Optimal Route Planning and Power Management for Hybrid UAV Using A* Algorithm
Jacob H. Jadischke, Mitch Wolff, Jon Zumberge, Brandon Hencey, Anhtuan D. Ngo · 2023
View Video Presentation: https://doi.org/10.2514/6.2023-4508.vid For the practical application of a gas-electric hybrid UAV, algorithms are needed to synthesize joint motion and power plans that satisfy path and energy constraints. Specifically, this paper considers the problem of finding an energy-optimal flight path toward a destination under the flight path constraints and battery-only operational constraints. Standard techniques such as reinforcement learning, or Dynamic Programming are computationally expensive. This paper investigates the performance of the A* algorithm on this problem to reduce the computational complexity and, by extension, the computational time. By constructing the objective as the minimization of unrecoverable energy expenditures, the A* algorithm, Dijkstra’s Algorithm, and a proposed iterative A* solution, were successfully implemented for this problem. The developed optimal control policies were observed to satisfy regions with battery-only restrictions and match the total route cost through comparison to the Dynamic Programming solution of the same environment. Results show that the use of the A* algorithm and Dijkstra’s algorithm obtain an optimal solution of 1.64-8.85 times faster than the optimal solution obtained through Dynamic Programming.