The main objective of AERIAL-CORE is the development of core technology modules and an integrated aerial cognitive robotic system that will have unprecedented capabilities on the operational range and safety in the interaction with people, or Aerial Co-Workers (ACW), for applications such as the inspection and maintenance of large infrastructures. The project will integrate aerial robots with different characteristics to meet the requirements of: (1) Long range (several kilometres) and local very accurate (subcentimetre) inspection of the infrastructure capability; (2) Maintenance activities based on aerial manipulation involving force interactions; and (3) Aerial co-working safely and efficiently helping human workers in inspection and maintenance. AERIAL-CORE technology modules will be based on Cognitive Mechatronics and apply cognitive capabilities to aerial morphing in order to combine long range endurance and hovering for local observations, manipulation involving force interactions, and co-working with humans. The project will develop: (1) Cognitive functionalities for aerial robots including perception based on novel sensors, such as event cameras, and data fusion techniques, learning, reactivity, fast on-line planning, and teaming; (2) Aerial platforms with morphing capabilities, to save energy in long range flights and perform a very accurate inspection; (3) Cognitive aerial manipulation capabilities, including manipulation while flying, while holding with one limb, and while hanging or perching to improve accuracy and develop greater forces; (4) Cognitive safe aerial robotic co-workers capable of physical interaction with people; and (5) Integrated aerial robotic system for the inspection and maintenance of large infrastructures. The system will be demonstrated in electrical power system inspection and maintenance, which is an application with a huge economic impact that also has implications in the safety of workers and in wildlife conservation.

In this proposal, we address the matter of transparency and explainability of AI using approaches inspired by control theory. Notably, we consider a comprehensive and flexible certification of properties of AI pipelines, certain closed-loops and more complicated interconnections. At one extreme, one could consider risk averse a priori guarantees via hard constraints on certain bias measures in the training process. At the other extreme, one could consider nuanced communication of the exact tradeoffs involved in AI pipeline choices and their effect on industrial and bias outcomes, post hoc. Both extremes offer little in terms of optimizing the pipeline and inflexibility in explaining the pipeline’s fairness-related qualities. Seeking the middle-ground, we suggest a priori certification of fairness-related qualities in AI pipelines via modular compositions of pre-processing, training, inference, and post-processing steps with certain properties. Furthermore, we present an extensive programme in explainability of fairness-related qualities. We seek to inform both the developer and the user thoroughly in regards to the possible algorithmic choices and their expected effects. Overall, this will effectively support the development of AI pipelines with guaranteed levels of performance, explained clearly. Three use cases (in Human Resources automation, Financial Technology, and Advertising) will be used to assess the effectiveness of our approaches.