Distributed multi-robot exploration under complex constraints

Programa de Doctorado en Biotecnología, Ingeniería y Tecnología QuímicaLínea de Investigación: Ingeniería InformáticaClave Programa: DBICódigo Línea: 19Mobile robots have emerged as a prime alternative to explore physical processes of interest. This is particularly relevant in situations that have a high risk for humans, like e.g. in search and rescue missions, and for applications in which it is desirable to reduce the required time and manpower to gather information, like e.g. for environmental analysis. In such context, exploration tasks can clearly benefit from multi-robot coordination. In particular, distributed multi-robot coordination strategies offer enormous advantages in terms of both system¿s efficiency and robustness, compared to single-robot systems. However, most state-of-the-art strategies employ discretization of robots¿ state and action spaces. This makes them computationally intractable for robots with complex dynamics, and limits their generality. Moreover, most strategies cannot handle complex inter-robot constraints like e.g. communication constraints. The goal of this thesis is to develop a distributed multi-robot exploration algorithm that tackles the two aforementioned issues. To achieve this goal we first propose a single-robot myopic approach, in which we build to develop a non-myopic informative path planner. In a second step, we extend our non-myopic single-robot algorithm to the multi-robot case. Our proposed algorithms build on the following techniques: (i) Gaussian Processes (GPs) to model the spatial dependencies of a physical process of interest, (ii) sampling-based planners to calculate feasible paths; (iii) information metrics to guide robots towards informative locations; and (iv) distributed constraint optimization techniques for multi-robot coordination. We validated our proposed algorithms in simulations and experiments. Specifically, we carried out the following experiments: mapping of a magnetic field with a ground-based robot, mapping of a terrain profile with two quadcopters equipped with an ultrasound sensor, and exploration of a simulated wind field with three quadcopters. Results demonstrate the effectiveness of our approach to perform exploration tasks under complex constraints.Universidad Pablo de Olavide de Sevilla. Departamento de Deporte e InformáticaPostprin

Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes

Information gathering (IG) algorithms aim to intelligently select a mobile sensor actions required to efficiently obtain an accurate reconstruction of a physical process, such as an occupancy map, or a magnetic field. Many recent works have proposed algorithms for IG that employ Gaussian processes (GPs) as underlying model of the process. However, most algorithms discretize the state space, which makes them computationally intractable for robotic systems with complex dynamics. Moreover, they are not suited for online information gathering tasks as they assume prior knowledge about GP parameters. This paper presents a novel approach that tackles the two aforementioned issues. Specifically, our approach includes two intertwined steps: (i) a Rapidly-Exploring Random Tree (RRT) search that allows a robot to identify unvisited locations, and to learn the GP parameters, and (ii) an RRT*-based informative path planning that guides the robot towards those locations by maximizing the information gathered while minimizing path cost. The combination of the two steps allows an online realization of the algorithm, while eliminating the need for discretization. We demonstrate that our proposed algorithm outperforms state-of-the-art both in simulations, and in a lab experiment in which a ground-based robot explores the magnetic field intensity within an indoor environment populated with obstacles

Decentralized Multi-Agent Exploration with Online-Learning of Gaussian Processes

Exploration is a crucial problem in safety of life applications, such as search and rescue missions. Gaussian processes constitute an interesting underlying data model that leverages the spatial correlations of the process to be explored to reduce the required sampling of data. Furthermore, multiagent approaches offer well known advantages for exploration. Previous decentralized multi-agent exploration algorithms that use Gaussian processes as underlying data model, have only been validated through simulations. However, the implementation of an exploration algorithm brings difficulties that were not tackle yet. In this work, we propose an exploration algorithm that deals with the following challenges: (i) which information to transmit to achieve multi-agent coordination; (ii) how to implement a light-weight collision avoidance; (iii) how to learn the data’s model without prior information. We validate our algorithm with two experiments employing real robots. First, we explore the magnetic field intensity with a ground-based robot. Second, two quadcopters equipped with an ultrasound sensor explore a terrain profile. We show that our algorithm outperforms a meander and a random trajectory, as well as we are able to learn the data’s model online while exploring

Robotic Information Gathering with Reinforcement Learning assisted by Domain Knowledge: an Application to Gas Source Localization

Gas source localization tackles the problem of finding leakages of hazardous substances such as poisonous gases or radiation in the event of a disaster. In order to avoid threats for human operators, autonomous robots dispatched for localizing potential gas sources are preferable. This work investigates a Reinforcement Learning framework that allows a robotic agent to learn how to localize gas sources. We propose a solution that assists Reinforcement Learning with existing domain knowledge based on a model of the gas dispersion process. In particular, we incorporate a priori domain knowledge by designing appropriate rewards and observation inputs for the Reinforcement Learning algorithm. We show that a robot trained with our proposed method outperforms state-of-the-art gas source localization strategies, as well as robots that are trained without additional domain knowledge. Furthermore, the framework developed in this work can also be generalized to a large variety of information gathering tasks

Swarm Technologies For Future Space Exploration Missions

Modern robotic platforms for in-situ space exploration are single-robots equipped with a number of specialized sensors providing scientists with unique information about a planet's surface. However, there is a number of exploration problems where large spatial apertures of the exploration system are necessary, requiring a completely new perspective on in-situ space exploration and it's required technologies. Large networks of robots, called swarm, pave the way: agents in a swarm span ad-hoc communication networks, localize themselves based on radio signals, share resources, process data and make inference over the network in a decentralized fashion. By cooperation, local information collected by agents becomes globally available. In this work we present our recent results in development of swarm technologies for future in-situ space exploration missions: a wireless system jointly used for communication and localization, and swarm navigation and exploration strategies to sample and reconstruct static spatial fields

Flow and Tableting Behaviors of Some Egyptian Kaolin Powders as Potential Pharmaceutical Excipients

The present work aimed at assessing the pharmaceutical tableting properties of some Egyptian kaolin samples belong to the Abu Zenima kaolin deposits (estimated at 120 million tons). Four representative samples were selected based on kaolinite richness and their structural order-disorder degree, and after purification, they were dried at 70 ºC and heated from room temperature up to 400 ºC (10 ºC/min). Mineralogy, micromorphology, microtexture, granulometry, porosimetry, moisture content, bulk and tapped density, direct and indirect flowability, and tableting characteristics are studied. Results indicated that purified kaolin samples were made up of 95–99% kaolinite, <3% illite, 1% quartz and 1% anatase. The powder showed mesoporous character (pore diameters from 2 to 38 nm and total pore volume from 0.064 to 0.136 cm3/g) with dominance of fine nanosized particles (<1 um–10 nm). The powder flow characteristics of both the ordered (Hinckley Index HI > 0.7, crystallite size D001 > 30 nm) and disordered (HI < 0.7, D001 < 30 nm) kaolinite-rich samples have been improved (Hausner ratio between 1.24 and 1.09) as their densities were influenced by thermal treatment (with some observed changes in the kaolinite XRD reflection profiles) and by moisture content (variable between 2.98% and 5.82%). The obtained tablets exhibited hardness between 33 and 44 N only from the dehydrated powders at 400 ºC, with elastic recovery (ER) between 21.74% and 25.61%, ejection stress (ES) between 7.85 and 11.45 MPa and tensile fracture stress (TFS) between 1.85 and 2.32 MPa, which are strongly correlated with crystallinity (HI) and flowability (HR) parameters. These findings on quality indicators showed the promising pharmaceutical tabletability of the studied Egyptian kaolin powders and the optimization factors for their manufacturability and compactability.This work has been funded by the Egyptian Cultural Affairs and Missions Sector (Plan 2013–2014), Ministry of Higher Education, in collaboration with the Group CTS-946 (Junta de Andalucía) and MINECO project CGL2016-80833-R (Spain), and the grant funded by Erasmus+ KA1 mobility program 2016/2017

Hectorite/Phenanthroline-Based Nanomaterial as Fluorescent Sensor for Zn Ion Detection: A Theoretical and Experimental Study

The development of fluorescent materials that can act as sensors for the determination of metal ions in biological fluids is important since they show, among others, high sensitivity and specificity. However, most of the molecules that are used for these purposes possess a very low solubility in aqueous media, and, thus, it is necessary to adopt some derivation strategies. Clay minerals, for example, hectorite, as natural materials, are biocompatible and available in large amounts at a very low cost that have been extensively used as carrier systems for the delivery of different hydrophobic species. In the present work, we report the synthesis and characterization of a hectorite/phenanthroline nanomaterial as a potential fluorescent sensor for Zn ion detection in water. The interaction of phenanthroline with the Ht interlaminar space was thoroughly investigated, via both theoretical and experimental studies (i.e., thermogravimetry, FT-IR, UV-vis and fluorescence spectroscopies and XRD measurements), while its morphology was imaged by scanning electron microscopy. Afterwards, the possibility to use it as sensor for the detection of Zn2+ ions, in comparison to other metal ions, was investigated through fluorescent measurements, and the stability of the solid Ht/Phe/Zn complex was assessed by different experimental and theoretical measurements

Lesson Learnt and Future of AI Applied to Manufacturing

This chapter touches on several aspects related to the role of Artificial Intelligence (AI) and Machine Learning (ML) in the manufacturing sector, and is split in different sub-chapters, focusing on specific new technology enablers that have the potential of solving or minimizing known issues in the manufacturing and, more in general, in the Industrial Internet of Things (IIoT) domain. After introducing AI/ML as a technology enabler for the IoT in general and for manufacturing in particular, the next four sections detail two key technology enablers (EdgeML and federated learning scenarios, challenges and tools), one most important area of the IoT system that needs to decrease energy consumption and increase reliability (reduce receiver Processing complexity and enhancing reliability through multi-connectivity uplink connections), and finally a glimpse at the future describing a promising new technology (Embodied AI), its link with millimetre waves connectivity and potential business impact

Supporting Information Hectorite/phenanthroline based nanomaterial as fluorescent sensor for Zn ions detection: a theoretical and experimental study

Figure S1. Change in fluorescence spectra of phenanthroline upon addition of different metal ionsPeer reviewe

Alluvial fans on volcanic islands: A morphometric perspective (São Vicente, Cape Verde)

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