# Hybrid Artificial Intelligence Strategies for Drone Navigation

**Authors:** Rubén San-Segundo, Lucía Angulo, Manuel Gil-Martín, David Carramiñana, Ana M. Bernardos  
**arXiv:** [2501.04472](https://arxiv.org/abs/2501.04472)  
**Date:** 2025-01-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2501.04472v1/

## Key points

- Objective: Enhance drone navigation capabilities
- Methodology: Combining deep learning models with rule-based engines tailored to the agent's state
- Navigation module: Integrates multiple strategies for decision-making process based on observation space
- Incorporating human decisions: Ensures a more adaptive and responsive system
- Evaluation methodology: Defining scenarios and analyzing performance of different strategies based on customized metrics
- Scenario 1 (reaching known targets): Achieves 90% task completion rate, reduces collisions with rule-based engine
- Scenario 2 (locating all targets efficiently): Reduces time required by 20% using reinforcement learning model
- Conclusion: Reinforcement learning is robust for drone navigation policies; hybrid AI approach essential for precision and reliability in critical situations

## Summary

The objective of this research is to enhance <kw1> drone navigation capabilities</kw1> by combining <kw2> deep learning models</kw2> with <kw3> rule-based engines</kw3>, tailored to the agent's state. The deep learning model is trained using reinforcement learning techniques, while the rule-based engine leverages expert knowledge to address specific scenarios. The navigation module integrates multiple strategies to elucidate the decision-making process of drones based on their observation space. Additionally, various mechanisms are implemented to incorporate human decisions into the navigation process, ensuring a more adaptive and responsive system. The authors propose an evaluation methodology that involves defining several scenarios and analyzing the performance of different strategies based on metrics customized for each scenario. Through their research, two primary navigation problems are investigated. In the first scenario focused on reaching known targets, the hybrid AI approach achieves an impressive 90% task completion rate while significantly reducing collisions due to the effectiveness of the rule-based engine. In contrast, in the second scenario aimed at locating all targets efficiently, there is a notable 20% reduction in time required by utilizing the reinforcement learning model. The conclusions drawn from this study emphasize that reinforcement learning serves as a robust strategy for learning policies governing drone navigation. However, in critical situations where precision and reliability are paramount, it is essential to complement reinforcement learning with a rule-based module to enhance task success rates. This hybrid AI approach offers a promising solution for optimizing drone navigation performance across diverse scenarios and environments.