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

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.

A Robust Strategy for UAV Autonomous Landing on a Moving Platform under Partial Observability

“…PPO was also used as an uncrewed traffic manager to lead autonomous uncrewed aircraft systems to their destinations while avoiding obstacles through continuous control. In [32], a fully autonomous UAV controller was developed to steer a drone toward a moving target using PPO. DDPG-RL Wind [33] DrQv2-RL Noisy Sensor [7] PID Missing Platform Detection [13] PID Wind [12] PID Noisy Sensor [34] MPC Wind [15] Visual Servo Wind…”

Vision-Based UAV Landing with Guaranteed Reliability in Adverse Environment

“…In addition, with the rapid evolution of machine learning techniques, learning-based landing algorithms are also implemented to conduct the auto-landing task. In [22], a Neural Network (NN) based controller combined with Reinforcement Learning (RL) and Proximal Policy Optimization (PPO) is proposed aiming to optimally control the drone approach to the landing target. A Deep Deterministic Policy Gradient (DDPG) algorithm based on the idea of Deep Q-Learning is proposed for correcting UAV's landing maneuver [23].…”