Distributed Machine Learning in Materials that Couple Sensing, Actuation, Computation and Communication
It addresses the problem of integrating machine learning into robotic materials for domains like structural health monitoring and wearable computing, but it is incremental as a review paper.
This survey reviews machine learning applications for intelligent materials with embedded distributed computation, focusing on tasks like damage detection and control, and explores approaches such as support vector machines and deep learning for adaptation to amorphous computing networks.
This paper reviews machine learning applications and approaches to detection, classification and control of intelligent materials and structures with embedded distributed computation elements. The purpose of this survey is to identify desired tasks to be performed in each type of material or structure (e.g., damage detection in composites), identify and compare common approaches to learning such tasks, and investigate models and training paradigms used. Machine learning approaches and common temporal features used in the domains of structural health monitoring, morphable aircraft, wearable computing and robotic skins are explored. As the ultimate goal of this research is to incorporate the approaches described in this survey into a robotic material paradigm, the potential for adapting the computational models used in these applications, and corresponding training algorithms, to an amorphous network of computing nodes is considered. Distributed versions of support vector machines, graphical models and mixture models developed in the field of wireless sensor networks are reviewed. Potential areas of investigation, including possible architectures for incorporating machine learning into robotic nodes, training approaches, and the possibility of using deep learning approaches for automatic feature extraction, are discussed.