A Taxonomic Classification and Identification System for Robots: Abstract

This is an abstract article to report a new research result on the taxonomy of robots, introducing a general taxonomic classification and identification system with a coding scheme that assigns taxonomic identifiers to robots and autonomous systems. While systematics and taxonomies have been extensively studied in biology, there is no general taxonomy that can identify and classify man-made systems in general. Machines are increasingly becoming complex, diverse, and most importantly autonomous, and as they pervasively co-exist with humans and animals in nature, the absence of a unified classification system presents significant challenges in safety, communication, and effective development of future machines. The key contributions of this research are three-fold. First on analysis: it examines in depth the current industry standards and academic literature and identifies major issues in building a taxonomy. Second on synthesis: it establishes a general principle to classify broad classes of machines and builds a coding scheme for consistent naming, identification, and classification. Third on applications: it provides exemplifying use cases to demonstrate the utility of the proposed system. The result is a general taxonomic classification system that categorizes machines not just by their physical attributes, but from the principled perspectives of autonomy, ecology, and anthropogenic factors. This interdisciplinary approach offers a robust, inclusive taxonomic framework that encompasses a broad spectrum of machines, including software and learning systems.