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One of the main issues within the field of social robotics is to endow robots with the ability to direct attention to people with whom they are interacting. Different approaches follow bio-inspired mechanisms, merging audio and visual cues to localize a person using multiple sensors. However, most of these fusion mechanisms have been used in fixed systems, such as those used in video-conference rooms, and thus, they may incur difficulties when constrained to the sensors with which a robot can be equipped. Besides, within the scope of interactive autonomous robots, there is a lack in terms of evaluating the benefits of audio-visual attention mechanisms, compared to only audio or visual approaches, in real scenarios. Most of the tests conducted have been within controlled environments, at short distances and/or with off-line performance measurements. With the goal of demonstrating the benefit of fusing sensory information with a Bayes inference for interactive robotics, this paper presents a system for localizing a person by processing visual and audio data. Moreover, the performance of this system is evaluated and compared via considering the technical limitations of unimodal systems. The experiments show the promise of the proposed approach for the proactive detection and tracking of speakers in a human-robot interactive framework.

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This work introduces a binary particle swarm optimization based approach to locate the optimal location for biomass-based power plants. The proposed algorithm also offers the supply area for the biomass plant. The optimal location can be addressed as a nonlinear optimization problem. The profitability index is the fitness function for the binary optimization algorithm. It is defined as the ratio between the net present value and the initial investment.The constraints for simulations are: the biomass power plant must be inside the supply area; the electric power generated by the plant is limited to 10 MW. Computer simulations have been performed using 15 particles in the swarm and 50 iterations. Simulation results show that the proposed approach provides high-quality solutions (the profitability index is about 1.8) with reduced computation time (about 170 times lower than that required for exhaustive search).