TIC-AUV · Towards Intelligent Cognitive AUVs

Towards Intelligent Cognitive AUVs

Recent advances in robotics have resulted in machines capable of performing many tasks autonomously. However, a significant obstacle to intelligent robots being used in real-world scenarios is their limited ability to cope with unexpected events and environments, to deal with faults, and to make smart decisions in response to changes in the world. The aim of this project is to develop a semantic knowledge-representation system that will make it easier for robots to handle such situations, and therefore achieve more persistent and long-term autonomy. An ontological representation for semantic data is used for several reasons: - it is easy to create and examine the concepts used by the system, and the attributes available for each concept, being a standard language with clear rules and widely adopted; - many tools exist to perform logical reasoning within the knowledge base; - an ontology represents a well-specified central data store that all the software components comprising the agent can make use of; - common ontologies are very useful for exchanging data between robotic systems created by different organisations. A proper knowledge representation system is pivotal for long term autonomy as it allows to store information, to reason on the acquired information and to augment the knowledge through reasoning. This further allows to have smarter planning and control systems which can query the knowledge base and be notified about important information relevant to the current plan. Overall, the concrete objectives of the project are: - develop a probabilistic world model system, which addresses both the external world, in order to aid localisation and to dynamically check consistency of current plan; and on the internal state of the vehicle, in order to aid internal fault management; - develop an active localisation system, based on both geometric and semantic information, which is general enough to accommodate for various and heterogeneous types of actions; - develop a fault management system, based on a probabilistic world model and – where applicable – on localisation information, in order to address a wide range of faults, from single component up to task level failures. The project has met the objectives, with encouraging results in semantic localisation and in fault management. Additionally, it has expanded its original scope to address localisation of Autonomous Surface Vessels in GPS-denied environments.

Data: CORDIS, © European Union