Hardware Design and Testing of SUPERball, A Modular Tensegrity Robot

We are developing a system of modular, autonomous "tensegrity end-caps" to enable the rapid exploration of untethered tensegrity robot morphologies and functions. By adopting a self-contained modular approach, different end-caps with various capabilities (such as peak torques, or motor speeds), can be easily combined into new tensegrity robots composed of rods, cables, and actuators of different scale (such as in length, mass, peak loads, etc). As a first step in developing this concept, we are in the process of designing and testing the end-caps for SUPERball (Spherical Underactuated Planetary Exploration Robot), a project at the Dynamic Tensegrity Robotics Lab (DTRL) within NASA Ames's Intelligent Robotics Group. This work discusses the evolving design concepts and test results that have gone into the structural, mechanical, and sensing aspects of SUPERball. This representative tensegrity end-cap design supports robust and repeatable untethered mobility tests of the SUPERball, while providing high force, high displacement actuation, with a low-friction, compliant cabling system

Design and Control of Compliant Tensegrity Robots Through Simulation and Hardware Validation

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center has developed and validated two different software environments for the analysis, simulation, and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ("tensile-integrity") structures have unique physical properties which make them ideal for interaction with uncertain environments. Yet these characteristics, such as variable structural compliance, and global multi-path load distribution through the tension network, make design and control of bio-inspired tensegrity robots extremely challenging. This work presents the progress in using these two tools in tackling the design and control challenges. The results of this analysis includes multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures. The current hardware prototype of a six-bar tensegrity, code-named ReCTeR, is presented in the context of this validation

Optimized parameter search for large datasets of the regularization parameter and feature selection for ridge regression

In this paper we propose mathematical optimizations to select the optimal regularization parameter for ridge regression using cross-validation. The resulting algorithm is suited for large datasets and the computational cost does not depend on the size of the training set. We extend this algorithm to forward or backward feature selection in which the optimal regularization parameter is selected for each possible feature set. These feature selection algorithms yield solutions with a sparse weight matrix using a quadratic cost on the norm of the weights. A naive approach to optimizing the ridge regression parameter has a computational complexity of the order with the number of applied regularization parameters, the number of folds in the validation set, the number of input features and the number of data samples in the training set. Our implementation has a computational complexity of the order . This computational cost is smaller than that of regression without regularization for large datasets and is independent of the number of applied regularization parameters and the size of the training set. Combined with a feature selection algorithm the algorithm is of complexity and for forward and backward feature selection respectively, with the number of selected features and the number of removed features. This is an order faster than and for the naive implementation, with for large datasets. To show the performance and reduction in computational cost, we apply this technique to train recurrent neural networks using the reservoir computing approach, windowed ridge regression, least-squares support vector machines (LS-SVMs) in primal space using the fixed-size LS-SVM approximation and extreme learning machines

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 ∘ C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region

Adverse Fetal and Neonatal Outcomes Associated with a Life-Long High Fat Diet: Role of Altered Development of the Placental Vasculature

Maternal obesity results in a number of obstetrical and fetal complications with both immediate and long-term consequences. The increased prevalence of obesity has resulted in increasing numbers of women of reproductive age in this high-risk group. Since many of these obese women have been subjected to hypercaloric diets from early childhood we have developed a rodent model of life-long maternal obesity to more clearly understand the mechanisms that contribute to adverse pregnancy outcomes in obese women. Female Sprague Dawley rats were fed a control diet (CON - 16% of calories from fat) or high fat diet (HF - 45% of calories from fat) from 3 to 19 weeks of age. Prior to pregnancy HF-fed dams exhibited significant increases in body fat, serum leptin and triglycerides. A subset of dams was sacrificed at gestational day 15 to evaluate fetal and placental development. The remaining animals were allowed to deliver normally. HF-fed dams exhibited a more than 3-fold increase in fetal death and decreased neonatal survival. These outcomes were associated with altered vascular development in the placenta, as well as increased hypoxia in the labyrinth. We propose that the altered placental vasculature may result in reduced oxygenation of the fetal tissues contributing to premature demise and poor neonatal survival

Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021

In July 2021 extreme rainfall across Western Europe caused severe flooding and substantial impacts, including over 200 fatalities and extensive infrastructure damage within Germany and the Benelux countries. After the event, a hydrological assessment and a probabilistic event attribution analysis of rainfall data were initiated and complemented by discussing the vulnerability and exposure context. The global mean surface temperature (GMST) served as a covariate in a generalised extreme value distribution fitted to observational and model data, exploiting the dependence on GMST to estimate how anthropogenic climate change affects the likelihood and severity of extreme events. Rainfall accumulations in Ahr/Erft and the Belgian Meuse catchment vastly exceeded previous observed records. In regions of that limited size the robust estimation of return values and the detection and attribution of rainfall trends are challenging. However, for the larger Western European region it was found that, under current climate conditions, on average one rainfall event of this magnitude can be expected every 400 years at any given location. Consequently, within the entire region, events of similar magnitude are expected to occur more frequently than once in 400 years. Anthropogenic climate change has already increased the intensity of the maximum 1-day rainfall event in the summer season by 3–19 %. The likelihood of such an event to occur today compared to a 1.2 $$^{\circ }$$C cooler climate has increased by a factor of 1.2–9. Models indicate that intensity and frequency of such events will further increase with future global warming. While attribution of small-scale events remains challenging, this study shows that there is a robust increase in the likelihood and severity of rainfall events such as the ones causing extreme impacts in July 2021 when considering a larger region