Infrastructure-Free Relative Localization: System Modeling, Algorithm Design, Performance Analysis, and Field Tests

Abstract

Relative localization is an essential part of autonomous multi-agent systems. Existing methods often require real-time communications, pre-installed infrastructure, and substantial computational resources. In this study, drawing inspiration from the collective behaviors of primitive animals, we propose an infrastructure-free 2D distributed relative localization framework utilizing onboard ranging sensors. We start with system modeling, based on which optimal sensor configuration and algorithm design are conducted. Subsequently, we perform a thorough performance analysis and validate the overall system design through field tests using unmanned ground vehicles (UGVs) equipped with ultra-wideband (UWB) ranging sensors and micro-controller units onboard. Contributions include the following: the geometric dilution of precision (GDOP) and Cramér-Rao lower bound (CRLB) are derived; a novel Euclidean distance matrix (EDM)-based trilateration algorithm and a maximum likelihood estimation algorithm are proposed; and comprehensive simulation and field tests are conducted to validate the viability of the proposed framework. Two use cases are considered: to localize a target sensor and to localize an agent. The theoretical, numerical, and experimental results will shed light on the design and optimization of relative localization systems, and our proposed framework holds potential for future extensions to 3D scenarios, different unmanned vehicle platforms, and multi-robot cooperative systems.