The Application of Deep Learning Techniques in Advanced Robotics for Enhancing Efficiency and Competitiveness in U.S. Manufacturing
Keywords:
Advanced Robotics, ManufacturingAbstract
Artificial intelligence (AI) has revolutionized our daily lives and possesses innumerable applications in the business domain, including cybersecurity, fraud detection, recommendation systems, predictive maintenance, computer vision, and natural language processing, to name a few. Deep learning is one such AI technique consisting of artificial neural networks with many layers (deep) and has made significant advances in recent years. The algorithms learn from raw data for the purpose of decision-making and have been very successful in applications above.
Elsewhere, deep learning in robotics allows data-driven learning and has witnessed advances in motion planning, visualization, and object manipulation. At the start of the 21st century, over 70,000 robots installed in the U.S. were performing inert and cumbersome tasks (i.e., lifting, pushing) while providing no flexibility or improvements to production capabilities. This changed with the advent of deep learning, and subsequently, there has been a steep rise in research at the intersection of deep learning and robotics.
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