Confined Space Underwater Positioning Using Collaborative Robots

TL;DR

The paper addresses the challenge of underwater localization in confined environments where GPS and fixed infrastructure are unavailable. It introduces Collaborative Aquatic Positioning (CAP), a leader-follower system in which a mobile surface robot assists localization of a submerged robot through sensor fusion and fiducial marker tracking, enabling infrastructure-free operation. Two variants are proposed: CAP-CPnP uses camera-based PnP with fiducial corners, and CAP-CD employs a depth-based Plücker-line formulation that relies on a single camera pixel, both yielding real-time pose estimates to guide autonomous missions. In experiments in a $4.8\times3.6\times2.0\ \mathrm{m}$ tank, CAP-CD achieves a mean error of $70.2\ \mathrm{mm}$ and CAP-CPnP $100.3\ \mathrm{mm}$ over 120 s, demonstrating robust, infrastructure-free underwater localization suitable for industrially constrained settings such as nuclear pond inspection. The results indicate CAP’s potential to enable repeatable autonomous underwater missions without fixed infrastructure and with applicability to a range of confined environments.

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.

Figures (24)

• Figure 1: The Saab Seaeye Tiger has spent five years working in nuclear storage ponds sella
• Figure 2: Hardware and system architecture. (a): MallARD's salient components and their layout. (b): MallARD's platform dimensions (c): Customised BlueROV2 equipped with a depth/pressure sensor
• Figure 3: Schematic block diagram showing MallARD's electrical architecture
• Figure 4: Schematic diagram of the principles of each part of the CAP system.(a) Overview of the proposed CAP system. (b) The coordinate frames abstracted from the CAP system, along with the Plücker line employed in the CAP-CD formulation. (c) is a simulated environment and (d) shows the laser scan, resulting body frame pose estimate and a map (the corner of a tank). (e) and (f): Tilting of the surface on the water surface due to the waves. (g): A flow diagram illustrating use of Extended Kalman Filter used to determine roll and pitch from IMU measurements. (h): Optical tracking system utilising fiducial markers and camera.
• Figure 5: Experimental field and setup. (a): Overview of the experimental tank. (b): Qualisys system setup and effective volume (c): MallARD and BlueROV2 deployed in the experimental pond. BlueROV2 is mounted with pearl marker for Qualisys system tracking.