Sensor fault detection and isolation (SFDI) is a fundamental topic in unmanned aerial vehicle (UAV) development, where attitude estimation plays a key role in flight control systems and its accuracy is crucial for UAV reliability. In commercial drones with low maximum take-off weights, typical redundant architectures, based on triplex, can represent a strong limitation in UAV payload capabilities. This paper proposes an FDI algorithm for low-cost multi-rotor drones equipped with duplex sensor architecture. Here, attitude estimation involves two 9-DoF inertial measurement units (IMUs) including 3-axis accelerometers, gyroscopes and magnetometers. The SFDI algorithm is based on a particle filter approach to promptly detect and isolate IMU faulted sensors. The algorithm has been implemented on a low-cost embedded platform based on a Raspberry Pi board. Its effectiveness and robustness were proved through experimental tests involving realistic faults on a real tri-rotor aircraft. A sensitivity analysis was carried out on the main algorithm parameters in order to find a trade-off between performance, computational burden and reliability.

A fault detection and isolation algorithm 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. Assuming the availability of double triaxial gyros, accelerometers, and magnetometers, the possibility that fault can occur both in input (gyros outputs) and in output (accelerometers and magnetometers outputs) to the kinematic nonlinear relations underlying attitude estimation must be taken into account. If an extended Kalman filter (EKF) can compensate for biases on gyroscopes, fault detection, for all sensors, is first obtained with a comparison between homogeneous sensor outputs. Then, the isolation of the faulted gyro is carried out through the analysis of the EKF bias estimates, whereas a set-based approach is used to isolate faulted accelerometers or magnetometers. For these sensors, isolation tests involve the solution of a linear programming problem on a moving time window in the discrete time domain. In order to show the practical applicability and robustness against measurement noise and different kind of faults, a set of simulations involving experimental data collected during flights of a tricopter UAV are discussed.

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.

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.