Association rule learning, which is also a form of unsupervised learning based on rules, is a well-researched technique for uncovering interesting correlations and common patterns among items in the datasets.
Published in Chapter:
Machine Learning and Optimization Applications for Soft Robotics
Mehmet Mert İlman (Manisa Celal Bayar University, Manisa, Turkey) and Pelin Yildirim Taser (Izmir Bakircay University, İzmir, Turkey)
Copyright: © 2023
|Pages: 17
DOI: 10.4018/978-1-6684-5381-0.ch002
Abstract
Due to their adaptability, flexibility, and deformability, soft robots have been widely studied in many areas. On the other hand, soft robots have some challenges in modeling, design, and control when compared to rigid robots, since the inherent features of soft materials may create complicated behaviors owing to non-linearity and hysteresis. To address these constraints, recent research has utilized different machine learning algorithms and meta-heuristic optimization techniques. First and foremost, the study looked at current breakthroughs and applications in the field of soft robots. Studies in the field are grouped under main headings such as modelling, design, and control. Fundamental issues and developed solutions were analyzed in this manner. Machine learning and meta-heuristic optimization-oriented methods created for various applications are highlighted in particular. At the same time, it is emphasized how the problems in each of the modeling, design, and control areas impact each other.