arXiv subjects

Pablo Rangel

Publications and source records attributed to Pablo Rangel.

3 recordsLinked to original sources

arXiv2026

N.E.O.N.-Bridge Geometry Determination: Turbulence Modeling of Individual N.E.O.N.-Bridge Segment

The N.E.O.N.-Bridge is a capstone project being developed by students at TAMUCC, under the oversight of Los Alamos National Laboratory. The project requires the development of a hull geometry for an autonomous bridge segment optimized to support onboard electronics and camera systems while maintaining stability in a dynamic water environment. Traditional ribbon bridge systems typically do not experience intense hydrodynamic loading due to current transportation and assembly methods, whereas the N.E.O.N-Bridge must continuously withstand forces from dynamic flow patterns. The requirement for a hull geometry to have both hydrodynamic design and rigidity, as in current ribbon bridges, has posed unique challenges. The current hull designs were evaluated through turbulent water-flow simulations performed with ANSYS Discovery. Boundary conditions were determined based on the forward motion of the bridge segment, simulating inlet and outlet flows, and the resulting pressure distribution. A waterline-based geometric constraint derived from the camera system's elevation enabled the simulation to model flow characteristics in an operational scenario. Velocity fields, pressure contours, and turbulent flow patterns were analyzed to identify areas of high loading and hydrodynamic inefficiencies. The findings will provide essential performance metrics that could be used to make design adjustments to the overall hull geometry. The simulation results will support improvements in stability, structural rigidity, and overall effectiveness of the hull geometry, advancing the development of the N.E.O.N-Bridge segments.

physics.flu-dyn↗

arXiv2025

A Machine Learning Approach to Automatic Fall Detection of Soldiers

Military personnel and security agents often face significant physical risks during conflict and engagement situations, particularly in urban operations. Ensuring the rapid and accurate communication of incidents involving injuries is crucial for the timely execution of rescue operations. This article presents research conducted under the scope of the Brazilian Navy's ``Soldier of the Future'' project, focusing on the development of a Casualty Detection System to identify injuries that could incapacitate a soldier and lead to severe blood loss. The study specifically addresses the detection of soldier falls, which may indicate critical injuries such as hypovolemic hemorrhagic shock. To generate the publicly available dataset, we used smartwatches and smartphones as wearable devices to collect inertial data from soldiers during various activities, including simulated falls. The data were used to train 1D Convolutional Neural Networks (CNN1D) with the objective of accurately classifying falls that could result from life-threatening injuries. We explored different sensor placements (on the wrists and near the center of mass) and various approaches to using inertial variables, including linear and angular accelerations. The neural network models were optimized using Bayesian techniques to enhance their performance. The best-performing model and its results, discussed in this article, contribute to the advancement of automated systems for monitoring soldier safety and improving response times in engagement scenarios.

cs.LG↗

arXiv2024

Spatially temporally distributed informative path planning for multi-robot systems

This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multistep prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets.

cs.RO↗