Robust Continuous System Integration for Critical Deep-Sea Robot Operations Using Knowledge-Enabled Simulation in the Loop
This work addresses safety-critical system integration for deep-sea robotics, though it appears incremental as it builds on existing simulation methods.
The paper tackles the challenge of ensuring safety and reliability in deep-sea robot operations by developing a simulation-in-the-loop platform that integrates real-world data to reduce discrepancies with simulated conditions, showing benefits in perception and self-localization tasks under varying environmental conditions.
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.