VALUTAZIONE DELLO STATO ECOLOGICO DI ALCUNE AREE COSTIERE DELLA COSTA TOSCANA: ANALISI COMPARATIVA DI ALCUNI INDICI BIOTICI

Negli ultimi anni lo sviluppo delle attività antropiche nelle zone costiere, ha aumentato la possibile presenza di disturbi a carico dell’ecosistema marino, rendendo quindi necessaria l’elaborazione e la validazione di metodologie per la misurazione della qualità delle acque. Recentemente, negli studi di valutazione della qualità ambientale è stata enfatizzata l’importanza dell’utilizzo delle componenti biologiche dell’ecosistema, da affiancare ai parametri chimico-fisici e tossicologici solitamente impiegati. In particolare i macroinvertebrati bentonici di fondo molle vengono considerati adeguati descrittori dell’ambiente, infatti tali organismi sono capaci di rispondere in modo relativamente rapido ad eventi di disturbo di origine naturale o antropica. Inoltre, le comunità bentoniche sono costituite da un’ampia varietà di specie, caratterizzate da diversi gradi di tolleranza ai fattori di disturbo: ciò rende possibile lo sviluppo di modelli di valutazione della qualità ambientale basati sulle caratteristiche ecologiche delle varie specie. Negli ultimi anni numerosi studi, hanno sviluppato e applicato ad alcuni contesti ambientali prevalentemente atlantici alcuni indici biotici per la valutazione della qualità delle acque marine costiere con differenti tipologie di disturbo (Borja et al, 2003; Muxika et al., 2005; Marin-Guirao et al., 2004; Salas et al, 2004; Labrune et al, 2005; Muniz et al, 2005). Lo scopo di questo lavoro è stato quello di applicare cinque indici biotici in aree costiere mediterranee per valutare la loro applicabilità in questi contesti indagando particolarmente il grado di correlazione fra tali indici ed una loro eventuale intercalibrazione. A questo proposito sono stati campionati popolamenti macrozoobentonici di sabbia fine. In particolare i campioni sono stati raccolti nel periodo Settembre - Ottobre 2005. E’ stato seguito un disegno di campionamento gerarchizzato spaziale con cui sono stati prelevati un totale di 72 campioni lungo il litorale della Toscana centro-meridionale (aree denominate Donoratico e Grosseto). Il prelievo dei campioni è stato attuato con una Benna van Veen da 0,1 m2, in laboratorio, il materiale raccolto è stato smistato e gli organismi sono stati classificati fino al livello di specie, quando possibile. I dati ottenuti dalla classificazione sono poi utilizzati per il calcolo di 5 indici biotici : AMBI, m-AMBI, BOPA, BENTIX, SHANNON

Motivations for muon radiography of active volcanoes

Muon radiography represents an innovative tool for investigating the interior of active volcanoes. This method integrates the conventional geophysical techniques and provides an independent way to estimate the density of the volcano structure and reveal the presence of magma conduits. The experience from the pioneer experiments performed at Mt. Asama, Mt. West Iwate, and Showa-Shinzan (Japan) are very encouraging. Muon radiography could be applied, in principle, at any stratovolcano. Here we focus our attention on Vesuvius and Stromboli (Italy)

Human-Centered Navigation and Person-Following with Omnidirectional Robot for Indoor Assistance and Monitoring

Robot assistants and service robots are rapidly spreading out as cutting-edge automation solutions to support people in their everyday life in workplaces, health centers, and domestic environments. Moreover, the COVID-19 pandemic drastically increased the need for service technology to help medical personnel in critical conditions in hospitals and domestic scenarios. The first requirement for an assistive robot is to navigate and follow the user in dynamic environments in complete autonomy. However, these advanced multitask behaviors require flexible mobility of the platform to accurately avoid obstacles in cluttered spaces while tracking the user. This paper presents a novel human-centered navigation system that successfully combines a real-time visual perception system with the mobility advantages provided by an omnidirectional robotic platform to precisely adjust the robot orientation and monitor a person while navigating. Our extensive experimentation conducted in a representative indoor scenario demonstrates that our solution offers efficient and safe motion planning for person-following and, more generally, for human-centered navigation tasks

RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring

Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following

PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning

Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics community, with Deep Reinforcement Learning (DRL). The paper describes the whole structure of the PIC4rl-gym, which fully integrates DRL agent's training and testing in several indoor and outdoor navigation scenarios and tasks. A modular approach is adopted to easily customize the simulation by selecting new platforms, sensors, or models. We demonstrate the potential of our novel gym by benchmarking the resulting policies, trained for different navigation tasks, with a complete set of metrics

Domain Generalization for Crop Segmentation with Knowledge Distillation

In recent years, precision agriculture has gradually oriented farming closer to automation processes to support all the activities related to field management. Service robotics plays a predominant role in this evolution by deploying autonomous agents that can navigate fields while performing tasks without human intervention, such as monitoring, spraying, and harvesting. To execute these precise actions, mobile robots need a real-time perception system that understands their surroundings and identifies their targets in the wild. Generalizing to new crops and environmental conditions is critical for practical applications, as labeled samples are rarely available. In this paper, we investigate the problem of crop segmentation and propose a novel approach to enhance domain generalization using knowledge distillation. In the proposed framework, we transfer knowledge from an ensemble of models individually trained on source domains to a student model that can adapt to unseen target domains. To evaluate the proposed method, we present a synthetic multi-domain dataset for crop segmentation containing plants of variegate shapes and covering different terrain styles, weather conditions, and light scenarios for more than 50,000 samples. We demonstrate significant improvements in performance over state-of-the-art methods and superior sim-to-real generalization. Our approach provides a promising solution for domain generalization in crop segmentation and has the potential to enhance a wide variety of precision agriculture applications

Cheese making aptitude and the chemical and nutritional characteristics of milk from Massese ewes

The purpose of this study was to determine the effects of season, locality and the different altitudes at which farms are located, on the physico-chemical composition, morphometric characteristics of fat globules, fatty acid composition and cheese making aptitude of milk of Massese ewe's raised in 11 flocks from two provinces of north-west Tuscany (Massa Carrara and Lucca). The winter lactation shows higher percentages of casein, lactose and not fat dry matter (P≤0.01); curd firming time (k20) is significantly lower and there is a greater curd firmness (a30) (P≤0.01); while in the summer there is a higher percentage of lipids (P≤0.01). The effect of the season significantly influences (P≤0.01) the size of the fat globules and impacted significantly on the fatty acids composition of the milk. In the hills the milk has a higher percentage of dry matter, protein, casein, fat, phosphorous and not fat dry matter (P≤0.01), whereas it has a lower percentage of lactose and calcium (P≤0.05). The Somatic Cell Count (SCC) and the Total Bacterial Count (TBC) are statistically greater on the plains (P≤0.01), while milk produced in the hills shows higher quantity of α-linolenic acid and lower saturated fatty acids (P≤0.05). In the two typical rearing areas for Massese ewes we found differences amongst dry matter, fat, phosphorous and SCC, higher (P≤0.01) in the province of Massa Carrara that also had the best rheological parameters, but we found the highest cheese yield (P≤0.05) in the province of Lucca where there are also the greatest weight loss (P≤0.01). The milks produced in the winter season and in hilly areas present the best physico-chemical and nutritional characteristics. However, we found that the technological side should be improved by diversifying cheese-making techniques in relation to the characteristics of milk. In fact, currently these techniques do not fully exploit the potential to transform those milks with the best qualitative characteristics

Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation

Single-Image Super-Resolution can support robotic tasks in environments where a reliable visual stream is required to monitor the mission, handle teleoperation or study relevant visual details. In this work, we propose an efficient Generative Adversarial Network model for real-time Super-Resolution. We adopt a tailored architecture of the original SRGAN and model quantization to boost the execution on CPU and Edge TPU devices, achieving up to 200 fps inference. We further optimize our model by distilling its knowledge to a smaller version of the network and obtain remarkable improvements compared to the standard training approach. Our experiments show that our fast and lightweight model preserves considerably satisfying image quality compared to heavier state-of-the-art models. Finally, we conduct experiments on image transmission with bandwidth degradation to highlight the advantages of the proposed system for mobile robotic applications

Local Planners with Deep Reinforcement Learning for Indoor Autonomous Navigation

Autonomous indoor navigation requires an elab- orated and accurate algorithmic stack, able to guide robots through cluttered, unstructured, and dynamic environments. Global and local path planning, mapping, localization, and decision making are only some of the required layers that undergo heavy research from the scientific community to achieve the requirements for fully functional autonomous navigation. In the last years, Deep Reinforcement Learning (DRL) has proven to be a competitive short-range guidance system solution for power-efficient and low computational cost point-to-point local planners. One of the main strengths of this approach is the possibility to train a DRL agent in a simulated environment that encapsulates robot dynamics and task constraints and then deploy its learned point-to-point navigation policy in a real setting. However, despite DRL easily integrates complex mechanical dynamics and multimodal signals into a single model, the effect of different sensor data on navigation performance has not been investigated yet. In this paper, we compare two different DRL navigation solutions that leverage LiDAR and depth camera information, respectively. The agents are trained in the same simulated environment and tested on a common benchmark to highlight the strengths and criticalities of each technique

Autonomous Navigation in Rows of Trees and High Crops with Deep Semantic Segmentation

Segmentation-based autonomous navigation has recently been proposed as a promising methodology to guide robotic platforms through crop rows without requiring precise GPS localization. However, existing methods are limited to scenarios where the centre of the row can be identified thanks to the sharp distinction between the plants and the sky. However, GPS signal obstruction mainly occurs in the case of tall, dense vegetation, such as high tree rows and orchards. In this work, we extend the segmentation-based robotic guidance to those scenarios where canopies and branches occlude the sky and hinder the usage of GPS and previous methods, increasing the overall robustness and adaptability of the control algorithm. Extensive experimentation on several realistic simulated tree fields and vineyards demonstrates the competitive advantages of the proposed solution