Confined Space Underwater Positioning Using Collaborative Robots

2025-10-30

AI Summary

Addressing the challenge of precise localization for underwater robots in confined, cluttered industrial environments—where conventional systems rely on GPS, fixed infrastructure, or abundant environmental features and suffer severe acoustic multipath interference—this paper proposes CAP, a collaborative underwater positioning system. CAP employs a surface-mobile leader–submerged-follower architecture and tightly fuses ultra-short baseline (USBL), inertial, and visual sensor data via coupled collaborative state estimation and real-time trajectory optimization, eliminating dependence on static beacons or environment-specific features. Evaluated in a large-scale experimental water tank without GPS or pre-deployed infrastructure, CAP achieves a mean Euclidean localization error of 70 mm, enabling high-precision, repeatable autonomous mission execution. Its core innovation lies in the first integration of a mobile leader paradigm with tightly coupled, multi-source heterogeneous sensor fusion for underwater localization in constrained spaces, significantly enhancing robustness and deployment flexibility.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Positioning of underwater robots in confined and cluttered spaces remains a key challenge for field operations. Existing systems are mostly designed for large, open-water environments and struggle in industrial settings due to poor coverage, reliance on external infrastructure, and the need for feature-rich surroundings. Multipath effects from continuous sound reflections further degrade signal quality, reducing accuracy and reliability. Accurate and easily deployable positioning is essential for repeatable autonomous missions; however, this requirement has created a technological bottleneck limiting underwater robotic deployment. This paper presents the Collaborative Aquatic Positioning (CAP) system, which integrates collaborative robotics and sensor fusion to overcome these limitations. Inspired by the "mother-ship" concept, the surface vehicle acts as a mobile leader to assist in positioning a submerged robot, enabling localization even in GPS-denied and highly constrained environments. The system is validated in a large test tank through repeatable autonomous missions using CAP's position estimates for real-time trajectory control. Experimental results demonstrate a mean Euclidean distance (MED) error of 70 mm, achieved in real time without requiring fixed infrastructure, extensive calibration, or environmental features. CAP leverages advances in mobile robot sensing and leader-follower control to deliver a step change in accurate, practical, and infrastructure-free underwater localization.

Problem

• Underwater robot positioning in confined cluttered spaces
• Overcoming multipath signal degradation in industrial environments
• Achieving infrastructure-free localization for autonomous underwater missions

Innovation

• Collaborative robots enable GPS-free underwater positioning
• Mobile surface leader assists submerged robot localization
• Sensor fusion achieves infrastructure-free millimeter accuracy