Organizations: Embodied Robotics and Automation Lab, University of South Florida, Tampa, FL 33620, USA · Computer Science Department, George Mason University, Fairfax, VA 22032, USA · Department of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA · Naval Architecture and Ocean Engineering at the Seoul National University, Seoul 08826, South Korea
Autonomous navigation in underwater cave environments is essential for search-and-rescue operations, scientific exploration, and emergency egress. Traditional navigation systems commonly depend on dense visual features for localization and mapping. In underwater caves, however, visual degradation can undermine feature-based localization, sonar-based mapping may yield overly conservative obstacle representations, and communication constraints preclude real-time human guidance. To address these limitations, we propose an autonomous underwater cave navigation framework that leverages a vision-language model (VLM) with Chain-of-Thought (CoT) reasoning to infer navigable directions from environmental cues, including light intensity gradients, passage morphology, and geometric complexity, captured through multimodal inputs comprising RGB imagery, depth maps, and sonar-based vertical-clearance measurements, thereby supporting safe 3D navigation through confined cave passages. High-fidelity simulations across multiple cave topologies demonstrate that the proposed framework completes all evaluated end-to-end traversals without collisions while maintaining safe clearance from cave boundaries.
Figures & tables
Fig. 1 : Real-world BlueROV operation in a representative underwater cave environment at Blue Grotto, Florida.
Fig. 2 : Overview of the proposed framework. Multimodal observations are rendered at each step, interpreted by a VLM under Chain-of-Thought prompting, and converted into a parameterized motion command that is applied directly to the vehicle pose.
Fig. 3 : Navigation trajectories in 5 simulated cave scenarios (S1–S5): first row is top-down views and second row is side views; orange: AUV trajectories.
Fig. 4 : VLM inference on a real image captured by a BlueROV2 at Blue Grotto
Oyster reefs are critical ecosystem species that sustain biodiversity, filter water, and protect coastlines, yet they continue to decline globally. Restoring these ecosystems requires regular underwater monitoring to assess reef health, a task that remains costly, hazardous, and limited when performed by human divers. Autonomous underwater vehicles (AUVs) offer a promising alternative, but existing AUVs rely on geometry-based navigation that cannot interpret scene semantics. Recent vision-language models (VLMs) enable semantic reasoning for intelligent exploration, but existing VLM-driven systems adopt an end-to-end paradigm, introducing three key limitations. First, these systems require the VLM to generate every navigation decision, forcing frequent waits for inference. Second, VLMs cannot model robot dynamics, causing collisions in cluttered environments. Third, limited self-correction allows small deviations to accumulate into large path errors. To address these limitations, we propose CORAL, a framework that decouples high-level semantic reasoning from low-level reactive control. The VLM provides high-level exploration guidance by selecting waypoints, while a dynamics-based planner handles low-level collision-free execution. A geometric verification module validates waypoints and triggers replanning when needed. Compared with the previous state-of-the-art, CORAL improves coverage by 14.28% percentage points, or 17.85% relatively, reduces collisions by 100%, and requires 57% fewer VLM calls.
Zhenqi Wu, Yuanjie Lu, Xuesu Xiao +1
University of South Florida · George Mason University
Safe underwater exploration requires a robot to understand where surrounding structures are located and which regions are available for motion. Existing vision-based underwater exploration systems commonly obtain this information indirectly by estimating monocular depth, unprojecting the geometry into 3D space, and accumulating it into a 2D bird's-eye-view occupancy map. This reliance on intermediate depth estimation is particularly problematic underwater, where scattering and wavelength-dependent attenuation degrade visual cues and limit the reliability of monocular depth estimates. We introduce AquaBEV-Nav, an underwater exploration framework that bypasses explicit monocular depth estimation through direct bird's-eye-view occupancy prediction. Built upon the CORAL hierarchical exploration framework, AquaBEV-Nav replaces its depth-based perception front end with AquaBEV. Given a single RGB frame, AquaBEV maps visual features into a learned polar representation, performs causal reasoning along the range dimension, and reconstructs local Cartesian occupancy without relying on intermediate depth prediction. The resulting occupancy map is accumulated into CORAL's persistent spatial memory, providing spatial context for VLM-based high-level planning and collision constraints for dynamics-aware local trajectory generation. Across ten simulated reef environments and six occupancy backbones evaluated under a single protocol, AquaBEV-Nav reaches 37.48 structure IoU and 53.2 target IoU, 88.95% closed-loop coverage with zero collisions.
Trung Tien Dong, Zhenqi Wu, Sahasra Kondapalli +3
1ERA Lab, University of South Florida, Florida, USA · University of Maryland- College Park, College Park, Maryland · 2YES Lab, University of South Florida, Florida, USA
Autonomous navigation in 3D is a fundamental problem for autonomy. Despite major advancements in terrestrial and aerial settings due to improved range sensors including LiDAR, compact sensors with similar capabilities for underwater robots have only recently become available, in the form of 3D sonars. This paper introduces a novel underwater 3D navigation pipeline, called SHRUMS (Sensor Hallucination for Robust Underwater Motion planning with 3D Sonar). To the best of the authors' knowledge, SHRUMS is the first underwater autonomous navigation stack to integrate a 3D sonar and demonstrate safe operation in complex 3D environments without reliance on visual input, thereby remaining independent of visibility conditions. To accommodate the intricacies of the novel sensor data stream while achieving real-time locally optimal performance, SHRUMS introduces the concept of hallucinating sensor measurements from non-existent sensors with convenient arbitrary parameters, tailored to application specific requirements. The proposed concepts are validated in simulations informed with real 3D sonar sensor data, in-pool clean-water trials, and field deployments in real conditions with significantly degraded visibility conditions.
Susheel Vadakkekuruppath, Lauritz R. Fosso, Michail Chatzispyrou +4
Susheel Vadakkekuruppath and Jason M. O’Kane are with the Department of Computer Science and Engineering, Texas A&M University, USA · Herman B. Amundsen and Marios Xanthidis are with the Aquaculture Robotics and Automation, SINTEF Ocean