A variabilidade espacial do desenvolvimento das plantas cultivadas em áreas extensas pode ser caracterizada com diversas estratégias, havendo sensores específicos e aqueles que indiretamente permitem inferir sobre o porte e biomassa. O uso de sensores de reflectância embarcados em plataformas orbitais, aéreas ou terrestres são fontes importantes de informação, mas não permitem obter informações de forma direta sobre a altura das plantas além de apresentarem alta sensibilidade às condições de iluminação no momento da coleta. Sensores ultrassônicos para mensurar a distância até o topo das plantas cultivadas são empregados comumente em operações nas quais se deseja manter uma distância entre órgãos das máquinas e as plantas, porém não permitem obter informações precisas sobre o porte das plantas, uma vez que usualmente são fixados em partes móveis, além de serem também sensíveis a variáveis do ambiente como poeira e ventos. Os radares são sensores ativos amplamente utilizados em aplicações em que se deseja mensurar distâncias. Há sistemas orbitais capazes de empregar radiação que penetra em dosséis de vegetações permitindo o cálculo de sua altura. O uso de radares em curtas distâncias requer dispositivos com capacidade de mensurar o tempo em alta resolução o que os torna caros. Através da modulação de sinais é possível o emprego de dispositivos mais simples mantendo a efetividade na determinação da distância. O objetivo deste trabalho foi investigar o uso de um tipo de sistema radar, de baixo custo, que pode ser acoplado a veículos terrestres para a mensuração de distâncias curtas. Foi desenvolvido um sistema de mensuração de distância baseado em micro-ondas moduladas. A modulação em duas frequências distintas permitiu obter medidas até alvos com características distintas e.g. alvos representativos do topo do dossel e da superfície do terreno. Houve uma relação exponencial entre a distância aos alvos e a frequência de batidas do sinal modulado. O sistema foi acoplado em um trator e caracterizou-se seu desempenho em alvos com dimensões conhecidas, demonstrando haver efeito do formato e tamanho do alvo sobre a qualidade da medida de distância. Também foi realizada a comparação com um sensor de reflectância do infravermelho em uma lavoura de milheto (Pennisetum glaucum). Os resultados demonstraram o potencial da técnica, com boas relações entre as leituras de frequência de batidas e distância do alvo quando este apresenta dimensões superiores ao campo de visada do sensor. As leituras de distância até o terreno na presença de um obstáculo foram, entretanto influenciadas pela distância do obstáculo ao sensor. Uma comparação com leituras de um sensor de reflectância foi realizada em uma lavoura que apresentava variabilidade espacial identificando uma relação inversa entre as leituras do sensor com as de reflectância. / Spatial variability of growth for row crops cultivated in extensive areas can be characterized using several strategies from specific sensors and those that allow indirectly infer about height and biomass. The use of reflectance sensors embedded in orbital, aerial or terrestrial platforms are important sources of information but do not allow direct information on plant height, besides being highly sensitive to lighting conditions at the time of collection. Ultrasonic sensors can be employed for the measurement of distances and are already used in many agricultural operations in which there is a requirement to maintain a distance between machine components and plants. Such sensors, however, doesn\'t allow to obtain information about the size of the plants, since they are usually fixed in moving parts, as well as being sensitive to environmental variables such as dust and wind. Radars are active sensors widely used in applications where distances should be measured. Many orbital radar systems are capable of employing radiation that penetrates vegetation canopies allowing the calculation of its height. The use of radars at short distances requires devices with the ability to measure time at a high resolution which make them expensive. Through the modulation of signals, it is possible to use simpler devices while maintaining the effectiveness in determining the distance. The objective of this research was to investigate the use of a low-cost radar system, which can be attached to terrestrial vehicles for the measurement of short distances. A distance measurement system based on modulated microwave radiantion was developed. Signal modulation in two frequencies allowed obtaining measurements from targets with distinct characteristics e.g. targets representative of the top of the canopy and terrain surface. There was an exponential relationship between distance to the targets and the beating frequency of modulated signals. The system was installed on a tractor and its performance was characterized in targets with known dimensions, demonstrating the effect of the size and shape of the target on the quality of the distance measurement. Comparison was also made with a reference infrared reflected sensor in a millet (Pennisetum glaucum) crop. The results demonstrated the potential of the technique, with good relations between the signal beating frequency and target distance readings when the target is larger than the sensor\'s footprint. Distance to the ground measurements when in the presence of an obstacle were influenced by the distance from the obstacle to the sensor. A comparison with readings of a reflectance sensor was performed in a crop that had spatial variability, an inverse relationship between sensor and reflectance readings was found.

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.

This thesis is concerned with the Distributed Information Gathering (DIG) problem in which a Sensor Network is tasked with building a common representation of environment. The problem is motivated by the advantages offered by distributed autonomous sensing systems and the challenges they present. The focus of this study is on Macro Sensor Networks, characterized by platform mobility, heterogeneous teams, and long mission duration. The system under consideration may consist of an arbitrary number of mobile autonomous robots, stationary sensor platforms, and human operators, all linked in a network. This work describes a comprehensive framework called Active Sensor Network (ASN) which addresses the tasks of information fusion, decistion making, system configuration, and user interaction. The main design objectives are scalability with the number of robotic platforms, maximum flexibility in implementation and deployment, and robustness to component and communication failure. The framework is described from three complementary points of view: architecture, algorithms, and implementation. The main contribution of this thesis is the development of the ASN architecture. Its design follows three guiding principles: decentralization, modularity, and locality of interactions. These principles are applied to all aspects of the architecture and the framework in general. To achieve flexibility, the design approach emphasizes interactions between components rather than the definition of the components themselves. The architecture specifies a small set of interfaces sufficient to implement a wide range of information gathering systems. In the area of algorithms, this thesis builds on the earlier work on Decentralized Data Fusion (DDF) and its extension to information-theoretic decistion making. It presents the Bayesian Decentralized Data Fusion (BDDF) algorithm formulated for environment features represented by a general probability density function. Several specific representations are also considered: Gaussian, discrete, and the Certainty Grid map. Well known algorithms for these representations are shown to implement various aspects of the Bayesian framework. As part of the ASN implementation, a practical indoor sensor network has been developed and tested. Two series of experiments were conducted, utilizing two types of environment representation: 1) point features with Gaussian position uncertainty and 2) Certainty Grid maps. The network was operational for several days at a time, with individual platforms coming on and off-line. On several occasions, the network consisted of 39 software components. The lessons learned during the system's development may be applicable to other heterogeneous distributed systems with data-intensive algorithms.