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.

翻译：多种截然不同的策略可被用于识别和表征未知环境或形状。近年来，一种颇具前景的方法（尤其在机器人领域）是将探索单元复杂度降至最低。本文证明，这种节约型策略可被推至极致：通过利用统计几何的力量并引入新的不变特征，我们展示了一个缺乏任何定向或观测系统的初级机器人，通过随机探索即可获取环境的全局信息，例如探索区域的面积和周长值。被探索形状具有任意几何特征，甚至可能为非连通图形。借助一个字典，这种最简机器人能够识别包括著名纪念碑在内的各种形状，甚至能读取文本。