Path Planning and Real-Time Collision Avoidance Based on the Essential Visibility Graph

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

Towards Digital Twin Implementation for Assessing Production Line Performance and Balancing

The optimization of production processes has always been one of the cornerstones for manufacturing companies, aimed to increase their productivity, minimizing the related costs. In the Industry 4.0 era, some innovative technologies, perceived as far away until a few years ago, have become reachable by everyone. The massive introduction of these technologies directly in the factories allows interconnecting the resources (machines and humans) and the entire production chain to be kept under control, thanks to the collection and the analyses of real production data, supporting the decision making process. This article aims to propose a methodological framework that, thanks to the use of Industrial Internet of Things—IoT devices, in particular the wearable sensors, and simulation tools, supports the analyses of production line performance parameters, by considering both experimental and numerical data, allowing a continuous monitoring of the line balancing and performance at varying of the production demand. A case study, regarding a manual task of a real manufacturing production line, is presented to demonstrate the applicability and the effectiveness of the proposed procedure

Clothoid-Based Path Planning for a Formation of Fixed-Wing UAVs

Unmanned aerial vehicles (UAVs) are playing an increasingly crucial role in many appli cations such as search and rescue, delivery services, and military operations. However, one of the significant challenges in this area is to plan efficient and safe trajectories for UAV formations. This paper presents an optimization procedure for trajectory planning for fixed-wing UAV formations using graph theory and clothoid curves. The proposed planning strategy consists of two main steps. Firstly, the geometric optimization of paths is carried out using graphs for each UAV, providing piece-wise linear paths whose smooth connections are made with clothoids. Secondly, the geometric paths are transformed into time-dependent trajectories, optimizing the assigned aircraft speeds to avoid collisions by solving a mixed-integer optimal control problem for each UAV of the flight formation. The proposed method is effective in achieving suboptimal paths while ensuring collision avoidance between aircraft. A sensitivity analysis of the main parameters of the algorithm was conducted in ideal conditions, highlighting the possibility of decreasing the length of the optimal path by about 4.19%, increasing the number of points used in the discretization and showing a maximum path length reduction of about 10% compared with the average solution obtained with a similar algorithm using a graph based on random directions. Furthermore, the use of clothoids, whose parameters depend on the UAV performance constraints, provides smoother connections, giving a significant improvement over traditional straight-line or circular trajectories in terms of flight dynamics compliance and trajectory tracking capabilities. The method can be applied to various UAV formation scenarios, making it a versatile and practical tool for mission planning

Smooth Path planning for Fixed-Wing Aircraft in 3D Environment Using a Layered Essential Visibility Graph

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

A Particle Filtering Approach for Fault Detection and Isolation of UAV IMU Sensors: Design, Implementation and Sensitivity Analysis

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

Upper body motion tracking system with inertial sensors for ergonomic issues in industrial environments

Recently, motion tracking techniques to acquire human body movements have broadened their range of applications from sports training, to rehabilitation, to ergonomics issues. For industrial applications, motion capture systems are largely used to acquire workers movements to improve working conditions, comfort, safety, etc. Several tracking solutions have been provided to analyze trunk and upper limbs movement based on different sensing technologies such as optical systems, audio systems, radar systems, magnetic systems, inertial systems and mechanical motion systems. Motion tracking with inertial sensors has been an active research area due to its several advantages. In this paper the authors introduce a motion tracking method of arbitrary human upper body motion. Low cost wearable inertial sensors and CPU unit are used in the proposed approach to track the upper body movement in 3D space and in real time

Application of game theory and evolutionary algorithm to the regional turboprop aircraft wing optimization

Nash equilibrium and evolutionary algorithm are used to optimize a wing of a regional turboprop aircraft, with the aim to compare different optimization strategies in the aircraft design field. Since the aircraft design field is very complex in terms of number of involved variables and space of analysis, it is not possible to perform an optimization process accounting for all possible parameters. This leads to the need to reduce the number of the variables to the most significant ones. A multi-objective optimization approach is here performed, paying attention to the variables which mainly influence the objective functions. Results of Nash-Genetic algorithm are compared against those of both a typical Pareto front and a scalarization, showing that the proposed approach locates almost all solutions on the Pareto front, while the scalarization results are confined only in a zone of this front. The optimization elapsed time for a single optimization point is less than 32% of an entire Pareto front, but the designer must initially choose the players’ cards assignment

Model predictive control for a multi-body slung-load system

In this paper we present a multi-level and distributed control system, based on a robust Model Predictive Control (MPC) technique, for a multi-body slung-load system. In particular, we consider a swarm of autonomous multi-copters which are connected by wires to a suspended payload. The payload reference trajectory is obtained through a constrained optimization, then the reference trajectory for each UAV is derived on the basis of the known shape of the formation, while taking into account operational constraints such as collision avoidance and cruise speed. Trajectory tracking is performed by a multi-level flight control system based on a MPC technique and a PID control system. Numerical simulations have been performed in order to test the control system in realistic scenarios. In particular, the multi-copters are modeled by the six Degrees-of-Freedom (6DOF) model, the constraint forces on the wires are calculated using the Udwadia–Kalaba equation and the external disturbances (atmospheric turbulence and gust) are included in the simulation. Simulation results are encouraging, thus making the proposed system an appealing candidate for similar applications

Trajectory Planning and Tracking for a Re-Entry Capsule with a Deployable Aero-Brake

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

Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs

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