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Scheduling safe movement of air traffic in crowded air spaces

Published online by Cambridge University Press:  26 July 2012

David W. Hildum  and
Stephen F. Smith
David W. Hildum*
Affiliation:
The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, USA; e-mail: hildum@cs.cmu.edu, sfs@cs.cmu.edu
Stephen F. Smith*
Affiliation:
The Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890, USA; e-mail: hildum@cs.cmu.edu, sfs@cs.cmu.edu
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Abstract

This paper considers the problem of generating conflict-free movement schedules for a set of vehicles that are operating simultaneously in a common airspace. In both civilian air traffic management and military air campaign planning contexts, it is crucial that the movements of different vehicles be coordinated so as to avoid collisions and near misses. Our approach starts from a view of airspace management as a 4D resource allocation problem, where the space in which vehicles must maneuver is itself managed as a capacitated resource. We introduce a linear octree representation of airspace capacity to index vector-based vehicle routes and efficiently detect regions of potential conflict. Generalizing the notion of contention-based search heuristics, we next define a scheduling algorithm that proceeds by first solving a relaxed version of the problem to construct a spatial capacity profile (represented as an octree), and then using spatio-temporal regions where demand exceeds capacity to make conflict-avoiding vehicle routing and scheduling decisions. We illustrate the utility of this basic representation and search algorithm in two ways. First, to demonstrate the overall viability of the approach, we present experimental results using data representing a realistically sized air campaign planning domain. Second, we define a more abstract notion of ‘encounter set’, which tolerates some amount of conflict on the assumption that on-board deconfliction processes can take appropriate avoidance maneuvers at execution time, and show that generation of this more abstract form of predictive guidance can be obtained without loss in computational efficiency.

Type
Articles
Information
The Knowledge Engineering Review , Volume 27 , Special Issue 3: Constraint Programming for Air Traffic Management , 26 July 2012 , pp. 309 - 331
Copyright
Copyright © Cambridge University Press 2012

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References

Beck, J. 1999. Texture Measurements as a Basis for Heuristic Commitment Techniques in Constraint-Directed Scheduling. PhD thesis, Department of Computer Science, University of Toronto.Google Scholar
Cesta, A., Oddi, A., Smith, S. F. 2002. A constraint-based method for project scheduling with time windows. Journal of Heuristics 8, 109–136.CrossRefGoogle Scholar
Coppenbarger, R. A., Lanier, R., Sweet, D., Dorsky, S. 2004. Design and development of the en route descent advisor (EDA) for conflict-free arrival metering. In Proceedings of the AIAA Guidance, Navigation and Control Conference, Providence, RI.CrossRefGoogle Scholar
Ennis, R. L., Zhao, Y. J. 2004. A formal approach to the analysis of aircraft protected zone. Air Traffic Control Quarterly 12(1), pp. 75–102.CrossRefGoogle Scholar
Flener, P., Pearson, J., Agren, M., Garcia Vello, C., Celiktin, M., Dissing, S. 2007. Air-traffic complexity resolution in multi-sector planning. Journal of Air Transport Management 13(6), 323–328.CrossRefGoogle Scholar
Gaede, V., Günther, O. 1998. Multidimensional access methods. ACM Computing Surveys (CSUR) 30(2), 170–231.CrossRefGoogle Scholar
Gargantini, I. 1982. Linear octrees for fast processing of three-dimensional objects. Computer Graphics and Image Processing 20, 365–374.CrossRefGoogle Scholar
Hildum, D. W., Smith, S. F. 2007. Constructing conflict-free schedules in space and time. In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS-07), Providence, RI.Google Scholar
Morton, G. 1966. A Computer Oriented Geodetic Data Base and a New Technique in File Sequencing. Technical report, IBM Ltd.Google Scholar
Myers, K. L., Smith, S. F., Hildum, D. W., Jarvis, P. A., de Lacaze, R. 2001. Integrating planning and scheduling through adaptation of resource intensity estimates. In Proceedings of the 6th European Conference on Planning, Toledo, Spain.Google Scholar
OR Concepts Applied 2002. Versatile Integrated Planner and Router (VIPR). DTIC Technical report, ADB279522.Google Scholar
Parker, A., Yaman, F., Nau, D., Subrahmanian, V. S. 2007. Probabilistic go theories. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, India.Google Scholar
Sadeh, N. 1991. Look-ahead Techniques for Micro-Opportunistic Job Shop Scheduling. PhD thesis, School of Computer Science, Carnegie Mellon University.Google Scholar
Samet, H. 1990. Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS. Addison-Wesley.Google Scholar
Smith, S. 1994. OPIS: a methodology and architecture for reactive scheduling. In Intelligent Scheduling Zweben, M. & Fox, M. (eds). Morgan Kaufmann.Google Scholar
Smith, S., Becker, M., Kramer, L. 2004. Continuous management of airlift and tanker resources: a constraint-based approach. Mathematical and Computer Modeling—Special Issue on Defense Transportation: Algorithms, Models and Applications for the 21st Century 39(6–8), 581–598.Google Scholar
Sridhar, B., Chatterji, G. B., Grabbe, S., Sheth, K. 2002. Integration of traffic flow management decisions. In Proceedings of the AIAA Guidance, Navigation and Control Conference, Washington DC.CrossRefGoogle Scholar
Sridhar, B., Sheth, K., Smith, P., Leber, W. 2005. Migration of FACET from Simulation Environment to Dispatcher Decision Support System. In Proceedings of the 24th Digital Avionics Systems Conference (DASC 2005), Washington, DC.Google Scholar
Stilman, B. 2000. Linguistic Geometry: From Search to Construction. Kluwer Academic Press.CrossRefGoogle Scholar
Yaman, F., Nau, D., Subrahmanian, V. S. 2005. Going far, logically. In Proceedings of the 19th International Joint Conference on Artificial Intelligence, Edinburgh, Scotland.Google Scholar
Zhou, Q., Smith, S. F. 2002. A Priority-Based Pre-Emption Algorithm for Incremental Scheduling with Cumulative Resources. Technical report, CMU-RI-TR-02-11, The Robotics Institute, Carnegie Mellon University.Google Scholar