Risk-Averse Planning and Plan Assessment for Marine Robots

TL;DR

This paper presents a method for marine robots that generates diverse high-level plans and assesses them through low-level simulation to select the most reliable and optimal plan, improving autonomous underwater vehicle operation.

Contribution

It introduces a novel approach combining high-level planning diversity with low-level simulation-based assessment for reliable AUV task execution.

Findings

Demonstrates the method's feasibility in realistic underwater simulations

Shows improved plan reliability and performance in diverse scenarios

Validates the approach's effectiveness in risk estimation and plan selection

Abstract

Autonomous Underwater Vehicles (AUVs) need to operate for days without human intervention and thus must be able to do efficient and reliable task planning. Unfortunately, efficient task planning requires deliberately abstract domain models (for scalability reasons), which in practice leads to plans that might be unreliable or under performing in practice. An optimal abstract plan may turn out suboptimal or unreliable during physical execution. To overcome this, we introduce a method that first generates a selection of diverse high-level plans and then assesses them in a low-level simulation to select the optimal and most reliable candidate. We evaluate the method using a realistic underwater robot simulation, estimating the risk metrics for different scenarios, demonstrating feasibility and effectiveness of the approach.