Autonomous Navigation System for a Delivery Drone
This addresses the problem of enabling faster and cheaper parcel deliveries using drones, but it is incremental as it builds on existing sensor fusion and control methods.
The paper tackled autonomous navigation for delivery drones by developing a system that generates and follows paths using GPS, IMU, and barometer data, with an Extended Kalman Filter improving landing precision via marker detection and UWB. Preliminary results show the system is viable for autonomous drone delivery.
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.