Efficient Feature Mapping Using a Collaborative Team of AUVs

2024-10-14

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

To address the challenge of collaborative localization of underwater features (e.g., isobaths) under weak acoustic communication, limited onboard computation, and high environmental uncertainty, this paper proposes an uncertainty-aware distributed level-set estimation framework. Methodologically, it formulates a rigorously Bayesian uncertainty-driven objective function, integrating decentralized cooperative control, distributed path planning, and lightweight uncertainty modeling to enable real-time collaborative mapping by AUV swarms under communication latency, intermittency, and computational constraints. Theoretical analysis establishes convergence guarantees and performance bounds. Experiments on physical AUV fleets demonstrate robustness and efficacy: the approach significantly improves isobath identification accuracy and cooperative efficiency. This work delivers a verifiable, deployable paradigm for collaborative perception in resource-constrained underwater multi-agent systems.

Technology Category

Intelligent Robots: Localization, Mapping, and NavigationConstraint Satisfaction and Optimization: Distributed CSP/OptimizationMultiagent Systems: Multiagent Systems under Uncertainty

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deploymentsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsResponsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources.

Problem

• Autonomous Underwater Vehicles
• Bathymetric Features Detection
• Underwater Communication Challenges

Innovation

• Underwater Topography Estimation
• Autonomous Underwater Vehicle (AUV)
• Real-world Implementation