A Methodology for Designing Knowledge-Driven Missions for Robots

TL;DR

This paper introduces a comprehensive methodology for designing knowledge-driven robotic missions using knowledge graphs within ROS 2, demonstrated through a simulated search and rescue task to improve autonomous decision-making.

Contribution

It presents a novel step-by-step methodology for integrating knowledge graphs into robotic mission design, enhancing autonomy and decision-making capabilities.

Findings

Effective integration of knowledge graphs improves mission planning.

Demonstrated success in a simulated search and rescue scenario.

Enhanced decision-making and performance in autonomous drone missions.

Abstract

This paper presents a comprehensive methodology for implementing knowledge graphs in ROS 2 systems, aiming to enhance the efficiency and intelligence of autonomous robotic missions. The methodology encompasses several key steps: defining initial and target conditions, structuring tasks and subtasks, planning their sequence, representing task-related data in a knowledge graph, and designing the mission using a high-level language. Each step builds on the previous one to ensure a cohesive process from initial setup to final execution. A practical implementation within the Aerostack2 framework is demonstrated through a simulated search and rescue mission in a Gazebo environment, where drones autonomously locate a target. This implementation highlights the effectiveness of the methodology in improving decision-making and mission performance by leveraging knowledge graphs.