Title

ARTIFICIAL INTELLIGENCE BASED DYNAMIC MISSION PLANNING USING VORONOI DIAGRAMS AND PREDICTIVE LAUNCH ACCEPTABILITY REGION APPROACH

Abstract

Abstract

In this thesis, a dynamic air-to-surface mission planning strategy based on Voronoi diagrams and predictive launch acceptability region is proposed for opportunity targets in order to strengthen decision support capabilities of aircraft. Air-to-surface missions are planned in ground support systems and loaded to aircraft before the mission begins. This means that all the waypoints which should be followed during an air-to-surface mission are planned according to various threats and geographical formations. However, opportunity targets sometimes endanger aircraft safety because pilots may be obliged to deviate from planned waypoints in order to destroy the target which is unexpectedly appeared. First of all, threats on battlefields are modeled by ellipsoids, and geographical formations are simulated by geoTIFFs. Then, predictive launch acceptability region queries are modeled, and a strategy is developed to designate a release state. Then, Voronoi diagram is generated according to threats in order to form a connected graph that will connect the start and the goal states. The shortest path between the start and the goal state in Voronoi diagram is derived by Dijkstra’s shortest path algorithm. A typical method is developed in order to optimize the output of Dijkstra’s shortest path algorithm. The optimized path is enhanced according to geographical formations by extracting the maximum envelope of elevation profile of the path using Hilbert transform. Finally, the proposed method is analyzed in terms of convergence rate, mean trajectory length, elapsed time and compared with previous work. Mean trajectory length, average execution time, and convergence rate are observed as 7192.6 m, 0.60sec, and %100, respectively. Results show that dynamic mission planning can be accomplished for opportunity targets using a predicted release state with a shorter trajectory, admissible elapsed time, and full convergence rate.

Supervisor(s)

Supervisor(s)

MUSTAFA RASIT OZDEMIR

Date and Location

Date and Location

2021-09-02 13:30:00

Category

Category

MSc_Thesis