This paper presents the development and implementation of neural control systems in mobile robots in obstacle avoidance in real time using ultrasonic sensors with complex strategies of decision-making in development (Matlab and Processing). An Arduino embedded platform is used to implement the neural control for field results.

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This paper presents a theoretical analysis of a DC-DC flyback converter variant applied in energy harvesting based on thermoelectric generators. The main contribution of the article is the analysis and obtaining the equations of the behavior of the converter with a rearrangement of the elements of the traditional flyback converter in such a way that the converter only processes part of the energy while the other part is delivered directly to the load. This is achieved by connecting the secondary of the flyback in series with the load, and this assembly, in turn, is placed in parallel with the primary and the voltage source. This configuration means that the topology can only be a boost topology; however, there are benefits such as partial power processing (R2P2) and reduced stress on converter components in both voltage and current; all this leads to increase the efficiency. A Low Frequency Averaging Analysis (LFAA) was used to determine the behavior of the proposed circuit, and a simple equivalent circuit to analyze was obtained. In order to validate the theoretical analysis, a circuit was simulated in Spice and implemented in an 18 W prototype. Experimental results showed that the converter has an efficiency of 92.65%. Moreover, the rearranged flyback processed only 56% of the input power.