# Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling
## 1 Introduction  
In this paper, we consider the planning of the fleet mission problem for UAVs with highly dynamic and unpredictable environment constraints [1; 2; 3; 4; 5]. Typical disruptions in deliveries by UAVs may be caused by ad hoc changes to the deliveries ordered or changing weather conditions, which affect the energy consumption of UAVs and result in their shorter range due to the depletion of batteries [6; 7]. For this reason, routing and scheduling a UAV fleet in partially known and unpredictable environments should take into account both projected changes in orders and weather conditions and selected categories of interference not included in previous forecasts. In other words, the planning of the UAVs' missions should take into account the possibility of implementing both proactive (e.g., taking into account the projected disturbances) and reactive (e.g., enabling contingency reactions) behaviour. UAV routes can be determined through proactive planning [6] and generated in an offline mode or in reactive planning [8; 9] while executed in an online mode. Routes determined in proactive planning guarantee the achievement of the planned mission's goal in terms of environmental parameters, which change at predetermined intervals. Scenarios corresponding to planned reactive rules do not guarantee reactive end-to-end paths employed in the routing process while adapting to changes in the environment during the execution of a mission [7; 9; 10; 11; 12; 13; 14; 15; 16]. In particular, the reactive routing strategies linking \"route discovery\" (proactive route planning) and \"route maintenance\" (adopting reactive rules) concepts [17] are responsible for the robustness of UAVs in terms of the changes in weather conditions. In this context, our study focuses on reactive planning [18; 19] of deliveries by the UAVs' fleet that are resistant to sudden atmospheric changes, resulting in a change in wind direction and intensity, rain or snow, precipitation, or unforeseen changesin delivery schedules caused by the cancellation of ordered deliveries or the notification of new orders. In this context, the studies presented are a continuation of the previously published work focused on methods for planning UAVs' fleet missions online [20; 21]. Previous research has focused on selected planning strategies for UAVs' fleet missions. These focus on arbitrarily chosen decomposition concepts for planned volumes of deliveries being phased out under submissions and are responsible for the partial distribution of supplies. Both variable weather conditions and ad hoc changing conditions for the volume of planned deliveries are taken into account. The aim of the research was to develop methods of predictive and/or reactive planning of acceptable options successively implemented under the supply of sub-missions, in particular the methods that enable their online use. The originality of the paper results from merging the proactive and reactive planning of UAV fleet missions. The developed model allows for predictive (i.e., taking into account forecasted weather conditions changing) and reactive (i.e., enabling the interruption of a drone's mission) planning for delivery missions in terms of the constraint satisfaction problem (CSP). This is easily implemented and commercially available in a constraint programming environment, e.g., IBM ILOG. In particular, the conditions guaranteeing the resistance function convexity, which consequently speed up the calculations (involved in the CSP solution) for the extended version of the UAVs' mission planning are developed (i.e., taking into account multiple depots). To summarise, the main objective of this paper is to introduce constraint programming based on a hybrid model. This is a combination of proactive and reactive planning and routing the UAVs' fleet delivery missions.