Frugal random exploration strategy for shape recognition using statistical geometry

TL;DR

This paper introduces a minimalistic exploration strategy for shape recognition that leverages statistical geometry to enable a simple, orientation-free robot to identify and analyze complex shapes and environments.

Contribution

It presents a novel frugal exploration approach that uses invariant features derived from statistical geometry, allowing minimal robots to recognize diverse shapes without orientation or observation systems.

Findings

A simple robot can access global shape information through random exploration.

Invariant features enable recognition of arbitrary and non-connected shapes.

The approach allows identification of complex shapes like monuments and text.

Abstract

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.