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

Relative localization is an essential part of autonomous multiagent systems. In this study, drawing inspiration from animals that achieve collective behaviors solely through individual perception of relative information, we propose an infrastructure-free 2-D 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 ultrawideband (UWB) ranging sensors and microcontroller units (MCUs) 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 (EDMT) algorithm and a maximum likelihood estimation (MLE) algorithm are proposed; the computational complexities of the proposed algorithms are compared with the state-of-the-art methods; 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 infrastructure-free relative localization systems, and our proposed framework holds potential for future extensions to 3-D scenarios, different unmanned vehicle platforms, and multirobot cooperative systems.