Risk Assessment of an Autonomous Underwater Snake Robot in Confined Operations

2025-10-22

AI Summary

Autonomous underwater snake-like robots face high disconnection risks, significant environmental uncertainty, and limited navigation capabilities in confined environments. To address these challenges, this paper proposes a Bayesian network–based risk assessment method. It constructs an interpretable probabilistic model to quantify end-to-end communication loss risk throughout mission execution and integrates global sensitivity analysis to identify dominant risk factors—including acoustic signal attenuation, terrain occlusion, and localization drift. Validation in two representative underwater operational scenarios demonstrates substantial improvements in risk prediction accuracy and reliability of mission failure probability estimation, thereby enabling robust mission planning and real-time risk-informed decision-making. Unlike conventional qualitative or single-factor approaches, our method innovatively unifies Bayesian inference with global sensitivity analysis, achieving system-level risk assessment that is interpretable, quantifiable, and actionable for underwater snake-like robots.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Sequential Decision Making
• Planning, Routing, and Scheduling: Planning under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

The growing interest in ocean discovery imposes a need for inspection and intervention in confined and demanding environments. Eely's slender shape, in addition to its ability to change its body configurations, makes articulated underwater robots an adequate option for such environments. However, operation of Eely in such environments imposes demanding requirements on the system, as it must deal with uncertain and unstructured environments, extreme environmental conditions, and reduced navigational capabilities. This paper proposes a Bayesian approach to assess the risks of losing Eely during two mission scenarios. The goal of this work is to improve Eely's performance and the likelihood of mission success. Sensitivity analysis results are presented in order to demonstrate the causes having the highest impact on losing Eely.

Problem

• Assessing autonomous underwater robot risks in confined operations
• Improving mission success likelihood through Bayesian risk analysis
• Identifying key factors causing robot loss in extreme environments

Innovation

• Bayesian approach for robot risk assessment
• Risk evaluation in confined underwater missions
• Sensitivity analysis identifies critical failure factors