12], [11], [10]. This shows that the speed of robotic visual recognition will soon become higher than that of a human.…”

Section: Related Workmentioning

confidence: 99%

Robotic catching using a direct mapping from visual information to motor command

Namiki

Ishikawa

2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422)

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Abstract-In this paper a robotic catching algorithm based on a nonlinear mapping of visual information to the desired trajectory is proposed. The nonlinear mapping is optimized by learning based on constraints of dynamics and kinematics. As a result a reactive and flexible motion is obtained owing to real-time high-speed visual information. Experimental results on catching a moving object using a high-speed vision system and a manipulation are presented.

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High speed grasping using visual and force feedback

Cited by 51 publications

References 8 publications

Cloud-Based Networked Visual Servo Control

Cloud-Based Networked Visual Servo Control

Adaptive Visual-Force Control in Unknown Workspaces

Robotic catching using a direct mapping from visual information to motor command

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