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The problem of generating an optimal flight trajectory with the objective of maximizing the coverage of specified target areas minimizing the total flight path, in the presence of a constrained environment, is solved via multi-objective Genetic Algorithms. A novel coverage model, based on the evaluation of a so called coverage potential field, is proposed. Sensitivity studies with increasing problem complexity allow validating procedure effectiveness as well as final solution reliability.

The introduction of new information and communication technologies (ICT) in factory environment is leading the world of manufacturing industry to a change. Indeed, we talk about Industry 4.0, the fourth industrial revolution, that facilitates the vision of a Smart Factory in which systems become cyber-physical, interact between themselves, monitor and validate physical processes, creating a virtual copy of the physical world and making decisions based on complex numerical analysis. Virtualization and simulation of production processes generate several benefits, in terms of costs and time, optimizing the assembly line design and studying human-machine interaction. Regarding the last topic, this paper proposes an innovative method for ergonomic analysis of workplaces on automotive assembly lines in a virtual environment. The method can represent an innovation for human-centered design of workplace in developing new products, reducing costs and improving job quality thanks to a preventive ergonomic approach.

One of the critical issues characterizing the manufacturing industries, which within Industry 4.0 era are experiencing the new human-centered approach in design, is related to working postures assumed by the workers in assembly activities. In order to study the motion of human body, the research team developed an inertial MoCap system, composed by IMUs. The system allows to estimate the attitude of the fundamental segments of the human body, by using a Kalman filtering, and to evaluate the posture angles assumed during the motion. The system is modular, composed by 4 modules, made of 4 sensors. From acquired data it is possible to code algorithms to online assess the desired ergonomic scores, making the system able to take decisions for the workplace design optimization. In this paper, the full body inertial MoCap system is presented, supported by a test case to prove the reliability of the system in industrial environment.

In the last decade, the increasing use of NanoSats and CubeSats has made the re-entry capsule an emerging research field needing updates in configuration and technology. In particular, the door to advancements in terms of efficiency and re-usability has been opened by the introduction of inflatable and/or deployable aerodynamic brakes and the use of on-board electronics for active control. Such technologies allow smaller sizes at launch, controlled re-entries, and safe recovery. This paper deals with the design of a guidance and control algorithm for the re-entry of a capsule with a deployable aero-brake. A trajectory optimization model is used both in the mission planning phase to design the reference re-entry path and during the mission to update the trajectory in case of major deviations from the prescribed orbit, thanks to simplifications aimed at reducing the computational burden. Successively, a trajectory tracking controller, based on Nonlinear Model Predictive Control (NMPC), is able to modulate the opening of the aero-brake in order to follow the planned trajectory towards the target. A robustness analysis was carried out, via numerical simulations, to verify the reliability of the proposed controller in the presence of model uncertainties, orbital perturbations, and measurement noise.

In recent years the use of Unmanned Aerial Vehicles (UAVs) has considerably grown in the civil sectors, due to their high flexibility of use. Currently, two important key points are making them more and more successful in the civil field, namely the decrease of production costs and the increase in navigation accuracy. In this paper, we propose a Kalman filtering-based sensor fusion algorithm, using a low cost navigation platform that contains an inertial measurement unit (IMU), five ultrasonic ranging sensors and an optical flow camera. The aim is to improve navigation in indoor or GPS-denied environments. A multi-rate version of the Extended Kalman Filter is considered to deal with the use of heterogeneous sensors with different sampling rates, and the presence of non-linearities in the model. The effectiveness of the proposed sensor platform is evaluated by means of numerical tests on the dynamic flight simulator of a quadrotor. Results show high precision and robustness of the attitude estimation algorithm, with a reduced computational cost, being ready to be implemented on low-cost platforms.

This paper deals with the design of a guidance control system for a swarm of unmanned aerial systems flying at a given altitude, addressing flight formation requirements that can be formulated constraining the swarm to be on the nodes of a triangular mesh. Three decentralized guidance algorithms are presented. A classical fixed leader–follower scheme is compared with two alternative schemes: the former is based on the self-identification of one or more time-varying leaders; the latter is an algorithm without leaders. Several operational scenarios have been simulated involving swarms with obstacles and an increasing number of aircraft in order to prove the effectiveness of the proposed guidance schemes.

In this paper, an optimal path search methodology for UAVs flying in 3D environments is presented, taking into account the presence of obstacles and other constraints deriving from flight dynamics. The so-called Essential Visibility Graph (EVG) is extended to the 3D case by describing the obstacles using a finite number of parallel planar layers at different altitudes. The resulting graph, called the Layered Essential Visibility Graph (LEVG), is based on an efficient branching algorithm and it is made up of a reduced number of nodes and edges thus assuring a limited computational burden. Once the optimal piece-wise linear path has been identified over the LEVG, aircraft performance related constraints, formulated in terms of turn and pull-up radii limits, can be taken into account via a smoothing procedure based on a 3D extension of Dubins' paradigm. This way an optimal flyable 3D path can be obtained. The implementation of a specific integer programming formulation within the graph search process ensures the full compliance of the optimal smoothed trajectory with the environmental constraints. The effectiveness of the proposed methodology is proved by means of numerical tests in complex operational scenarios over a real terrain morphology and an urban environment.

Current research on Unmanned Aerial Vehicles (UAVs) is focusing on the ability of performing complex tasks by means of cooperation over many aircraft, with the scope of reducing costs and increasing the reliability. However, the use of a cooperative formation deals with several challenges to coordinate a group of autonomous vehicles. A distributed situational awareness becomes an essential requirement towards the objective. In this paper, a Decentralized Moving Horizon Estimator (DMHE) is presented with the scope of distributing the computational burden and limiting the requirements about communication and software complexity besides avoiding the vulnerability of a centralized architecture to faults. The proposed algorithm merges the consensus theory with a moving horizon estimator to overcome Kalman filtering problems in the presence of constraints on any disturbance or state variables. The decentralization of the scheme is obtained by decomposing the overall estimation problem in several optimization sub-models whose convergence is guaranteed by consensus. A preliminary sensitivity analysis was performed to evaluate the results of the proposed strategy and the significance of its main parameters.

Safety in the operations of UAVs (Unmanned Aerial Vehicles) depends on the current and future reduction of technical barriers and on the improvements related to their autonomous capabilities. Since the early stages, aviation has been based on pilots and Air Traffic Controllers that take decisions to make aircraft follow their routes while avoiding collisions. RPA (Remotely Piloted Aircraft) can still involve pilots as they are UAVs controlled from ground, but need the definition of common rules, of a dedicated Traffic Controller and exit strategies in the case of lack of communication between the Ground Control Station and the aircraft. On the other hand, completely autonomous aircraft are currently banned from civil airspace, but researchers and engineers are spending great effort in developing methodologies and technologies to increase the reliability of fully autonomous flight in view of a safe and efficient integration of UAVs in the civil airspace. This paper deals with the design of a collision avoidance system based on a Distributed Model Predictive Controller (DMPC) for trajectory tracking, where anticollision constraints are defined in accordance with the Right of Way rules, as prescribed by the International Civil Aviation Organization (ICAO) for human piloted flights. To reduce the computational burden, the DMPC is formulated as a Mixed Integer Quadratic Programming optimization problem. Simulation results are shown to prove the effectiveness of the approach, also in the presence of a densely populated airspace.

This paper describes the control architecture and the control laws of a new concept of Modular Iron Bird aimed at reproducing flight loads to test mobile aerodynamic control surface actuators for small and medium size aircraft and Unmanned Aerial Vehicles. The iron bird control system must guarantee the actuation of counteracting forces. On one side, a hydraulic actuator simulates the hinge moments acting on the mobile surface due to aerodynamic and inertial effects during flight, on the other side, the actuator to be tested applies an active hinge moment to control the angular position of the same surface. Reference aerodynamic and inertial loads are generated by a flight simulation module to reproduce more realistic conditions arising during operations. The design of the control action is based on a dynamic model of the hydraulic plant used to generate loads. This system is controlled using a Proportional Integral Derivative control algorithm tuned with an optimization algorithm taking into account the closed loop dynamics of the actuator under testing, uncertainties and disturbances in the controlled plant. Numerical simulations are presented to show the effectiveness of the proposed architecture and control laws.

This paper deals with the design of a decentralized guidance and control strategy for a swarm of unmanned aerial vehicles (UAVs), with the objective of maintaining a given connection topology with assigned mutual distances while flying to a target area. In the absence of obstacles, the assigned topology, based on an extended Delaunay triangulation concept, implements regular and connected formation shapes. In the presence of obstacles, this technique is combined with a model predictive control (MPC) that allows forming independent sub-swarms optimizing the formation spreading to avoid obstacles and collisions between neighboring vehicles. A custom numerical simulator was developed in a Matlab/Simulink environment to prove the effectiveness of the proposed guidance and control scheme in several 2D operational scenarios with obstacles of different sizes and increasing number of aircraft.

This paper is focused on the design of a Model Predictive Control (MPC) algorithm for a micro-satellite with a mass of about 20 kg, equipped with an umbrella-like deployable front structure. This control device allows the vehicle to maneuver and track a prescribed trajectory during the re-entry phase by changing the aerobrake surface. The proposed MPC controller is aimed at minimizing the error between the desired target position at an altitude of about 30 km, after which the satellite follows an uncontrolled ballistic trajectory. A single control move is updated at a sampling rate of 0.1 Hz trying to balance performance with computational burden for a possible real time implementation. To prove that the proposed MPC strategy implies a limited loss of performance, a comparison with MPC controllers optimizing more than one control move has been carried out.

In order to test aircraft actuators moving aerodynamic control surfaces, it becomes more and more important to have flexible facilities which are able to produce a variety of loading conditions with a given accuracy. The so-called Modular Iron Bird (MIB), which is now in the design phase, will be able to reproduce aerodynamic and inertial loads for a large category of aircraft. The MIB control system has to guarantee the application of a desired time history of forces through a hydraulic system, while the aircraft actuator is driven in closed-loop to guarantee a given aerodynamic surface deflection. Therefore, two controllers are fighting each other, and this makes the control problem challenging. In this paper, a nonlinear model-based predictive control strategy is proposed. Numerical results allow to test the effectiveness of this control strategy and its benefit with respect to the classical PID approach.

This paper deals with a novel procedure to generate optimum flight paths for multiple unmanned aircraft in the presence of obstacles and/or no-fly zones. A real-time collision avoidance algorithm solving the optimization problem as a minimum cost piecewise linear path search within the so-called Essential Visibility Graph (EVG) is first developed. Then, a re-planning procedure updating the EVG over a selected prediction time interval is proposed, accounting for the presence of multiple flying vehicles or movable obstacles. The use of Dubins curves allows obtaining smooth paths, compliant with flight mechanics constraints. In view of possible future applications in hybrid scenarios where both manned and unmanned aircraft share the airspace, visual flight rules compliant with International Civil Aviation Organization (ICAO) Annex II Right of Way were implemented. An extensive campaign of numerical simulations was carried out to test the effectiveness of the proposed technique by setting different operational scenarios of increasing complexity. Results show that the algorithm is always able to identify trajectories compliant with ICAO rules for avoiding collisions and assuring a minimum safety distance as well. Furthermore, the low computational burden suggests that the proposed procedure can be considered a promising approach for real-time applications.

This article proposes a novel approach to the Distributed State Estimation (DSE) problem for a set of co-operating UAVs equipped with heterogeneous on board sensors capable of exploiting certain characteristics typical of the UAS Traffic Management (UTM) context, such as high traffic density and the presence of limited range, Vehicle-to-Vehicle communication devices. The proposed algorithm is based on a scalable decentralized Kalman Filter derived from the Internodal Transformation Theory enhanced on the basis of the Consensus Theory. The general benefit of the proposed algorithm consists of, on the one hand, reducing the estimation problem to smaller local sub-problems, through a self-organization process of the local estimating nodes in response to the time varying communication topology, and on the other hand, of exploiting measures carried out nearby in order to improve the accuracy of the local estimates. In the UTM context, this enables each vehicle to estimate both its own position and velocity, as well as those of the neighboring vehicles, using both on board measurements and information transmitted by neighboring vehicles. A numerical simulation in a simplified UTM scenario is presented, in order to illustrate the salient aspects of the proposed algorithm.

The applicability of a novel algorithm identifying optimal paths for Unmanned Aerial Vehicles in 2-D dynamic environments has been preliminarily assessed in this paper. Optimality is evaluated taking path length or flight time as objective functions. Flight trajectories, compliant with both air vehicle and environmental constraints, are made up of a finite number of circular arcs and straight lines. Such a geometrical sequence is converted into a finite sequence of two bit-coded basic flight manoeuvres. Identification of optimum path is obtained coupling such a manoeuvering model with a Particle Swarm Optimizer (PSO). To deal with the problem of a dynamic environment a recurrent planning strategy has been developed. Following this approach, final path is obtained by performing both the path planning and the path tracking phase within a series of constant time windows. While the air vehicle is tracking the current sub-optimal trajectory, the algorithm identifies a new sub-optimal trajectory, based on the obstacles estimated position at the end of the current time window, that will be tracked by the air vehicle in the next time window. This way, the computed path turns out to be a piecewise sequence of sub-optimal trajectories reaching the destination point. The identification of obstacles future position is carried out by the algorithm only by monitoring their current position. To test the effectiveness of the proposed flight path planner, we set different 2-D scenarios with obstacles having both constant and variable speed.

This paper deals with the problem of flight path planning for unmanned fixed-wing air vehicles (UAVs) in complex 3D environments. Flight paths must be compliant with both mission constraints defined in terms of no-fly zones, obstacles and destination points and aircraft performance constraints such as maximum flight path angle and minimum turn radius.Trajectory generation is addressed as a minimum cost path search using a novel layered Essential Visibility Graph whose arcs and corresponding weights are obtained via an efficient branching algorithm to reduce computational time. The resulting path is a piecewise trajectory composed by only circular arcs and straight segments, according to Dubins paradigm. To prove the effectiveness of the proposed method, operational scenarios derived from real terrain morphology have been used.

This paper proposes the application of a hybrid consensus on information (CI) and consensus on measurements (CM) algorithm, named HCMCI, to deal with the Distributed State Estimation (DSE) problem for a set of cooperating UAVs equipped with heterogeneous on board sensors and limited range communication devices. The benefit of the proposed algorithm is to allow to each vehicle to estimate position and velocity of the entire set of vehicles using only its own on board sensors and the information received only from the adjacent vehicles. The meaning of the theoretical conditions required to guarantee the stability of the estimate are discussed, with respect to the particular application. Numerical simulation are presented of different flight scenarios meaningful for the UAS Traffic Management (UTM) context. © 2019 IEEE.

This paper deals with the problem of generating 3D flight paths for a swarm of cooperating Unmanned Aerial Vechicles (UAVs) flying in a formation having a prespecified shape, in the presence of polygonal obstacles, no-fly zones and other non cooperative aircraft. UAVs are modeled as Dubins flying vehicles with bounds on the turning radius and flight path climb/descent angle. A Reduced Visibility Graph (RVG) based method, connecting selected nodes by means of circular arcs and segments, is adopted to minimize the length of each path. Then, to keep as much as possible the formation shape while flying between obstacles, the RVG is refined with the addition of so called Rendez-Vous Waypoints (RVWs). These are placed between groups of obstacles where it is impossible to maintain the desired formation. Waypoints locations and UAVs paths are optimized using a bi-level game theoretic approach based on the leader-follower Stackelberg model, where the lower level and upper level problems are the search of the shortest paths and the optimal locations of waypoints respectively. Such an approach allows to fly between obstacles, dispersing the formation and forcing UAVs to recompose it at given waypoints (RVWs) beyond groups of obstacles. Collision avoidance among UAVs and possible non-cooperative aircrafts, called intruders, is then achieved solving a set of linear quadratic optimization problems based on an original geometric based formulation. The effectiveness of the proposed approach is shown by means of numerical simulations where RVWs positions are optimized via a genetic algorithm.

This paper deals with a distributed path planning and Collision Avoidance System (CAS) for multiple aircraft sharing the same airspace. The algorithm is compliant with aircraft limits on maximum turn rate and with environmental constraints resulting from no-fly zones and/or obstacles. The avoidance maneuver is calculated on the basis of the Essential Visibility Graph and Dubins curves. Numerical examples reproducing urban-like environments show the effectiveness of the proposed approach.

Since the early development for health purposes in 1950s, motion tracking systems have been strongly developed for several applications. Nowadays, using Micro Electro-Mechanics Systems (MEMS) technologies, these systems have become compact and light, being popular for several applications. Looking at the manufacturing industry, such as the automotive one, ergonomic postural analyses are a key step in the workplaces design and motion tracking systems represent fundamental tools to provide data about postures of workers while carrying out working tasks, in order to assess the critical issues according to ISO 11226 standard. The aim of this work is to present an experimental wearable inertial motion tracking system, developed at the Dept. of Engineering of the University of Campania “Luigi Vanvitelli” in collaboration with Linup S.r.l., composed by several low-cost inertial measurement units (IMU). The system allows to estimate the orientation of selected human body segments and to analyze the postures assumed during the working tasks. To increase the flexibility of use, the system is highly modular: it’s composed by 4 independent modules in full-body configuration, each one made of 3 or 4 inertial units. In this paper, the overall system is presented, supported by several test cases, carried out in Fiat Chrysler Automobile (FCA) assembly lines, to test the system reliability in industrial environments. Furthermore, an automatic posture analysis code is presented to evaluate the postural critical issue of the workplaces.

The study of human factors is fundamental for the human-centered design of Smart Workplaces. IIoT (Industrial Internet of Things) technologies, mainly wearable devices, are becoming necessary to acquire data, whose analysis will be used to make decision in a smart way. For industrial applications, motion-tracking systems are strongly developing, being not invasive and able to acquire high amounts of data related to human motion in order to evaluate the ergonomic indexes in an objective way, as well as suggested by standards. For these reasons, a modular inertial motion capture system has been developed at the Department of Engineering of the University of Campania Luigi Vanvitelli. By using low cost Inertial Measurement Units – IMU and sensor fusion algorithms based on Extended Kalman filtering, the system is able to estimate the orientation of each body segment, the posture angles trends and the gait recognition during a working activity in industrial environment. From acquired data it is possible to develop further algorithms to online asses ergonomic indexes according to methods suggested by international standards (i.e. EAWS, OCRA, OWAS). In this paper, the overall ergonomic assessment tool is presented, with an extensive result campaign in automotive assembly lines of Fiat Chrysler Automobiles to prove the effectiveness of the system in an industrial scenario.

In the Industry 4.0 and digital revolution era, the world of manufacturing industry is experiencing an innovative reconfiguration of design tools and methodologies, with a different approach to the production processes organization. The design philosophy is changing, integrating to engineering contribution interpretative aspects (design thinking), executive practices (design doing) and cognitive aspects (design cultures). The design becomes human–centered. The new Virtual Reality technologies allow to validate performances of designed products and production processes by means of virtual prototypes in a virtual simulated environment. This approach generates several benefits to the companies, in terms of costs and time, and allows optimizing the assembly line design and related workplaces, by improving workers’ benefits too. This paper proposes an innovative method to validate the design of workplaces on automotive assembly lines in a virtual environment, based on ergonomic approach, according to ERGO – Uas system, applied by FCA (Fiat Chrysler Automobiles) groups, that integrates UAS method for measurement and EAWS method for biomechanical effort evaluation. Creating 3D virtual scenarios allows to carry on assembly tasks by virtual manikins in order to be evaluated from different points of view. In particular, data coming from the simulation can be used to assess several ergonomic indexes, improving safety, quality and design. The analysis is supported by the use of a motion capture system, developed by the University of Campania and composed of wearable inertial sensors, that estimates the attitude of fundamental human segments, using sensor fusion algorithms based on Kalman filtering. In this way, it is possible to make a further design validation, assessing the EAWS index basing on posture angles trends evaluated. This method can represent an innovation for human-centered design of workplace in developing new products, reducing costs and improving job quality.

The Unmanned Aerial Vehicles (UAVs) integration in the civil air traffic will contribute to the reduction of technical barriers related to safety and operational challenges associated with enabling routine UAV access to the civil airspace. While manned aircraft involve pilots and the Air Traffic Controller to take decisions, and follow their preassigned paths to avoid collisions with other aircraft, UAVs still need the definition of algorithms and rules. In this paper, a collision avoidance algorithm based on International Civil Aviation Organization (ICAO) rules to resolve possible conflicts among aircraft that are on a collision course while flying to their respective destinations is proposed. The proposed algorithm is based on the combination of collision prediction, speed optimization and inverse proportional navigation algorithms. Different strategies are activated on the basis of the UAV status and in particular on the evaluation of the risk level leading the UAV in a de-confliction or avoidance mode. Numerical simulations are presented to show the effectiveness of the proposed approach in the presence of many UAVs.

This paper deals with the problem of generating optimal 2D flight paths compliant with mission constraints resulting from no-fly zones and/or obstacles, and from flight mechanics limitations as minimum coordinate turn radius and maximum climb and descent angles. The optimization problem is converted into a minimum cost path search within a so called Essential Visibility Graph whose arcs and corresponding weights are obtained via an efficient branching algorithm in view of possible real-time implementations. A possible extension to 3D paths is also presented. Finally, numerical examples are reported with a discussion on the computational aspects.

A two-stage optimization model for flight path planning of cooperative UAVs in formation flight in the presence of polygonal obstacles and no-fly zones is proposed. Adopting a Visibility Graph (VG) approach, the virtual formation leader plans its flight path composed of circular arcs and segments connecting obstacles vertices. Then groups of obstacles, being not permeable by the flight formation without UAVs separation or formation shape deformation, are clustered and the VG is revised with the addition of so called rendez-vous waypoints, forcing the formation to be recomposed at a given location beyond groups of obstacles. Such rendez-vous waypoints are optimized at a higher hierarchical level with respect to the flight path optimization leading to a Stackelberg game. The validity of the proposed approach and optimization model is shown by means of numerical simulations where flight paths are obtained calculating shortest path on the revised Visibility Graph and waypoints positions are optimized via a genetic algorithm. Finally, anti-collision among UAVs is achieved via a simple potential based method.

In this paper a Fault Tolerant system for the attitude estimation of an Unmanned Aerial Vehicle (UAV) using low cost magnetometers, accelerometers, and gyroscopes, implemented in an Inertial Measurement Unit (IMU) is proposed. An approach based on the Unscented Kalman Filter (UKF) for Detection, Isolation and Reconfiguration is investigated in the presence of a Hardware Duplex IMU mounted on board for flight control purposes. A state automaton, with comparative logics, detects if anomalies occurr on one of the two IMUs; then based on a comparison between the measured variables and their UKF estimations, the fault is isolated and identified. The proposed approach is an alternative to standard Hardware Triplex IMU architectures based on triple physical redundancy. At the expense of a higher computational cost and a small delay in isolating faults, the UKF based approach allows to limit the number of IMUs to two, or to guarantee a fail safe behavior in the presence of a double fault, even if contemporary, with Triplex IMUs. Experimental results on the implementation of a multiple IMU platform on a quadrotor flight control board are finally presented.

This paper presents the application of a distributed attitude and position estimation algorithm to a swarm of cooperating UAVs with heterogeneous sensors on board. The algorithm, based on a Consensus Extended Kalman Filtering (CEKF) to account for nonlinearities, is implemented assuming kinematic relationships. Numerical simulations are presented on different flight scenarios to evaluate the benefits of dealing with prior and novel information in a separate way on the basis of recent theoretical results on CEKF. Inertial and vision sensors are supposed to be mounted on board of the aircraft. Realistic flight scenarios are analyzed in the light of possible time communication delays among the agents. © 2015 IEEE.

The problem of detecting and isolating sensor faults (sensor fault detection and isolation-SFDI) on a general aviation aircraft, in the presence of external disturbances, is considered. The proposed approach consists of an extended Kalman observer applied to an augmented aircraft plant, where some integrators are added to the output variables subject to faults. The output of the integrators should be ideally zero in the absence of model uncertainties, external disturbances and sensor faults. A threshold-based decision making system is adopted where the residuals are weighted with gains coming from the solution to an optimization problem. The proposed nonlinear observer was tested both numerically on a large database of simulations in the presence of disturbances and model uncertainties and on input-output data recorded during real flights. In this case, the possibility of successfully applying the proposed technique to detect and isolate faults on inertial and air data sensors, modelled as step or ramp signals artificially added to the real measurements, is shown.

In this paper the use of different observer schemes based on Kalman Filtering for the detection and isolation of aircraft abrupt and incipient sensor faults on Inertial Measurement Units (IMUs) is discussed. The possibility of using a dynamic 6DoF model of the aircraft is explored and compared with the use of a purely kinematic model. Both the possibilities are investigated assuming that two IMUs are available on board, and the analytic redundancy provided by the observers is used to vote the healthy one, when a fault occurs on accelerometers, gyros or magnetometers. The proposed schemes are applied to simulated flight data of a General Aviation aircraft generated in the presence of disturbances and uncertainties. Preliminary experimental results using two low cost IMUs are also shown for possible applications to improve safety and reliability of small Unmanned Air Vehicles.

This paper describes an algorithm for the control of a swarm of UAVs based on decentralized MPC. For each UAV, our algorithm first determines the trajectory taking into account the obstacles and the constraints on the aircraft performance. Then basing on a robust MPC algorithm, optimal guidance laws are calculated and tracked by the UAVs by means of local PIDs controllers. Our approach also allows us to take into account moving obstacles and constraints on the minimum distance between the vehicles. Validation of the approach is obtained by means of simulations where for each UAV a 6-DOF model is used.