Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Synopsis of recent progress on camera calibration for 3D machine vision
The robotics review 1
Connectionist robot motion planning: a neurally-inspired approach to visually-guided reaching
Connectionist robot motion planning: a neurally-inspired approach to visually-guided reaching
CVGIP: Image Understanding - Special issue on purposive, qualitative, active vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online estimation of Image Jacobian Matrix for uncalibrated dynamic hand-eye coordination
International Journal of Systems, Control and Communications
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Assembly robots that use an active camera system for visual feedback can achieve greater flexibility, including the ability to operate in an uncertain and changing environment. Incorporating active vision into a robot control loop involves some inherent difficulties, including calibration, and the need for redefining the servoing goal as the camera configuration changes. In this paper, we propose a novel self-organizing neural network that learns a calibration-free spatial representation of 3D point targets in a manner that is invariant to changing camera configurations. This representation is used to develop a new framework for robot control with active vision. The salient feature of this framework is that it decouples active camera control from robot control. The feasibility of this approach is established with the help of computer simulations and experiments with the University of Illinois Active Vision System (UIAVS).