An Innovative Mission Management System for Fixed-Wing UAVs

This paper presents two innovative units linked together to build the main frame of a UAV Mis- sion Management System. The first unit is a Path Planner for small UAVs able to generate optimal paths in a tridimensional environment, generat- ing flyable and safe paths with the lowest com- putational effort. The second unit is the Flight Management System based on Nonlinear Model Predictive Control, that tracks the reference path and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves on-line (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that min- imizes the tracking error with respect to the ref- erence path, driving the aircraft far from sensed obstacles and towards the desired trajectory

A Comprehensive Robust Adaptive Controller for Gust Load Alleviation

The objective of this paper is the implementation and validation of an adaptive controller for aircraft gust load alleviation. The contribution of this paper is the design of a robust controller that guarantees the reduction of the gust loads, even when the nominal conditions change. Some preliminary results are presented, considering the symmetric aileron deflection as control device. The proposed approach is validated on subsonic transport aircraft for different mass and flight conditions. Moreover, if the controller parameters are tuned for a specific gust model, even if the gust frequency changes, no parameter retuning is require

The Design of GDPR-Abiding Drones Through Flight Operation Maps: A Win–Win Approach to Data Protection, Aerospace Engineering, and Risk Management

Risk management is a well-known method to face technological challenges through a win–win combination of protective and proactive approaches, fostering the collaboration of operators, researchers, regulators, and industries for the exploitation of new markets. In the field of autonomous and unmanned aerial systems, or UAS, a considerable amount of work has been devoted to risk analysis, the generation of ground risk maps, and ground risk assessment by estimating the fatality rate. The paper aims to expand this approach with a tool for managing data protection risks raised by drones through the design of flight maps. The tool should allow UAS operators choosing the best air corridor for their drones based on the so-called privacy by design principle pursuant to Article 25 of the EU data protection regulation, the GDPR. Among the manifold applications of this approach, the design of fly zones for drones can be tailored for public authorities in the phase of authorization of new operations, much as for national Data Protection authorities that have to control the lawfulness of personal data processing by UAS operations. The overall aim is to present the first win–win approach to data protection issues, aerospace engineering challenges, and risk management methods for the threats posed by this technology

Hybrid game evolutionary algorithm for mission path planning of aerial survey tasks

The aim of this paper is to implement a Game-Theory based offline mission path planner for aerial inspection tasks of large linear infrastructures. Like most real-world optimisation problems, mission path planning involves a number of objectives which ideally should be minimised simultaneously. The goal of this work is then to develop a Multi-Objective (MO) optimisation tool able to provide a set of optimal solutions for the inspection task, given the environment data, the mission requirements and the definition of the objectives to minimise. Results indicate the robustness and capability of the method to find the trade-off between the Pareto-optimal solutions