Search results

The purpose of this study is to define configuration requirements needed to define “harmless” small unmanned aerial systems (sUAS) by taking into account mathematical models and ballistic approaches, already defined for small projectiles and now modified, with appropriate assumptions. These safety requirements are related to structures, materials, weight and dimensions, such as the radius of curvature.

In this paper, the state-of-the-art forensic and ballistic physics models are presented, and then safety requirements are defined for sUAS. This big challenge is due to a lack of regulation because each country and each regulatory authority have published their own regulations, where the imposed requirements are different because the mathematical models, and the related assumptions, are different from one country to another. Therefore, proper assumptions are introduced in these models to obtain safety requirements to define harmless final configuration.

If the final configuration meets the requirements defined in the methodology, there are no severe injuries on the human body, in case of impact with the sUAS. In other words, the inoffensive configuration can also be used in overcrowded areas without risks for the human life.

The final configuration proposed in this paper has some design features substantially modified, compared to the conventional configuration, and in particular, the sUAS could be the first harmless multicopter certified in Italy.

In the past decades, both civil and military applications of small unmanned aerial systems (sUASs) have been on the rise. The sUASs guarantee the performance of dangerous, dull, duly and dirty missions, according to the 4D rule. The purpose of this study is to describe, some ethical, operational and safety challenges occur owing to the use of sUASs at over-crowded areas or in emergency scenarios. After an overview of the current sUAS regulations, some strategic configuration elements will be analysed to improve these systems and to define safe and inoffensive sUASs. Nevertheless, some problems have not been completely overcome.

The unmanned vehicles are nowadays applied for different kinds of applications. Search and rescue (S&R) missions; terrain surveillance and monitoring after natural disasters, such as earthquakes and landslides; and transportation of medical equipment and cartography are some examples of the most renowned and important civil missions of sUAS. In all these scenarios, some challenges could be encountered. First, the use of sUASs could compromise the privacy of unaware citizens who are in the area of application. Moreover, even if the unmanned vehicle works according to national and international regulations, there are some hazards both for the ground operators and for the population, because these sUASs could impact the human body after a flight failure.

In this paper, current principal regulations will be analysed, identifying some differences and discrepancies among them. Moreover, some considerations about the configuration elements are introduced to define the safe use of sUASs. Nevertheless, the privacy challenge is quite complicated to be overcome definitely.

Considering some challenges related to the civil applications of sUASs, new unmanned configurations could be developed to guarantee safety and data protection of unaware people.

The purpose of this paper is to report the research activity of Politecnico di Torino concerning the MicroHawk platform (micro‐aerial vehicles – MAVs) and to present the design and the development of a basic flight simulator for educational/training purpose.

A simulator is an easy‐to‐use system for the analysis of maneuver response, the dynamic study and the evaluation of the aircraft flying and handling qualities for different aircraft categories. The software implementation, including the definition of mathematical model, the visual scenario and the real‐time data analysis graphic interface, are delineated in this paper. In addition to this experimental phase, an important effort is done to incorporate simulation into the autopilot tuning process.

An intense flight activity is carried out to test the flight control system performances of the MicroHawk platform and to establish general procedures to ensure the correct operation of all subsystems. The automatic flight of MAVs has been studied with success for territorial surveillance and map project.

In order to simplify the use of these platforms by the end‐user, a software interface will be designed to calculate automatically the flight plan, ensuring the desired trajectory design and collision avoidance.

The autopilot simulation integrated with vehicle's dynamics can be used to reduce the platform set‐up time and the risk of losing the prototype. The simulator training permits to study flight complex plane, in order to obtain better platform performances in real conditions. Starting from a simple scenario, it is possible to set up and upgrade the mission at any time during the simulation.

The purpose of this paper is to present an original design procedure for a flight control system.

An optimization process, based on a genetic algorithm (GA), is used to meet the frequency domain handling qualities requirements in the longitudinal plane for an unconventional platform characterized by nonlinear aerodynamics. The parameters are implemented in the search process as fitness functions related to the expected magnitude of bandwidth and delay for an existing micro aerial vehicle. The bandwidth and the delay of the longitudinal short‐term attitude response are estimated before and after the inclusion of the flight control system in the simulation model, and the parameters are compared with the expected handling qualities levels. A qualitative analysis of handling qualities levels is also performed by implementing the augmented aircraft in a simulator with a realistic visual environment.

The results show that an optimal search process based on a GA can implement the handling qualities requirements with a computational procedure that is straightforward.

Even if the requisites for bandwidth and delay implemented in the search process are general in use as no specific aircraft response type is taken as a reference for the estimation of handling qualities requirements, only future experimental work will provide insight for the definition of specific Level 1 boundaries for micro aerial vehicles in remotely piloted flight.

The virtual environment is useful to test remote piloting with unconventional onboard visual cues. This is important in applications in which technical limitations may preclude complete real time data link during flight tests in the first development phase of the vehicle.

The purpose of this paper is to present a novel approach for trajectory tracking of UAVS. Research on unmanned aircraft is constantly improving the autonomous flight capabilities of these vehicles to provide performance needed to use them in even more complex tasks. The UAV path planner (PP) plans the best path to perform the mission. This is a waypoint sequence that is uploaded on the flight management system providing reference to the aircraft guidance, navigation and control system (GNCS). The UAV GNCS converts the waypoint sequence in guidance references for the flight control system (FCS) that, in turn, generates the command sequence needed to track the optimum path.

A new guidance system (GS) is presented in this paper, based on the graph search algorithm kinematic A* (KA*). The GS is linked to a nonlinear model predictive control (NMPC) system that tracks the reference path, solving online (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with genetic algorithm (GA). The GA finds the command sequence that minimizes the tracking error with respect to the reference path, driving the aircraft toward the desired trajectory. The same approach is also used to demonstrate the ability of the guidance laws to avoid the collision with static and dynamic obstacles.

The tracking system proposed reflects the merits of NMPC, successfully accomplishing the task. As a matter of fact, good tracking performance is evidenced, and effective control actions provide smooth and safe paths, both in nominal and off-nominal conditions.

The GNCS presented in this paper reflects merits of the algorithms implemented in the GS and FCS. As a matter of fact, these two units work efficiently together providing fast and effective control to avoid obstacles in flight and go back to the desired path. KA* was developed from graph search algorithms. Maintaining their simplicity, but improving their search logics, it represents an interesting solution for online replanning. The results show that the GS uploads the collision avoidance path continuously during flight, and it obtains straightforward the reference variables for the FCS, thanks to the KA* model.