*3.2. Tactical Path Planning*

Tactical planner is aimed at fast finding a path that can be followed by the UAV if safety and effectiveness of the strategic path are jeopardized during the flight. Optimality is non-accounted for at this level, and the first feasible alternative is considered as tactical solution. Therefore, all the information sources, which are only used for the aim of cost definition (i.e., landing site location and ground risk) are discarded at tactical level, and their modification cannot trigger any level of tactical replanning. Conversely, updates of GNSS maps, fixed and mobile obstacles and wind conditions may call for trajectory update. As far as wind modification is concerned, it not only alters the UAV energy consumption, but it also modifies the zones in which the UAV is allowed to fly due to the maximum admissible wind velocity. Tactical path planner assumes the ownship has a lower priority than the other UAVs, so it has to maneuver in case of conflict. It implements three different solutions, referred to as levels in Figure 3, that are specifically designed to counteract several events that could occur during the tactical phase. The three levels are characterized by an increasing level of computational complexity and their performance is summarized below. For further algorithmic details, the reader is referred to [16].


of the trajectory because it is informed to return to the strategic path. The global nature of this approach avoids sequential replanning if multiple unfavorable events are experienced by the UAV, thus saving time. Due to the spatial modification of the trajectory, this solution is not only able to deal with both fixed and mobile obstacle geometries, but it can also be used to counteract wind velocity and GNSS coverage maps alteration. The heuristic nature of the RRT makes this solution non-deterministic. In addition, a higher computational time is experienced with respect to the previous approach. However, since only a local modification is provided to the strategic path, its optimality remains almost unaltered while also providing a small increase in flight time, as demonstrated in [16].

3. *Level 3* of the tactical planning provides a global modification of the trajectory starting from its last non-corrupted point. From that point, a completely new trajectory is recomputed with an algorithm still based on RRT, but not informed to return to the strategic trajectory. This solution, which completely alters the path after the unfavorable event, should be chosen when a significant modification of the flight conditions has been experienced with respect to the scenario available at the strategic level. Due to the similar algorithmic scheme, this level shares the same heuristic nature of *Level 2*, as well as the higher computational time with respect to *Level 1*.

Among the proposed solutions, the one to be chosen during tactical phase is not trivial to be identified. Although a simple geometry consideration can be made to deem whether to use *Level 1* solution or not, this is not true for the other two levels and their output must be compared to this aim. Therefore, the tactical planner is conceived to run all the levels sequentially and compare their solutions, if available, to choose the one with the minimum cost. A flowchart of the tactical planning is reported in Figure 4.

**Figure 4.** Tactical path planner flowchart.

*Level 1*, which has the lowest computational cost, is the first to be run (it runs only if compliant to the conflict geometries), then *Level 2* is queried. The level sequence runs until a timeout, and the tactical output is picked among all the available solutions at that time. During the flight, the current path is checked for any unfavorable event (i.e., contingency). If the time to the contingency (*tcoll*) is greater than the replanning timeout (*trep*), the tactical replanning levels are run sequentially and the best cost path is selected to update the flight plan. In the case no available solution is found by tactical planning or *tcoll* <sup>≤</sup> *trep*, contingency landing actions are activated.

#### **4. Use Cases**

Strategic and tactical pipeline have been applied to two test case scenarios. The first represents an urban air taxi problem, specifically designed to transport passengers from airport to business center and port. The second scenario includes the delivery of medical supplies from the mainland to an island, thereby saving time with respect to ship-based transportation. Two scenarios have been identified in the Naples area and its surroundings and are reported in Figure 5. Risk, landing site, wind and GNSS coverage maps estimated with three different thresholds, i.e., *D*<sup>1</sup> = 2, *D*<sup>2</sup> = 3, *D*<sup>3</sup> = 4 are reported both for air taxi and medical delivery cases in Figures 6 and 7, respectively.

**Figure 5.** Test case scenarios. (**a**) Air taxi scenario. Top, lateral view and satellite map. (**b**) Medical delivery scenario. Top, lateral view and satellite map.

**Figure 6.** Air taxi scenario. (**a**) Risk and (**b**) landing site intensity maps. (**c**) Top view of wind direction, identified by blue vectors. GNSS coverage maps associated to (**d**) *D*<sup>1</sup> = 2, (**e**) *D2* = 3 and (**f**) *D*<sup>3</sup> = 4.

Because the two identified scenarios have a huge extension, the GNSS coverage map has been computed only in a portion of the environment which is closer to the start or the end point of the trajectory. They were obtained using a starting time of 11:30 UTC of 19th April 2022. A time interval of 20 min has been considered, with a time span of 5 min. The ground grid has 5 m spacing. A uniform wind intensity of 5 m/s has been used for both missions.

The air taxi scenario is reported in Figure 5a. It envisages transfers from Capodichino airport to Naples business center or port. The three locations have been represented on the map with an asterisk, a cross and a circle, respectively. Both the top and lateral views have been reported in the Earth north up (ENU) coordinate frame originated at 40◦51 56 N, 14◦17 20 E, as well as the rectangles where GNSS coverage maps have been computed. For the sake of concreteness, the satellite map of the identified area is also reported. The lateral view highlights the high slope of the scenario. A maximum flight altitude estimated above the ground level and equal to 150 m has been assumed. Two missions have been considered, i.e.:


The medical delivery scenario involves the Procida island and its closest city on the mainland, i.e., Monte di Procida, which is about 4 km far. Even if several ship connections, taking about 16 min, are organized in the summer season (from June to September), very few transportations (twice a day and only on weekdays) are foreseen in the winter season, making impossible to directly deliver urgent medicines. A dedicated drone service could not only spare time but also be operated on-demand. The scenario extension (both in the lateral and top view) and its satellite view have been reported in Figure 5b. The start and arrival location includes a pharmacy in Monte di Procida (40◦47 20 N 14◦3 0 E and 100 m altitude) and the Procida local medical unit (40◦45 25 N 14◦1 11 E and 60 m altitude), which are reported with a cross and a circle in the figure, respectively. A maximum flying altitude of 150 m above the terrain level (or the sea level when the aircraft flies in the Procida channel) has been assumed.

#### **5. Results**

Strategic and tactical planning have been carried out assuming as aircraft a DJI M300 RTK [27], whose main parameters are reported in Table 1. Navigation performance of the IMU sensor has also been included, which is assumed to be the one of the medium grade IMU HG1120CA50 from Honeywell [28]. The positioning error threshold is assumed to be equal to 2 m so that any trajectory which overcomes this value at least once during the flight must be discarded. In order to trigger every tactical level to output a solution, in this work, tactical information is only limited to intruder trajectory updates. The entire path planning pipeline results will be detailed in the medical delivery scenario, which involves a single mission, in Section 5.1. On the other hand, results related to the air taxi scenario are shown in Section 5.2.

**Table 1.** Vehicle specifics.


<sup>1</sup> Only accelerometer parameters are included since navigation error covariance propagation is run with a simplified approach.
