🤖 AI Summary
In multi-robot systems lacking fixed base stations, direct visual perception, and operating under full mobility, achieving high-accuracy relative localization remains challenging. Method: This paper proposes an infrastructure-free UWB-based relative localization framework that eliminates reliance on static anchors for the first time. It introduces a geometric dilution of precision (GDOP)-aware UWB ranging error loss function and designs a deep reinforcement learning (DRL)-based active controller to dynamically optimize robot poses and enhance localization observability in real time. Contribution/Results: Evaluated jointly on simulation and physical testbeds, the framework reduces relative localization error by up to 60% compared to state-of-the-art methods, significantly improving robustness and accuracy in dynamic, infrastructure-free environments.
📝 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 infrastructureless 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 infrastructureless 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.