Underwater robots that use sonar navigation in turbid and visually degraded environments face several challenges. This includes sensor blackouts, visually similar rock formations, and misleading canyon geometries (geometry-induced ambiguity). While operating sonar at high resolution can help in improving navigation accuracy, it can, however, impact the computational and energy costs. This paper implements a predictive scheduling system that anticipates moments when the navigation estimator may become unstable. This approach is able to select the suitable sonar processing mode to match the current environmental condition and predict ahead, thereby balancing the trade-offs between sensor fidelity and resource consumption. The proposed approach monitors scan-matching performance and estimator uncertainty to predict impending navigation degradation and escalates sensing fidelity only when necessary. The framework is evaluated in three simulated environments with distinct perceptual characteristics: sparse, cluttered, and corridor-like environments, as well as on a real forward-looking sonar dataset (UATD). Results showed that in the challenging environments characterized by fluctuating geometries, the scheduler was able to reduce its reliance on continuous high-fidelity sensing while maintaining estimator stability. In the structured corridor environment, the scheduler was conservative in its action as it switched only a few times, reflecting sustained observability conditions. With the real sonar data, the scheduler chose the high-fidelity operation as it sensed persistent degradation. It is able to prioritize reliability over energy efficiency. The study shows that integrating uncertainty-aware sensing adaptation proves to be effective for managing sensing degradation while reducing reliance on continuous high-fidelity operation in environments where lower-cost modes remain sufficient.
Training technicians to diagnose and resolve hydroelectric plant faults requires exposure to multi-stage system issues that are difficult, expensive, and hazardous to recreate in real facilities. This work presents a scenario-driven virtual reality (VR) hydroelectric training environment built around a static facility layout and hand-based low-fidelity interactions. The system models turbine bays, reservoir areas, drain gates, intake zones, and mechanical components, and each scenario is instrumented with fine-grained telemetry capturing object-manipulation events such as debris removal, reservoir cleaning, component replacement, alignment attempts, and disposal accuracy. The environment presents learners with fault sequences in which symptoms (e.g., low flow, unstable pressure, abnormal vibration) evolve logically from underlying causes such as reservoir debris accumulation or mechanical wear. Each episode includes structured cues, root-cause relationships, and expected multi-step procedures, allowing the system to collect performance metrics such as task order, alignment offsets, cleanup completeness, safety adherence, and timing. In addition to supporting realistic practice, the platform enables repeatable, controlled experimentation with trainee behavior across varied fault conditions. Preliminary demonstrations using scripted interaction traces show that this telemetry can differentiate correct diagnostic pathways from common errors and support data-driven evaluation of trainee proficiency. Overall, this work introduces a fully instrumented VR platform for hydroelectric scenario training and establishes a foundation for future adaptive, personalized, or automated assessment systems.
Human–robot collaboration (HRC) is increasingly used in industrial environments to improve flexibility and productivity. As humans and robots operate in shared workspaces, safety remains a central requirement during system design and operation. An important challenge in the planning phase is the placement of safety sensors, which affects the ability to detect humans and prevent hazardous situations. In current industrial practice, sensor layout design is often based on manual planning and expert experience. This process requires significant effort and may lead to blind spots caused by occlusions or complex cell geometries. This paper presents a systematic approach for generating sensor layouts in human–robot collaboration applications. The proposed method analyzes the geometry of an industrial cell to identify occluded regions that limit human detection. A heatmap representation of potential human movement paths toward hazardous zones is used to describe the spatial distribution of risk within the workspace. Based on this information, suitable sensor positions are derived to improve coverage of critical areas while reducing redundant sensing. The approach is evaluated using several industrial use cases with different layouts and collaboration modes. The results show that the generated sensor layouts provide improved coverage of safety-relevant areas compared to manually designed configurations, while reducing planning effort. In addition, the method can be integrated into digital twin environments, allowing sensor layouts to be assessed and adapted during the design phase. The proposed approach supports the development of safer and more efficient human–robot collaboration systems.
This paper presents the design, implementation, and experimental validation of a novel underwater robotic quadrotor-float developed to address the challenges of remotely navigating, mapping, and sampling seafloor methane seeps. The proposed platform integrates the passive stability of a Lagrangian float with the active control authority of an underwater quadrotor, enabling precise depth tracking in dynamic underwater environments. The mechanical architecture adopts a modular design that combines a 3D-printed structural frame with a commercially available watertight enclosure to house onboard electronics, facilitating rapid prototyping and future upgrades. Actuation is achieved using four electric motor–propeller thrusters aligned along the heave axis, while depth feedback is provided by a high-resolution pressure sensor. A PID-based control strategy is implemented to regulate the vertical motion of the platform and achieve accurate depth tracking. The performance of the quadrotor-float is experimentally evaluated through a series of progressively complex tests conducted in a laboratory water tank, a deep-water swimming pool, and an outdoor natural water body. Experimental results demonstrate reliable tracking of both step and time-varying depth reference signals, with centimeter-level accuracy under nominal operating conditions and in the presence of environmental disturbances. These results validate the feasibility of the proposed quadrotor-float concept for near-seafloor operations and highlight its potential to improve maneuverability, reliability, and data or sample collection success in methane seep exploration. To the best of the authors’ knowledge, this work represents the first pool-scale experimental demonstration of closed-loop reference depth tracking using a robotic quadrotor-float in complex underwater scenarios.
Sea-level rise poses a growing threat to coastal structures, requiring building-level assessments that move beyond traditional 2D flood maps toward richer sensing and environment modeling. This study develops a 3D-mapping workflow to quantify building-level exposure to sea-level rise (SLR), using Old Dominion University (ODU) as a case study. Python, GeoPandas, rasterio, and Leafmap with a MapLibre backend are used for multisource sensor fusion, combining LiDAR-derived USGS DEM data, NASA GIBS satellite imagery, and OpenStreetMap footprints into a machine-readable 3D environment model. Building geometries are extruded by estimating ground elevations from the DEM and heights from available attributes. Exposure is evaluated by comparing building elevations to projected water levels for SLR scenarios between 1.5 m and 4.0 m. Results show an abrupt, nonlinear increase in flooded structures at higher SLR levels, revealing spatial clusters of vulnerability. These building-level risk maps provide actionable information for campus planners and local emergency managers to prioritize adaptation, retrofitting, and evacuation planning. The workflow shows how open-source tools and multi-sensor geospatial data can create a practical 3D sensing and sensor-fusion framework for assessing climate-resilient infrastructure. This framework can be expanded with real-time sensing. It can support autonomous navigation, robotic inspection, and environmental monitoring in flood-prone coastal areas. Such an approach enables more adaptive, cost-effective, and scalable solutions for infrastructure monitoring and disaster response.
