Monitoring weeder robots and anticipating their functioning by using advanced topological data analysis
This work addresses the challenge of anticipating robot functioning for agricultural automation, but it appears incremental as it applies existing topological methods to a new domain.
The paper tackles the problem of monitoring weeder robots by analyzing their complex trajectories using topological data analysis, showing that topological descriptors are influenced by both the robot's environment and its maintenance state.
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.