Control of a robotic manipulator to grasp a moving target using vision

Houshangi, N.

doi:10.1109/robot.1990.126048

Cited by 55 publications

(29 citation statements)

References 9 publications

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“…The task of the control subsystem is to keep the relative position between the end effector and the object by generating a robot movement in order to compensate the movement of the object (see figure 17). Due to the delay introduced by the vision subsystem, in the majority of the reported experimental systems some type of estimator is used to determine the future position of the object from previous visual information (Houshangi, 1990), (Wang & Varshney, 1991), (Westmore & Wilson, 1991), (Corke, 1993). Nevertheless, we will demonstrate in this work that it is possible to eliminate the estimator while achieving performances similar to those obtained using an estimator, just by selecting and tuning an adequate control subsystem.…”

Section: Task 2: Tracking Of a Moving Objectmentioning

confidence: 91%

Designing and Building Controllers for 3D Visual Servoing Applications under a Modular Scheme

Bachiller¹,

Cerrada²,

Cerrada³

2006

Industrial Robotics: Programming, Simulation and Applications

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Section: Task 2: Tracking Of a Moving Objectmentioning

confidence: 91%

Designing and Building Controllers for 3D Visual Servoing Applications under a Modular Scheme

Bachiller¹,

Cerrada²,

Cerrada³

2006

Industrial Robotics: Programming, Simulation and Applications

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“…Early work by Houshangi (1990) described a system for online visual servoing of a robot manipulator to grasp a moving target. This early work executed a top grasp on a cylinder, constrained to move on a flat surface while viewed from a ceiling-mounted camera, i.e., the problem was reduced to 2D tracking of a circle on a plane, and execution of a pre-programmed grasp on the circle.…”

Section: Related Workmentioning

confidence: 99%

“…The main contribution of this work was the development of fast algorithms for computing optical flow at real-time frame rates, using the low-power computers of the time, and predictively extrapolating the motion of the toy train to overcome computational time delays. As with Houshangi (1990), the actual grasp planning in this work was relatively simplistic, and worked by simply grasping the toy train from above, while aligning the gripper's jaws with the direction of the train's motion. The work of Smith and Papanikolopoulos (1995) was similarly limited, with a rectangular block, moving in a straight line, being grasped from above.…”

Section: Related Workmentioning

confidence: 99%

Dynamic grasp and trajectory planning for moving objects

et al. 2018

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This paper shows how a robot arm can follow and grasp moving objects tracked by a vision system, as is needed when a human hands over an object to the robot during collaborative working. While the object is being arbitrarily moved by the human co-worker, a set of likely grasps, generated by a learned grasp planner, are evaluated online to generate a feasible grasp with respect to both: the current configuration of the robot respecting the target grasp; and the constraints of finding a collision-free trajectory to reach that configuration. A task-based cost function enables relaxation of motion-planning constraints, enabling the robot to continue following the object by maintaining its end-effector near to a likely pre-grasp position throughout the object's motion. We propose a method of dynamic switching between: a local planner, where the hand smoothly tracks the object, maintaining a steady relative pre-grasp pose; and a global planner, which rapidly moves the hand to a new grasp on a completely different part of the object, if the previous graspable part becomes unreachable. Various experiments are conducted using a real collaborative robot and the obtained results are discussed.

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“…These challenges are the major reason for a limited performance in the tracking and grasping process which can be solved via use of predictive algorithms. [7] developed a system to grasp moving targets using a static camera and precalibrated camera-manipulator transform. [8] proposed a control theory approach for grasping using visual information.…”

Section: Introductionmentioning

confidence: 99%

Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object in the Presence of Obstacles without Cameras

Chaabaani¹,

Bellamine²,

Gasmi³

2016

IJCTE

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Abstract-Many of researchers working on robotic grasping tasks assume a stationary or fixed object, others have focused on dynamic moving objects using cameras to record images of the moving object and then they treated their images to estimate the position to grasp it. This method is quite difficult, requiring a lot of computing, image processing… Hence, it should be sought more simple handling method. Moreover, the majorities of robotic arms available for humanoid applications are complex to control and yet expensive. In this paper, we are going to detail the requirements to manipulating a 7-DoF WAM robotic arm equipped with the Barrett hand to grasp and handle any moving objects in the 3-D environment in the presence of obstacles and without using the cameras. We used the OpenRAVE simulation environment. We use an extension of RRT-JT algorithm that interleaves exploration using a Rapidly-exploring Random Tree with exploitation using Jacobian-based gradient descent to control the 7-DoF WAM robotic arm to avoid the obstacles, track a moving object, and grasp planning. We present results in which a moving mug is tracked, stably grasped with a maximum rate of success in a reasonable time and picked up by the Barret hand to a desired position.

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Control of a robotic manipulator to grasp a moving target using vision

Cited by 55 publications

References 9 publications

Designing and Building Controllers for 3D Visual Servoing Applications under a Modular Scheme

Designing and Building Controllers for 3D Visual Servoing Applications under a Modular Scheme

Dynamic grasp and trajectory planning for moving objects

Controlling a Humanoid Robot Arm for Grasping and Manipulating a Moving Object in the Presence of Obstacles without Cameras

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