This study proposes an entire hardware and software architecture from operator input to motor command for the autonomous area coverage mission using multiple unmanned aerial vehicles. Despite the rapid growth of commercial drone services, there are many limitations on operations, such as a low decision-making autonomy and the need for experienced operators to intervene in the whole process. For performing the area coverage mission more efficiently and autonomously, this study newly designs an optimization problem that allocates waypoints created to cover that area to unmanned aerial vehicles. With an optimized list of waypoints, unmanned aerial vehicles can fill the given areas with their footprints in a minimal amount of time and do not overlap each other during the mission. In addition, this study performs both various simulations for quantitative analysis and an outdoor experiment through real hardware implementation in order to verify the performance sufficiently. The methodologies developed in this study could be applied to endless applications using unmanned aerial vehicles equipped with mission-specific sensors.

A two-stage stochastic programming algorithm based on particle swarm optimization is proposed for an optimal and robust design in the aircraft sequencing and scheduling problem. The first and second stage decision problems are defined as aircraft sequencing and scheduling, respectively, and the uncertainties that arise from the aircraft flight time between two successive fixes are considered. Particle swarm optimization is utilized for a randomized search to make the first stage decision under incomplete information about uncertain parameters. In addition, the random key representation is adopted to apply particle swarm optimization to a discrete aircraft sequencing problem. Then, the second stage decision is made using mixed integer linear programming after the realization of the uncertain parameters. The performance of the proposed algorithm is verified by numerical simulations.

This paper proposes a new sequencing and scheduling algorithm for the point merge system based on mixed integer linear programming considering the uncertain flight time of aircraft. In the first stage of the proposed algorithm, for a static environment, deterministic robust solutions are determined. To consider the uncertainty, an extra buffer is introduced in the sequencing and scheduling algorithm, and the buffer size is analytically derived by generating a deterministic robust counterpart problem. In the second stage, to compensate for unforeseen situations under a dynamic environment, the static solution determined in the first stage is adjusted by using the proposed heuristic algorithm, with a sliding time window to reduce the computational load. The performances of the proposed algorithm are verified via numerical simulations based on historical data analysis.

In this study, we propose a practical path planning method that combines the A* search algorithm and minimum snap trajectory generation. The A* search algorithm determines a set of waypoints to avoid...

An optimal scheduling algorithm of the point merge system that considers a holding pattern is presented based on mixed-integer linear programming. The route structure of the point merge system is constructed by adopting virtual fixes, and appropriate constraints are included to integrate a holding pattern into the formulation of point merge system. The performance of the proposed point merge system holding algorithm is demonstrated by comparing with the point merge system and first-come first-served algorithms via Monte Carlo simulation. Simulation reveals that the point merge system holding algorithm shows the best performance in terms of delay reduction and intervention rate by air traffic controllers. The proposed point merge system holding algorithm is expected to assist human air traffic controllers as a decision support tool.