Active perception based on deep reinforcement learning for autonomous robotic damage inspection

In this study, an artificial intelligence framework is developed to facilitate the use of robotics for autonomous damage inspection. While considerable progress has been achieved by utilizing state-of-the-art computer vision approaches for damage detection, these approaches are still far away from being used for autonomous robotic inspection systems due to the uncertainties in data collection and data interpretation. To address this gap, this study proposes a framework that will enable robots to select the best course of action for active damage perception and reduction of uncertainties. By doing so, the required information is collected efficiently for a better understanding of damage severity which leads to reliable decision-making. More specifically, the active damage perception task is formulated as a Partially Observable Markov Decision Process, and a deep reinforcement learning-based active perception agent is proposed to learn the near-optimal policy for this task. The proposed framework is evaluated for the autonomous assessment of cracks on metallic surfaces of an underwater nuclear reactor. Active perception exhibits a notable enhancement in the crack Intersection over Union (IoU) performance, yielding an increase of up to 69% when compared to its raster scanning counterpart given a similar inspection time. Additionally, the proposed method can perform a rapid inspection that reduces the overall inspection time by more than two times while achieving a 15% higher crack IoU than that of the dense raster scanning approach.