Mobile Robot-Assisted Mapping of Materials in Unknown Environments
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Active perception by robots of surrounding objects and environmental elements may involve contacting and recognizing material types such as glass, metal, plastic, or wood. This perception is especially beneficial for mobile robots exploring unknown environments, and can increase a robot's autonomy and enhance its capability for interaction with objects and humans. This paper introduces a new multi-robot system for learning and classifying object material types through the processing of audio signals produced when a controlled solenoid switch on the robot is used to tap the target material. We use Mel-Frequency Cepstrum Coefficients (MFCC) as signal features and a Support Vector Machine (SVM) as the classifier. The proposed system can construct a material map from signal information using both manual and autonomous methodologies. We demonstrate the proposed system through experiments using the mobile robot platforms installed with Velodyne LiDAR in an exploration-like scenario with various materials. The material map provides information that is difficult to capture using other methods, making this a promising avenue for further research.
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