Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging

Cooperative Relative Localization in MAV Swarms with Ultra-wideband Ranging

Liu, Changrui (TU Delft Aerospace Engineering)

de Croon, G.C.H.E. (mentor)
Pfeiffer, S.U. (mentor)
Mazo, M. (graduation committee)
de Wagter, C. (graduation committee)

Delft University of Technology

Aerospace Engineering | Control & Simulation

2022-11-23

Relative localization (RL) is essential for the successful operation of micro air vehicle (MAV) swarms. Achieving accurate 3-D RL in infrastructure-free and GPS-denied environments with only distance information is a challenging problem that has not been satisfactorily solved. In this work, based on the range-based peer-to-peer RL using the ultra-wideband (UWB) ranging technique, we develop a novel UWB-based cooperative relative localization (CRL) solution which integrates the relative motion dynamics of each host-neighbor pair to build a unified dynamic model and takes the distances between the neighbors as bonus information. Observability analysis using differential geometry shows that the proposed CRL scheme can expand the observable subspace compared to other alternatives using only direct distances between the host agent and its neighbors. In addition, we apply the kernel-induced extended Kalman filter (EKF) to the CRL state estimation problem with the novel-designed Logarithmic-Versoria (LV) kernel to tackle heavy-tailed UWB noise. Sufficient conditions for the convergence of the fixed-point iteration involved in the estimation algorithm are also derived. Comparative Monte Carlo simulations demonstrate that the proposed CRL scheme combined with the LV-kernel EKF significantly improves the estimation accuracy owing to its robustness against both the measurement outliers and incorrect measurement covariance matrix initialization. Moreover, with the LV kernel, the estimation is still satisfactory when performing the fixed-point iteration only once for reduced computational complexity.

Cooperative Localization
Relative Localization
Ultra-wideband
Kernel method
Kalman Filtering
Observability

http://resolver.tudelft.nl/uuid:1136170f-3c4b-43b8-8b43-09e1e52d3bfd

2023-12-31

Student theses

master thesis

© 2022 Changrui Liu