Exploring Concepts of Operations for On-Demand Passenger Air Transportation

In recent years, a surge of interest in "flying cars" for city commutes has led to rapid development of new technologies to help make them and similar on-demand mobility platforms a reality. To this end, this paper provides analyses of the stakeholders involved, their proposed operational concepts, and the hazards and regulations that must be addressed. Three system architectures emerged from the analyses, ranging from conventional air taxi to revolutionary fully autonomous aircraft operations, each with vehicle safety functions allocated differently between humans and machines. Advancements for enabling technologies such as distributed electric propulsion and artificial intelligence have had major investments and initial experimental success, but may be some years away from being deployed for on-demand passenger air transportation at scale

Towards an autonomous landing system in presence of uncertain obstacles in indoor environments

The landing task is fundamental to Micro air vehicles (MAVs) when attempting to land in an unpredictable environment (e.g., presence of static obstacles or moving obstacles). The MAV should immediately detect the environment through its sensors and decide its actions for landing. This paper addresses the problem of the autonomous landing approach of a commercial AR. Drone 2.0 in presence of uncertain obstacles in an indoor environment. A localization methodology to estimate the drone's pose based on the sensor fusion techniques which fuses IMU and Poxyz signals is proposed. In addition, a vision-based approach to detect and estimate the velocity, position of the moving obstacle in the drone's working environment is presented. To control the drone landing accurately, a cascade control based on an Accelerated Particle Swarm Optimization algorithm (APSO) is designed. The simulation and experimental results demonstrate that the obtained model is appropriate for the measured data

Perching with fixed wings

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (leaves 43-46).Human pilots have the extraordinary ability to remotely maneuver small Unmanned Aerial Vehicles (UAVs) far outside the flight envelope of conventional autopilots. Given the tremendous thrust-to-weight ratio available on these small machines [1, 2], linear control approaches have recently produced impressive demonstrations that come close to matching this agility for a certain class of aerobatic maneuvers where the rotor or propeller forces dominate the dynamics of the aircraft [3, 4, 5]. However, as our flying machines scale down to smaller sizes (e.g. Micro Aerial Vehicles) operating at low Reynold's numbers, viscous forces dominate propeller thrust [6, 7, 8], causing classical control (and design) techniques to fail. These new technologies will require a different approach to control, where the control system will need to reason about the long term and time dependent effects of the unsteady fluid dynamics on the response of the vehicle. Perching is representative of a large class of control problems for aerobatics that requires and agile and robust control system with the capability of planning well into the future. Our experimental paradigm along with the simplicity of the problem structure has allowed us to study the problem at the most fundamental level. This thesis presents methods and results for identifying an aerodynamic model of a small glider at very high angles-of-attack using tools from supervised machine learning and system identification. Our model then serves as a benchmark platform for studying control of perching using an optimal control framework, namely reinforcement learning. Our results indicate that a compact parameterization of the control is sufficient to successfully execute the task in simulation.by Rick E. Cory.S.M

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed

Transformational Autonomy and Personal Transportation: Synergies and Differences Between Cars and Planes

Highly automated cars have undergone tremendous investment and progress over the past ten years with speculation about fully-driverless cars within the foreseeable, or even near future, becoming common. If a driverless future is realized, what might be the impact on personal aviation? Would self-piloting airplanes be a relatively simple spin-off, possibly making travel by personal aircraft also commonplace? What if the technology for completely removing human drivers turns out to be further in the future rather than sooner; would such a delay suggest that transformational personal aviation is also somewhere over the horizon or can transformation be achieved with less than full automation? This paper presents a preliminary exploration of these questions by comparing the operational, functional, and implementation requirements and constraints of cars and small aircraft for on-demand mobility. In general, we predict that the mission management and perception requirements of self-piloting aircraft differ significantly from self-driving cars and requires the development of aviation specific technologies. We also predict that the highly-reliable control and system automation technology developed for conditionally and highly automated cars can have a significant beneficial effect on personal aviation, even if full automation is not immediately feasible

Planning and reconfigurable control of a fleet of unmanned vehicles for taxi operations in airport environment

The optimization of airport operations has gained increasing interest by the aeronautical community, due to the substantial growth in the number of airport movements (landings and take-offs) experienced in the past decades all over the world. Forecasts have confirmed this trend also for the next decades. The result of the expansion of air traffic is an increasing congestion of airports, especially in taxiways and runways, leading to additional amount of fuel burnt by airplanes during taxi operations, causing additional pollution and costs for airlines. In order to reduce the impact of taxi operations, different solutions have been proposed in literature; the solution which this dissertation refers to uses autonomous electric vehicles to tow airplanes between parking lots and runways. Although several analyses have been proposed in literature, showing the feasibility and the effectiveness of this approach in reducing the environmental impact, at the beginning of the doctoral activity no solutions were proposed, on how to manage the fleet of unmanned vehicles inside the airport environment. Therefore, the research activity has focused on the development of algorithms able to provide pushback tractor (also referred as tugs) autopilots with conflict-free schedules. The main objective of the optimization algorithms is to minimize the tug energy consumption, while performing just-in-time runway operations: departing airplanes are delivered only when they can take-off and the taxi-in phase starts as soon as the aircraft clears the runway and connects to the tractor. Two models, one based on continuous time and one on discrete time evolution, were developed to simulate the taxi phases within the optimization scheme. A piecewise-linear model has also been proposed to evaluate the energy consumed by the tugs during the assigned missions. Furthermore, three optimization algorithms were developed: two hybrid versions of the particle swarm optimization and a tree search heuristic. The following functional requirements for the management algorithm were defined: the optimization model must be easily adapted to different airports with different layout (reconfigurability); the generated schedule must always be conflict-free; and the computational time required to process a time horizon of 1h must be less than 15min. In order to improve its performance, the particle swarm optimization was hybridized with a hill-climb meta-heuristic; a second hybridization was performed by means of the random variable search, an algorithm of the family of the variable neighborhood search. The neighborhood size for the random variable search was considered varying with inverse proportionality to the distance between the actual considered solution and the optimal one found so far. Finally, a tree search heuristic was developed to find the runway sequence, among all the possible sequences of take-offs and landings for a given flight schedule, which can be realized with a series of taxi trajectories that require minimum energy consumption. Given the taxi schedule generated by the aforementioned optimization algorithms a tug dispatch algorithm, assigns a vehicle to each mission. The three optimization schemes and the two mathematical models were tested on several test cases among three airports: the Turin-Caselle airport, the Milan-Malpensa airport, and the Amsterdam airport Schiphol. The cost required to perform the generated schedules using the autonomous tugs was compared to the cost required to perform the taxi using the aircraft engines. The proposed approach resulted always more convenient than the classical one

Model of on-line control for the smart object by communication technologies

Based on analysis of opportunities of usage existing models, methods and techniques for on-line monitoring and control of unmanned dynamic objects, a mathematical model of remote estimation of distance to wire-controlled unmanned mobile object is proposed. The presence of model and techniques of remote estimation of distance to wirecontrolled unmanned object increases the reliability of control for exclusion of critical situations in the strict radio counteraction conditions and powerful electromagnetic jamming setting during control