Indoor autonomous navigation requires a precise and accurate localization system able to guide robots through cluttered, unstructured and dynamic environments. Ultra-wideband (UWB) technology, as an indoor positioning system, offers precise localization and tracking, but moving obstacles and non-line-of-sight occurrences can generate noisy and unreliable signals. That, combined with sensors noise, unmodeled dynamics and environment changes can result in a failure of the guidance algorithm of the robot. We demonstrate how a power-efficient and low computational cost point-to-point local planner, learnt with deep reinforcement learning (RL), combined with UWB localization technology can constitute a robust and resilient to noise short-range guidance system complete solution. We trained the RL agent on a simulated environment that encapsulates the robot dynamics and task constraints and then, we tested the learnt point-to-point navigation policies in a real setting with more than two-hun dred experimental evaluations using UWB localization. Our results show that the computational efficient end-to-end policy learnt in plain simulation, that directly maps low-range sensors signals to robot controls, deployed in combination with ultra-wideband noisy localization in a real environment, can provide a robust, scalable and at-the-edge low-cost navigation system solution.
Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-Wideband
01.01.2021
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
Vision-Based Indoor UAV Navigation with Deep Reinforcement Learning
Springer Verlag | 2025
|Automatic navigation using deep reinforcement learning
Europäisches Patentamt | 2023
|Automatic Navigation Using Deep Reinforcement Learning
Europäisches Patentamt | 2019
|