Christfried Webers

Motion Control of Mobile Robots—From Static Targets to Fast Drives in Moving Crowds

Motion control of vehicles under uncertain, noisy, and discontinuous positioning is essential in autonomous navigation in unknown environments. This article suggests two methods for motion control, where the initial parameters of the on-line control are physically explainable, the resulting trajectory as well as the control parameters are asymptotically converging and glitches in the localization are handled robustly. The differences to a known method based on Lyapunov functions are discussed theoretically as well as in terms of actual motion measurements. Physical experiments with landbound vehicles show the reliability and limitations of these different methods in setups employing static attractors, systematically moving targets and fast, unpredictable moving targets in highly dynamical environments. Mainly due to the physical meaning of the control parameters the adaptation to actual kinematics and dynamics is significantly simplified.

kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity

Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or “samples”) in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.

Robust tracking under uncertainties

Robust tracking of vehicles under uncertain, noisy, and discontinuous positioning is a significant part of autonomous navigation in unknown environments. The article suggests two methods for track control, where the initial parameters of the online control are physically explainable, the resulting track as well as the control parameters are asymptotically converging and glitches in the localization are handled robustly. Practical experiments with landbound vehicles show the reliability and limitations of the method in various environments in setups for following simple attractors. Due to the physical meaning of the control parameters the adaptation to changed kinematic or dynamics is significantly simplified.