Adaptive continuous‐space informative path planning for online environmental monitoring

Autonomous mobile robots are increasingly employed to take measurements for environmental monitoring, but planning informative, measurement‐rich paths through large three‐dimensional environments is still challenging. Designing such paths, known as the informative path planning (IPP) problem, has been shown to be NP‐hard. Existing algorithms focus on providing guarantees on suboptimal solutions, but do not scale well to large problems. In this paper, we introduce a novel IPP algorithm that uses an evolutionary strategy to optimize a parameterized path in continuous space, which is subject to various constraints regarding path budgets and motion capabilities of an autonomous mobile robot. Moreover, we introduce a replanning scheme to adapt the planned paths according to the measurements taken in situ during data collection. When compared to two state‐of‐the‐art solutions, our method provides competitive results at significantly lower computation times and memory requirements. The proposed replanning scheme enables to build models with up to 25% lower uncertainty within an initially unknown area of interest. Besides presenting theoretical results, we tailored the proposed algorithms for data collection using an autonomous surface vessel for an ecological study, during which the method was validated through three field deployments on Lake Zurich, Switzerland. Spatiotemporal variations are shown over a period of three months and in an area of 350 m × 350 m × 13 m. Whereas our theoretical solution can be applied to multiple applications, our field results specifically highlight the effectiveness of our planner for monitoring toxic microorganisms in a pre‐alpine lake, and for identifying hot‐spots within their distribution.     

From software extensions to product lines of dataflow programs

Dataflow programs are widely used. Each program is a directed graph where nodes are computations and edges indicate the flow of data. In prior work, we reverse-engineered legacy dataflow programs by deriving their optimized implementations from a simple specification graph using graph transformations called refinements and optimizations. In MDE speak, our derivations were PIM-to-PSM mappings. In this paper, we show how extensions complement refinements, optimizations, and PIM-to-PSM derivations to make the process of reverse engineering complex legacy dataflow programs tractable. We explain how optional functionality in transformations can be encoded, thereby enabling us to encode product lines of transformations as well as product lines of dataflow programs. We describe the implementation of extensions in the \(\mathtt{ReFlO}\) tool and present two non-trivial case studies as evidence of our work’s generality.     

A Monge-Ampère enhancement for semi-Lagrangian methods

Demanding the compatibility of semi-Lagrangian trajectory schemes with the fundamental Euler expansion formula leads to the Monge-Ampère (MA) nonlinear second-order partial differential equation. Given standard estimates of the departure points of flow trajectories, solving the associated MA problem provides a corrected solution satisfying a discrete Lagrangian form of the mass continuity equation to round-off error. The impact of the MA enhancement is discussed in two diverse limits of fluid dynamics applications: passive tracer advection in a steady cellular flow and in fully developed turbulence. Improvements of the overall accuracy of simulations depend on the problem and can be substantial.     

Homotopy-based surface reconstruction with application to?acoustic signals

This work introduces a new algorithm for surface reconstruction in ℝ³ from spatially arranged one-dimensional cross sections embedded in ℝ³. This is generally the case with acoustic signals that pierce an object non-destructively. Continuous deformations (homotopies) that smoothly reconstruct information between any pair of successive cross sections are derived. The zero level set of the resulting homotopy field generates the desired surface. Four types of homotopies are suggested that are well suited to generate a smooth surface. We also provide derivation of necessary higher order homotopies that can generate a C ² surface. An algorithm to generate surface from acoustic sonar signals is presented with results. Reconstruction accuracies of the homotopies are compared by means of simulations performed on basic geometric primitives.     

A Novel Approach Makes Higher Order Wavelets Really Efficient for Radiosity

Since wavelets were introduced in the radiosity algorithm ⁵, surprisingly little research has been devoted to higher order wavelets and their use in radiosity algorithms. A previous study ¹³ has shown that wavelet radiosity, and especially higher order wavelet radiosity was not bringing significant improvements over hierarchical radiosity and was having a very important extra memory cost, thus prohibiting any effective computation. In this paper, we present a new implementation of wavelets in the radiosity algorithm, that is substantially different from previous implementations in several key areas (refinement oracle, link storage, resolution algorithm). We show that, with this implementation, higher order wavelets are actually bringing an improvement over standard hierarchical radiosity and lower order wavelets.     

Proving the Correctness of the Standardized Algorithm for ABR Conformance

Conformance control for ATM cells is based on a real-time reactive algorithm which delivers a value depending on inputs from the network. This value must always fit with a well defined theoretical value. We present here the correctness proof of the algorithm standardized for the ATM transfer capability called ABR. The proof turned out to produce a key argument during the standardization process of ABR.     

2 1/2 D Visual Servoing with Respect to Unknown Objects Through a New Estimation Scheme of Camera Displacement

Classical visual servoing techniques need a strong a priori knowledge of the shape and the dimensions of the observed objects. In this paper, we present how the 2 1/2 D visual servoing scheme we have recently developed, can be used with unknown objects characterized by a set of points. Our scheme is based on the estimation of the camera displacement from two views, given by the current and desired images. Since vision-based robotics tasks generally necessitate to be performed at video rate, we focus only on linear algorithms. Classical linear methods are based on the computation of the essential matrix. In this paper, we propose a different method, based on the estimation of the homography matrix related to a virtual plane attached to the object. We show that our method provides a more stable estimation when the epipolar geometry degenerates. This is particularly important in visual servoing to obtain a stable control law, especially near the convergence of the system. Finally, experimental results confirm the improvement in the stability, robustness, and behaviour of our scheme with respect to classical methods.     

Dissimilarity-based classification of chromatographic profiles

This paper proposes a non-parametric method for the classification of thin-layer chromatographic (TLC) images from patterns represented in a dissimilarity space. Each pattern corresponds to a mixture of Gaussian approximation of the intensity profile. The methodology comprises various phases, including image processing and analysis steps to extract the chromatographic profiles and a classification phase to discriminate among two groups, one corresponding to normal cases and the other to three pathological classes. We present an extensive study of several dissimilarity-based approaches analysing the influence of the dissimilarity measure and the prototype selection method on the classification performance. The main conclusions of this paper are that, Match and Profile-difference dissimilarity measures present better results, and a new prototype selection methodology achieves a performance similar or even better than conventional methods. Furthermore, we also concluded that simplest classifiers, such as k-NN and linear discriminant classifiers (LDCs), present good performance being the overall classification error less than 10% for the four-class problem.     

Quantitative comparison of thermal and solutal transport in a T-mixer by FLIM and CFD

The development and adoption of lab-on-a-chip and micro-TAS (total analysis system) techniques requires not only the solving of design and manufacturing issues, but also the introduction of reliable and quantitative methods of analysis. In this work, two complementary tools are applied to the study of thermal and solutal transport in liquids. The experimental determination of the concentration of water in a water–methanol mixture and of the temperature of water in a microfluidic T-mixer are achieved by means of fluorescence lifetime imaging microscopy (FLIM). The results are compared to those of finite volume simulations based on tabulated properties and well-established correlations for the fluid properties. The good correlation between experimental and modelled results demonstrate without ambiguity that (1) the T-mixer is an adiabatic system within the conditions, fluids and flow rates used in this study, (2) buoyancy effects influence the mixing of liquids of different densities at moderate flow rates (Reynolds number Re ≪ 10⁻²), and (3) the combination of FLIM and computational fluid dynamics has the potential to be used to measure the thermal and solutal diffusion coefficients of fluids for a range of temperatures and concentrations in one single experiment. As such, it represents a first step towards the full-field monitoring of both the extent and the kinetics of a chemical reaction.

---