Ontological framework for high-level task replanning for autonomous robotic systems

Several frameworks for robot control platforms have been developed in recent years. However, strategies that incorporate automatic replanning have to be explored, which is a requirement for Autonomous Robotic Systems (ARS) to be widely adopted. Ontologies can play an essential role by providing a structured representation of knowledge. This paper proposes a new framework capable of replanning high-level tasks in failure situations for ARSs. The framework utilizes an ontology-based reasoning engine to overcome constraints and execute tasks through Behavior Trees (BTs). The proposed framework was implemented and validated in a real experimental environment using an Autonomous Mobile Robot (AMR) sharing a plan with a human operator. The proposed framework uses semantic reasoning in the planning system, offering a promising solution to improve the adaptability and efficiency of ARSs. •Proposing a framework that leverages semantic reasoning to optimize task planning.•Optimized execution based on Behavior Trees and an ontology-based reasoning engine.•The framework has the capability of replanning high-level tasks in failure situations.•The proposed framework was implemented and validated in practice.