Abstract
Drone based consignment delivery system is considered to be the future of retail marketing where unmanned aerial vehicles are employed for door delivery of items. This results in a fast, efficient and accurate delivery system which is not affected by geographic conditions and can be used for delivery of items to areas where human reachability is difficult. Also, it can ensure that the customers avoid close contact with strangers, especially in the era of a pandemic like COVID 19. However, the system poses a number of challenges too, the most important of them being the limited battery life of drones. Though newer batteries provide better flight times, the battery discharges faster with increased number of landings and take-offs. Hence the flight of the drone has to be kept optimal for efficient delivery of consignments. In this paper, we propose a delivery system using drones where both the delivery agency and the customers collaborate together to distribute the consignments and model a novel framework for finding the optimal path of delivery drones. We employ unsupervised learning approaches to find the landing locations for drones, based on the concentration of customers. We use a nature inspired optimization algorithm using the fundamental principles of particle physics to get rid of the practical limitations of k-means, a widely used centroid based clustering technique. Besides, we address the case where the customers are spread in such a way that their partitions are not well-separated. Finally, we model, experiment and evaluate our methods on two different datasets.