Integration of UWB Radar on Mobile Robots for Continuous Obstacle and Environment Mapping

2025-11-30

AI Summary

To address the failure of conventional vision- and LiDAR-based sensors in visually degraded environments—such as darkness, smoke, and low-reflectivity surfaces—this paper proposes a UWB radar-based obstacle detection and anchor-free environmental mapping method for mobile robots. The approach leverages channel impulse responses from UWB channels 5 and 9, implementing a three-stage signal processing pipeline: peak detection, followed by SNR- and phase-difference-based filtering, and finally AoA–range joint clustering to mitigate multipath interference and noise. Crucially, it eliminates reliance on fixed anchors, enabling visual-feature-free UWB-SLAM in dynamic settings. Experimental results demonstrate 82.36% detection accuracy and 89.46% recall under channel 9, with robust identification of diverse materials—including metal, concrete, and low-reflectivity plywood—thereby significantly enhancing perception robustness in complex, unstructured environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Vision for Robotics & Autonomous Driving

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

This paper presents an infrastructure-free approach for obstacle detection and environmental mapping using ultra-wideband (UWB) radar mounted on a mobile robotic platform. Traditional sensing modalities such as visual cameras and Light Detection and Ranging (LiDAR) fail in environments with poor visibility due to darkness, smoke, or reflective surfaces. In these visioned-impaired conditions, UWB radar offers a promising alternative. To this end, this work explores the suitability of robot-mounted UWB radar for environmental mapping in dynamic, anchor-free scenarios. The study investigates how different materials (metal, concrete and plywood) and UWB radio channels (5 and 9) influence the Channel Impulse Response (CIR). Furthermore, a processing pipeline is proposed to achieve reliable mapping of detected obstacles, consisting of 3 steps: (i) target identification (based on CIR peak detection), (ii) filtering (based on peak properties, signal-to-noise score, and phase-difference of arrival), and (iii) clustering (based on distance estimation and angle-of-arrival estimation). The proposed approach successfully reduces noise and multipath effects, resulting in an obstacle detection precision of at least 82.36% and a recall of 89.46% on channel 9 even when detecting low-reflective materials such as plywood. This work offers a foundation for further development of UWB-based localisation and mapping (SLAM) systems that do not rely on visual features and, unlike conventional UWB localisation systems, do not require on fixed anchor nodes for triangulation.

Problem

• UWB radar enables obstacle detection in low-visibility conditions
• It maps environments without fixed anchors or visual features
• The method reduces noise and multipath for reliable mapping

Innovation

• UWB radar enables obstacle detection in low-visibility conditions
• Processing pipeline filters noise and multipath effects for mapping
• Anchor-free UWB mapping without fixed nodes or visual features