الفهرس 
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Abstract
This thesis offers 2D path and trajectory planning for unmanned aerial vehicle (UAV) in a static environment with known obstacles. The path produced must satisfy all kinematic and dynamic constraints of UAVs like maximum and minimum curvature bounds and minimum bending energy. The path also meets the safety conditions with all obstacles, borders in the environment and inter-collision between UAVs while minimizing the length of the curve joining the start and goal points of UAV.
To produce a feasible flyable and smooth path for UAV, there are two proposed methodologies in this thesis whether the environment is simple or complex.
1- When the environment is simple, the methodology used is the Pythagorean hodograph curve (PH curve) of order five because it is the best method used for path planning for UAV, make the path more flexible, and used in many researches. Then, the genetic algorithm optimization technique (GA) is used and combined with the previous method to get the optimal curve parameters for a feasible flyable and smooth path planning for UAV. Finally, to prove the efficiency of this methodology, apply it to get the optimal path planning for a single UAV joining initial and goal points. Also, apply it to get the optimal path planning for multiple UAVs satisfying all kinematic and dynamic constraints of UAVs especially avoiding the inter-collision between them.
2- When the environment is complex where the order of the path is greater than five, the proposed methodology is a combination of three path planning methods. These methods are Voronoi diagram, Dijkstra algorithm, and PH curve.
Voronoi diagram is used to divide the environment into collision-free spaces based on the vertices of all obstacles and borders in the environment. Then, use Dijkstra algorithm to get the shortest path between initial and goal points for UAV in the Voronoi diagram but this path is not smooth and has sharp edges so, PH curve of order two is used between each two consecutive points in the shortest path produced by Dijkstra algorithm to get the flyable and smooth path satisfying all movement’s conditions. Finally, the GA optimization algorithm is used to get the optimal curve parameters for each one of PH curves to get the optimal path for UAV.
Matlab simulation is used to simulate the mission with the proposed methodologies for producing the optimal path planning for UAVs satisfying all kinematic and dynamic constraints to prove the efficiency of these methodologies.