Adaptive Experimental Design for Path-following Performance Assessment of Unmanned Vehicles

TL;DR

This paper proposes an adaptive experimental methodology using statistically designed experiments to evaluate path-following performance of Unmanned Surface Vehicles, emphasizing simulation before field trials.

Contribution

It introduces a two-step adaptive experimental procedure based on DoE for USV path-following assessment, providing specific guidelines for such experiments.

Findings

Validated the approach on Charlie USV simulator

Highlighted importance of extensive simulations before field trials

Developed empirical models for system performance

Abstract

The definition of Good Experimental Methodologies (GEMs) in robotics is a topic of widespread interest due also to the increasing employment of robots in everyday civilian life. The present work contributes to the ongoing discussion on GEMs for Unmanned Surface Vehicles (USVs). It focuses on the definition of GEMs and provides specific guidelines for path-following experiments. Statistically designed experiments (DoE) offer a valid basis for developing an empirical model of the system being investigated. A two-step adaptive experimental procedure for evaluating path-following performance and based on DoE, is tested on the simulator of the Charlie USV. The paper argues the necessity of performing extensive simulations prior to the execution of field trials.