Computationally Adaptive Multi-Objective Trajectory Optimization for UAS with Variable Planning Deadlines

This paper presents a new approach which allows for the computation and optimization of feasible 3D flight trajectories within real time planning deadlines, for Unmanned Aerial Systems (UAS) operating in environments with obstacles present. Sets of candidate flight trajectories have been generated through the application of maneuver automaton theory, where smooth trajectories are formed via the concatenation of predefined trim and maneuver primitives; generated using aircraft dynamic models. During typical UAS operations, multiple objectives may exist, therefore the use of multi-objective optimization can potentially allow for convergence to a solution which better reflects overall mission requirements. Multiple objective optimization of trajectories has been implemented through weighted sum aggregation. However, real-time planning constraints may be imposed on the multi-objective optimization process due to the existence of obstacles in the immediate path. Thus, a novel Computationally Adaptive Trajectory Decision (CATD) optimization system has been developed and implemented in simulation to dynamically manage, calculate and schedule system execution parameters to ensure that the trajectory solution search can generate a feasible solution, if one exists, within a given length of time. The inclusion of the CATD potentially increases overall mission efficiency and may allow for the implementation of the system on different UAS platforms with varying onboard computational capabilities. This approach has been demonstrated in this paper through simulation using a fixed wing UAS operating in low altitude environments with obstacles present.(12)