UAVs Trajectory Optimization for Data Pick Up and Delivery with Time Window

• Published in: Drones (Basel) Vol. 5; no. 2; pp. 27 - 27:22
• Main Authors: Khoufi, Ines, Laouiti, Anis, Adjih, Cedric, Hadded, Mohamed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2021; MDPI
• Subjects: Computer Science; Connectivity; Cost control; Customer services; Drone aircraft; Drone vehicles; Emergency response; Genetic algorithms; Heuristic; intermittent connectivity; MOEA; multi-objective optimization; Multiple objective analysis; Networking and Internet Architecture; Optimization; pickup and delivery; Radio communications; Rechargeable batteries; Route selection; Sorting algorithms; time window; Trajectory optimization; Trajectory planning; UAVs; Unmanned aerial vehicles; Vehicles; Windows (intervals); NSGA-II; Pickup and delivery; Time window; Multi-objective optimization; MOEA; Intermittent connectivity; UAVs
• ISSN: 2504-446X; 2504-446X
• DOI: 10.3390/drones5020027

Unmanned Aerial Vehicles (UAVs), also known as drones, are a class of aircraft without the presence of pilots on board. UAVs have the ability to reduce the time and cost of deliveries and to respond to emergency situations. Currently, UAVs are extensively used for data delivery and/or collection to/from dangerous or inaccessible sites. However, trajectory planning is one of the major UAV issues that needs to be solved. To address this question, we focus in this paper on determining the optimized routes to be followed by the drones for data pickup and delivery with a time window with an intermittent connectivity network, while also having the possibility to recharge the drones’ batteries on the way to their destinations. To do so, we formulated the problem as a multi-objective optimization problem, and we showed how to use the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve this problem. Several experiments were conducted to validate the proposed algorithm by considering different scenarios.