**1. Introduction**

The market of unmanned aerial systems (UAS) has witnessed a rapid growth in the last decade, fostering research towards innovative algorithms and instruments to enhance their reliability, autonomy and safety. Urban air mobility (UAM) represents the next frontier of the UAS market, and a huge effort is being carried out in that direction in view of a wide variety of applications. As an example, missions such as urban taxi [1], inspection and surveillance [2] could experience a significant performance improvement (e.g., reduction of mission time) if performed by highly autonomous unmanned aerial vehicle (UAV). The development of urban air mobility will completely change our cities with a huge quantity of UAVs that require to be accommodated in the airspace. The increased UAV density also calls for new regulations [3] that are being developed to bridge the gaps between novel technologies and urban safety requirements [4]. In this framework, path planning and traffic management tasks are of paramount relevance to meet these safety requirements and enable UAV access to a traffic dense airspace.

UAV path planning, aiming to define the best route from a start to a goal point, is a widely discussed problem in the open literature, and several algorithms have been developed to specifically accommodate mission needs. In general, path planning requires accounting for UAV dynamic constraints, energy consumption (and thus wind and weather conditions [5–8]) and fixed and mobile obstacles to meet safety and effectiveness requirements [9]. When it comes to urban scenarios, navigation issues can arise for vehicles using the classical GNSS/inertial fusion scheme due to the non-nominal GNSS coverage conditions, which is experienced next to buildings and infrastructures. Indeed, these structures cause GNSS signal shadowing or deviation, leading to either signal obstruction or multipath phenomena, respectively. In these scenarios, path effectiveness, should also account

for the capability to follow the planned trajectory while keeping a bounded navigation error and fulfilling the required navigation performance (RNP) [10]. Conversely, safety requirements are also linked to the need for minimizing the ground risk [11]. Several works in the open literature have addressed planning problems by accounting for ground risk mitigation [12,13], GNSS coverage fault [14], wind condition and its effect to energy consumption [6]. Nevertheless, an integrated framework which tackles all these aspects altogether is required to effectively enable safe access to the urban airspace.

The "SMARTGO" (gnsS-enabled urban air Mobility through Ai-powered environmentawaRe Techniques for strateGic and tactical path planning Operations) project [15], funded by the Italian Space Agency (ASI) and carried out by a consortium composed by the University of Naples "Federico II" (as coordinator), TopView S.r.l. and Euro.Soft S.r.l. aims to develop strategic and tactical path planning algorithms that can handle several information levels to meet urban environment requirements and constraints. Besides path planning approaches, the project also aims to gather and synthesize relevant information about the urban environment useful for path planning and at defining innovative Uspace services based on the project outcomes. Data gathering includes processing satellite images with innovative algorithms, which are also based on artificial intelligence for terrain classification and ground risk estimation. Tackling both planner design and information gathering bridges the gap between data retrieval and usage and ensures information collection and representation is specifically tailored to planner's needs.

This paper briefly describes the main algorithmic outcomes of the project in terms of path planning and focuses on the application of the entire algorithmic chain to real-world test cases that have been specifically identified for the project's needs. A brief overview of the project and a preliminary version of both the strategic and tactical algorithms have already been presented in [15]. Then, a more detailed version of the tactical framework and the developed approaches to tackle with unpredicted events during flight has been described in [16]. In addition, a detailed description of the strategic path planning algorithm is reported in [17]. This work extends the previous contributions of the authors by providing the following innovative points:


The remainder of this paper is structured as follows. Section 2 analyzes the constraints to be taken into account at both tactical and strategic level while also detailing the procedure for information retrieval. Both strategic and tactical path planning algorithms are summarized in Section 3. Test cases and their related information to be used at path planning level are reported in Section 4, and Section 5 shows the tactical and strategic path planning results. Finally, Section 6 draws conclusive remarks.

#### **2. Environment and Vehicle Based Constraints**

Risk, weather and no fly zone information, as well as 3D geometries, mobile obstacles and vehicle specifications and constraints must be taken into account to plan for a safe and effective path. *Vehicle-based constraints* include maximum airspeed and flight path angle, as well as battery capacity, maximum allowed wind velocity and navigation system performance. *Environment-based constraints* can, in general, be divided into two categories depending on whether they are connected to the airspace or not. Airspace-based constraints include (but are not limited to) no-fly-zones, maximum and minimum flight altitudes, traffic information, contingency landing site location and possible airspace structure and/or speed rules, as foreseen, for instance, in the geovectoring approach [18]. On the other hand, the other environmental constraints include general information about the scenario such as 3D geometry, weather information and estimate of ground risk.

Because the majority of the developed planning approaches are designed to deal with spatial based information, SMARTGO information gathering and organization aims at simplifying most of the aforementioned sources in multi-dimensional maps. This is done to reduce the information processing to be carried out at the path planning level. Specifically, the following maps are defined and used as planning inputs:

	- a. Class 1 includes low-risk areas such as natural and rural ones;
	- b. Class 2 includes industrial areas characterized by low people density;
	- c. Class 3 includes urban environments. In this scenario a subclassification is performed to distinguish between buildings and populated areas such as squares and streets;
	- d. Class 4 includes critical infrastructures (e.g., train stations and hospitals) and it is again divided in various subcategories.

An example of risk level over a portion of Naples city (Italy) is reported in Figure 1.


6. **Traffic information** is provided via vehicle-to-vehicle (V2V) or infrastructure-tovehicle (I2V) communications. This work assumes the entire flight plan of the other vehicles is fed to the ownship both in the strategic phase and during the flight. Flight plan information of the intruder is stored in 3D time-varying occupancy maps, detailed in [16]. *N* occupancy maps varying with time are used to prevent continuously checking for intruder possible collision, each one covering a time interval equal to Δ*t*. The *n*-th occupancy map is used for checking collision in the time segment going from *tn*−<sup>1</sup> to *tn* (*tn* <sup>=</sup> *<sup>t</sup>*<sup>0</sup> <sup>+</sup> *<sup>n</sup>*Δ*t*, being *<sup>t</sup>*<sup>0</sup> the starting time of the mission). The representation of the intruder in each occupancy map is given by its path during the associated time interval enlarged with time and spatial margins. The nature of traffic maps allows them to be merged with the fixed obstacle maps so as to speed up the collision check operation.

**Figure 1.** Risk map over a portion of the Naples city center, Italy.

**Figure 2.** GNSS coverage maps as a function of the DOP threshold. *D*<sup>1</sup> < *D*<sup>2</sup> < *D*3.

#### **3. Path Planning Framework**

Planning framework, whose flowchart is shown in Figure 3, foresees two phases, i.e., the strategic phase (detailed in Section 3.1), which is aimed at evaluating an optimized trajectory for the UAV before the flight, and a tactical phase (described in Section 3.2), which continuously checks the trajectory during the flight and takes action in the case an

unpredicted event occurs that compromises the trajectory safety and effectiveness. The need for decomposing the planning approach in two phases [21] comes from guaranteeing path optimality while reducing the computational cost during flight, as tactical replanning is only demanded at finding deviations from the nominal (strategic) trajectory. To ensure the latter condition, a strategic path planner must be carried out with the largest amount of available information, following the better-informed, better-planned logic.

**Figure 3.** Strategic and tactical planning flowchart.

Both strategic and tactical solutions are conceived to deal with all the set information reported in Section 2, or a subset of them in a scalable and adaptive way. Due to the large amount of information to be dealt with and the huge dimension of the scenario where the planner is considered to operate (with mission ranges in the order of few kilometers), sampling based approaches, such as rapidly exploring random trees (RRT) [22] and its modifications, are preferred in this work to graph search method, such as A\* [23] because of the high cost linked to sampling all the nodes belonging to the environment. Feasible segments to add to the solution tree are those which:

