Morris, Robert (NASA Ames Research Center) | Pasareanu, Corina S. (NASA Ames Research Center) | Luckow, Kasper (Carnegie Mellon University) | Malik, Waqar (NASA Ames Research Center) | Ma, Hang (University of Southern California) | Kumar, T. K. Satish (University of Southern California) | Koenig, Sven (University of Southern California)
This paper explores the problem of managing movements of aircraft along the surface of busy airports. Airport surface management is a complex logistics problem involving the coordination of humans and machines. The work described here arose from the idea that autonomous towing vehicles for taxiing aircraft could offer a solution to the 'capacity problem' for busy airports, the problem of getting more efficient use of existing surface area to meet increasing demand. Supporting autonomous surface operations requires continuous planning, scheduling and monitoring of operations, as well as systems for optimizing complex human-machine interaction. We identify a set of computational subproblems of the surface management problem that would benefit from recent advances in multi-agent planning and scheduling and probabilistic predictive modeling, and discuss preliminary work at integrating these components into a prototype of a surface management system.
This paper describes a subset of the findings of a simulation study conducted to explore the usefulness and usability of a prototype of the Surface Management System (SMS), designed to assist in expediting the movements of aircraft on the surface of a large airport. In particular, this paper focuses on the use of SMS by Traffic Management Coordinators (TMCs) in airport Air Traffic Control Towers. The overall study included three airport tower air traffic controllers supported by a traffic management coordinator working in a full mission simulation of air and surface traffic environment at the Dallas-Ft.
Schurr, Nathan (Aptima, Inc.) | Good, Richard (Aptima, Inc.) | Alexander, Amy (Aptima, Inc.) | Picciano, Paul (Aptima, Inc.) | Ganberg, Gabriel (Aptima, Inc.) | Therrien, Michael (Aptima, Inc.) | Beard, Bettina L. (NASA Ames Research Center) | Holbrook, Jon (San Jose State University Research Foundation)
To meet the growing demands of the National Airspace System (NAS) stakeholders and provide the level of service, safety and security needed to sustain future air transport, the Next Generation Air Transportation System (NextGen) concept calls for technologies and systems offering increasing support from automated systems that provide decision-aiding and optimization capabilities. This is an exciting application for some core aspects of Artificial Intelligence research since the automation must be designed to enable the human operators to access and process a myriad of information sources, understand heightened system complexity, and maximize capacity, throughput and fuel savings in the NAS.. This paper introduces an emerging application of techniques from mixed initiative (adjustable autonomy), multi-agent systems, and task scheduling techniques to the air traffic control domain. Consequently, we have created a testbed for investigating the critical challenges in supporting the early design of systems that allow for optimal, context-sensitive function (role) allocation between air traffic controller and automated agents. A pilot study has been conducted with the testbed and preliminary results show a marked qualitative improvement in using dynamic function allocation optimization versus static function allocation.
DLR has set up a number of projects to increase flight safety and economics of aviation. Within these activities one field of interest is the development and validation of systems for pilot assistance in order to increase the situation awareness of the aircrew. The basic idea behind these systems is the principal of an ''electronic copilot''. All flight phases ("gate-to-gate") are taken into account, but as far as approaches, landing and taxiing are the most critical tasks in the field of civil aviation, special emphasis is given to these operations. Especially under adverse weather conditions the situation awareness of pilots is decreased in these critical flight phases due to the reduced visual range.