In the context of Wireless Sensor Networks (WSNs), the Minimal Exposure Path (MEP) represents an important metric to evaluate the quality of network services. Most of the current literature is concentrated on stationary sensors, while few works have addressed the existence of moving nodes. Mobile Wireless Sensor Networks (MWSNs) present a great potential for detecting invaders compared to static ones, but dealing with dynamic sensors significantly increases the coverage complexity since the overall exposure of the sensor field depends on time. Therefore, in this letter, we propose an approach to compute Minimal Exposure Paths in time-varying fields based on a control optimization method called semi-Lagrangian (SL) scheme, in such a way that an intruder will be able to penetrate the dynamic field with the lowest exposure. The SL has already been proven to reach the optimal Minimal Exposure Path (MEP) on static WSNs, but concerning dynamic nodes, the proof is much more complicated. Then, we propose a heuristics that provides convergence of the SL algorithm to a result we conjecture to be the optimal one. Results with different time-varying sensor models in obstacle-free and cluttered environments have been presented and discussed.
A critical metric of the coverage quality in Wireless Sensor Networks (WSNs) is the Minimal Exposure Path (MEP), a path through the environment that least exposes a mobile target to the sensor nodes detection. Many approaches have been proposed in the last decades to solve this optimization problem, ranging from classic grid-based and Voronoi-based planners to meta-heuristics. However, most of them are limited to specific sensing models and obstacle-free spaces. Still, none of them guarantee an optimal solution, and the state-of-the-art is expensive in terms of execution time. Therefore, in this paper, we propose a novel method, called SL-MEP, that models the MEP as an optimal control problem and solves it by using a semi-Lagrangian (SL) scheme. This framework is shown to converge to the optimal MEP while it incorporates different homogeneous and heterogeneous sensor models and geometric constraints (obstacles). Experiments show that our method dominates the state-of-the-art, improving the results by approximately 10% with a relatively lower execution time.
O interesse em relação às pesquisas sobre Veículos Aéreos Não Tripulados (VANTs) vem aumentando de forma significativa nos últimos anos. O quadrotor é um tipo de VANT que possui diversas vantagens e aplicações. O acoplamento dinâmico e o comportamento altamente não-linear impõem um carácter desafiador ao controle destas aeronaves. Neste trabalho são desenvolvidos controladores nebulosos adaptativos diretos capazes de resolver problemas de seguimento de trajetória. Uma nova abordagem usando diferenciador robusto e exato é proposta para estimar parâmetro não conhecido da lei de adaptação.
O quadrotor é um tipo de VANT (Veículos Aéreos Não Tripulados) que possui diversas vantagens e aplicações. O acoplamento dinâmico e o comportamento altamente não-linear impõem um carácter desafiador ao controle destas aeronaves. Neste trabalho um controlador nebuloso adaptativo capaz de resolver um problema de rastreamento de trajetória é desenvolvido. Os parâmetros do controlador são estimados e otimizados pelo uso de um algoritmo genético, já que a utilização de controladores nebulosos implica no ajuste de vários parâmetros e, à medida que a complexidade do processo aumenta, torna-se difícil estabelecer a configuração ideal destes componentes de um sistema nebuloso. Através de resultados obtidos por simulação computacional, mostrou-se que o controlador projetado é eficiente para resolver o problema proposto e que o algoritmo genético conseguiu otimizar os seus parâmetros.
This paper proposes augmenting the traditional quadratic event-triggered conditions for Takagi-Sugeno (TS) fuzzy models, by also considering triggering conditions defined by a variation of the membership function values. Suitable Linear Matrix Inequality (LMI) conditions are presented that make full use of the proposed modified triggering conditions to co-design the control law and triggering conditions. Under some mild assumptions, the closed loop system is guaranteed to avoid the Zeno behavior for its triggering instants. A numerical example is presented to illustrate the proposed conditions.
We develop a new adaptive gain-scheduling control scheme for continuous-time linear systems with polytopic uncertainties. The gain-scheduled control law is proposed as a convex sum of a fixed set of controller gains, exploiting the polytopic representation of the system uncertainty, which is not possible with classical robust control results in the literature. To realize this scheme, an adaptation law is proposed to adaptively provide the tuning parameter for the gain-scheduling implementation. The admissible domain of the stabilizing control feedback gains, defined by the fixed set of controller gains, can be determined offline by solving a set of linear matrix inequality constraints over a scalar line search. Using Lyapunov-based arguments, the proposed design conditions and the adaptation law ensure that all closed-loop signals are bounded. In particular, if the uncertain parameters are not time-varying, then the system states asymptotically converge to the origin. Theoretical arguments and appropriate numerical illustrations are provided to demonstrate the effectiveness of the proposed control scheme.
This article presents a new observer design framework for a class of nonlinear descriptor systems with unknown but bounded inputs. In the presence of unmeasured nonlinearities, that is, premise variables, designing nonlinear observers is known as particularly challenging. To solve this problem, we rewrite the nonlinear descriptor system in the form of a Takagi–Sugeno (TS) fuzzy model with nonlinear consequents. This model reformulation enables an effective use of the differential mean value theorem to deal with the mismatching terms involved in the estimation error dynamics. These nonlinear terms, issued from the unmeasured nonlinearities of the descriptor system, cause a major technical difficulty for TS fuzzy-model-based observer design. The descriptor form is treated through a singular redundancy representation. For observer design, we introduce into the Luenberger-like observer structure a virtual variable aiming at estimating the one-step ahead state. This variable introduction allows for free-structure decision variables involved in the observer design to further reduce the conservatism. Using Lyapunov-based arguments, the observer design is reformulated as an optimization problem under linear matrix inequalities with a single line search parameter. The estimation error bounds of both the state and the unknown input can be minimized by means of a guaranteed ℓ∞-gain performance level. The interests of the new ℓ∞ TS fuzzy observer design are clearly illustrated with two physically motivated examples.
This paper presents an H∞ event-triggered state-feedback controller design for continuous-time nonlinear systems via convex optimization techniques. The proposal is based on an exact discretization and its quasi-linear parameter varying representation. Thus, two sets of design conditions in terms of linear matrix inequalities by means of both parameter-dependent and quadratic Lyapunov functions are proposed. The proposed conditions also provide an estimate to system’s domain of attraction and an extra set of conditions is presented for a guaranteed minimum time between events. Well-known examples are employed to illustrate the effectiveness of the proposal.
This paper proposes a new procedure for discretizing nonlinear systems described by Takagi-Sugeno fuzzy models. The discretization procedure consists of obtaining a linear auxiliary system that approximates the Takagi-Sugeno model over a sampling instant. By discretizing this auxiliary system, a norm bounded uncertain linear discrete-time system is found, which is capable of representing the fuzzy model. This auxiliary system, as well as the norm bounded uncertainty, is found by solving an optimization problem with Linear Matrix Inequality (LMI) constraints. To illustrate the discretization procedure, a constant state observer is synthesized based on simple LMI conditions and then applied to a real nonlinear Chua's circuit. Additionally, a state-feedback controller based on our discretization approach is synthesized and we obtain larger maximum sampling periods than other tested strategies from the literature.
O objetivo deste trabalho é apresentar e comparar duas estratégias de filtros para estimação de estados aplicados em uma Câmara Termoeletricamente Controlada (CTC). A CTC é composta por cinco sensores digitais de temperatura que representam os estados do sistema. A aplicação dos métodos é executada em duas etapas. Na primeira etapa, os filtros são implementados de forma off-line no software MATLAB R2018a, a partir dos dados reais do sistema. Enquanto, na segunda etapa, os filtros são aplicados em tempo real no sistema físico. São apresentados dois teoremas para a obtenção dos parâmetros dos filtros robustos tendo como base o lema de Finsler e o bounded real lemma, utilizando a norma H∞ como critério de desempenho, uma vez que emprega-se um modelo politópico incerto para descrever a dinâmica da CTC. O problema de filtragem ótima para o sistema é resolvido por meio de Desigualdades Matriciais Lineares. Os resultados obtidos pelos dois métodos são comparados graficamente e por meio da métrica do Erro Quadrático Médio.
This paper proposes a novel state estimator for discrete-time linear systems with Gaussian noise. The proposed algorithm is a fixed-gain filter, whose observer structure is more general than Kalman one for linear time-invariant systems. Therefore, the steady-state variance of the estimation error is minimized. For white noise stochastic processes, this performance criterion is reduced to the square H2 norm of a given linear time-invariant system. Then, the proposed algorithm is called observer H2 filter (OH2F). This is the standard Wiener-Hopf or Kalman-Bucy filtering problem. As the Kalman predictor and Kalman filter are well-known solutions for such a problem, they are revisited.
Neste artigo, é proposta uma nova abordagem de aproximação adaptativa baseada na técnica de backstepping para o controle do ângulo de arfagem (modo de operação SISO) de um sistema eletromecânico conhecido como Twin-Rotor MIMO System -- TRMS. Aproximadores universais na lei de controle são usados para estimar a parte desconhecida da dinâmica, usando modelos nebulosos Takagi-Sugeno (TS). De modo a se reduzir a complexidade no projeto do controlador por backstepping, filtros de comando são utilizados para evitar o cálculo explícito das derivadas temporais das ações de controle virtuais. Uma modificação de zona morta, desligando a lei de adaptação na região em que não se pode garantir a convergência da função de Lyapunov, é utilizada de modo a evitar o problema de deriva dos parâmetros. A eficácia do projeto do controlador é investigada por meio de simulações numéricas, enfatizando-se a redução no número de parâmetros a serem estimados.
O uso de inversor com filtro de saída LC permite a geração de tensões senoidais com baixa distorção harmônica, adequada para sistemas de fonte de alimentação ininterrupta. No entanto, o projeto do controlador se torna mais difícil para sistemas deste tipo. Este artigo apresenta a formulação de um esquema de controle preditivo, projetado no referencial síncrono, para a tensão de um conversor de dois níveis. Para o projeto do controlador o modelo em espaço de estados é aumentado considerando que a referência e o erro integral também são estados do sistema. Assim sendo, o controlador usa o modelo para prever, em cada intervalo de amostragem, o comportamento da tensão de saída e das correntes no indutor para um horizonte de predição finito. Em seguida, uma função de custo quadrática, sujeita as restrições do problema, é otimizada gerando os sinais que são comparados com uma onda portadora originando o PWM senoidal para as chaves semicondutoras. A estratégia proposta é demonstrada em detalhes e validada com simulações para diferentes cenários de carga.
This work provides analytical upper bounds on the discretization error of uncertain linear systems. The Tensor Product Model Transformation is used to approximate the derived discretized system,with a reduced number of vertices. Digital state feedback controllers are then designed for the discretized system, for comparison to other available works in the current literature.
This paper investigates an alternative approach for the discretization of uncertain time-invariant continuous-time linear systems which allows to employ higher sampling times. The approach consists in creating an artificial discrete-time descriptor system whose discretization error behaves similarly to the one obtained with double the sampling rate of the original system. The resulting discrete-time descriptor model is compounded of homogeneous polynomially parameter-dependent matrices and additive norm bounded terms related to the discretization residual error. A new linear matrix inequality condition is proposed for the synthesis of a robust digital state feedback control law that certifies the closed-loop stability of the hybrid system. Numerical examples are presented to illustrate how larger sampling times can be used in the proposed method when compared to other works in the literature.
More than 40 years after fuzzy logic control appeared as an effective tool to deal with complex processes, the research on fuzzy control systems has constantly evolved. Mamdani fuzzy control was originally introduced as a model-free control approach based on expert's experience and knowledge. Due to the lack of a systematic framework to study Mamdani fuzzy systems, we have witnessed growing interest in fuzzy model-based approaches with Takagi-Sugeno fuzzy systems and singleton-type fuzzy systems (also called piecewise multiaffine systems) over the past decades. This paper reviews the key features of the three above types of fuzzy systems. Through these features, we point out the historical rationale for each type of fuzzy systems and its current research mainstreams. However, the focus is put on fuzzy model-based approaches developed via Lyapunov stability theorem and linear matrix inequality (LMI) formulations. Finally, our personal viewpoint on the perspectives and challenges of the future fuzzy control research is discussed.
This letter proposes a new l∞ observer design for fuzzy descriptor systems with unknown inputs. The descriptor form is treated using a singular redundancy system representation. To keep the consistency of the resulting fuzzy observer structure, we make use of a virtual variable playing the role of the one-step ahead state estimate. As a result, the observer gain can be constructed with free-structure decision variables to reduce the design conservatism. Using a membership-function-dependent Lyapunov function, the observer design is reformulated as a convex optimization problem with a single line search parameter. In particular, the error bounds of both the state and the unknown input estimations can be minimized through the guaranteed l∞ performance level. The effectiveness of our result is demonstrated with a challenging real-world application on robot manipulators.
O objetivo deste trabalho é apresentar e comparar dois métodos de estimação de estado, realizados off-line, que são aplicados em uma câmara termeletricamente controlada, que consiste de uma câmara equipada com cinco sensores digitais de temperatura e um atuador de resfriamento/aquecimento composto de módulos Peltier. Os estados do sistema consistem nas temperaturas medidas pelos cinco sensores digitais de temperatura. Neste trabalho, os métodos escolhidos para estimar os estados foram o Filtro de Partículas e um Observador Fuzzy Takagi- Sugeno projetado com condições baseadas em desigualdades matriciais lineares. Uma comparação entre as duas abordagens de estimativa de estado é realizada e ilustrada pelos resultados obtidos e é apresentada no artigo.
Este trabalho tem como objetivo fazer a modelagem de um sistema termoelétrico, usando as series de Volterra em conjunto com as Funções de Base Ortonormal (FBO). Neste trabalho, emprega-se otimização multiobjetivo para obter o polo ótimo da FBO e reduzir o número de parâmetros necessários para a identificação do modelo. O algoritmo utilizado é da classe dos Multiobjective Evolutionary Algorithms (MOEA) e o sistema utilizado para ilustrar a aplicação da técnica é uma câmara térmica com temperatura variada por meio de módulos Peltier.
As Indústrias 4.0 caracterizam uma revolução tecnológica nas infraestruturas críticas. A crescente necessidade de conectividade e acesso a informação rompe com os padrões dos modelos de sistemas supervisórios anteriores. O aumento da utilização dos sistemas ciberfísicos possibilitam grandes melhorias no controle de processos industriais. Por outro lado, a interconectividade está relacionada à redução da segurança, o que deixa as infraestruturas vulneráveis a ataques digitais. Esse trabalho apresenta um método para detecção de ataques utilizando um estimador de estados com erro quadrático mínimo.
Este trabalho apresenta a aplicação de uma lei de controle preditiva explícita em um sistema térmico real. É apresentada uma estratégia de sistema aumentado para garantir erro em estado estacionário nulo para uma entrada em degrau, além das características construtivas do sistema considerado. Por fim, comparam-se os resultados obtidos pelo controlador em simulação e no sistema real.
This paper presents a novel application of the Tensor Product Model Transformation: the approximation of fuzzy control and estimation laws. In order to illustrate this application, a thermoelectric controlled chamber was built using peltier coolers and an H Bridge. By using 5 digital temperature sensors, a Takagi-Sugeno discrete time fuzzy model of the system was found with system identification techniques. A control and an estimation law were designed using state of the art LMI conditions for fuzzy systems. By making use of the Tensor Product Model Transformation, these control and estimation laws were approximated/simplified and implemented on a microcontroller. The results obtained from these simplified laws show that this is a viable option and allows the use of cheap microcontrollers in cases where it would not be able to implement the control and estimation laws.
Most of the discretization approaches for uncertain linear systems make use of the series representation of the matrix exponential function and truncate the summation after a certain order. This usually leads to discrete-time uncertain polytopic models described by polynomial matrices with multiple indexes, which usually means that the higher the order used in the approximation, the higher the number of linear matrix inequalities (LMI) needed. This work, instead, proposes an approach based on a grid of the possible values for the matrix exponential function and an application of the tensor product model transformation technique to find a suitable polytopic model. Numerical examples are presented to illustrate the advantages and the applicability of the proposed technique.
This paper provides a computational method to study the asymptotic stability of piecewise multi-affine (PMA) systems. Such systems stem from a class of fuzzy systems with singleton consequents and can be used to approximate any smooth nonlinear system with arbitrary accuracy. Based on the choice of piecewise Lyapunov functions, stability conditions are expressed as a feasibility test of a convex optimization with linear matrix inequality constraints. The basic idea behind these conditions is to exploit the parametric expressions of PMA systems by means of Finsler's lemma. Numerical examples are given to point out the effectiveness of the proposed method.
This paper deals with the output tracking control problem for nonlinear networked control systems (NCSs) described by Takagi-Sugeno (T-S) fuzzy models. Due to the existence of inherent constraints in NCS as communication time-delays and limitation of data transmission capacity, a recent event-triggered scheme proposed in the literature is implemented to reduce the bandwidth utilization. On the other hand, the communication time-delay imposes an asynchronously operation between the proposed T-S fuzzy controller and the T-S fuzzy system handled via new LMI based conditions. Furthermore, a synchronous model operation is also proposed in this paper which allows to design a linear controller which is much simpler to implement. The main results are derived following the selection of an appropriate fuzzy Lyapunov Krasovskii Functional (LKF) together with alternative integral inequalities which lead to less conservative conditions when compared to recent results in the literature. An example illustrates the effectiveness of the proposed output tracking control for NCSs fuzzy models.
Unknown nonstationary processes require modeling and control design to be done in real time using streams of data collected from the process. The purpose is to stabilize the closed-loop system under changes of the operating conditions and process parameters. This paper introduces a model-based evolving granular fuzzy control approach as a step toward the development of a general framework for online modeling and control of unknown nonstationary processes with no human intervention. An incremental learning algorithm is introduced to develop and adapt the structure and parameters of the process model and controller based on information extracted from uncertain data streams. State feedback control laws and closed-loop stability are obtained from the solution of relaxed linear matrix inequalities derived from a fuzzy Lyapunov function. Bounded control inputs are also taken into account in the control system design. We explain the role of fuzzy granular data and the use of parallel distributed compensation. Fuzzy granular computation provides a way to handle data uncertainty and facilitates incorporation of domain knowledge. Although the evolving granular approach is oriented to control systems whose dynamics is complex and unknown, for expositional clarity, we consider online modeling and stabilization of the well-known Lorenz chaos as an illustrative example.
The tensor‐product (TP) model transformation is a numerical technique that finds a convex representation, akin to a Takagi‐Sugeno (TS) fuzzy model, from a given linear parameter varying (LPV) model of a system. It samples the LPV model over a limited domain, which allows the use of the higher order singular value decomposition (HOSVD) and convex transformations that leads to the TS representation of the LPV model. In this paper, we discuss different strategies that could be used on the sampling step of the TP model transformation (which in turn lead to different membership function properties of a TS fuzzy model). Additionally, this paper discusses how the other steps could be used to reduce the number of rules of a given TS fuzzy model. In cases where nonzero singular values were discarded in the rule reduction, we also show how to obtain an uncertain model that covers the original.
This paper presents new less conservative stability analysis conditions for Takagi–Sugeno fuzzy systems subject to interval time-varying delay. The methodology is based on the direct Lyapunov method allied with an appropriate Lyapunov–Krasovskii functional choice and the use of the integral inequalities, Finsler lemma, Newton–Leibniz formula manipulations and convex combination properties. Particularly, the main result differs from previous ones since the positiveness of the Lyapunov–Krasovskii functional is guaranteed by new relaxed conditions. Two examples illustrate the effectiveness of the proposed methodology.
Improvements of recent stability conditions for continuous-time Takagi-Sugeno (T-S) fuzzy systems are proposed. The key idea is to bring together the so-called local transformations of membership functions and new piecewise fuzzy Lyapunov functions. By relying on these special local transformations, the associated linear matrix inequalities that are used to prove the system's stability can be relaxed without increasing the number of conditions. In addition, to enhance the usefulness of the proposed methodology, one can choose between two different sets of conditions characterized by independence or dependence on known bounds of the membership functions time derivatives. A standard example is presented to illustrate that the proposed method is able to provide substantial improvements in some cases.
As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions. In an outdoor environment, however, terrain characteristics play a major role on the robot's motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, since the environment conditions may greatly vary. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. This article presents an algorithm for localization of mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques.
Este artigo apresenta a elaboração e desenvolvimento de um módulo didático experimental, dinâmico, incerto e de baixo custo, direcionado às disciplinas de modelagem e controle de sistemas dinâmicos. O modelo proposto ́e de fácil uso e com vasta aplicabilidade, podendo ser utilizado para compreender diversos conceitos, como estabilidade, linearidade, sistemas de fase não mínima e de fase mínima, abordagens nos domínios do tempo e da frequência, entre outros. Além disso, o módulo possibilita aos alunos dos cursos de Engenharia Elétrica e de Controle e Automação realizar a modelagem em espaço de estados de um sistema de baixo custo e com diferentes configurações de estados e saídas. Em níveis mais avançados, também lhes permite projetar controladores para o módulo que funcionem independentemente do comportamento escolhido para o sistema, isto é, estável ou instável.
When using fuzzy Lyapunov functions for continuous-time Takagi-Sugeno fuzzy systems, it is common to have to deal with the membership functions’ time derivative. Several upper-bound inequalities have been proposed in the literature in order to deal with these derivatives in a Linear Matrix Inequality setting. In this article, we extend and compare some of these inequalities in the context of generating the largest estimate of the domain of attraction while synthesizing non-PDC control laws.
When using fuzzy Lyapunov functions for continuous-time Takagi-Sugeno fuzzy systems, it is common to have to deal with the membership functions’ time derivative. Several upper-bound inequalities have been proposed in the literature in order to deal with these derivatives in a Linear Matrix Inequality setting. In this article, we extend and compare some of these inequalities in the context of generating the largest estimate of the domain of attraction while synthesizing non-PDC control laws.
This paper aims to compare alternative time delay relaxations for a class of nonlinear systems controlled via network and described by Takagi-Sugeno fuzzy models. In this regard, three alternatives were proposed and compared with a very recent relaxation proposed in the literature. Basically, the changes are made at two strategic points. The first point is the Lyapunov functional proposed and the second one is related to the introduction of different integral inequalities conditions. A numerical example of a network-based fuzzy tracking control systems is presented to highligth the advantages of the alternatives relaxations.
This paper is concerned with controller design for H-infinity disturbance rejection in continuous-time Takagi-Sugeno models. The control law belongs to a more general class than the well-known parallel distributed compensation: it is based on a recent approach which employs progressively more complex nested convex sums while preserving the use of a quadratic Lyapunov function. The results thus obtained are parameter-dependent linear matrix inequalities which allow logarithmic search of feasible solutions. Examples are provided to illustrate the aforementioned contributions.
Many Takagi-Sugeno (TS) synthesis and analysis conditions can be expressed as positive/negative definiteness conditions of fuzzy summations. In this regard, several sufficient conditions have been proposed in the literature that are asymptotically exact (i.e. necessary and sufficient as their number tends to infinity). However, since these conditions do not take the membership function shapes into account (aside from the fact that they add up to one and are greater than zero), the exactness of these conditions is only true when the membership functions can assume any possible combination of values inside the standard simplex. By making use of the membership functions shapes, in this paper we propose a triangulation (or simplicial partition) based approach that generates asymptotically exact conditions for fuzzy summations with a possibly smaller growing rate of added simplices per iteration than that observed in recently published algorithms. The set of conditions are Linear Matrix Inequalities (LMIs), for which efficient numerical routines are available.
Este artigo apresenta o uso de uma técnica de controle fuzzy por modelo de referência, baseada em resultados de LMIs recentes na literatura, aplicada ao modelo longitudinal de um caça militar a jato. Para poder aplicar tal técnica, este trabalho também faz uso da técnica de transformação do modelo via produto tensorial para modelar a aeronave como um sistema fuzzy. Resultados das simulações são apresentados ao final do artigo.
Uma premissa fundamental para que um robô móvel consiga executar suas tarefas é que ele possua conhecimento de sua localização. Portanto, este artigo aborda o cálculo da atitude de robôs móveis navegando em ambientes que possuem terrenos irregulares. A partir das informações provindas de diferentes sensores e utilizando-se o UKF para o cálculo da atitude foi possível obter bons resultados para a localização em ambientes externos.
A robot’s knowledge of its location is a fundamental information on mobile robotics, allowing greater autonomy in decision-making problems. Thus, this paper proposes a technique for localization of Unmanned Aerial Vehicles based on Computer Vision sensing and using the Monte Carlo Localization method. Only natural landmarks already present in the environment are used, avoiding the need for manual insertion of recognizable landmarks. For the identification of landmarks the Scale Invariant Feature Transform algorithm (SIFT) is used. We present results from a model of a small autonomous aerial vehicle in a nonstructured environment.
As a mobile robot navigates through an indoor environment, the condition of the floor is of low (or no) relevance to its decisions, because it does not expect to find holes, rocks or other severe imperfections. In an outdoor environment, terrain characteristics play a major role on the robot’s motion. Without an adequate assessment of terrain conditions and irregularities, the robot will be prone to major failures, as an external environment is typically unstructured and pavement conditions may greatly vary. Since the assumption of horizontal flat support areas is no longer valid, a mobile robot pose needs to be fully described in the three dimensional space. As such, it may assume any orientation about the three axes of its reference frame, which leads to a full six degrees of freedom configuration. The added three degrees of freedom have a major bearing on position and velocity estimation due to higher time complexity of classical techniques such as Kalman filters and particle filters. Therefore, one challenge is the identification of good models for the robotic system, which includes sensors and actuators, but that are also fast to initialize and to compute. One interesting approach that fits these requirements is the complementary filtering, which allows the summation of reliable signals in different frequency bandwidths, resulting in more accurate values in the time domain. This technique has been successfully applied to estimate the three-dimensional orientation of mobile robots, with the major advantages of having low computational cost, faster dynamic responses and simple adjustment of the parameters of the algorithm. However, the technique is basically used to calculate the angles of orientation, not taking into account signals that measure the position of the robot. This article presents a localization system for mobile robots based on the complementary filtering technique to estimate the localization and orientation, through the fusion of data from IMU, GPS and compass. The main advantages are the low complexity of implementation and the high quality of the results for the case of navigation in outdoor environments (uneven terrain). The results obtained through this system are compared positively with those obtained using more complex and time consuming classic techniques.
O objetivo deste artigo é mostrar um arcabouço desenvolvido para utilização de robôs móveis na coleta eficiente de dados em uma rede de nós sensores. Esse arcabouço é composto, basicamente, de três partes principais: i) protocolo de comunicação empregado entre o robô e os nós sensores espalhados no ambiente para transferir os dados coletados, ii) localização baseada em Transformada de Hough e Filtro de Kalman e iii) controle aplicado a um robô não holonômico utilizando um método baseado em campos de potencial para mover o robô em direção aos sensores. Simulações e experimentos são mostrados visando validar a metodologia proposta.
We propose and implement a hybrid navigational strategy for mobile robots for data collection tasks in sparsely deployed sensor networks. Our approach presents an alternative to the routing algorithms used in wireless sensor networks which focus on the minimization of energy consumption of each node which is mainly due to data transmission. The method has two layers: i) Reactive Layer in which the data collected from a sensor node is modelled as a potential function whose gradient attracts the robot and ii) Planning Layer, that dictates the action the robot must take to collect sensor data. Results from both simulation and real world experiments showing the performance of the proposed methodology are shown, compared and discussed.