Autonomous systems are artificial systems capable of performing a variety of tasks with a high degree of autonomy. Cognitive Dynamic Systems (CDSs) are one of the possible approaches that allow us to face the challenges of autonomous systems design. CDSs aim to develop rules of behavior over time through learning from continuous experiential interactions with the surroundings. By exploiting these rules, CDSs can deal with environmental dynamics and uncertainties, and have therefore leveraged the automation of tasks with complex perception-action cycles including surveillance, inspection, predictive maintenance, cognitive radio, traffic control, and robot-mediated industrial and domestic applications. This paper presents an overview of this Special Session, featuring works in the fields of inspection and predictive maintenance of infrastructures, that address challenges associated with autonomy, including perception, decision-making, and adaptation.