UAVS FLIGHT ROUTES OPTIMIZATION IN CHANGING WEATHER CONDITIONS – CONSTRAINT PROGRAMMING APPROACH

The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination

Problem marszrutyzacji floty dronów

W artykule rozważany jest problem dostarczania towarów w sieci dystrybucyjnej, w której operacje transportu realizowane są przez flotę bezzałogowych statków powietrznych (BSP). Szczególny nacisk położony jest na możliwość uwzględniania warunków pogodowych, w których odbywa się transport. Rozważany problem sprowadza się do wyznaczenia marszruty gwarantującej bezkolizyjne dostarczenie towaru do węzła, przy uwzględnieniu warunków pogodowych oraz poziomu baterii dronów. Zaproponowany został deklaratywny model, którego implementacja w środowisku programowania IBM ILOG pozwala na rozwiązywanie tego typu problemów w trybie on-line. Przedstawione eksperymenty obliczeniowe, potwierdzają poprawność opracowanego modelu.A problem of delivering goods in a distribution network is considered in which, transport operations are carried out by a fleet of unmanned aerial vehicles (UAV). The weather conditions in which a transport operations take place and a UAV battery level are taken into account. The above-mentioned weather conditions and the battery level affect the determination of the route guaranteeing collision-free delivery. The goods must be delivered to a specific node in a given time window. Specifying the route is the focus of this study. Solutions maximizing the level of customer satisfaction are sought. Computational experiments which show impact of the weather conditions to on route determination are presented

UAVS flight routes optimization in changing weather conditions – constraint programming approach

The problem of delivering goods in a distribution network is considered in which a fleet of Unmanned Aerial Vehicles (UAV) carries out transport operations. The changing weather conditions in which the transport operations take place and the UAVs energy capacity levels influenced by the weather conditions are taken into account as factors that affect the determination of a collision-free route. The goods must be delivered to the customers in a given time window. Establishing the routes are the focus of this study. Solutions maximizing the level of customer satisfaction are focused and the computational experiments presented in the study show the impact of weather conditions on route determination

Reactive Planning-Driven Approach to Online UAVs Mission Rerouting and Rescheduling

The presented problem concerns the route planning of a UAV fleet carrying out deliveries to spatially dispersed customers in a highly dynamic and unpredictable environment within a specified timeframe. The developed model allows for predictive (i.e., taking into account forecasted changing weather conditions) and reactive (i.e., enabling contingency UAVs rerouting) delivery mission planning (i.e., NP-hard problem) in terms of the constraint satisfaction problem. Due to the need to implement an emergency return of the UAV to the base or handling ad hoc ordered deliveries, sufficient conditions have been developed. Checking that these conditions are met allows cases to be eliminated if they do not guarantee acceptable solutions, thereby allowing the calculations to be sped up. The experiments carried out showed the usefulness of the proposed approach in DSS-based contingency planning of the UAVs’ mission performed in a dynamic environment