Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis

TL;DR

This paper applies advanced topological data analysis to analyze and classify the complex trajectories of autonomous weeder robots, aiming to improve monitoring and anticipation of their functioning.

Contribution

It introduces a novel approach using topological descriptors and persistence homology to analyze robot trajectories affected by environment and state.

Findings

Topological descriptors vary with environment and robot state.

Persistence homology effectively captures trajectory features.

Method enables classification and pattern recognition of robot paths.

Abstract

The present paper aims at analyzing the topological content of the complex trajectories that weeder-autonomous robots follow in operation. We will prove that the topological descriptors of these trajectories are affected by the robot environment as well as by the robot state, with respect to maintenance operations. Topological Data Analysis will be used for extracting the trajectory descriptors, based on homology persistence. Then, appropriate metrics will be applied in order to compare that topological representation of the trajectories, for classifying them or for making efficient pattern recognition.