Abstract

Abstract

Abstract
The manufacturing process of thermoregulation products with polyester (PES) fabric and conductive polymers such as poly(3,4-ethylenedioxythiophene) doped with poly(styrene sulfonate) (PEDOT:PSS) with proper wearability, comfort, and high performance is still a challenge due to low adhesion, environment instability and nonuniform coatings. This study presents a simple and effective method for producing thermoregulatory PES fabrics using the Joule heating effect. Textiles treated with dielectric barrier discharge (DBD) plasma were functionalized with PEDOT:PSS incorporating secondary dopants, such as dimethyl sulfoxide (DMSO) and glycerol (GLY). PEDOT:PSS was used because it does not compromise the mechanical properties of base materials. DBD plasma treatment was applied to PES to improve the substrate's functional groups and consequently increase adhesion and homogeneity of the PEDOT:PSS on the substrate. The polymer were applied to the textiles by dip-pad-drycure method ensuring uniform distribution and homogeneous heating of the materials. The samples' conductivity, impedance, potential and Joule effect, and their morphological, chemical and thermal properties were studied. Control samples without plasma treatment and secondary dopants were also prepared. The results showed that the DBD-treated samples, coated with 5 layers of PEDOT:PSS, doped with DMSO 7 % (w/v), displayed the best conductivity and Joule effect performance reaching 44.3 degrees C after 1 h.

Abstract
The calibration of sensors stationed along a cable in marine observatories is a time-consuming and expensive operation that involves taking the mooring out of the water periodically. In this paper, we present a method that allows an underwater vehicle to approach a mooring, in order to take reference measurements along the cable for in-situ sensor calibration. We use the vehicle's Mechanically Scanned Imaging Sonar (MSIS) to identify the cable's reflection within the sonar image. After pre-processing the image to remove noise, enhance contour lines, and perform smoothing, we employ three detection steps: 1) selection of regions of interest that fit the cable's reflection pattern, 2) template matching, and 3) a track-before-detect scheme that utilized the vehicle's motion. The later involves building a lattice of template matching responses for a sequence of sonar images, and using the Viterbi algorithm to find the most probable sequence of cable locations that fits the maximum speed assumed for the surveying vessel. Performance is explored in pool and sea trials, and involves an MSIS onboard an underwater vehicle scanning its surrounding to identify a steel-core cable. The results show a sub-meter accuracy in the multi-reverberant pool environment and in the sea trial. For reproducibility, we share our implementation code.

Abstract
Underwater long-endurance platforms are crucial for continuous oceanic observation, allowing for sustained data collection from a multitude of sensors deployed across diverse underwater environments. They extend mission durations, reduce maintenance needs, and significantly improve the efficiency and cost-effectiveness of oceanographic research endeavors. This paper investigates the closed-loop depth control of actuation systems employed in underwater vehicles, focusing on the energy consumption of two different mechanisms: variable buoyancy and propeller actuated devices. Using a prototype previously developed by the authors, this paper presents a detailed model of the vehicle using both actuation solutions. The proposed model, although being a linear-based one, accounts for several nonlinearities that are present such as saturations, sensor quantization, and the actuator brake model. Also, it allows a simple estimation of the energy consumption of both actuation solutions. Based on the developed models, this study then explores the intricate interplay between energy consumption and control accuracy. To this end, several PID-based controllers are developed and tested in simulation. These controllers are used to evaluate the dynamic response and power requirements of variable buoyancy systems and propeller actuated devices under various operational conditions. Our findings contribute to the optimization of closed-loop depth control strategies, offering insights into the trade-offs between energy efficiency and system effectiveness in diverse underwater applications.

Abstract
Autonomous Mobile Robots (AMR) are seeing an increased introduction in distinct areas of daily life. Recently, their use has expanded to intralogistics, where forklift type AMR are applied in many situations handling pallets and loading/unloading them into trucks. One of the these vehicles requirements, is that they are able to correctly identify the location and status of pallets, so that the forklifts AMR can insert the forks in the right place. Recently, some commercial sensors have appeared in the market for this purpose. Given these considerations, this paper presents a comparison of the performance of two different approaches for pallet detection: using a commercial off-the-shelf (COTS) sensor and a custom developed application based on Artificial Intelligence algorithms applied to an RGB-D camera, where both the RGB and depth data are used to estimate the position of the pallet pockets.