Mobile robot navigation based on ad-hoc RF communication

Mestrado em Engenharia Electrónica e TelecomunicaçõesActualmente a utilização de redes de sensores sem fios, com nós quer estáticos quer moveis, é cada vez mais apelativa. Desde simples aplicações de monitorização, como por exemplo parâmetros ambientais, até aplicações complexas de busca e salvamento, a localização dos vários nós da rede é fundamental. No caso de mobilidade na rede acresce ainda a necessidade de uma capacidade de navegação eficiente. Dado o facto de que em muitas das aplicações de redes de sensores sem fios, como por exemplo operações de busca e salvamento em que o tempo de resposta tem de ser obrigatoriamente curto, é impossível fazer previamente o planeamento e a implementação de uma infra-estrutura, torna-se imprescindível a utilização de métodos de localização que não dependam de pontos conhecidos. No âmbito desta dissertação são estudadas técnicas de localização e navegação relativas, baseadas simplesmente no sinal RF das comunicações sem fios. Relativamente à localização foram realizados testes com diferentes parâmetros relacionados com as comunicações. Estes são importantes devido à necessidade de estudar o impacto destes factores no cálculo da topologia da rede. O trabalho desenvolvido relativamente à navegação foi avaliado experimentalmente, com incidência na avaliação comparativa dos diversos métodos propostos, i.e., um método oblívio baseado em direcções aleatórias e outro baseado na técnica MLE - Maximum Likelihood Estimator. Apresentam-se nesta dissertação os respectivos resultados que permitem verificar o melhor desempenho em convergência para o objectivo usando MLE à custa de maior custo computacional. Em particular, foi possível fazer um robô móvel percorrer um trajecto entre dois faróis de RF, navegando apenas com informação de RSS.Nowadays the usage of wireless sensor networks, with either static or mobile nodes, has been an area of growing interest. From the simplest applications of monitoring, i.e. environmental parameters, to the most complex search and rescue applications, the localization of the various nodes of the network is fundamental. In the situation at which the network has mobility there is additionally a need of the ability to efficiently navigate. Due to the fact that in many of the applications, i.e. search and rescue situations where the time of action is critical, is impossible to perform a previous planning and building of a framework, anchor free relative localization methods become indispensable. In this dissertation several relative localization and navigation techniques, based only on the RF signal of the wireless communications, are studied. On the subject of localization, different parameters related with the communications were tested. These are significant because of the necessity of studying the impact of such factors in calculating the network topology. On the subject of navigation the resulting work was experimentally evaluated, with emphasis on the comparative evaluation of the several methods presented in this dissertation, namely a simple oblivious method based on random directions and another one based on MLE - Maximum Likelihood Estimator. The results show the superiority of MLE concerning the speed of getting to the target at the cost of extra computations. In particular, in the scope of this dissertation we have made a small autonomous robot move between to RF beacons, using RSS information, only

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora