# A Methodology for Designing Knowledge-Driven Missions for Robots

arXiv: https://arxiv.org/abs/2601.20797 · HTML: https://mlsanity.com/paper/2601.20797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 28 Jan 2026.

## Builds on (most cited first, 1 of 1)

- [Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems](https://mlsanity.com/paper/2303.18237/) (2023)

## Abstract (arXiv)

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.