Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing

TL;DR

This paper introduces a lightweight, camera-only autonomous UAV landing system on moving platforms, utilizing neural networks trained with reinforcement learning, achieving precise landings with minimal deviation in real-world tests.

Contribution

The paper presents a novel lightweight UAV landing system relying solely on camera input, trained with reinforcement learning, suitable for low-power platforms and independent of external sensors.

Findings

Average landing deviation of 15cm from target center

Successful real-world testing with 40 landing attempts

System operates without external communication or sensors

Abstract

While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several missions, their inability of reliable and consistent autonomous landing poses a major setback for deploying such systems truly autonomously. In this paper we present an autonomous UAV landing system for landing on a moving platform. In contrast to existing attempts, the proposed system relies only on the camera sensor, and has been designed as lightweight as possible. The proposed system can be deployed on a low power platform as part of the drone payload, whilst being indifferent to any external communication or any other sensors. The system relies on a Neural Network (NN) based controller, for which a target and environment agnostic simulator was created, used in training and testing of the proposed system, via Reinforcement Learning (RL) and Proximal Policy optimization (PPO) to optimally control and steer the drone towards landing on the target. Through real-world testing, the system was evaluated with an average deviation of 15cm from the center of the target, for 40 landing attempts.