A Vision-based Control Framework for Real-time Autonomous UUV Operations

Erik Tjærand Frøland; Marco Job; Md Ether Deowan; Eleni Kelasidi

cs.ROAug 5, 2026

A Vision-based Control Framework for Real-time Autonomous UUV Operations

Authors: Erik Tjærand Frøland, Marco Job, Md Ether Deowan, Eleni Kelasidi

Organizations: Dept. of Mechanical and Industrial Engineering, NTNU, Norway · Oceaneering AS, Norway

Abstract

This paper presents a fully integrated vision-based framework for real-time and robust localization, autonomous navigation, and mapping for unmanned underwater vehicles (UUVs) in dynamic, visually challenging environments. The proposed pipeline enables both net-relative and global localization while generating continuous 3D maps of the surroundings in real-time. The framework was validated on synthetic datasets with ground truth and tested onboard an UUV during autonomous net-relative navigation experiments. Results demonstrate real-time performance and enhanced robustness, supporting vision-driven autonomous navigation and enabling the field deployment of marine robots for critical inspection and mapping tasks in complex underwater environments.

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Aug 5, 2026 · cs.ROJ/K move · Enter open · S save

Unified Planning-Learning Framework for Robust UUV Navigation Under Partial Observability

Md Ether Deowan, Eleni Kelasidi

Department of Mechanical and Industrial Engineering, Faculty of Engineering, NTNU, Trondheim, Norway.

This paper presents an observation-only autonomy framework for Unmanned Underwater Vehicles (UUVs) navigation in dynamic underwater environments that integrates persistent occupancy mapping, global clearance-aware planning, and risk-aware local control. The proposed pipeline constructs occupancy maps solely from onboard sonar and depth image observations, adapts a clearance-constrained global planner (GP) to provide long-horizon structure, and integrates a reinforcement learning (RL) policy to handle short-range tracking and reactive avoidance. To further support decision-making under partial observability, the system learns a compact latent state representation from onboard sensor data, encoding environmental structure, obstacle dynamics, and uncertainty. Behavior tree (BT) distillation with staged supervision is introduced to improve safety and training stability, while an uncertainty-calibrated distillation mechanism reweights teacher guidance using online latent-model uncertainty, emphasizing uncertain regimes during learning, with time-to-collision (TTC) and clearance cues remaining explicit in planning and local policy features. To demonstrate the efficacy of the framework, a reproducible multi-seed evaluation protocol is established in high-fidelity GPU-accelerated simulation using NVIDIA Isaac Sim, and performance is benchmarked against BT-only and standard RL baselines. The results obtained demonstrate improved robustness and safety under dynamic conditions, thus providing a general pipeline with a unified hybrid planning learning architecture and a reproducible methodology for robust UUV autonomy under partial observability.

Autonomous Underwater Vehicles Autonomous Drone Navigation Partial Observability Navigation

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Abstract

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