This paper proposes a human localization method in informationally structured space based on sensor network. First, we explain informationally structured space, robot partners, and sensor networks developed in this study. Next, we apply a fuzzy spiking neural network to extract a person from the measured data by the sensor network. Furthermore, we propose a learning method of fuzzy spiking neural network based on the time series of measured data. Finally, we discuss the effectiveness of the proposed methods through experimental results in a living room.
With user expectations for user-friendly robots growing, the question of what makes a robot “user-friendly” continues to be debated. With the human need to add an “emotional” aspect to robots – “humanoid” shape such as Asimo and “fuzzy-feely” appeal such as Paro – we propose an emotional model based on location-dependent memory for partner robots. Focusing on the functions of emotion in social interaction, our proposed model is based on emotions, feelings, and mood and “episodic” memory related to changes in feeling. We propose map building and behavior control based on the emotional model. Experimental results demonstrate the feasibility of the emotional model and related behavior based on location-dependent memory.
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