eFlight: RL Scheme for Autonomous Drones to Efficiently Fly through Obstacles

The flight time of uncrewed autonomous vehicles (UAVs) is constrained by its battery capacity, restricting its application in long-duration missions. To address this challenge, we propose eFlight, a hybrid scheme that uses a reinforcement-learning heuristic to augment A* for path finding. eFlight reduces both node expansions and computation time while finding energy-efficient paths in obstacle-dense 3D airspace. We compare eFlight with conventional path-planning algorithms for point-to-point flights on areas of various dimensions and with various obstacle densities. The results show that eFlight achieves a dual advantage: finding low-energy paths with short computation times. In high-density obstacle environment, eFlight identifies the lowest energy consumption path in 89.5% of the trials. Compared to the baseline scheme, eFlight reduces computation time by 90.6% ± 26.6% and energy by 7.13% ± 9.96%.