Taxonomic Robot Identifiers: Toward General Classification and Oversight for Autonomous Systems

摘要

This article introduces a newly developed system of taxonomic identifiers that classify and identify robotic and autonomous systems based on their fundamental attributes. Robotic products have grown increasingly complex, diverse, autonomous, and pervasively ubiquitous in recent years, rapidly outgrowing traditional definitions and categorization standards. However, current classification methods struggle to adapt to these advancements. New generations of robotic products are no longer limited to traditional mechatronic devices; they now expand into various forms, including cyber-physical systems, software platforms, nanorobots, organismic systems, soft and flexible robots, and developmental robotics. Without effective tools to identify and compare their fundamental differences, efficient development is stifled due to duplicated and entrenched efforts, while social adoption and safety are also impacted. This article directly addresses these challenges and introduces key innovations in taxonomic classification and identification systems. It first examines in depth the current industry standards and academic literature, identifying major issues in building an effective taxonomy. To resolve these problems, the paper establishes new fundamental principles to classify broad classes of machines and develops a coding system for consistent naming, identification, classification, and organization. It then provides practical use cases to demonstrate and validate the utility and adaptability of the proposed system. The result is a novel, unified taxonomic framework that identifies robots and autonomous systems from the perspectives of anthropogenic, ecological, and phylogenetic factors. This biologically grounded, interdisciplinary approach expands traditional classification boundaries and offers a robust and inclusive framework that encompasses a broad spectrum of technologies, including software platforms and nanorobots, thereby future-proofing the taxonomy in anticipation of the imminent emergence of intelligent robots with new capabilities.