Eduardo do Valle Simões

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This paper describes a fully embedded distributed evolutionary system that is able to achieve collision-free navigation in a few hundreds of trials. It reports the first experimental proof of the embedded evolution concept applied to the evolution of morphology and an unstructured control circuit of a population of six real robots in real time. Evolution(More)
This paper presents a vision system to be embedded in a mobile robot, both of them implemented using recon-figurable computing technology. The vision system captures gestures by means of a digital color camera, and then performs some pre-processing steps in order to use the image as input to a RAM-based neural network. The set of recognized gestures can be(More)
This work addresses the real time control of the Khepera mobile robot [1] navigation in a maze with reflector walls. Boolean Neural Networks such as RAM [2] and GSN [3] models are applied to drive the vehicle, following a light source, while avoiding obstacles. Both neural networks are implemented with simple logic and arithmetic functions (NOT, AND, OR,(More)
This work describes a framework for a GSN (Goal Seeking Neuron) Boolean neural network fast prototyping into a user-programmable gate array. This system provides a VHDL language description of the trained network, allowing the direct implementation of the circuit on an academic FPGA (Field-Programmable Gate Array). A GSN software tool was designed to train(More)
This article describes the implementation of a strategy that selects, destroys, and replaces some individuals of a population of six real autonomous mobile robots. This strategy was called Predation. We introduce Predation as a methodology for improving the performance of an embedded evolutionary system developed for the automatic design of robotic(More)
Although embedded systems have been around for quite a long time, just in recent years they have attracted major industry and academic interest. There is a perception that a computing paradigm shift is taking place, and so the need to provide computer science students with the required expertise in the field. In this paper we describe our experience of(More)
The utilization of Field-Programmable Gate Arrays (FPGA) to implement Artificial Neural Networks (ANN) becomes very attractive since it allows fast hardware design and modification at low costs. This work presents a comparison between two implementation strategies of ANN hardware design: the VLSI-Full Custom approach and FPGA. For that reason, three(More)