Development of an Ant Colony Optimization Algorithm to improve Maintenance Process Efficiency

Efficient maintenance policies are of fundamental importance because of their fallbacks into the safety and economics of air traffic operations. Usually the optimization of maintenance process is limited to a resource optimization in position and number. But it should be considered that maintenance tasks are performed by man whose excessive workload has negative falls-out not only for workers well-being but also for process safety and efficiency. Thus, in maintenance process optimization it is necessary to take into account also ergonomic aspects of workplace. This gives rise to an optimization of the maintenance process by using an ergonomic approach. In this way, the result of the optimization could allow improvements in the quality of the work of maintenance, but also a greater efficiency of the whole maintenance system. An ant colony optimization algorithm has been developed in order to optimize the system efficiency. This kind of algorithm natively permits to improve man movements into the workplace; furthermore the optimization of the workplace ergonomics has been added. To do this, an objective function of efficiency levels has been determined, linked to any task performed by man. Some protocols have been created on the basis of a literature survey and experimental results. This paper illustrates an applied research in which a method for the optimization of the maintenance process efficiency has been developed in order to show the applicability of a tool offering benefits on both sides: the maintenance process and the related human factors

Multiobjective evolutionary-based optimization methods for trajectory planning of a quadrotor UAV

This thesis describes the main research activity developed in a three years PhD program on flight dynamics. Optimization and UAVs flight control have been the main focus with methodological contributions on optimization, numerical and experimental work. Unmanned Aerial Vehicles (UAV) captured the attention of both research and industrial worlds as a replacement for expensive human-piloted vehicles. In the last decade, they became widely used for several applications in which humans could be unnecessary or in some cases too in danger. Many laboratories in the area of flight control, but also in the areas of robotics and control engineering in general, made significant research experiences on quadrotors. A collaboration between University of Naples "Parthenope" and the Second University of Naples is aimed at designing and using UAVs for educational and research purposes. More than one quadrotor was built, tested in flight and used as a platform for testing flight control and navigation systems. Several optimization problems may be encountered in the design of an UAV. During the design phase, they arise from the choice of the hardware, the design and layout of the structure, the aerodynamics. On the other hand, for the Guidance Navigation and Control system, the management of single or fleets of UAVs requires the solution of many non-linear optimization problems. For this reason a multi-objective general purpose optimization software has been developed, integrating evolutionary methods, as genetic algorithm and ant colony, with game theory paradigms, as Nash and Stackelberg equilibria. These methods have been primarily used to solve trajectory optimization problems with the scope of searching efficient flight trajectories in the presence of constraints. The thesis is developed around the flight control of a quadrotor UAV. The following are the main steps of the work described in this thesis: - dynamic and aerodynamic modelling oriented to flight control design; - development of a distributed general purpose optimization software implementing Game Theory based paradigms and Ant Colony algorithm hybridization; - Application of the above optimization methods to trajectory planning; - Numerical simulations and flight experiments. In Chapter 2, the quadrotor platform is described, together with the mathematical modelling and the design of the low level flight control system (attitude and speed control). In Chapter 3 the structure of the general purpose optimization software, mainly focused on the game theory layer and the ant colony algorithm is presented. In Chapter 4 the objectives of the optimization software are described and solved. Finally, in the Chapter 5, numerical simulations and flight tests are are shown

Distributed Reactive Model Predictive Control for Collision Avoidance of Unmanned Aerial Vehicles in Civil Airspace

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

A mixed probabilistic–geometric strategy for UAV optimum flight path identification based on bit-coded basic manoeuvres

This paper presents a novel algorithm identifying optimal flight trajectories for Unmanned Aerial Vehicles compliant with environmental constraints. Such constraints are defined in terms of obstacles, fixed way-points and selected destination points. Optimality is evaluated taking the minimum path length as the specific objective function. The proposed path planning strategy is based on an original trajectory modelling coupled with a Particle Swarm optimizer (PSO). Flight paths starting from a specified point and ending at a selected destination point are divided into a finite number of segments made up of circular arcs and straight lines. In the proposed approach such a geometrical sequence is replaced with a finite sequence of binary-coded basic manoeuvres. This novel formulation allows to easily handle the manoeuvres sequence with a fixed number of integer variables taking advantage of PSO capability in handling discrete variables; moreover the use of mixed-type variables provides the optimization procedure a useful flexibility in the “decision making” modelling and operational scenarios definition as well. Specific geometric-based linear obstacle avoidance models have been implemented in addition to suitable penalty functions. The use of these models forces each path to be consistent with the environmental constraints favouring the identification of feasible trajectories with a reduced number of iterations and particles. The path planning model has been developed with particular care devoted to reduce computational effort as well as to improve algorithm capability in handling general-shaped obstacles both in 2-D and 3-D environments. Various applications have been performed in order to test the effectiveness of the proposed flight path generator. Applicability of the proposed optimization model also to vehicles with VTOL and hovering capabilities has been preliminarily assesse

Spatial interaction models: facility location using game theory

Facility location theory develops the idea of locating one or more facilities by optimizing suitable criteria such as minimizing transportation cost, or capturing the largest market share. The contributions in this book focus an approach to facility location theory through game theoretical tools highlighting situations where a location decision is faced by several decision makers and leading to a game theoretical framework in non-cooperative and cooperative methods. Models and methods regarding the facility location via game theory are explored and applications are illustrated through economics, engineering, and physics. Mathematicians, engineers, economists and computer scientists working in theory, applications and computational aspects of facility location problems using game theory will find this book useful

Spatial interaction models: facility location using game theory

This book aims to provide a comprehensive overview of facility location models that can be investigated in a game theoretical environment. Facility location theory develops the idea of locating one or more facilites optimizing suitable criteria such as minimizing transportation cost, capturing the largest market share, etc.. and a huge number of papers have been devoted to this reaserch. In this volume we focus on situations where the location decision is faced by several decision makers, leading to a game theoretical framework in a noncooperative way, as well as in a cooperative one. Some chapters are surveys of models and methods regarding this part of the facility location using game theory, other chapters illustrate applications in dierent contexts such as economics, engineering, physics, etc. This makes the book useful for a broader audience of researchers working on theory, applications and computational aspects. We would like to express our thanks to all the contributors of chapters and also the evaluable assistance of the Springer sta for the publication of this book

Spatial interaction models: facility location using game theory

This book aims to provide a comprehensive overview of facility location models that can be investigated in a game theoretical environment. Facility location theory develops the idea of locating one or more facilites optimizing suitable criteria such as minimizing transportation cost, capturing the largest market share, etc.. and a huge number of papers have been devoted to this reaserch. In this volume we focus on situations where the location decision is faced by several decision makers, leading to a game theoretical framework in a noncooperative way, as well as in a cooperative one. Some chapters are surveys of models and methods regarding this part of the facility location using game theory, other chapters illustrate applications in dierent contexts such as economics, engineering, physics, etc. This makes the book useful for a broader audience of researchers working on theory, applications and computational aspects. We would like to express our thanks to all the contributors of chapters and also the evaluable assistance of the Springer sta for the publication of this book

A Multiple Leader Stackelberg-Nash Model with Genetic Algorithm

We present a computational methodology to reach a Stackelberg - Nash solution for a hierarchical 2+n person game via genetic algorithm (GA). There are two players acting as leaders in a two level leader-follower model, the rest of players play a noncooperative game and react to the optimal decision taken by the two leaders who also play a noncooperative game between themselves. The idea of the Stackelberg-Nash GA is to bring together genetic algorithms and the leader-follower strategy in order to process a genetic algorithm to build the solution. The follower players, as well as the leader players, make their decisions simultaneously at each step of the evolutionary process, solving a Nash equilibrium problem. In this model the uniqueness of the Nash equilibrium of the follower players has been supposed. Applications to global emission games, together with some test cases, will be illustrated

N leader--M follower coaliton games with genetic algorithms and applications

In this paper we present a computational methodology to reach a Stackelberg - Nash solution for a hierarchical 2+n person game via genetic algorithm evolution process. There are two players acting as leaders in a two level leader-follower model: the rest of players play a Nash game and react to the optimal decision taken by the two leaders who also play a Nash game between themselves. The idea of the Stackelberg-Nash GA is to bring together genetic algorithms and the leader-follower strategy in order to process a genetic algorithm to build the solution. The follower players, as well the leader players, make their decisions simultaneously at each step of the evolutionary process, playing a so called Nash game. In this model the uniqueness of the Nash equilibrium at the lower level problem has been supposed. The algorithm convergence is illustrated by means of examples and test cases