Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.     

A method to select a successful interoperability solution through a simulation approach

Enterprise applications and software systems need to be interoperable in order to achieve seamless business across organizational boundaries and thus realize virtual networked organizations. Our proposition can be considered as an interoperability project selection approach and is based on three steps: (1) Modelling both collaborative business processes and potential related interoperability projects; (2) Evaluating the accessibility of each project regarding the current state of the organization; (3) Simulating each project and assessing the associated performance. These results are finally projected on a comparison matrix used as a decision support to select the most appropriate interoperability solution. An application case extracted from the French aerospace sector demonstrates the applicability and the benefits of the proposition.     

Prediction of visual attention with deep CNN on artificially degraded videos for studies of attention of patients with Dementia

Multimedia Tools and Applications - Studies of visual attention of patients with Dementia such as Parkinson’s Disease Dementia and Alzheimer Disease is a promising way for non-invasive...     

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.     

Region-based Statistical Analysis of 2D PAGE Images

A new comprehensive procedure for statistical analysis of two-dimensional polyacrylamide gel electrophoresis (2D PAGE) images is proposed, including protein region quantification, normalization and statistical analysis. Protein regions are defined by the master watershed map that is obtained from the mean gel. By working with these protein regions, the approach bypasses the current bottleneck in the analysis of 2D PAGE images: it does not require spot matching. Background correction is implemented in each protein region by local segmentation. Two-dimensional locally weighted smoothing (LOESS) is proposed to remove any systematic bias after quantification of protein regions. Proteins are separated into mutually independent sets based on detected correlations, and a multivariate analysis is used on each set to detect the group effect. A strategy for multiple hypothesis testing based on this multivariate approach combined with the usual Benjamini-Hochberg FDR procedure is formulated and applied to the differential analysis of 2D PAGE images. Each step in the analytical protocol is demonstrated by using an actual dataset. The effectiveness of the proposed methodology is shown using simulated gels in comparison with the commercial software packages PDQuest and Dymension. We also introduce a new procedure for simulating gel images.     

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.     

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.     

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.

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