Software-in-the-loop and hardware-in-the-loop paradigms and their use for research and development of autonomous underwater vehicles

The development and field testing of autonomous robots are complex tasks for a number of reasons. The involved logistics are often quite complicated, and the risk of damaging the equipment under evaluation is very high; this could, on occasion, represent a considerable time and monetary overload. Such difficulties are greatly magnified in the development and testing of the underwater platforms designed to operate in hazardous environments, such as underground flooded mines, the main focus of the UNEXUP project, funded under EIT Raw Materials, and its predecessor, the horizon 2020 UNEXMIN project. These unique field working conditions involve a high risk of permanent loss of the platform in case of failure or error and a high risk of casualties in rescue attempts entailing direct human actions. 

In contrast, software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing paradigms are powerful tools for preliminary system validation and algorithm benchmarking where troubleshooting is simplified through the use of a controlled environment. They provide a time- and cost-effective solution for testing, thus having a crucial role in the development of advanced autonomous robotic platforms. These two paradigms are possible thanks to the evolution of simulation models, which have achieved astonishing completeness and can realistically simulate not only system dynamics but also all the operational components of a robotic platform, such as their sensor readings, even corrupted with realistic noise. SIL experiments involve software uniquely, and are thus performed without the real robot platform but its simulation model, making them an ideal tool for testing specific algorithms or software modules. On the other hand, HIL experiments involve the real robot’s hardware, either the complete robotic platform or only parts of it, thus providing a more realistic testing environment. 

In this work, we illustrate the vast range of aspects during the development of a robotic platform that can dramatically benefit from the use of the combination of SIL and HIL testing, especially in those technical applications where field trials show severe operational difficulties. We show how these testing paradigms provide a solid basis for evaluating parts or modules of a system, which, besides being convenient for the development of advanced robotic platforms by multiple teams, also bridges the gap between algorithm design and the testing of a complete platform. Then, we show how SIL and HIL can substitute parts of real environments, and enrich aspects of real data to focus on specific testing situations not easily controllable, or even dangerous, in field tests. We demonstrate how we can create augmented environments by introducing virtual obstacles and even complete virtual maps into the available experimental setup, such as a real submersible inside a water tank, thus testing complex maneuvers and simulating possible real scenarios with minimum risk of compromising the equipment. We show how these environments are beneficial not only when developing autonomous submersibles but also when training human operators of non-autonomous ones in a so-called human-in-the-loop configuration.