2023/08/09 by Samuel Hidalgo‐Caballero, Samuel Hidalgo-Caballero, Alvaro Cassinelli +7 · 1 voice
Computer Science · Physics and Astronomy · #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Physical sciences #Mathematical Physics (math-ph) #Robotics (cs.RO) #cs.RO #math-ph
paper · pdf · doi:10.48550/arxiv.2308.04848
openalex publication_date 2023/08/09 · arxiv published 2023/08/09 · arxiv updated 2023/08/09 · openalex created_date 2023/08/11 · openalex updated_date 2026/08/01
Very distinct strategies can be deployed to recognize and characterize an unknown environment or a shape. A recent and promising approach, especially in robotics, is to reduce the complexity of the exploratory units to a minimum. Here, we show that this frugal strategy can be taken to the extreme by exploiting the power of statistical geometry and introducing new invariant features. We show that an elementary robot devoid of any orientation or observation system, exploring randomly, can access global information about an environment such as the values of the explored area and perimeter. The explored shapes are of arbitrary geometry and may even non-connected. From a dictionary, this most simple robot can thus identify various shapes such as famous monuments and even read a text.