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Risk Assessment of an Autonomous Underwater Snake Robot in Confined Operations

Abdelrahman Sayed Sayed

TL;DR

The paper tackles the risk of losing an autonomous underwater snake robot (Eely) in confined operations by developing a Bayesian risk model that integrates hazard identification, failure probabilities, and conditional dependencies within a Dynamic Bayesian Network (DBN). It demonstrates dynamic risk evolution over time and identifies the most influential factors, revealing higher risk in confined environments due to environmental complexity and control challenges. The work further shows how the BN can be extended to a decision network (DN) for online, autonomous risk-informed decision making, outlining decision nodes and online reasoning mechanisms. The findings offer a practical framework to enhance mission reliability for Eely and other modular AUVs, with future directions including more scenarios and behavior-based control integrations.

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.

Risk Assessment of an Autonomous Underwater Snake Robot in Confined Operations

TL;DR

The paper tackles the risk of losing an autonomous underwater snake robot (Eely) in confined operations by developing a Bayesian risk model that integrates hazard identification, failure probabilities, and conditional dependencies within a Dynamic Bayesian Network (DBN). It demonstrates dynamic risk evolution over time and identifies the most influential factors, revealing higher risk in confined environments due to environmental complexity and control challenges. The work further shows how the BN can be extended to a decision network (DN) for online, autonomous risk-informed decision making, outlining decision nodes and online reasoning mechanisms. The findings offer a practical framework to enhance mission reliability for Eely and other modular AUVs, with future directions including more scenarios and behavior-based control integrations.

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.
Paper Structure (18 sections, 1 equation, 6 figures, 8 tables)

This paper contains 18 sections, 1 equation, 6 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Risk Modeling Using BNs
  3. Definition of Risk
  4. Bayesian Networks
  5. Construction of Bayesian Risk model
  6. HAZID
  7. Failure Probabilities
  8. Conditional Probability Tables
  9. Launching the BN Model
  10. Dynamic Simulation
  11. Sensitivity Analysis
  12. Pros and Cons of the BN Model
  13. Risk-Based Decision Making
  14. Decision Nodes
  15. Online Reasoning
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Triple joint snake robot from Eelume
  • Figure 2: DBN for Losing Eely during Confined Environments Operations
  • Figure 3: Dynamic Simulation Results for Seabed Mapping Operations
  • Figure 4: Dynamic Simulation Results for Confined Environments Operations
  • Figure 5: Sensitivity tornado diagram for losing Eely during Seabed Mapping Operations
  • ...and 1 more figures