Coordination of En Route Air Traffic Conflicts Resolution Modelling Methods

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Survey of Coordination of En Route Air Traffic Conflicts Resolution Modelling Methods 
Huy-Ho Abstract— The en-route conflict resolution remains a major concern for Air Traffic Management (ATM), especially in core European airspace where the current Air Traffic Control (ATC) system is approaching its capacity limits. In this paper we discuss the emphasis of the coordination of conflict resolution actions. Indeed, coordination of conflict resolution is required to reach a global solution for clusters involving many aircraft. A number of such models have already been proposed and some of them applied in practice, but there has been no cohesive discussion or comparative evaluation of these approaches. This paper presents a summary of coordination of conflict resolution approaches. Index Terms— conflict detection and resolution, coordination of conflict resolution. I. INTRODUCTION he purpose of Air Traffic Management (ATM) is to enable Tairspace users to meet their schedules according to their preferred flight profiles without compromising safety levels. To provide safe and efficient aircraft movements, the current approach comprises two main activities: Air Traffic Control (ATC) and Air Traffic Flow Management (ATFM). Both ATC and ATFM are ground based services. The ATC provides tactical, safe separation between aircraft and between aircraft and obstacles. The main goal of ATC is to guarantee security and to give aircraft optimal trajectories to fly from one airport to an other. The Air Traffic Flow Management deals with the allocation of scarce capacity resources such as routes and terminal operations time slots. In the USA, airport capacity is the main problem. This problem exists also in Europe on the biggest airport. But in Europe, and mainly in France, En Route capacity is the critical point. There is also a problem linked with controller workload such as monitoring workload (the monitoring of the aircraft in the controller’s sector), resolution workload (the resolution of conflict) and coordination workload (a task that each controller must perform when a aircraft enters or leaves its sector). Thereby, the tools for air traffic control system, in particular, for conflict management as well as for ground- based Collaborative Decision Making (CDM) are necessary to optimize conflict resolution solutions. In other words, it aims at increasing capacity of controller. Huy-Hoang Nguyen is with the Heudiasyc Laboratory, UMR CNRS 6599, University of Technology of Compiègne, Centre de Recherches de Royallieu, BP 20 529, F-60205 Compiègne cedex, France and EUROCONTROL Experimental Centre, Centre de Bois des Bordes, BP15, F-91222 Brétigny sur Orge cedex, France. (e-mail: huy-hoang.nguyen@eurocontrol.int). ang Nguy en Additionally, the steady growth of traffic in core European airspace could lead to complex situations where separation standards may be infringed by several aircraft in a transitive configuration, called clusters of potential conflicts. It is necessary to treat large cluster of conflicts without inducing too much the costs of maneuvering to aircraft. Costs typically include fuel and time. Consequently, solutions to large cluster of potential conflicts are needed. Accordingly, coordination of conflict resolution is required to reach a global solution for clusters involving many aircraft. This paper provides a summary and evaluation of the approaches that have been used to perform coordination of conflict resolution. The objective of the paper is to point out the advantages and disadvantages of each method. The paper is organized as follows: Section 2 recalls quickly conflict detection and resolution. In Section 3, we review the approaches that have been proposed for the coordination problem and we discuss some of the most relevant methods. II. CONFLICT DETECTION &RESOLUTION (CD&R) The conflict detection and resolution has been a major topic in ATM research. The air traffic conflict detection and resolution process consists of several tasks to ensure separation or avoid collisions depending on the scope of the system. Firstly, based on the information available, future position can then be estimated and potential conflicts can be predicted. Conflict detection is based on the estimation of future vehicle position and through the application of predefined metrics on the situation in order to decide whether or not a conflict is present. This metric may include a sole parameter (e.g., distance) or may be a combination of several parameters (e.g., distance, time and maneuvering cost). After the detection of a conflict, a conflict resolution phase requires appropriate maneuver action and information distribution to all aircraft involved in the conflict. Following the literature research, an important number of different modeling approaches (more than 60 methods ) have been applied in the past for conflict detection and resolution in aerospace. These models include a wide variety of techniques from varying viewpoints, but are all intended to provide an analytical basis for designing and evaluating conflict detection and resolution systems. A. Conflict Detection In order to ensure safety of aircraft traffic operations, adequate separation must be maintained. A conflict occurs 1 when an aircraft’s protected zone is violated. The protected zone is currently defined by en route ATC standards as a circular zone of 5 nautical mile radius and a height of 2000 ft altitude (-1000 ft to +1000ft). In other words, a conflict between two aircraft is called effective if at some instant of time, the minimal distance between these two aircraft called Closest Point of Approach (CPA) is inferior to the minimal separation standard (see Figure 1). The conflict detection phase, permits to detect conflicts only with aircraft for which an intrusion of the protected zone takes place in the near future, which is defined by using a fixed look-ahead time for T minutes . A new conflict is detected when an intrusion of the protected zone is predicted, and the time of this intrusion is within the look-ahead time. The conflict detection uses the current state (position and altitude) and trend vector (ground speed, track and vertical speed) to Aircraft 1 Aircraft 2 
Figure 1. A Conflict detect conflicts.  For a global resolution of case of more than two aircraft simultaneously in conflicts, clusters of aircraft involved in these conflicts will be determined and identified during the look-ahead time. Recall that a cluster is the transitive closure on all aircraft pairs involved in a conflict during the look-ahead time; that mean if A conflicts with B, and B conflicts with both A and C, then the cluster consists of A, B and C (see Figure 2). B. Conflict Resolution Once a conflict is detected it must also be resolved. Generally, a conflict situation will be resolved by maneuvering horizontally (heading change) or maneuvering vertically (altitude change) or speed change of aircraft. Over the years, various methods for resolving conflict situations have been proposed. Some methods use force field techniques, others use genetic algorithms, rule-based methods, or optimization techniques. Kuchar and Yang have also given an overview of various approaches to conflict detection and resolution problem. Force field approaches model each aircraft as a changed The protected zone is also often referred to as Protected Airspace Zone, which is a definition that originates from Radio Technical Commission for Aeronautics (RTCA) A C 
 B Figure 2. Cluster of three aircraft involving in conflicts {A, B, C} particle and use modified electrostatic equations to determine resolution maneuvers. The repulsive forces between aircraft are used to define the maneuver each performs to avoid a collision , . Optimized conflict resolution can involve a rule-based decision , or determining which of several avoidance options minimizes a given cost function. The Traffic Alert and Collision Avoidance System (TCAS), for example, searches through a set of potential climb or descend maneuvers and chooses the least-aggressive maneuver that still provides adequate protection. Algorithms for resolving three- dimensional conflicts involving multiple aircraft are presented in . These algorithms are based on trajectory optimization methods and provide resolution actions that minimize a certain cost function. Krozel et al. have used an approach based on optimal control theory (OCT). They have developed an algorithm for the resolution of conflicts involving two aircraft. This algorithm is based on the maximization of the inter- aircraft distance at the Point of Closest Approach. Game theory (Game) is used for conflict resolution by Tomlin et al. . Recently, Nicolas Durand describes a mathematical programming model using a heuristic method based on genetic algorithms (GA) to optimally resolve conflict whereby the global optimization function aims at minimizing the overall cost incurred. III. THE COORDINATION PROBLEM En Route capacity is a problem mainly in Europe. Airspace is divided in control sectors, each sector being managed by two air traffic controllers. However, the capacity of a sector is limited. A controller can not handle more than a certain number of aircraft in its sector. Additionally, as air traffic keeps increasing, a controller must be able to manage clusters of conflicts. And then, a global solution to clusters involving many aircraft in conflicts, more than two aircraft, is needed. Consequently, the coordination problem of conflict resolution appears and must be solved. 
 
REFERENCES  J. Kuchar and L. Yang, “A Review of Conflict Detection and Resolution Modeling Methods”, IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 4, December 2000, pp. 179-189.  M.S.V. V. Clari, R.C.J. Ruigrok, J.M. Hoekstra and H.G. Visser, “Cost-Benefit Study of Free Flight With Airborne Separation Assurance”, Air Traffic Control Quarterly, Vol. 9 (4) pp. 287-309, 2001.  N. Durand, . Optimisation des Trajectoires pour la Résolution de Conflits En-Route ., Ph.D Thesis, INPT, Toulouse, France 1996.  K. Zeghal, “A Review of Different Approaches Based on Force Fields for Airborne Conflict Resolution”, AIAA-98-4240, in Proc. AIAA Guidance, Navigation, and Control Conference, Boston, MA, August 10-12, 1998, pp. 818-827.  V. Duong, “FREER-1 Research memo Dynamic Models for Airbone ATM Capability”, Eurocontrol, June 1996.  V. Duong, E. Hoffman, F. Floc’hic, J.P. Nicolaon, A. Bossu, “Extended Flight Rules (EFR) to apply to the resolution of encounters in autonomous airborne separation”, Technical Report, Eurocontrol, September 1996.  J. Kosecka, C.Tomlin, G. Pappas and S. Sastry, “Generation of Conflict Resolution Maneuvers for Air Traffic Management”, International Conference on Robotics and Intelligent Systems, September 1997.  G. Granger, N. Durand, J.M. Alliot, “Token allocation strategy for free-flight conflict solving”, IJCAI'01.  J. Carlier, V. Duong, D. Nace, H.H. Nguyen, “Using Disjunctive Scheduling for Optimally Solving En-Route Conflicts”, AIAA/IEEE 21st Digital Avionics Systems Conference, Irvine, California, USA, October 27-31, 2002 F. Medioni, . Méthodes d'Optimisation pour l'Evitement Aérien: Systèmes Centralisés, Systèmes Embarqués ., Ph.D Thesis, ENAC, France, December 1998. G. Granger, N. Durand, J.M. Alliot,“Optimal Resolution of En Route th Conflicts”, 4 ATM R&D Seminar, SantaFe, USA, December 2001. K. Zeghal, “Towards the Logic of an Airborne Collision Avoidance System which Ensures Coordination with Multiple Cooperative Intruders”, ICAS-94-8.6.4, ICAS Proceedings, Vol. 3, pp. 2208-2218, 1994. Y. Iijima, H. Hagiwara and H. Kasai, “Results of Collision Avoidance Maneuver”, Experiments Using a Knowledge-Based Autonomous Piloting System, Journal of Navigation, Vol. 44 no. 2, 1991. J. Krozel, T. Mueller and G. Hunter, “Free Flight Conflict Detection and Resolution Analysis”, Paper AIAA-96-3763, AIAA Guidance, Navigation and Control Conference, California, 1996. J. P. Wangermann and R. F. Stengel, “Optimization and Coordination of Multi-Agent Systems Using Principled Negotiation”, AIAA-96-3853, in Proc. 1996 AIAA Guidance, Navigation, and Control Conference, San Diego, CA, July 29-31, 1996. M.S. Eby, “A Seft-Organizational Approach for Resolving Air Traffic Conflicts”, Lincoln Laboratory Journal, Vol. 7, No. 2, 1994. J.W. Andrews, “A Relative Motion Analysis of Horizontal Collision Avoidance”, SAFE Journal, Vol. 8, No. 2, 1978. C. Tomlin, G.J. Pappas and S. Sastry, “Conflict Resolution for Air Traffic Management: a Case Study in Multi-Agent Hybrid Systems”, University of California at Berkeley, 1996. D. Love, “TCAS III: Bringing Operational Compatibility to Airborne st Collision Avoidance”, AIAA/IEEE 8 Digital Avionics Systems Conference, 1988. Radio Technical Committee on Aeronautics (RTCA), “Minimum Performance Specifications for TCAS Airborne Equipment”, Docum ent No. RTCA/DO185, Washington, D.C. September 1983. P.K. Menon and G. D. Sweriduk , ”Optimal Strategies for Free-Flight Air Traffic Conflict Resolution”, Journal of Guidance, Control, and Dynamics, Vol 22, No 2, March-April 1999. R. Schild and J.K. Kuchar, “Operational efficiency of maneuver coordination rules for an airborne separation assurance system”, 3rd USA/Europe Air Traffic Management R&D Seminar Napoli, 13-16 June 2000. R. Mandiau, S. Piechowiak, ”Conflict Solving Into the Multi-Agent Distributed Planning”, Universite de Valenciennes, 1998. N. Jennings, “Cooperation in Industrial Multi-Agent Systems”, World Scientific Publishing Co. Pte. Ltd., Singapore, 1994. J. P. Wangermann, R. F. Stengel, ”Optimization and Coordination of Multiagent Systems Using Principled Negotiation”, Journal of Guidance, Control and Dynamics, Vol. 22, No. 1, January -February 1999. C. Goodchild, M.A. Vilaplana and S. Elefante, “Co-operative Optimal rd Airborne Separation Assurance in Free Flight Airspace”, 3 Usa/Europe Air Traffic Management R&D Seminar, Napoli, 13th-16th June 2000. TERMINOLOGY AND ABBREVIATIONS ADS-B Automatic Dependent Surveillance - Broadcast ATC Air Traffic Control ATM Air Traffic Management ATFM Air Traffic Flow Management CDM Collaborative Decision Making CD&R Conflict Detection and Resolution CPA Closest Point of Approach DAI Distributed Artificial Intelligence EFR Extended Flight Rules FREER Free-Route Experimental Encounter Resolution GA Genetic Algorithms OCT Optimal Control Theory RTCA Radio Technical Commission for Aeronautics TCAS Traffic Alert and Collision Avoidance System VFR Visual Flight Rules BIOGRAPHY Huy-Hoang Nguyen obtained an Engineering degree in Computer Science from the Polytechnic University of Ho Chi Minh City, a Master degree in Computer Science from the Institut de la Francophonie pour l’Informatique, and  is currently a Ph.D. candidate at the University of Technology of Compiègne, France. His interests include operations research, and in particular, scheduling and optimization problems.

duanpingnpu 发表于 2011-10-10 20:53:00

thaks thanks

hanyunxiang 发表于 2011-10-17 22:14:46

好的谢谢啊

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Coordination of En Route Air Traffic Conflicts Resolution Modelling Methods