Hybrid Artificial Intelligence Strategies for Drone Navigation

TL;DR

This paper presents hybrid AI strategies combining deep reinforcement learning and rule-based systems to improve drone navigation, achieving high task completion rates and reduced search times in different scenarios.

Contribution

It introduces a hybrid AI framework integrating deep learning and expert rules for drone navigation, with an evaluation methodology across multiple scenarios.

Findings

90% task completion in target reaching scenario

20% reduction in search time for locating targets

Enhanced collision avoidance through rule-based engine

Abstract

Objective: This paper describes the development of hybrid artificial intelligence strategies for drone navigation. Methods: The navigation module combines a deep learning model with a rule-based engine depending on the agent state. The deep learning model has been trained using reinforcement learning. The rule-based engine uses expert knowledge to deal with specific situations. The navigation module incorporates several strategies to explain the drone decision based on its observation space, and different mechanisms for including human decisions in the navigation process. Finally, this paper proposes an evaluation methodology based on defining several scenarios and analyzing the performance of the different strategies according to metrics adapted to each scenario. Results: Two main navigation problems have been studied. For the first scenario (reaching known targets), it has been possible to obtain a 90% task completion rate, reducing significantly the number of collisions thanks to the rule-based engine. For the second scenario, it has been possible to reduce 20% of the time required to locate all the targets using the reinforcement learning model. Conclusions: Reinforcement learning is a very good strategy to learn policies for drone navigation, but in critical situations, it is necessary to complement it with a rule-based module to increase task success rate.