Coordinated Robot Navigation via Hierarchical Clustering

We introduce the use of hierarchical clustering for relaxed, deterministic coordination and control of multiple robots. Traditionally an unsupervised learning method, hierarchical clustering offers a formalism for identifying and representing spatially cohesive and segregated robot groups at different resolutions by relating the continuous space of configurations to the combinatorial space of trees. We formalize and exploit this relation, developing computationally effective reactive algorithms for navigating through the combinatorial space in concert with geometric realizations for a particular choice of hierarchical clustering method. These constructions yield computationally effective vector field planners for both hierarchically invariant as well as transitional navigation in the configuration space. We apply these methods to the centralized coordination and control of $n$ perfectly sensed and actuated Euclidean spheres in a $d$-dimensional ambient space (for arbitrary $n$ and $d$). Given a desired configuration supporting a desired hierarchy, we construct a hybrid controller which is quadratic in $n$ and algebraic in $d$ and prove that its execution brings all but a measure zero set of initial configurations to the desired goal with the guarantee of no collisions along the way.Comment: 29 pages, 13 figures, 8 tables, extended version of a paper in preparation for submission to a journa

Probing the loss origins of ultra-smooth Si3N4\mathrm{Si_3N_4} integrated photonic waveguides

On-chip optical waveguides with low propagation losses and precisely engineered group velocity dispersion (GVD) are important to nonlinear photonic devices such as soliton microcombs. Yet, despite intensive research efforts, nonlinear integrated photonic platforms still feature propagation losses orders of magnitude higher than in standard optical fiber. The tight confinement and high index contrast of integrated waveguides make them highly susceptible to fabrication induced surface roughness. Therefore, microresonators with ultra-high Q factors are, to date, only attainable in polished bulk crystalline, or chemically etched silica based devices, that pose however challenges for full photonic integration. Here, we demonstrate the fabrication of silicon nitride ($\mathrm{Si_3N_4}$) waveguides with unprecedentedly smooth sidewalls and tight confinement with record low propagation losses. This is achieved by combining the photonic Damascene process with a novel reflow process, which reduces etching roughness, while sufficiently preserving dimensional accuracy. This leads to previously unattainable \emph{mean} microresonator Q factors larger than $5\times10^6$ for tightly confining waveguides with anomalous dispersion. Via systematic process step variation and two independent characterization techniques we differentiate the scattering and absorption loss contributions, and reveal metal impurity related absorption to be an important loss origin. Although such impurities are known to limit optical fibers, this is the first time they are identified, and play a tangible role, in absorption of integrated microresonators. Taken together, our work provides new insights in the origins of propagation losses in $\mathrm{Si_3N_4}$ waveguides and provides the technological basis for integrated nonlinear photonics in the ultra-high Q regime

Investigation of the coupling asymmetries at double-slit interference experiments

Double-slit experiments inferring the phase and the amplitude of the transmission coefficient performed at quantum dots (QD), in the Coulomb blockade regime, present anomalies at the phase changes depending on the number of electrons confined. This phase change cannot be explained if one neglects the electron-electron interactions. Here, we present our numerical results, which simulate the real sample geometry by solving the Poisson equation in 3D. The screened potential profile is used to obtain energy eigenstates and eigenvalues of the QD. We find that, certain energy levels are coupled to the leads stronger compared to others. Our results give strong support to the phenomenological models in the literature describing the charging of a QD and the abrupt phase changes.Comment: conference paper, 50th anniversary of Aharonov-Bohm effec

Clustering-Based Robot Navigation and Control

In robotics, it is essential to model and understand the topologies of configuration spaces in order to design provably correct motion planners. The common practice in motion planning for modelling configuration spaces requires either a global, explicit representation of a configuration space in terms of standard geometric and topological models, or an asymptotically dense collection of sample configurations connected by simple paths. In this short note, we present an overview of our recent results that utilize clustering for closing the gap between these two complementary approaches. Traditionally an unsupervised learning method, clustering offers automated tools to discover hidden intrinsic structures in generally complex-shaped and high-dimensional configuration spaces of robotic systems. We demonstrate some potential applications of such clustering tools to the problem of feedback motion planning and control. In particular, we briefly present our use of hierarchical clustering for provably correct, computationally efficient coordinated multirobot motion design, and we briefly describe how robot-centric Voronoi diagrams can be used for provably correct safe robot navigation in forest-like cluttered environments, and for provably correct collision-free coverage and congestion control of heterogeneous disk-shaped robots.For more information: Kod*la

Discriminative Measures for Comparison of Phylogenetic Trees

In this paper we introduce and study three new measures for efficient discriminative comparison of phylogenetic trees. The NNI navigation dissimilarity $d_{nav}$ counts the steps along a “combing” of the Nearest Neighbor Interchange (NNI) graph of binary hierarchies, providing an efficient approximation to the (NP-hard) NNI distance in terms of “edit length”. At the same time, a closed form formula for $d_{nav}$ presents it as a weighted count of pairwise incompatibilities between clusters, lending it the character of an edge dissimilarity measure as well. A relaxation of this formula to a simple count yields another measure on all trees — the crossing dissimilarity $d_{CM}$. Both dissimilarities are symmetric and positive definite (vanish only between identical trees) on binary hierarchies but they fail to satisfy the triangle inequality. Nevertheless, both are bounded below by the widely used Robinson–Foulds metric and bounded above by a closely related true metric, the cluster-cardinality metric $d_{CC}$. We show that each of the three proposed new dissimilarities is computable in time O($n^2$) in the number of leaves $n$, and conclude the paper with a brief numerical exploration of the distribution over tree space of these dissimilarities in comparison with the Robinson–Foulds metric and the more recently introduced matching-split distance. For more information: Kod*La