*Article* **Systemic Performance Analysis on Zoning for Unmanned Aerial Vehicle-Based Service Delivery**

**Casper Bak Pedersen † , Kasper Rosenkrands † , Inkyung Sung \* and Peter Nielsen**

Operations Research Group, Department of Materials and Production, Aalborg University, 9220 Aalborg, Denmark; cbpe17@student.aau.dk (C.B.P.); krosen17@student.aau.dk (K.R.); peter@mp.aau.dk (P.N.)

**\*** Correspondence: inkyung\_sung@mp.aau.dk

† These authors contributed equally to this work.

**Abstract:** A zoning approach that divides an area of interest into multiple sub-areas can be a systemic and strategic solution to safely deploy a fleet of unmanned aerial vehicles (UAVs) for package delivery services. Following the zoning approach, a UAV can be assigned to one of the sub-areas, taking sole ownership and responsibility of the sub-area. As a result, the need for collision avoidance between units and the complexity of relevant operational activities can be minimized, ensuring both safe and reliable execution of the tasks. Given that the zoning approach involves the demand-server allocation decision, the service quality to customers can also be improved by performing the zoning properly. To illuminate the benefits of the zoning approach to UAV operations from a systemic perspective, this study applies clustering techniques to derive zoning solutions under different scenarios and examines the performance of the solutions using a simulation model. The simulation results demonstrate that the zoning approach can improve the safety of UAV operations, as well as the quality of service to demands.

**Keywords:** unmanned aerial vehicle; zoning; unmanned aircraft system traffic management; clustering; collision avoidance; drone package delivery

### **1. A Zoning Approach: A Systemic Solution for Successful Airspace Control**

Unmanned aerial vehicles (UAVs), or drones, are a game changer in many business and public sectors because of their ability to exploit aerial dimensions and inexpensive operating cost. However, the applications of UAVs are often made with a single or a few UAVs in a relatively safe operation area. One of the reasons for such limited UAV applications is the difficulty in air traffic control and collision avoidance for UAVs [1] for large-scale deployments. Although there have been dramatic advances in technologies for collision avoidance (e.g., artificial intelligence for path finding [2]) and UAV flight system automation, the technologies are not at the desired level to resolve safety issues completely.

This difficulty becomes more significant when multiple UAVs are deployed in a relatively small area at the same time in a dynamic environment. When no cyclical stability on UAV tasks can be identified, this difficulty becomes even worse [3]. This situation can be found in UAV application scenarios with a delivery function, such as UAV-based logistics [4,5], humanitarian/emergency aid operations [6,7], or in cooperation with other unmanned systems [8,9]. These applications are where the demands for services are expected to exceed the supply levels that can be fulfilled with traditional means of transportation, and the task can be performed better with exploitation of the aerial dimension. Therefore, a large-scale UAV fleet deployment is desired to handle the increasing service demands and to maximize the service quality.

A key solution to realize large-scale UAV operations is an unmanned aircraft traffic management (UTM) system, which manages airspace for multiple UAV operations through real-time control. Following the increasing volume of UAV operations and the need for

**Citation:** Pedersen, C.B.; Rosenkrands, K.; Sung, I.; Nielsen, P. Systemic Performance Analysis on Zoning for Unmanned Aerial Vehicle-Based Service Delivery. *Drones* **2022**, *6*, 157. https://doi.org/ 10.3390/drones6070157

Academic Editors: Ivana Semanjski, Antonio Pratelli, Massimiliano Pieraccini, Silvio Semanjski, Massimiliano Petri and Sidharta Gautama

Received: 9 June 2022 Accepted: 23 June 2022 Published: 26 June 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

UTM systems, federal regulatory agencies such as the European Aviation Safety Agency (EASA) and the Federal Aviation Administration (FAA) in the United States have conducted several projects and discussed the concepts of UTM and regulations of UAVs [10,11].

While airspace for UAVs is increasingly regulated, the feasibility and performance of UTM is still far behind the desired level, especially considering the envisioned future air traffic densities. Unlike the well-established and -functioning air traffic management (ATM) systems for manned aircraft, the following aspects of UAVs challenge the development of UTM [12]:


These key differences between manned aircraft and UAVs underline the complexity of the development and implementation of a UTM system. UTM implementation involves novel challenges, including decentralization of governing authority over large-scale UAV operations and interactions with pilots to share crucial flight and safety data [13]. When narrowing down the UTM for the safety aspects of UAVs, safety separation standards, collision risk prediction, and collision avoidance can be listed as the critical research topics, and there needs to be significant advances for full- and large-scale UAV operations [14].

Considering the complexity of the involved design and decision-making problems in the UTM, it is clear that a centralized air traffic control (ATC) with a full synchronization of large-scale UAV operations is extremely difficult to achieve. In the context of ATM, the ATC-related decision-making authority is distributed among flight crews, the air traffic service providers, and aeronautical operational control organizations in order to reduce reliance on the centralized ATC [15]. In the context of UTM, however, it is difficult to give UAVs the freedom for their path and speed selection in real time, because full automation of path finding and conflict resolution for large-scale UAV operations in a decentralized manner is highly complex [14,16,17].

As a systemic solution to the difficulties of UTM implementation, a zoning approach, where an operational area for UAVs is decomposed into a set of sub-areas and a maximum of one UAV can be deployed within a sub-area at a given time, can be considered [18]. By this approach, the flight paths of UAVs in different zones do not overlap, and it is likely that UAVs remain well clear with or without minimum control efforts. The flight planning and control problems involved in a UTM can also be made simple, as the interaction level between UAVs are minimized by the zoning approach.

As such, the workload for airspace control and collision avoidance can be dramatically reduced by the zoning approach. Importantly, this reduced workload can be further distributed to the sub-UTMs for each zone in favor of the zoning approach. Given that most of UAV control systems are still operated by humans, this zoning approach also brings advantages to UAV service providers, that is, to reduce the manpower for UAV deployment and corresponding operating costs. The expected benefits of the zoning approach are summarized as follows:


On the other hand, one can question the negative impacts of the zoning approach on the service-to-customers level, as the means to serve demands is restricted by the approach. Indeed, the set of demands or the area a UAV can cover is limited by a zoning solution in terms of volume, size, and location of the demands. Therefore, considering the zoning approach as a demand-server allocation problem, it is important to derive a zoning solution so that the resulting service level to customers is kept at an acceptable level, while fully exploiting the advantages of the zoning approach.

The aim of this study is to demonstrate the advantages of the zoning approach from the UAV safety and service quality perspectives. Specifically, we answer the following questions:


To answer these questions, we analyze the performance of the zoning approach from a systemic perspective. We first generate package-delivery-like scenarios, where multiple demand nodes in an area are served by multiple UAVs. We group the demand nodes using clustering techniques and derive a zoning solution accordingly. The performance of the proposed zoning approach is examined under multiple demand configurations using a simulation model, compared to other benchmark service strategies.

#### **2. Related Work: Zoning in Literature**

UAV application areas are numerous and include those domains where automation, low operation costs, and aerial dimension exploitation can bring values. Otto et al. [19] present a comprehensive review on UAV applications, classifying the UAV-involved tasks in the applications and relevant planning problems. Mukhamediev et al. [20] also present detailed UAV use cases in various industry applications, highlighting relevant data management and processing tasks.

The UAV applications where the concept of zoning is relevant can be further classified into two classes: area coverage and package delivery. The area coverage class addresses tasks for searching and monitoring an area, whereas the package delivery class addresses transportation-related activities.

By the nature of the zoning approach, one can easily find its link to the area coverage class, where efficient use of units by properly assigning responsible areas to the units is critical to achieve a service/mission goal (e.g., to minimize the time it takes to search an area). Fu et al. [21] propose a local Voronoi decomposition algorithm for exploration task allocation to multi-agents. In this approach, Voronoi regions of each agent are calculated based on the agents' positions, and the agents move only within their regions to avoid overlapping tasks. Miao et al. [22] apply a map decomposition approach to assign submaps to cleaning robots so that large-scale cleaning areas are effectively distributed and assigned to the robots. Xiao et al. [23] apply area segmentation for UAV coverage planning in a grid map. Once a take-off location is determined, an area of interest is specified as a set of grids and further divided into sub-areas so that the energy required to cover the sub-areas are balanced and collisions between UAVs can be avoided. For three-dimensional coverage path planning, where different coverage paths depending on altitudes of a region of interest are generated [24], multiple UAVs can be assigned to different altitudes for coverage operations.

The zoning approach is also found in the package delivery application class, the focus of the zoning approach in this study. UAV-based package delivery is a UAV application with increasing demands and demonstrated profits. To realize this service concept with minimum barriers to its implementation, various practical issues, such as preserving privacy, have been addressed [25].

The main focus of the zoning approach in this class is collision avoidance and ease of UAV traffic management, the critical challenges for package delivery by UAVs [26]. Amazon, who first introduced the concept of package delivery by UAVs, proposes an airspace model where civil airspace is segregated by altitudes based on vehicle capability [27]. In this model, a low-speed localized traffic area is used for the UAVs without

sophisticated sense-and-avoid technology, whereas a high-speed transit area is used for well-equipped vehicles. The model also includes a no fly zone to buffer the UAV operations from current aviation operations. Feng and Yuan [28] apply space zoning to a low-altitude airspace that divides the space into upper, buffer, safe, and bottom zones according to space height restrictions in order to construct flight corridors for UAVs. Sung and Nielsen [18] propose a zoning approach that divides a service area with a single UAV station into zones. They allow at most a single UAV to fly within a zone and investigate the expected service level with these zoning practice using a simulation.

Note that the clustering approaches applied to UAV routing problems for package delivery services can also be seen as a special case of the zoning approach for tactical decision-making. In particular, clustering has been actively applied to truck-assisted UAV package delivery services, where a truck visits multiple spots following a path to support UAVs' operations (e.g., recharging and (un)loading payloads). Under this service delivery scheme, delivery points are grouped into a set of clusters, and a route for a truck to visit the clusters and routes for UAVs within the clusters are derived to maximize the service delivery performance [29–31].

As reviewed, the concept of zoning has been addressed from different perspectives in the literature. In general, the zoning approach is designed to optimize the UAV performance for a single service instance (tactical/operational) under the area coverage class, whereas the zoning approach is applied to design a UAV traffic management system for multiple service instances (strategic) under the package delivery class. The zoning approach for the package delivery class is further separated by the restriction level on a zone. Sung and Nielsen [18] apply the most restricted practice, which allows only a single UAV to fly within a zone at a given time, whereas the other studies separate airspace by altitudes and allow multiple UAV operations in a zone.

Our study is aligned with the work of Sung and Nielsen [18]. It is difficult to assume a full connectivity between UAVs during their operations and 100% reliable UAV control logic mainly due to the dynamics in the airspace and the absence of a decent UTM. Considering this fact, operating UAVs with zoning, as proposed by Sung and Nielsen [18], seems more appropriate for safety and service reliability reasons and can increase the feasibility of large-scale UAV deployment.

Based on the review, the contributions of our study can be noted as follows. We first describe a systemic and strategic solution for application of UAVs to package delivery scenarios, which allows UAV deployment at a large scale and reduces reliance on dramatic advances in UAV navigation and control technology. Next, in contrast to the work of Sung and Nielsen [18], we apply clustering techniques for zoning to address multiple UAV stations in a service area, which can naturally be seen in a large-scale service scenario. Last, we test the performance of the zoning approach under different demand distribution configurations to clearly illuminate the expected benefits of the proposed zoning approach.

#### **3. The Zoning Problem and the Considered Solution Approach**
