Intrinsic parametrization for approximation
Computer Aided Geometric Design
Multilayer feedforward networks are universal approximators
Neural Networks
A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Neural Computation
Robust fitting by nonlinear neural units
Neural Networks
Grouping and parameterizing irregularly spaced points for curve fitting
ACM Transactions on Graphics (TOG)
Model based vision as feedback for virtual reality robotics environments
VRAIS '95 Proceedings of the Virtual Reality Annual International Symposium (VRAIS'95)
VRAIS '98 Proceedings of the Virtual Reality Annual International Symposium
Marker Tracking and HMD Calibration for a Video-Based Augmented Reality Conferencing System
IWAR '99 Proceedings of the 2nd IEEE and ACM International Workshop on Augmented Reality
Augmented Reality for Programming Industrial Robots
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning
Robotics and Computer-Integrated Manufacturing
| Hi-index | 0.00 |
This paper discusses the benefits of applying Augmented Reality (AR) to facilitate intuitive robot programming, and presents a novel methodology for planning collision-free paths for an n degree-of-freedom (DOF) manipulator in an unknown environment. The targeted applications are where the end-effector is constrained to move along a visible 3D path/curve, which position is unknown, at a particular orientation with respect to the path, such as arc welding and laser cutting. The methodology is interactive as the human is involved in obtaining the 3D data points of the desired curve to be followed through performing a number of demonstrations, defining the free space relevant to the task, and planning the orientations of the end-effector along the curve. A Piecewise Linear Parameterization (PLP) algorithm is used to parameterize the data points using an interactively generated piecewise linear approximation of the desired curve. A curve learning method based on Bayesian neural networks and reparameterization is used to learn and generate 3D parametric curves from the parameterized data points. Finally, the orientation of the end-effector along the learnt curve is planned with the aid of AR. Two case studies are presented and discussed.