Autonomous UAV Navigation Using Reinforcement Learning

Abstract

Unmanned aerial vehicles (UAV) are commonly used for missions in unknown environments, where an exact mathematical model of the environment may not be available. This paper provides a framework for using reinforcement learning to allow the UAV to navigate successfully in such environments. We conducted our simulation and real implementation to show how the UAVs can successfully learn to navigate through an unknown environment. Technical aspects regarding to applying reinforcement learning algorithm to a UAV system and UAV flight control were also addressed. This will enable continuing research using a UAV with learning capabilities in more important applications, such as wildfire monitoring, or search and rescue missions.

References in corpus (1)

• A Distributed Control Framework for a Team of Unmanned Aerial Vehicles for Dynamic Wildfire Tracking

Cited by in corpus (5)

• Cooperative and Distributed Reinforcement Learning of Drones for Field Coverage
• Reinforcement Learning for UAV Autonomous Navigation, Mapping and Target Detection
• Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
• Proportional integral derivative controller assisted reinforcement learning for path following by autonomous underwater vehicles
• Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization