A real-time network approach for including obstacles and flight dynamics in UAV route planning

Abstract

The procedure presented within considers the problem of calculating flight time for unmanned aerial vehicles (UAVs) while incorporating a subset of important flight dynamics characteristics in an operational field that contains obstacles. These flight time calculations are parameter inputs in mission planning and dynamic reassignment problems. The addition of pseudonodes and the addition of penalties into the associated edge weights are the basis for how the network generation procedure includes flight dynamics. The procedure includes a method for handling pop-up targets or obstacles in a dynamic reassignment problem. To guarantee that the optimal path includes flight dynamics, a selective Dijkstra's algorithm computes the shortest path. A complex mission plan consisting of thirty targets and three obstacles is the largest test scenario of nine developed scenarios. Our network generation procedure, along with the shortest path calculations of all 992 node pairs of interest, solves in approximately one second. This procedure allows for the fast computation needed to generate parameters for use in a dynamic domain such as mission planning and dynamic reassignment algorithms.

Parçali Hücresel Geneti̇k Algori̇tma İle İnsansiz Hava Araci Performansina Dayali Yol Planlama

“…Literatürde yol planlama algoritmalarında uçuş mekaniği açısından çok sayıda kısıt dikkate alınmıştır [67][68][69][70]. Önerilen algoritmalarda uçuş süresi, azami irtifa, azami tırmanma hızı, azami yatış açısı gibi geometrik kısıtlar olarak ifade edilebilecek kısıtlar dikkate alınmaktadır.…”

“…İHA performans parametreleri hesaplanırken dönüş performansı önemli bir faktördür. Manevra sırasındaki asgari dönüş yarıçapı, hız ve yük faktörü, azami dönüş hızı, hız, hareket sırasındaki yük faktörü hızı dikkate alınmaktadır [70]. Algoritma, yalnızca izin verilen dönüş hızları aralığında yörüngeler oluşturarak aracın manevra kabiliyetini aşmamasını sağlamaktadır.…”

Volcanic Ash Region Path Planning Based on Improved A-Star Algorithm

“…In the aspect of path planning, Myers et al proposed an improved Dijkstra algorithm model based on the consideration of aircraft performance to realize the dynamic redistribution of nodes [3]. ree assumptions are added to the mathematical model of multiobjective dynamic resource management to improve the accuracy of dynamic path planning.…”

The role of unmanned aerial vehicles in military communications: application scenarios, current trends, and beyond

“…It is obvious that many field measurements are required in order to re-model the new uplink and downlink channels that will be used, while at the same time new protocols will also have to be developed for the cellular-connected UAVs. 45,46 This will possibly result in spectrum re-allocation and of course in new spectrum management techniques that will be necessary in order to cope with the incorporation of the increasing number of new users in the network. It should be noted that the architectures of the future cellular networks should carefully take into consideration the coexistence of terrestrial and aerial mobile users and the possible implications that this coexistence might have on the efficiency and the QoS (Quality of Service) of the whole cellular network.…”