1,954 research outputs found
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Trajectory filtering and prediction for automated tracking and grasping of a moving object
The authors explore the requirements for grasping a moving object. This task requires proper coordination between at least three separate subsystems: real-time vision sensing, trajectory-planning/arm-control, and grasp planning. As with humans, the system first visually tracks the object's 3D position. Because the object is in motion, this must be done in real-time to coordinate the motion of the robotic arm as it tracks the object. The vision system is used to feed an arm control algorithm that plans a trajectory. The arm control algorithm is implemented into two steps: filtering and prediction and kinematic transformation computation. Once the trajectory of the object is tracked, the hand must intercept the object to actually grasp it. Experimental results are presented in which a moving model train was tracked, stably grasped, and picked up by the system
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Automated tracking and grasping of a moving object with a robotic hand-eye system
An attempt to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object is described. The interplay of hand-eye coordination in dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system is explored. The goal is to build an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system built addresses three distinct problems in using robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system operates at approximately human arm movement rates. Experimental results in which a moving model train is tracked, stably grasped, and picked up by the system are presented. The algorithms developed to relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks
Realtime tracking and grasping of a moving object from range video
In this paper we present an automated system that is able to track and grasp a moving object within the workspace of a manipulator using range images acquired with a Microsoft Kinect sensor. Realtime tracking is achieved by a geometric particle filter on the affine group. Based on the tracked output, the pose of a 7-DoF WAM robotic arm is continuously updated using dynamic motor primitives until a distance measure between the tracked object and the gripper mounted on the arm is below a threshold. Then, it closes its three fingers and grasps the object. The tracker works in real-time and is robust to noise and partial occlusions. Using only the depth data makes our tracker independent of texture which is one of the key design goals in our approach. An experimental evaluation is provided along with a comparison of the proposed tracker with state-of-the-art approaches, including the OpenNI-tracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.Peer ReviewedPostprint (author’s final draft
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Real-time visual servoing
A real-time tracking algorithm in conjunction with a predictive filter to allow real-time visual servoing of a robotic arm that is tracking a moving object is described. The system consists of two calibrated (but unregistered) cameras that provide images to a real-time, pipeline-parallel optic-flow algorithm that can robustly compute optic-flow and calculate the 3-D position of a moving object at approximately 5-Hz rates. These 3-D positions of the moving object serve as input to a predictive kinematic control algorithm that uses an α-β-γ filter to update the position of a robotic arm tracking the moving object. Experimental results are presented for the tracking of a moving model train in a variety of different trajectories
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