The growth of the nascent UAS industry will be affected by the airspace coordination rules between drones because these rules can impact business profitability. Few analyses have been reported to support design of commercial UAS operations in low-altitude commercial urban airspace. Analysis of minimum horizontal separation is critical for designing safe and efficient UAS delivery systems. In this paper a constructive simulation model is used to analyze and evaluate proposed UAS airspace traffic. A high density of delivery drones could create a bottleneck in a drone-based supply chain very quickly, especially when a high minimum horizontal separation standard is required. This paper proposes a simple idea on how to organize low-altitude UAS traffic, and evaluates the idea using a simulation model. Additional implications and future work needed in relation to UAS-based delivery are also discussed.
This paper is concerned with the lower portion of Class G airspace, which includes elevations up to 1,200 feet above the surface where there is no Air Traffic Control (ATC) designated to manage that airspace (FAA 2015). Current Federal Aviation Agency (FAA) regulations do not facilitate pro-business climate for UAS-based low-altitude delivery systems. FAA requires aircraft to be 1,000 feet above the highest obstacle within a horizontal distance of 4 nautical miles from the course to be flown and does not provide enabling guidance for the use of autonomous UAS in this airspace (FAA 2015). The lack of FAA rules does not allow for explicit description of future UAS-related businesses operations, but this indicates an opportunity to investigate management options by using simulation models that can inform the FAA about business perspectives related to this endeavor. More well-defined system and structural rules are needed to both guarantee safety and provide economic justification due to potential crowding of future low-altitude urban airspace. For instance, a mix of centralized and decentralized controls of UAS could offer both resilient and redundant operation.
Infrastructure that could enable safe and widespread use of low-altitude airspace for UAS operations does not yet exist; therefore, NASA has initiated research, within the 2014-2020 timeline, that should lead to the development of a prototype UAS traffic management (UTM) solution (Kopardekar 2014). Low altitude UTM could provide services such as dynamic configuration, dynamic geo-fencing, severe weather and wind avoidance, congestion management, terrain avoidance, route planning and re-routing, separation management, sequencing and spacing, and contingency management (Kopardekar 2014). Initially, the UTM system is expected to support low-altitude airspace delivery of goods and services via UAS operations in remote areas and then migrate to increasingly denser areas, eventually managing airspace over urban areas (Kopardekar 2014).
Mohammed et al. (2014) discussed several UAS-based related business and technical challenges in the context of smart cities. Foina et al. (2015) proposed a UTM that consists of three main components: electronic identification plate, ground identification equipment, and a Traffic Routing System (TRS). It introduces an air parcel model dividing low altitude airspace into a 3-D air parcel map. TRS calculates efficient and collision free trajectory assuming straight flying path between waypoints. This proposed system considers a pilot-operated UAS, which can be a major drawback to achieving cost efficient delivery system.
This paper builds upon the research reported in Balaban et al. (2016), where concepts of the future UAS business delivery operations were introduced and a theoretical constructive simulation model was used to analyze selected factors.
Figure 1: Structure of the simulation model.
Figure 1 presents the main components of a simulation model and the Modeling and Simulation (M&S) methods used. Agent-based Modeling (ABM) is used to represent the overall multi-level model structure that allows for embedding processes, behaviors, and interactions of actors (i.e. delivery businesses, customers, orders, and drones). Discrete Event Simulation (DES) represents product deliveries that involve processing orders and utilize UASs to represent the resources required to process the orders. Orders and UASs combine properties of both agents and entities because they are (1) part of DES processes and (2) include internal behaviors that trigger other agents or monitor conditions triggered by other agents. State charts (SC) represent drone states during operations and their transactions with customers.