Autonomous robots do not fully understand their open environments, their complex missions, their intricate realisations, and the unexpected events that affect their performance. An improvement in the capability to understand autonomous robots is needed. This project tries to provide a solution to this including a theory of understanding, a theory of awareness, reusable software assets to apply these theories in real robots, augment flexibility of manufacturing robots, and augment human alignment of social robots.
In summary, the project will develop a cognitive architecture for autonomous robots based on a formal concept of understanding, supporting value-oriented situation understanding and self-awareness to improve robot flexibility, resilience and explicability.
The project includes three use cases – these are:
- Manufacturing system testbed: In the manufacturing testbed we will address two scenarios: mobile manipulators and manufacturing of large parts.
- Inspection by drones testbed: A multiagent scenario with several drones to accomplish an industrial inspection mission will be used to test the resilience of the whole system.
- Social interaction testbed: Demonstration of an enhanced performance in tasks for social robots in domestic environments. PAL Robotics will take part here with our social robot ARI.