*5.1. Medical Delivery Scenario*

Strategic path planning results are obtained using in STEP I *αs* = *αw* = 1, *αr* =4, *αl* = 2 as weighting factors, thus privileging trajectories which reduce the ground risk and are closer to the landing sites. STEP I paths are reported in Figure 8, along with the strategic obstacles' paths. A trajectory for each GNSS challenging map is obtained, with the cost breakdown reported in Table 2. The costs are estimated over the smoothed trajectory computed in STEP II. Results of trajectory flyability test (maximum trajectory positioning error lower than Δpmax) are also reported in the Table. All the trajectories are compliant with navigation requirements, and *D*<sup>2</sup> (highlighted in green in Table 2), which minimizes the cost function (*f*) is picked as the strategic (nominal) solution. Computational time of each solution estimated on an Intel i7 pc with a 2.59 GHz processing unit has been also reported in the Table, demonstrating the planner requires less than one minute for output each trajectory.

**Figure 8.** STEP I strategic solution and strategic mobile obstacles. Medical delivery scenarios. *αs* = *αw* = 1, *αr* =4, *αl* = 2.


**Table 2.** Medical delivery scenario. Strategic solution costs breakdown.

Tactical deconfliction accounts for unknown (tactical) obstacles that the UAV has to avoid during the flight. With the aim of testing tactical planner performance, these trajectories have been specifically designed in order to intersect the strategic path. Information about these trajectories is transferred to the UAV by the U-Space Service Provider (USSP) or via a vehicle-to-vehicle data link. This information, together with the UAVs flight plans known in the strategic phase, must be taken into account to generate a safe and collision free path. The trajectory costs obtained after tactical deconfliction are reported in Table 3, along with the maximum navigation error, the computation time and the overall flight time that (except for *Level 1* approach, which experiences a huge time delay) does not increase significantly. Using the same GNSS coverage map accounted for in the strategic path definition as a boundary allows keeping the navigation error smaller than Δpmax. As expected, the lowest cost solution is the one associated with *Level 2*, which is specifically designed to produce local variation from the strategic path by keeping its cost function almost unaltered. Because the *Level 1* 3D trajectory coincides with the strategic one, all the spatial based costs (risk, landing site and path length) are equal. However, this is not true for the energy cost, which is increased due to the high waiting time to avoid tactical obstacles. As far as the computation times are concerned, *Level 1* solution, based on a

deterministic approach, gives the smallest contribution. On the other hand, about 5 s are required to solve the deconflictions for spatial based solutions. These values are compatible with typical values of tactical replanning cut off time which is of the order of 10 s.

**Table 3.** Medical delivery scenario. Tactical solution costs breakdown.


Tactical results are reported in Figure 9, either for spatial based solution (i.e., associated to *Level 2* and *3*) and time scaling results (*Level 1*), which are depicted in Figure 9a,b, respectively. Figure 9a shows both the lateral and the top view of the *Level 2* and *3* trajectories by also reporting the information of strategic and tactical intruders (top view) and the GNSS coverage map associated to the nominal trajectory, i.e., whose threshold is *D*<sup>2</sup> (lateral view). The *Level 3* solution has a larger deviation from the strategic path than *Level 2*, as expected. This deviation from the optimal path produces an increase of the trajectory cost. Figure 9b compares the velocity history of the strategic path with respect to the tactical one, noting the huge delay produced by the time scaling approach. Indeed, the ownship is slowed down twice and the avoidance of the second intruder produces a huge velocity reduction (near to zero) and a very long waiting time to avoid collision.

**Figure 9.** Tactical solution—Medical delivery scenario. (**a**) *Level 2* and *3* trajectories. (**b**) *Level 1* velocity norm history.
