LiDAR-based drone navigation with reinforcement learning

TL;DR

This paper presents a reinforcement learning-based autonomous drone control system using LiDAR data, trained in simulation and successfully tested in real forest navigation scenarios.

Contribution

It introduces a novel RL-based drone navigation approach utilizing LiDAR data, with a custom simulator and real-world implementation on Nvidia Jetson Nano.

Findings

Drone successfully navigated through forest avoiding trees

Reinforcement learning with PPO effectively trained the control system

System demonstrated reliable real-world performance

Abstract

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a~key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest.