ARCog: an aerial robotics cognitive architecture

Abstract

E cient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower level data interfaces, e.g., sensors and actuators, up to higher level data, such as reasoning and decision. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision making, several key issues remain open. A problem is a selection, coordination, and decision making related to the several specialized tasks required for fulfilling mission objectives. This research work addresses decision making for a cognitive architecture used to Unmanned Aerial Vehicle (UAV) missions, called ARCog, an acronym for Aerial Robotics Cognitive Architecture. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of state-of-the-art technology in the field. The system is designed to provide high-level decision making. Besides, the architecture enables real-time decision-making with intelligent social behavior among the agents. The main algorithms used to test the architecture in an outdoor environment are detailed as well as their results. The experiments confirmed the success of the proposed cognitive architecture. The partial results have shown technical feasibility and e ectiveness of the ARCog in di erent scenarios, such as Search and Rescue, Inspection and Surveillance missions.