## 1 Introduction
(4) This system can provide 'last-mile' delivery or delivery at the doorstep of the customer. (5) It reduces carbon emissions as drones do not heavily rely on fossil fuels. Although the overall prospect may sound promising, the underlying scenario of drone-based delivery requires further investigation. Numerous constraints have a direct impact on the performance of drone-based delivery systems. First, drones have limited battery capacity. Thus, they can only fly for a limited time which may be insufficient for the drone to cover a certain area. Thus, charging stations (CSs) must be established to guarantee  
Figure 1: General scenario of a drone-based delivery system. longer flying time. The CSs can be utilized as platforms where drones land and have their batteries changed or recharged. Second, currently, drones cannot carry bulky packages or travel longer distances due to limited battery capacity. Third, due to extreme weather conditions (e.g., heavy wind, snow, and rain) drone flights can significantly impact the performance of drone-based delivery systems. Finally, the service providers need to ensure minimum delivery cost because most users would be unwilling to use the service if they perceive the delivery cost to be unaffordable. Thus, the service providers need to consider various costs in terms of drone maintenance, recharging, and operational strategies. Thus, in the logistics industry, every company willing to integrate drone-based delivery service is faced with numerous challenges and important issues in deploying the system at a reduced cost. These include delivery mission design and planning, drone routing and charging, and multiple parcel deliveries on a single mission. These dynamic factors make the overall planning more complex and challenging. We identified that the technical issues for drone routing in drone-based delivery systems are coupled with trajectory design, charging strategy, and security aspect. The trajectory design considers efficient planning of the waypoint and visiting order of customers or delivery locations according to drone's flight time. Since drones have the limited flight time due to the constrained battery capacity for long distance and, sometimes, they carry out multiple deliveries in a single mission, a charging strategy is required. Thus, the charging strategy considers the battery replacement or replenishment strategy to extend the drone flight time by placing charging stations at optimal places. Finally, the security aspect ensures the required security and privacy in drone-based delivery systems as drones contain the sensitive information of both propriators and customers (i.e., user/product information and trajectory data). In drone-based delivery systems, trajectory planning can be classified based on the delivery method type. Researchers have considered single drone-based delivery [9], truck-drone collaborative delivery [10], public transport and drone collaborative delivery [11], and multi-drone-based delivery with nearby CSs for multiple customers [12]. The overall trajectory or route planning to visit the customers/delivery points or CSs are derived as different variants of the travelling salesman problem (TSP) [10; 12; 13; 14]. Many mathematical models such as mixed integer linear programming (MILP) [12], heuristic method [15; 16], and reinforcement learning [6] have been adopted to obtain a sub-optimal solution under different objectives considering several constrains. The main objective of trajectory planning in drone-based delivery systems is minimizing the average delivery time [11] and maximizing coverage area to serve maximum number of customers in a single charge [17]. The major constraints in trajectory planning are limited battery of drones and their constrained payload capacity, limited number of drones and CSs for the given area, restriction on the placement locations of CSs, presence of static and dynamic obstacles, restricted area for flying, threshold time to complete the delivery, and different uncertainties based on the weather conditions.