Robust Continuous System Integration for Critical Deep-Sea Robot Operations Using Knowledge-Enabled Simulation in the Loop

TL;DR

This paper introduces a simulation-in-the-loop approach for deep-sea robot systems, enabling thorough testing and benchmarking of components like perception and localization under realistic conditions to enhance safety and reliability.

Contribution

It presents a high-fidelity, knowledge-enabled simulation platform that reduces the simulation-reality gap for critical deep-sea robot operations.

Findings

Improved perception and localization performance under variable conditions

Effective benchmarking of system components in simulated and real environments

Enhanced safety and reliability in deep-sea robot operations

Abstract

Deep-sea robot operations demand a high level of safety, efficiency and reliability. As a consequence, measures within the development stage have to be implemented to extensively evaluate and benchmark system components ranging from data acquisition, perception and localization to control. We present an approach based on high-fidelity simulation that embeds spatial and environmental conditions from recorded real-world data. This simulation in the loop (SIL) methodology allows for mitigating the discrepancy between simulation and real-world conditions, e.g. regarding sensor noise. As a result, this work provides a platform to thoroughly investigate and benchmark behaviors of system components concurrently under real and simulated conditions. The conducted evaluation shows the benefit of the proposed work in tasks related to perception and self-localization under changing spatial and environmental conditions.