arXiv subjects

Johannes Mootz

Publications and source records attributed to Johannes Mootz.

3 recordsLinked to original sources

arXiv2026

Control Barrier Functions with Audio Risk Awareness for Robot Safe Navigation on Construction Sites

Construction automation increasingly requires autonomous mobile robots, yet robust autonomy remains challenging on construction sites. These environments are dynamic and often visually occluded, which complicates perception and navigation. In this context, valuable information from audio sources remains underutilized in most autonomy stacks. This work presents a control barrier function (CBF)-based safety filter that provides safety guarantees for obstacle avoidance while adapting safety margins during navigation using an audio-derived risk cue. The proposed framework augments the CBF with a lightweight, real-time jackhammer detector based on signal envelope and periodicity. Its output serves as an exogenous risk that is directly enforced in the controller by modulating the barrier function. The approach is evaluated in simulation with two CBF formulations (circular and goal-aligned elliptical) with a unicycle robot navigating a cluttered construction environment. Results show that the CBF safety filter eliminates safety violations across all trials while reaching the target in 40.2% (circular) vs. 76.5% (elliptical), as the elliptical formulation better avoids deadlock. This integration of audio perception into a CBF-based controller demonstrates a pathway toward richer multimodal safety reasoning in autonomous robots for safety-critical and dynamic environments.

cs.RO↗

arXiv2025

Building Information Models to Robot-Ready Site Digital Twins (BIM2RDT): An Agentic AI Safety-First Framework

The adoption of cyber-physical systems and jobsite intelligence that connects design models, real-time site sensing, and autonomous field operations can dramatically enhance digital management in the construction industry. This paper introduces BIM2RDT (Building Information Models to Robot-Ready Site Digital Twins), an agentic artificial intelligence (AI) framework designed to transform static Building Information Modeling (BIM) into dynamic, robot-ready digital twins (DTs) that prioritize safety during execution. The framework bridges the gap between pre-existing BIM data and real-time site conditions by integrating three key data streams: geometric and semantic information from BIM models, activity data from IoT sensor networks, and visual-spatial data collected by robots during site traversal. The methodology introduces Semantic-Gravity ICP (SG-ICP), a point cloud registration algorithm that leverages large language model (LLM) reasoning. Unlike traditional methods, SG-ICP utilizes an LLM to infer object-specific, plausible orientation priors based on BIM semantics, improving alignment accuracy by avoiding convergence on local minima. This creates a feedback loop where robot-collected data updates the DT, which in turn optimizes paths for missions. The framework employs YOLOE object detection and Shi-Tomasi corner detection to identify and track construction elements while using BIM geometry as a priori maps. The framework also integrates real-time Hand-Arm Vibration (HAV) monitoring, mapping sensor-detected safety events to the digital twin using IFC standards for intervention. Experiments demonstrate SG-ICP's superiority over standard ICP, achieving RMSE reductions of 64.3%--88.3% in alignment across scenarios with occluded features, ensuring plausible orientations. HAV integration triggers warnings upon exceeding exposure limits, enhancing compliance with ISO 5349-1.

cs.RO↗

arXiv2025

Advancing Accessible Hand-Arm Vibration Safety Monitoring: ISO-Compliance with Wearable Sensors and Transfer Functions

Field workers are frequently exposed to hazardous vibrations, increasing the risk of Hand-Arm Vibration Syndrome (HAVS) and other long-term health problems. ISO 5349-1 provides guidelines for measuring vibration exposure. However, this standard was established in controlled conditions using high-quality accelerometers directly attached to power tool handles. This study investigates an alternative, wearable sensor-based data collection process and develops an error-minimization transfer function that derives values comparable to ISO benchmarks for safety monitoring. Experiments are performed with subjects hammer drilling into concrete while vibrations are measured using three accelerometers at different sampling frequencies. The transfer function maps vibration data across sensor positions by accounting for damping effects. The findings indicate a significant reduction in acceleration between the palm and upper arm, highlight the impact of sampling frequency on data accuracy, and enable accurate comparison of true hand-arm vibration levels with existing standard limits to allow accessible, real-time, and cost-effective HAVS prevention.

eess.SP↗