Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband

TL;DR

This paper presents a robust indoor navigation system combining deep reinforcement learning-based local planning with ultra-wideband localization, trained in simulation and tested extensively in real environments, demonstrating resilience to noise and environment changes.

Contribution

It introduces a low-cost, power-efficient point-to-point navigation method using deep RL trained in simulation and deployed with UWB localization for indoor robots.

Findings

The RL-based navigation policy is robust to UWB noise.

The system achieves over 200 real-world tests with consistent performance.

End-to-end simulation training simplifies deployment in real environments.

Abstract

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-hundred 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.