As the minimum fuel point-to-point optimal trajectories of a fuel cell powered unmanned air vehicle (UAV) are different from the minimum distance point-to-point optimal trajectories when the height differences between the initial positions and the final positions are significant, optimal route plans and flight paths based on the Dubins vehicle may not be fuel optimal. In this paper, a new method is proposed to solve three-dimensional (3D) minimum fuel route planning and path generation problems for a fuel cell powered UAV. The first step in the proposed method is to develop a fuel consumption cost model for the minimum fuel point-to-point optimal trajectories. In the second step, a genetic algorithm with different heading algorithms is implemented to find the minimum fuel route plan for a given list of waypoints. Finally, the minimum fuel flight path is generated by connecting the waypoints with minimum fuel point-to-point optimal trajectories. With the proposed method, the resulting 3D route plan and flight path are both dynamically feasible and fuel optimal. In this paper, we extend route planning problems from two dimensions to three dimensions, as is common with other route planning problems. We extend the optimization objective from minimizing the distance to minimizing the fuel, and we extend the dynamic constraints from the two-dimensional dynamics to the 3D point mass UAV dynamics including the propulsion system characteristics.
An Efficient Path-Generation Method for Virtual Colonoscopy