Autonomous Navigation System for a Delivery Drone

TL;DR

This paper introduces an autonomous drone navigation system for parcel delivery, combining GPS, IMU, barometer, marker detection, UWB, and Kalman filtering to ensure precise landing and smooth path following, validated through real experiments.

Contribution

It presents a novel integrated navigation and control system for autonomous drone delivery, combining multiple sensors and algorithms for improved accuracy and safety.

Findings

The system successfully navigates and delivers parcels in real-world tests.

Kalman filter improves landing precision significantly.

Vector field control ensures smooth drone movement.

Abstract

The use of delivery services is an increasing trend worldwide, further enhanced by the COVID pandemic. In this context, drone delivery systems are of great interest as they may allow for faster and cheaper deliveries. This paper presents a navigation system that makes feasible the delivery of parcels with autonomous drones. The system generates a path between a start and a final point and controls the drone to follow this path based on its localization obtained through GPS, 9DoF IMU, and barometer. In the landing phase, information of poses estimated by a marker (ArUco) detection technique using a camera, ultra-wideband (UWB) devices, and the drone's software estimation are merged by utilizing an Extended Kalman Filter algorithm to improve the landing precision. A vector field-based method controls the drone to follow the desired path smoothly, reducing vibrations or harsh movements that could harm the transported parcel. Real experiments validate the delivery strategy and allow to evaluate the performance of the adopted techniques. Preliminary results state the viability of our proposal for autonomous drone delivery.