Practical Approach to Mission Autonomy of Multiple Unmanned Air Mobilities

Extensive research has been conducted on mission autonomy for multiple aircraft and air mobility operations because of the increasing demand for developing manned-unmanned teaming. Besides a few studies, research focusing on real terrain environments and non-linear dynamics for multiple large unmanned aircraft operations for high-speed maneuvers remains lacking. This study proposes an integrated framework that includes mission planning for low-level flight control laws in 3D terrain environments with complex non-linear flight dynamics. A genetic algorithm with double chromosomes was used for task allocation, and a rapidly exploring random tree star with a line-of-sight path optimization method was used for fast and optimal path planning. Further, spline interpolation was applied to generate flyable paths and trajectory commands for control laws. A demonstration was performed based on non-linear flight dynamics with incremental backstepping-based trajectory tracking control laws. In addition to integrating these methods, a trajectory envelope was introduced to consider the effects of the complex non-linear dynamics. The integrated method predicts whether the given trajectory will be stable for the selected rotorcraft unmanned aerial vehicles by checking the trajectory before the flight and converting it to a continuous connection of the maneuver library using flight regime recognition. Based on this simulation, the proposed framework for multiple aircraft operations with 3D terrain and non-linear flight dynamics proves its capabilities.