Unmanned Aerial Vehicle (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.

This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm‐based method for minimizing pollution from fuel combustion during aircraft take‐off and landing at airports. This algorithm uniquely integrates the optimization of both landing gates and take‐off/landing runways, considering the correlation between engine operation time and pollutant levels. The approach employs advanced constraint handling techniques to manage the intricate time and resource limitations inherent in airport operations. Additionally, the study conducts a thorough sensitivity analysis of the model, with a particular emphasis on the mutation factor and the type of penalty function, to fine‐tune the optimization process. This dual‐focus optimization strategy represents a significant advancement in reducing environmental impact in the aviation sector, establishing a new standard for comprehensive and efficient airport operation management.