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An Approach to Airborne Conflict Prediction

Lakshmi Vempati*

CDM Technologies Inc, San Luis Obispo, CA, 93401

Hisham Assal† Collaborative Agent Design (CAD) Research Center, San Luis Obispo, CA, 9340

Existing conflict alerting systems provide less than acceptable support to air traffic controllers due to lack of integration of all the necessary information such as weather, terrain and airspace information when predicting conflicts. The final integration and interpretation of this information lies with the air traffic controller. The rate at which air traffic has grown in the past few years has not only created congestion but also an increase in controller workload. Future concepts such as free flight will enable pilots to choose autonomous flight paths. Such arbitrary selection of flight paths can lead to potential conflicts. It is therefore essential that an autonomous monitoring system is vital to not only support future concepts such as free flight but also provide relief to controllers and reduce congestion. This paper presents a ground-based autonomous airspace monitoring tool that integrates information from airborne sensors, radar, weather, terrain, airspace, flight rules and pilot intent.

I. Introduction uring the last few years considerable research has been underway in concepts such as free flight in order to reduce congestion and delays in the National Airspace. Free Flight is a future concept that will allow pilots to

choose autonomous flight paths to satisfy individual requirements such as minimum fuel, minimum time, shortest distance and so on. The goal of free flight is to enable the pilot freedom to select flight paths in real time. Such arbitrary selection of flight paths by individual aircraft can lead to potential conflicts and loss of separation. Considering that at any one time there can be as many as 5000 aircraft zigzagging to various destinations over US airspace, there are ample opportunities for trajectories to intersect and create a loss of separation leading to potential mid air collisions. To prevent such occurrences, an autonomous monitoring and prediction tool that can monitor the airspace and predict conflicts is essential.

Under current Air Traffic Management (ATM) aircraft fly specific routes determined by air traffic controllers. A combination of information resources such as aircraft flight rules, ground tracks, controller pilot communication and trajectory computation is utilized to achieve this. There have been many approaches presented in the area of air traffic Conflict Detection and Resolution (CD&R) for inclusion onboard aircraft. Previous research has concentrated on the type of onboard information and equipment necessary on the flight deck to enable pilots of autonomous aircraft to determine pilot specific flight paths and maintain separation.

Existing conflict detection and resolution algorithms can be broadly classified as state based or intent based.

State based algorithms employ current physical parameters such as location, altitude, vertical speed and ground speeds. Intent based approaches incorporate future maneuvers which the aircraft will perform in solving such problems. For successful resolution to occur in a timely fashion a combination of both state and intent based approaches is necessary.

The focus of the research effort described in this paper is to present an approach for the development of an

intelligent, autonomous airspace monitoring tool to predict conflicts by incorporating information from various sources such as weather, airspace, performance, communication and pilot intent. It is the natural extension of the

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work presented in Ref. 1. where the basic architecture for an intelligent airspace monitoring tool was first presented to provide airspace monitoring support for deviation prediction and security threat level analysis. This paper proposes how a system developed employing such architecture, can be very easily extended to include conflict prediction and resolutions.

II. Background The problem of airborne collision avoidance can be divided into three key aspects namely monitoring, prediction

and resolution. The complex and dynamic nature of air traffic warrants a continuous monitoring of the airspace to not only detect current conflicts but also predict future conflicts to provide sufficient time to prevent their occurrence. Once a conflict is identified a reasonable solution should necessarily prevent further conflicts and ultimately restore the conflicting aircrafts to original flight paths. Considerable research has been conducted in the area as noted by some of the references cited 3-7.

Current FAA regulations prescribe the minimum requirements for separation maintenance based on the airspace

through which the aircraft is flying. A minimum of 5 miles lateral and 1000 feet vertical separation is applicable through most airspace. In the vicinity of most major airports (within 40 miles) this is reduced to 3 miles and 1000 feet. For flights above FL410 and FL600 vertical separation is increased to 2000 feet and 5000 feet respectively. Therefore, depending on the airspace the necessary separation minima applies. Existing systems such as Conflict Alert employ fixed separation criteria to determine possible conflicts.

A conflict can occur under various circumstances. For example: 1) Head on – when two aircraft headed in opposite directions converging towards a point 2) Same direction – when two aircraft headed in the same direction with faster aircraft attempting to overtake

slower aircraft. 3) Lateral – when two aircraft heading in the same direction, converge towards a point 4) Altitude passing – an aircraft is either climbing from lower altitude through the same altitude to a higher

altitude or descending from a higher altitude through to a lower altitude. In order to accurately predict conflict, the trajectories of all aircraft within a prescribed range must be accurately

projected into the future. Existing systems employ current state information to project using nominal, worst case or probabilistic approaches to project. But neither of these approaches provides an accurate projection of the aircraft future trajectory. If pilot intent information is available as is possible with implementation of future systems such as Controller Pilot Data Link, then accurate computation is possible.

III. Airspace Monitoring Tool The architecture for the intelligent airspace monitoring tool is shown in Fig. 2. The representation of the

information plays a vital role in the design. Employing the rich representation afforded by developing an ontology for the NAS, the airspace monitoring tool incorporates information such as flight plans, airspace, aircraft, clearances, communications, navigation, and weather. The Ontology describing the NAS domain serves as the key element that provides the common semantics and context rich representation for information sharing.

A. Ontology Based Modeling The representation of domain knowledge in any field is the backbone for developing systems and tools to

support that field. Knowledge representation provides a means for managing the domain complexity and a common understanding for systems that share this representation2. Many knowledge representation paradigms attempted to capture more aspects of domain complexity in many fields with varying degrees of success. With the advent of the world wide web and the explosion of information on the Internet, the need for a formal mechanism to represent knowledge and share it among disparate applications has become more evident and led to the development of the knowledge representation paradigm, known as ontology.

Ontology is the study of categories of things that exist in a given domain and the relationships that may exist

among them8. The application of such study in the area of computer systems leads to formal representation of the entities of a system and the various types of relationships that affect their interaction. Examples of the types of relationships include 'inheritance', 'part/whole', 'association'. Another form of relationship is 'patterns' which

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describes common ways to group together the entities or concepts, and 'roles' which describes the context for a given concept or object.

The result of ontology-based modeling is an ontology for the subject matter with clear definitions of the concepts

and types of objects in this domain in a formal language. The ontology is typically defined in multiple levels of abstractions to make use of generic concepts such as time, organization, plan, etc. The resulting ontology should cover enough concepts in the domain to support all the tasks that are performed within this field. Ontology also serves as a documentation of knowledge for the system.

Figure 1 provides a sample model of a part of the ontology, capturing the details of a flight plan. A flight plan is essential for all flight under instrument flight rules (IFR). Some key information contained in a flight plan are the originating and destination airports, route of flight, fuel onboard, estimated time en-route and so on. During the course of a flight, the original flight plan can undergo modification due to amended clearances. By capturing the relationships of an aircraft to its flight plan, clearances, the route, and hence indirectly the terrain the flight is being conducted in, the flight rules defining minimum altitude, airways, navigational aids, airways and so on, it is possible to develop intelligent agents that can utilize all the rich information to predict conflicts. Figure 2 provides the architecture diagram for the airspace monitoring tool. The ontology provides a common semantics for integration of information from external systems such as flight plan databases, weather, geospatial information, airspace and flight rules. In future systems when communication via data links is prevalent, it can be integrated to provide more accurate predictions.

Figure 1 Sample Ontology Model

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B. Collaborative Agents Agent-based software systems provide software components that perform tasks on their own without the explicit

intervention of the user. They monitor the environment, in which they reside, and react to changes according to their tasks. Agents also communicate amongst themselves to exchange information. The complexity of agent tasks, added to the complexity of the environment, requires sophisticated techniques for the development and management of the system.

Figure 2 Airspace Monitoring Tool Architecture

Ontology-based modeling provides such a technique. The ontology provides an environment for managing the

complexity of the domain knowledge and provides agents with high level concepts, with which they can reason logically and provide meaningful answers. Main types of agents include: planning agents, which generate plans (sequences of actions) based on the current information and the stated objectives; resource allocation agents, which finds the appropriate resources for a given task; Truth Maintenance agents, which maintain the validity of assessments in the system; and monitoring agents, which generate alerts as unfavorable conditions develop within the environment.

IV. Intelligent Tracking The proposed approach incorporates ontological models as common representation for information sharing

among the various systems. Each system maintains the most up-to-date information state by employing a

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r2

r1

h

Prediction Zone

Protected Zone

publish/subscribe and/or query mechanism. Different agents are modeled as clients to the information sharing framework (described above), with interest profiles to perform the actions and/or tasks needed.

For each aircraft represented in the system, a Monitoring Agent monitors the path of the aircraft in relation to other aircraft in the vicinity of the current aircraft. For each aircraft a spherical zone is defined as described in the next sub-section. The Monitor Agent is triggered to monitor an aircraft the moment a flight plan for the aircraft is activated. The agent monitors location updates of all aircraft within a geographical area in the vicinity of the owned aircraft as defined by the prediction zone.

A. Conflict Prediction For the purpose of this work a cylindrical protected

zone is defined surrounding each aircraft. The bounds of this cylindrical protected zone (see figure 3) is defined by a varying radius, r1 and a variable height h. Further each aircraft is also defined by a spherical prediction zone. The radius r2 of this spherical area is defined as a factor of the airspeed of individual aircraft and congestion level of the airspace through which the aircraft is flying. The radius of the spherical prediction zone is larger for higher speed aircraft than for lower speed aircraft. The intersection of any two aircraft cylindrical protected zones indicates loss of separation between them. When the spherical predicted zones of any two aircraft intersect, the projected trajectory is computed up to the predicted zone bounds for the concerned aircraft at discrete time intervals. The number of discrete time intervals is defined as a factor of congestion level. In highly congested areas where there is less maneuverability, a larger number of discrete time intervals are used to better predict conflicts.

For each aircraft the projected trajectory within each predicted zone is computed using the current location of the

aircraft and the original flight plan or pilot (intent) or controller amended flight plan information if available. By considering flight plan information, projected trajectories are not merely straight lines but take into consideration information such as whether a change in course was necessary to conform to the flight plan, whether a climb or descent was necessary such as when approaching the destination airport or to maintain minimum altitude requirements or conform to amended clearances.

For each location update (or as configured) the Monitoring Agent applies various rules to compute and assess

whether collision is imminent. Some of the rules applied are as follows: 1) Straight line intersection with the path of any other aircraft within the prediction zone for the owned

aircraft 2) Spherical intersection of prediction zone of owned aircraft with any other aircraft 3) Cylindrical intersection of protected zone of owned aircraft with any other aircraft The straight line intersection rule is used to determine whether the two paths are skewed or intersect. For any

two lines defined in three dimensions in parametric form where line (1) passes through (x1, y1) and (x2, y2) and line (2) passes through (x3, y3) and (x4, y4):

x=x1 +(x2-x1)s (1)

x=x3 +(x4-x3)t (2)

are skewed if:

(x1-x3).[(x2-x1)x(x4-x3)] != 0 (3)

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The spherical rule is implemented in two parts: the basic rule checks the distance between the two aircraft to determine if the aircraft are within each other’s spherical radii. If the position violates this rule, which means the two sphere’s intersect, the point of intersection and the curve of intersection is computed using:

x=(d2-r2 +R2)/2d (4)

y2 + z2 = [4d2R2-(d2-r2+R2)2]/4d2 (5)

where it is assumed that the x-axis is centered at the center of one of the spheres, the distance between the two spherical centers is d, and the radii of the two sphere’s are r and R respectively.

If either one or both occur, the Monitor Agent alerts the Conflict Agent. The Conflict Agent simulates the path

of the two conflicting aircraft by estimating states using point mass equations utilizing each aircrafts weight and performance data, accurate flight plan information of the aircraft, weather and winds information if available, to the bounds of the curve of intersection in discrete time intervals. If at any discrete point the cylindrical zones intersect, a Conflict Alert is triggered. Like the spherical rule, the basic cylindrical rule first checks if the distance between the two aircraft (if at same altitude) is less than the sum of the radii of the two cylinders. For vertically displaced aircraft, the height rule is applied to determine if it is violated. For laterally separated aircraft both radial difference and height differences are considered.

For the application of each of the rules, the agent retrieves the most current separation rules to apply based on the

route segment being traversed by each aircraft and based on the performance of each aircraft. The ontological representation of information facilitates describing all the specific relations of the concerned aircraft and its relation to the environment. Moreover any future changes can be readily reflected by altering the existing route of the aircraft within the system. Therefore both the existing state and future state (i.e. the pilot intent) can be incorporated dynamically with changing situations, facilitating accurate predictions of impending conflicts.

V. Conclusion An approach to airborne conflict prediction is proposed. The approach utilizes ontological information

representation to provide not only a common view of the airspace domain for interaction among the various systems but also as a means to enable dynamic interaction among the systems. Due to the highly configurable nature of representing airspace rules and conventions, they can very easily be revised to accurately represent rapidly changing situations. Further work is necessary to fully implement the system and apply it to more complex situations to determine viability in a real-time environment. Further work is also needed to generate conflict resolution trajectories.

References 1Vempati, L., and Assal H., “An Intelligent Threat Level Assessment to Aircraft Deviation in the National Airspace System,”

AIAA Infotech@Aerospace, Sept 26-29, Arlington, VA., AIAA 2005-7140 2Pohl, J., “Information-Centric Decision-Support Systems: A Blueprint for Interoperability”, Office of Naval Research

(ONR) Workshop hosted by the CAD Research Center in Quantico, VA 2001. 3Shandy, S., and Valasek, J., “Intelligent Agent for Aircraft Collision Avoidance,” AIAA-2001-4055, 2001. 4Mondoloni, S., and Conway, S., “An Airborne Conflict Resolution Approach Using A Genetic Algorithm,” AIAA 2001-

4054 2001. 5Rong, J., Bokadia, S., Shandy, S., and Valasek, J., “Hierarchical Agent Based System for General Aviation CD&R Under

Free Flight,” AIAA 2002-4553, Guidance, Navigation and Control Conference and Exhibit, August 5-8, 2002, Monterey, CA. 5Portillo, I. A., and Atkins, E. M., “Adaptive Trajectory Planning for Flight Management Systems,” AIAA 2002-1073 2002. 6Kuchar, J. K., and Yang, L C., “An Review of Conflict Detection and Resolution Modeling Methods,” IEEE Transactions

on Intelligent Transportation Systems, Vol. 1, No. 4, December 2000 pp. 179-189. 7Krozel, J., “Intelligent Tracking of Aircraft in the National Airspace System”, AIAA Guidance, Navigation, and Control

Conference, Monterey, CA, Aug., 2002. 8Chandrasekaran B. et al, “What are Ontologies and Why Do We Need Them?”, IEEE Intelligent Systems Vol. 14 No.1,

January/February 1999.