Grasping of a moving object with a robotic hand-eye system

Benameur, K.; Bélanger, Pierre

doi:10.1109/iros.1998.724636

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…In the past, many different approaches have been developed for tracking and grasping of moving objects [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20]. Most of these methods use stereo images to compute the 3D pose for grasping.…”

Section: Related Workmentioning

confidence: 99%

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Realtime tracking and grasping of a moving object from range video

Husain

Colomé

Dellen

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-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 realtime 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 OpenNItracker. The developed system is integrated with ROS and made available as part of IRI's ROS stack.

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Section: Related Workmentioning

confidence: 99%

“…Based on the tracked results a real-time motion planning method is proposed. Simulation results have also been provided in [16] for an integrated sensing and actuation system for grasping a moving object.…”

Section: Related Workmentioning

confidence: 99%

Realtime tracking and grasping of a moving object from range video

Husain

Colomé

Dellen

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…Under this circumstance, the tracking problem can be converted into a trajectory planning problem in the sense of planning a trajectory which leads the robot to an anticipated rendezvous point [4]. For this problem, the prediction, planning and execution (PPE) method is proven to be an effective choice in solving corresponding issues [5]. Unfortunately, in most practical dynamic target tracking problems, the precise kinematic model for the target is either too difficult to obtain or needs complex mathematical descriptions, which leads to a result of an unfaithful prediction.…”

Section: Introductionmentioning

confidence: 99%

Persistent tracking of a dynamic target by a mobile robot

Liang

Fang

Yang

2014

Proceedings of the 33rd Chinese Control Conference

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A novel pursuing algorithm, together with a new experiment method, is introduced to drive a nonholonomic mobile robot tracking a dynamic target in the framework of pursuit-evasion game. Specifically, a pan-tilt with an on-board camera is attached to the mobile robot to construct a tracking system, which greatly improves the maneuverability compared with the traditional method. To keep the target close enough to the vision center of the camera, a control technique is devised according to its kinematic model and Lyapunov theory. By utilizing game theory, the viability set is determined. A tracking controller is employed to guarantee persistent tracking for the dynamic target, where the relative position between the target and the robot is successfully acquired from the visual measurements by the utilization of geometric analysis. Both simulation and experimental results are provided to illustrate the performance of the controller.

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“…Thus, time-optimal tracking and capturing method, such as prediction, planning and execution (PPE) methods, can be employed well [5], [6]. However, for a fast maneuvering object, whose velocity and acceleration varies randomly and quickly, visual-feedbackbased systems perform tracking and capturing thus objects become more and more difficult because it is difficult to predict its location and velocity in a long term.…”

Section: Introductionmentioning

confidence: 99%

Modeling human intelligence and application to space object capturing

Huang

Liang³

2005 IEEE International Conference on Information Acquisition

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It is a great challenge for a robot in space to track and capture a free-flying object in the future space operations. In previous research, most of them employed model-based method which requires robot model in advance. However, it is difficult and time-consuming to obtain the precise mathematical model of robots. Moreover, the computer installed on the space robot is usually not so powerful due to the restriction of weight and volume, thus it is infeasible to computer dynamics parameters for real-time control. To facilitate the computation complexity, we present an approach based on human learning skill for tracking and catching objects. With human-teaching demonstration, the space robot is able to learn and abstract human tracking and capturing skill using an efficient neural-network learning architecture that combines flexible Cascade Neural Networks with Node-Decoupled Extended Kalman Filtering (CNN-NDEKF)[1]. The goal of skill learning is to obtain the most likely human performance from all the training examples and to transfer this skill to the space robot system by trained cascade neural network. We investigate the learning position trajectory in Cartesian space and position trajectory in Joint space respectively. Especially, learning position trajectory in Joint space is useful to avoid the complex inverse kinematics of space robot and to lower the computation cost. The simulation results attest that this approach is useful and feasible in tracking trajectory planning and capturing of space robot. The proposed approach provides a feasible way to plan the tracking trajectory of space robot by learning human demonstration skill. The learning is significant in eliminating sluggish motion planning and correcting a motion command that the operator may mistakenly generate. It will be found useful in various other applications, such as human action recognition in man-machine interfaces, real-time training, and agile manufacturing.Index Terms-Space robot, learning approach, human skill, CNN-NDEFK. * This work is partially supported by Chinese High-Tech Program in Aerospace.

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Grasping of a moving object with a robotic hand-eye system

Cited by 5 publications

References 4 publications

Realtime tracking and grasping of a moving object from range video

Realtime tracking and grasping of a moving object from range video

Persistent tracking of a dynamic target by a mobile robot

Modeling human intelligence and application to space object capturing

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