Precise Payload Delivery via Unmanned Aerial Vehicles: An Approach Using Object Detection Algorithms
This addresses the precision issue in autonomous drone delivery for applications like logistics or emergency response, representing a domain-specific incremental improvement.
The paper tackles the problem of imprecise payload delivery by drones using GPS by proposing a novel navigation method that integrates deep-learning-based computer vision to identify and align with a target, achieving a 500% increase in average horizontal precision over conventional approaches.
Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. By relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of the GPS network and the availability and strength of the GPS connection, which may be severely restricted by the weather condition at the time and place of operation. In this work we describe the development of a micro-class UAV and propose a novel navigation method that improves the accuracy of conventional navigation methods by incorporating a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position. This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches.