Convolutional‐Generative Adversarial Network: Data‐Driven Mechanical Inverse Method for Intelligent Tactile Perception

Abstract

Reconstructing structural parameters of a system according to a known limited strain information is a typical mechanical inverse problem. [1] Among the many applications of the inverse problem, tactile perception has attracted a lot of attention from researchers recently. [2,3] Tactile perception plays a key role in physical interaction between human and the environment, and to ensure human's safety and stability. [4,5] Based on the signal of tactile perception, brain interprets it and then outputs the structural parameters of the object, such as the size, shape, texture, and hardness. [6][7][8][9][10] Comparing with other perception methods, the mechanical signal extracted by tactile perception is limited, while the sensed structural parameters of the object is complex. [11] In addition to the important application related to human, tactile perception also plays a critical role in robot interactions. [12][13][14][15] As shown in Figure 1, a conceptual robot of tactile perception consists of sensing and processing systems. Tactile sensors including strain gauges and data acquisition board are integrated into the fingers, which are used to evaluate the features of the target object, such as shape and structural parameters. Researches have shown that the state of the system can be evaluated by limited strain response. [16,17] However, when the features of the target object get more complex, limited strain response will bring difficulties to prediction. What is worse, in the long-term use of the tactile system, possible damage of strain sensors will lead to further loss of strain information. [18,19] The limited strain input results in significantly decreasing the accuracy rate of predicting structural parameters. Therefore, to create robots to perceive, explore, and manipulate their environments, an algorithm to process the tactile information like human needed to be developed, which is also the key factor that limits the development of robotic tactile perception.For the tactile perception, it would be much more difficult to solve the mechanical inverse problem by utilizing the aforesaid classical methods, such as Tikhonov regularization [20] and TV regularization. [21] The difficulties originate from two aspects. On the one hand, the strain field of structure needs to satisfy the mechanical governing equations and boundary conditions, which is intricate owing to the complexity of the loading and structure under real working conditions. Besides, the shape and pattern of microstructures severely affect the localized spatial relations of the strain data. Therefore, we need to consider both the global and local relations of the mechanical field when predicting structures. On the other hand, the predicted structure contains numerous randomly distributed microstructures, which will engender large categories while using the traditional classification algorithm. Generally, the problem of low prediction accuracy arises when the strain data measured are limited and the categories need to be distinguished are diverse. To ove...

Mechanical metamaterials

“…After appropriate training, the network is able to solve the forward problem efficiently. Dedicated networks can also be able to solve the inverse problem [279][280][281]. Concerning machine learning for mechanical metamaterials, a limited number of publications has been published.…”