*3.1. Strategic Path planning*

The strategic path planning approach developed within the SMARTGO project is characterized by two steps, which are depicted in Figure 3. The first step is based on a custom version of the batch informed three (BIT\*) algorithm [24] embedded in the open motion planning library (OMPL) framework [25]. More details about STEP I algorithmic implementation are provided in [17]. Both constant (e.g., fixed obstacles and NFZ, landing site, risk and wind maps, as well as GNSS coverage volumes) and time varying information (i.e., traffic) are used as input in this step. The final path is a 4D trajectory which is constrained to the feasible trajectory conditions reported in Section 2. An optimized trajectory is obtained by minimizing:

$$f(s) = \alpha\_s \mathbb{C}\_s(s) + \alpha\_l \mathbb{C}\_l(s) + \alpha\_r \mathbb{C}\_r(s) + \alpha\_w \mathbb{C}\_w(s) \tag{1}$$

Costs *Cx* and weighting factor *α<sup>x</sup>* can be referred to path length (*s*), landing site (*l*), risk (*r*) and energy (*w*) information. Landing site and risk costs are obtained by integrating the normalized version of the landing and risk maps along the trajectory. Energy cost is obtained by using a simplified model based on rotor theory and described by [17]. *Cs* represents the path length.

STEP I is repeated several (*J*) times, while the GNSS coverage map input changes as a function of the DOP. The second step selects the minimum cost solution among the *J* available ones. The trajectories are first smoothed with polynomial trajectory planning [26] and then navigation state covariance propagation is performed to verify path navigation feasibility, i.e., the fact that positioning error is always lower than a positioning error threshold (Δpmax). Any solutions not fulfilling this requirement is discarded and the 4D strategic (nominal) path is obtained as the one with minimum cost among the remaining alternatives.
