Robot localization has been researched by scientists from the very beginning of robotics. Knowing its position and orientation is an essential task for an autonomous system in many circumstances.
Very often, it is a prerequisite to perform more complex tasks.
The marine domain is no exception. In most - if not all - real life scenarios, the robot location is an essential information.
Marine robots are increasingly used to perform a great variety of tasks, ranging from oil\&gas applications to defense, from marine biology to underwater archaeology. In all of these scenarios, the robot location is fundamental.
There are several challenges to perform underwater localization. The lack of GPS signal is the most evident one. In order to overcome this, various acoustic-based solution can be employed, like for example Long BaseLine (LBL) acoustic positioning system. This requires to deploy acoustic transponders as aid for the vehicle, which can compute its location with a triangulation from the data received by the transponders. The drawback of this technique is however the need to actively deploy external transponders, which cause additional cost, time and logistic challenges. Additionally, the GPS location at the drop-in point might not be the same than the GPS on the seabed, especially in deep sea, with strong currents.
Many offshore infrastructures are located in environments which fall into this category.
For this reason, several techniques have been used in the past years to allow an underwater vehicle to determine its location based entirely on the on-board sensor suite.
Geometric approaches were developed based on distance sensors like sonar, in parallel with geometric approaches developed in land robotics, based on laser scanners.
In recent years there has been a substantial interest from the research community to explore semantic aspects of knowledge representation, and its influence in the vehicle's tasks. Generally speaking, robots still lack the high-level abstraction capability typical of humans. This is a complex problem, as it aims to shift the paradigm from sensor processing into a more organized, long-term knowledge structure in robotics systems, with possibility of augment, reasoning and learn.
This project represents a step in this direction, with the of use of semantic information in processes traditionally covered only by geometric approaches.
To the best of the researcher's knowledge, no significant semantic approach for localization in the marine robotics domain is currently available in the related literature.
Several application domains can benefit from the research carried out in this project. Inspection of subsea structures such as oil platforms using autonomous underwater vehicles (AUVs) is a prominent example. In this domain, the original plans for structures can generally be made available to the AUV in advance of a mission. However, frequently these structures have changed or moved when the AUV actually reaches them. If the robot has a pre-programmed survey path, unexpected obstacles can cause it to abort the mission. Reactive obstacle avoidance algorithms can improve the robustness but a proper knowledge representation framework, able to incorporate newly discovered information, is desirable when the aim is for the robot to be ``intelligently" autonomous, situation-aware and able to use dynamic world knowledge. Moreover, fault awareness - let alone fault management strategies - has been explored in research but very rarely integrated in a more complex system.
Other important application areas in the marine domain which would benefit from this research are defence, archaeology and marine biology. In all these domains, a cognitive vehicle would export capabilities and functionalities which are currently out of the reach of conventional commercial AUVs, unlocking greater opportunities in the field, reducing risks and cost.
On a societal dimension, this work is very well aligned with the United Nations Sustainable Development Goals (SDG), in particular with SDG14 - Oceans, and was even featured at the UN Oceans Conference.
