Effective exploration of the environment is a critical aspect of adaptive behaviour, enabling animals to identify food sources, potential mates, refuge locations, and other important resources. The particular strategies used during exploratory behaviours depend on a variety of factors including context, personality traits and natural ecology. Weakly electric fish rely specifically on a short-range electric sense to search and locate objects in their environment in low-light conditions. However, little is known about the exploratory strategies used. We characterized the exploratory movements of two species of weakly electric fish, Apteronotus leptorhynchus and Apteronotus albifrons, in a laboratory setting. Our results suggest that there are behavioural differences between species in their exploratory strategies. Apteronotus albifrons spent more time in the open, travelled at a slower speed when out in the open, and had a higher total feeding time. Interestingly, Apteronotus leptorhynchus had a higher total displacement and preference for wall-following. A subsequent study on the behavioural function of wall-following in the two species suggested that wall-following is used for exploration in weakly electric fish, rather than for protection, and is not an artifact of restricted movement and tank shape.

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CoordenaÃÃo de AperfeÃoamento de Pessoal de NÃvel Superior / Este trabalho investiga o grau de influÃncia que a inclusÃo de mecanismos de memÃria de curta duraÃÃo (MCD) exercem sobre o desempenho de classificadores neurais quando aplicados em tarefas de navegaÃÃo de robÃs. Em particular, trata da navegaÃÃo do tipo Wall Following. Para este fim, quatro conhecidas arquiteturas neurais (Perceptron LogÃstico, Perceptron Multicamadas, Mistura de Especialistas e rede de Elman) sÃo usadas com o intuito de associar diferentes padrÃes de leituras sensoriais com quatro classes de aÃÃes prÃ-determinadas. Todas as etapas dos experimentos - aquisiÃÃo dos dados, seleÃÃo e treinamento das arquiteturas em simulador, alÃm da execuÃÃo das mesmas em robà mÃvel real (SCITOS G5) - sÃo escritas em detalhes. Os resultados obtidos sugerem que a tarefa de seguir paredes, formulada como um problema de classificaÃÃo de padrÃes, à nÃo-linearmente separÃvel, resultado este que favorece a rede MLP quando os classificadores sÃo treinados sem MCD. Contudo, se mecanismos de MCD sÃo usados, entÃo atà mesmo uma rede linear à capaz de executar a tarefa de interesse com sucesso / This work reports results of an investigation on the degree of influence that the inclusion of short-term memory mechanisms has on the performance of neural classifiers when applied to robot navigation tasks. In particular, we deal with the well-known strategy of navigating by âwall-followingâ. For this purpose, four neural architectures (Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman network) are used to associate different sensory input patterns with four predetermined action categories. All stages of the experiments - data acquisition, selection and training of the architectures in a simulator and their execution on a real mobile robot - are described. The obtained results suggest that the wall-following task, formulated as a pattern classification problem, is nonlinearly separable, a result that favors the MLP network if no memory of input patterns are taken into account. If short-term memory mechanisms are used, then even a linear network is able to perform the same task successfully

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This thesis describes development of control algorithm for a wheelchair. Wheelchair should be capable of tracking and following a wall or a similar flat surface. Thesis is supposed to be an extension of the previous concept, whose purpose was to allow remote telepresence control of this wheelchair. SRF08 ultrasonic range finders are used to measure distance from the wall. Furthermore, image processing for mark detection is discussed. Purpose of these marks is to increase precision during final phase of the parking.