Infrastructure-less UWB-based Active Relative Localization

TL;DR

This paper presents an active, infrastructure-less UWB-based relative localization method for multi-robot systems, using deep reinforcement learning to adapt robot positions and significantly reduce localization errors without requiring static platforms.

Contribution

It introduces a novel active localization approach that removes the need for static platforms, utilizing a specialized anchor placement and a new loss function trained with deep reinforcement learning.

Findings

Up to 60% reduction in localization error compared to state-of-the-art methods.

Effective in both simulation and real-world experiments.

Eliminates the need for static platforms in UWB-based localization.

Abstract

In multi-robot systems, relative localization between platforms plays a crucial role in many tasks, such as leader following, target tracking, or cooperative maneuvering. State of the Art (SotA) approaches either rely on infrastructure-based or on infrastructure-less setups. The former typically achieve high localization accuracy but require fixed external structures. The latter provide more flexibility, however, most of the works use cameras or lidars that require Line-of-Sight (LoS) to operate. Ultra Wide Band (UWB) devices are emerging as a viable alternative to build infrastructure-less solutions that do not require LoS. These approaches directly deploy the UWB sensors on the robots. However, they require that at least one of the platforms is static, limiting the advantages of an infrastructure-less setup. In this work, we remove this constraint and introduce an active method for infrastructure-less relative localization. Our approach allows the robot to adapt its position to minimize the relative localization error of the other platform. To this aim, we first design a specialized anchor placement for the active localization task. Then, we propose a novel UWB Relative Localization Loss that adapts the Geometric Dilution Of Precision metric to the infrastructure-less scenario. Lastly, we leverage this loss function to train an active Deep Reinforcement Learning-based controller for UWB relative localization. An extensive simulation campaign and real-world experiments validate our method, showing up to a 60% reduction of the localization error compared to current SotA approaches.