# Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling
## 7 Conclusions  
The investigated cases of dynamic navigation of vehicles carrying out a common mission focus on the planning problem of the proactive and reactive driven hybrid routing of UAV fleet delivery missions. The class of considered cases includes situations related to planning UAV routes under changed weather conditions exceeding those previously forecasted and/or changes to previously agreed terms of order fulfilment. These occur, for example, in networks allowing the presence of impatient consumers [35; 36]. Examples of such situations illustrate cases of automatic navigation of the UAV to a charging station when the battery drops to a certain level, designating a route bypassing the delivery point that refuses to accept the delivery and serves another previously un-planned delivery point in its place. Consequently, the need to react in such situations enforces the establishment of condition-action rules that allow the designation of appropriate possible end-to-end routes enabling emergency safe completion of the mission or its continuation in a modified version. The case under consideration where UAVs depart from and return to one of multiple depot locations is the multi-depot extension of the previously considered contingency planning problem. Since the related problem of UAV mission planning has proven to be NP-hard, its constraint satisfaction-driven implementation has been proposed. A long-term objective of this study is to develop dedicated DSS software. With this in mind, we employ the declarative modelling framework, mostly because of its fast prototyping capability. The main advantage of the proposed approach follows from the open structure of the proposed model. This allows several other variables and restrictions to be taken into account (e.g., related to the cost of a mission, distribution system infrastructure, heterogeneity of UAVs, etc.). Computational results show that the permissible size of the distribution network (90 nodes and 2 UAVs) where considered reactive planning aims at a routing delivery design for a fleet mission makes the proposed approach suitable for online applications. The scope of the current paper is limited to the modelling of robust delivery scenarios for the selected disturbances (a change in weather conditions). In general, we can consider the online re-planning of UAVs' missions for disturbances implied by factors such as change in order (customer resignation, the appearance of a new customer), change in the delivery date, change in UAV fleet (failure of a UAV), change in the structure of the distribution network (shutdown of the UAVs base), etc. Taking into account these types of additional disturbances requires the reference model to be extended with appropriate decision variables each time. A similarly developed model (dedicated to the decision problem) can also be easily extended to the needs of optimization problems related to minimizing energy consumption, the size of the UAV fleet, the number of UAV depots, etc. Thanks to the approved relaxation of the nonlinear constraints, the above-mentioned modifications (extensions) will not significantly affect the computational complexity of the problem under consideration. In our future research, we want to take into account the uncertain nature of the real-world variables that are not deterministic. Thus, a fuzzy approach could be applied to the UAVs' mission planning problem while allowing for a more accurate estimation of the timeliness of deliveries, and distribution through multi-depot and multi-fleet delivery planning subject to constraints imposed by appearance and a range of varying time windows. Finally, the model might benefit from being added to different aspects related to the size of fleets with heterogeneous UAVs and coordination of different UAV fleets operating independently in a shared area. [PERSON], [PERSON] and [PERSON] prepared the literature review. [PERSON] and [PERSON] formulated the problem and proposed the model. [PERSON] and [PERSON] conceived and designed the experiments.