LAUSR

Material perception is one of the several essential abilities required by robots, and it also poses significant challenges in real-world field applications. In harsh environments, the existing material recognition methods have the problem of low accuracy. In this paper, ultrasonic technology is applied to the field of material recognition, which is derived from the predatory strategy of echolocation animals. We propose a novel noncontact material recognition method of surrounding objects for robots based on ultrasonic echo signals, which can be used in external extreme environments. This method primarily adopts the 16-dimensional feature vector extracted from intrinsic mode functions that we gain from empirical mode decomposition as inputs of machine learning algorithms to recognize different materials, and we use K-nearest neighbor, decision tree, and support vector machine algorithms on the feature vector set to decide the best classifier, and its acoustic theoretical model is established additionally. The experimental results validate the accuracy of the acoustic theoretical model and the effectiveness of the proposed method. Compared with the existing methods, the proposed method improves the low accuracy and the poor recognition effect in ultrasonic material recognition. This method provides a new idea for robots to recognize and perceive materials in extreme environments.