Contraction, Robustness, and Numerical Path-Following Using Secant Maps

Numerical Path Following

Shape Optimization of an abrupt contraction using numerical streamlining

This research was conducted to find a reliable technique to shape an abrupt contraction for minimizing the energy loss. The method may find broader applications in design of variety of transitional cross-sections in hydraulic structures. The streamlines in a 2-D contraction were calculated through solving the potential flow equations in rectangular and curvilinear coordinates. The natural cubic...

Qualitative Localization using Vision and Odometry for Path Following in Topo-metric Maps

We address the problem of navigation in topometric maps created by using odometry data and visual loopclosure detection. Based on our previous work [6], we present an optimized version of our loop-closure detection algorithm that makes it possible to create consistent topo-metric maps in real-time while the robot is teleoperated. Using such a map, the proposed navigation algorithm performs qual...

Contraction kernels and combinatorial maps

Graph pyramids are made of a stack of successively reduced graphs embedded in the plane. Such pyramids overcome the main limitations of their regular ancestors. The graphs used in the pyramid may be region adjacency graphs, dual graphs or combinatorial maps. Compared to usual graph data structures, combinatorial maps offer an explicit encoding of the orientation of edges around vertices. Each c...

Signal Classifiers Using Self-organizing Maps: Performance and Robustness

This paper explores the use of self-organizing maps as a mechanism for performing unsupervised learning for signal classification. Approaches using unsupervised learning have a key advantage over traditional approaches that utilize neural networks and support vector machines because they do not require a training phase. We develop signal classifiers using self-organizing maps and explore their ...