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Semantic AAAI 2012 July 23rd, 2012 University of Tabriz Faculty of Electrical and Computer Engineering In The Name of God A new method for Conflict.

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Presentation on theme: "Semantic AAAI 2012 July 23rd, 2012 University of Tabriz Faculty of Electrical and Computer Engineering In The Name of God A new method for Conflict."— Presentation transcript:

1 Semantic Cities @ AAAI 2012 July 23rd, 2012 University of Tabriz Faculty of Electrical and Computer Engineering In The Name of God A new method for Conflict Detection and Resolution in Air Traffic Management By: Farnaz Derakhshan Hojjat Emami

2 Contents 2-28 References 7 Conflict Detection and Resolution Process 2 Imperialist Competitive Algorithm (ICA) 4 Content Introduction 1 Semantic Cities @ AAAI 2012 Our Proposed Model 5 Test Results & Conclusion 6 Graph Coloring Problem (GCP) 3

3 Introduction (Air Traffic Control)  Air traffic: F “Aircraft operating in the air or on an airport surface, exclusive of loading ramps and parking areas” (Federal Aviation Regulations and Aeronautical Information Manual, 2010 edition)  Air Traffic Control: F A service operated by appropriate authority to promote the safe, orderly, and expeditious flow of air traffic (Federal Aviation Regulations and Aeronautical Information Manual, 2010 edition) Introduction Semantic Cities @ AAAI 2012 3-28

4 Introduction (Air Traffic Control)  Air traffic control is a complicated task, involving multiple and dynamic controls and have high level of granularity.  having a reliable, safe and efficient air traffic management is a fundamental and critical need in aviation industry. Introduction Semantic Cities @ AAAI 2012 4-28

5 Introduction (Free Flight)  Free flight is a new concept presented potentially to solve problems in the current air traffic management system. F Free flight method has many advantages (such as less fuel consumption, minimum delays, reduction of the workload of the air traffic control centers, high efficiency, and has distributed nature) F The most notable problem in free flight method is the occurrence of conflicts between different aircrafts’ flights. Introduction 5-28 Semantic Cities @ AAAI 2012

6 Introduction  One of the fundamental challenges in the current air traffic management and especially in the free flight is detection and resolution of conflicts.  We presented a new model for conflict detection and resolution between aircrafts in airspace using graph coloring problem.  We mapped the conflict detection and resolution problem to graph coloring problem.  To solve graph coloring problem we used imperialist competitive algorithm. Introduction 6-28 Semantic Cities @ AAAI 2012

7 Conflict Detection and Resolution Process  Conflict: F Conflict is the event in which two or more than two aircrafts experience a loss of minimum separation from each other  Conflict Detection: F The process of deciding when conflict (conflict between aircrafts) will occur  Conflict Resolution: F specifying what action and how should be to resolve conflicts Conflict Detection and Resolution Process 7-28 Semantic Cities @ AAAI 2012

8 Conflict Detection and Resolution (General view) 8-28 No Yes Conflict Resolution Conflict Detected? Environment (airspace) Conflict Detection Operating area- Traffic Information – weather conditions and … Operator or Agent Semantic Cities @ AAAI 2012 Figure.1: Conflict Detection and Resolution (General view)

9 Graph Coloring Problem (GCP)  GCP is an optimization problem which finds an optimal coloring for a given graph G. (Jensen and Toft 1995)  GCP is one of the NP-hard problems  GCP is a practical method of representing many real world problems including: F Time scheduling F Frequency assignment F Register allocation F Circuit board testing F…F… Graph Coloring Problem (GCP) 9-28 Semantic Cities @ AAAI 2012

10 Graph Coloring Problem (Cont)  Given an undirected graph G=(V, E) with a set of vertices V and a set of edges E; F A K-coloring of G includes assigning a color to each vertex of V, such that neighboring vertices have different colors (labels). F GCP implemented by using a conflict minimization algorithm Graph Coloring Problem (Cont) 10-28 Semantic Cities @ AAAI 2012 12 34 12 34 An Undirected Graph before Coloring |V| = 4 |E| = 4 Chromatic number = 2 |K| = 2 (Colors : Red, Green) Colored Graph Figure.2: an example of coloring a graph

11 Search Strategies Complete Deterministic Evolutionary Programming Evolutionary Strategy Genetic Algorithms Genetic Programming Heuristic (Non-Deterministic) Estimation of Distribution Algorithm Evolutionary Algorithms Population-based Search Strategies Imperialist Competitive Algorithm Point-based 11-28 Semantic Cities @ AAAI 2012

12 Imperialist Competitive Algorithm (ICA)  Imperialist Competitive Algorithm (ICA) is Novel Socio-politically Motivated Optimization Strategy. F Proposed by Atashpaz-Gargari and Lucas, 2007. F is inspired by sociopolitical process of imperialism !! F since in late inception, it has been used in many applications. F has shown good convergence and global minimum achievement. F has a lot to do with. Introduction(ICA) 12-28 Semantic Cities @ AAAI 2012

13 A Big Picture of ICA A Big Picture Figure.3: A big picture of ICA (Gargari and Lucas, 2007) 13-28 Semantic Cities @ AAAI 2012

14 Flowchart of the ICA start Is there an empire With no colonies Is there an empire With no colonies Yes Done Compute the total cost of all empires No is there a colony in an empire which has lower cost than that of imperialist is there a colony in an empire which has lower cost than that of imperialist Exchange the positions of that Imperialist & colony Exchange the positions of that Imperialist & colony Initialize the Empires pick the weakest colony from the weakest empire and give it to the empire that has the most likelihood to posses it pick the weakest colony from the weakest empire and give it to the empire that has the most likelihood to posses it Eliminate this empire Yes move the colonies toward the imperialist move the colonies toward the imperialist Yes stop condition satisfied stop condition satisfied * * * * No End 14-28 Semantic Cities @ AAAI 2012 Figure.4: Flowchart of the ICA

15 Our Proposed Model  In our model, the main strategy is based on: "Prevention is better than cure“  we mapped the conflict detection and resolution problem to graph coloring problem.  we map the aircrafts congestion area to a corresponding graph based on aircrafts condition in airspace.  imperialist competitive algorithm has shown great performance in both convergence rate and better global optimal achievement, therefore we used this algorithm to solve graph coloring problem rather than other evolutionary algorithms.  In our model, a Global approach is used for resolving the multiple conflicts. (the entire traffic situation is examined simultaneously, and it has the high capabilities) Our Proposed Model 15-28 Semantic Cities @ AAAI 2012

16 Our Proposed Model start Solving the GCP using ICA (Conflict Resolution process) Solving the GCP using ICA (Conflict Resolution process) Map the Congestion Area to a Graph (Making Adjacency Matrix) Map the Congestion Area to a Graph (Making Adjacency Matrix) Monitor the Environment Project current states to future states by using nominal propagation method Project current states to future states by using nominal propagation method Detect the Congestion Area Compute Distance between all Aircrafts in Congestion Area Detect the Congestion Area Compute Distance between all Aircrafts in Congestion Area Define Problem Parameters And other Traffic Parameters Define Problem Parameters And other Traffic Parameters 16-28 Send new Flight Paths Plan to Existing Aircrafts in Congestion Area Send new Flight Paths Plan to Existing Aircrafts in Congestion Area End Figure.5: Block diagram of our proposed model Semantic Cities @ AAAI 2012 Our Proposed Model

17 Creating the Graph  here, the nodes of graph indicate aircrafts in the congestion area and every edge between two nodes indicates a probable conflicts in the future time step, if the aircrafts continue their current flight plan. Creating the Graph 17-28 Semantic Cities @ AAAI 2012 Figure.6: Graphical display of an example conflict detection & resolution scenario (creating the graph)

18 Conflict Detection  we use a simple conflict prediction method.  we used nominal state propagation method for prediction conflicts between aircrafts.  the current position, heading and speed of existing aircrafts in congestion area used for mapping the current location to the future states.  in the future states if distance between two aircraft is less than a predefined reliable distance threshold we say a conflict is going to occur. (i.e. when in future states the protected zones of two aircraft is overlapped then system report a conflict)  here, we focused only horizontal plan dimension Conflict Detection 18-28 Semantic Cities @ AAAI 2012

19 Conflict Resolution  we map the congestion area to a state space graph.  we replace solving the conflicts between different aircrafts with coloring the corresponding graph.  we used imperialist competitive algorithm to solve graph coloring problem  a colored plan for graph is a reliable solution for conflicts problem  in the conflict resolution process, for each aircraft, we allocate a flight path in which this aircraft will has a reliable distance with other aircrafts and there is no risk of conflict.  In our model, we can assume each flight path as five directional options namely: main line, deviation to right of the main line, deviation to left of the main line, top of the main line and bottom of the main line. Conflict Resolution 19-28 Semantic Cities @ AAAI 2012

20 Test Results  To evaluate our proposed model, we use random flights model (Archibald et al. 2008)  here all aircrafts are constrained to fly at the same altitude and at a constant speed.  Small and instantaneous heading changes for each aircraft are the only maneuvers of resolving conflicts.  We used supposed scenarios that these samples contain 2, 3, 4, 5, 6, 8, 12, 16, 20 and 30 aircrafts in congestion area.  In air traffic control we deal with a multi objective problem.  In this paper, our goal is providing a safe, reliable and efficient strategy for solving conflicts between aircrafts. Test Results 20-28 Semantic Cities @ AAAI 2012

21 Performance Metrics F The ideal state in conflict resolution model is that the aircrafts are able to track their destination without deviation or with minimal deviation from their original path. F Maneuvers that are used in the conflict resolution methods, causes the aircraft to be diverted from the ideal and optimal mainstream.  System efficiency metric:  measures the degree to which the aircrafts in the system are able to follow direct and linear flight paths to their destinations.  In this paper, we define a performance criterion for each aircraft same as follows: Performance Metrics 21-28 Semantic Cities @ AAAI 2012

22 Performance Metrics (cont)  here, we define a performance criterion for each aircraft same as follows:  We try to select routes with lowest cost when we redirect the aircrafts’ main routes (i.e. the lowest deviation from the main flight path for each aircraft) Performance Metrics (cont) 22-28 ideal and optimal flight path for an aircraft the amount of deviation from mainstream of aircrafts Semantic Cities @ AAAI 2012

23 Performance Metrics (cont)  System Efficiency:  how much this criterion is closer to “1” indicates good performance of the system and how much this criterion is closer to “0” indicates poor performance  our proposed model for solving GCP for higher dimensions (for a great number of aircrafts that are in the congestion area) acts as a good way. Performance Metrics (cont) 23-28 Total number of aircrafts Performance of each aircraft Semantic Cities @ AAAI 2012

24 Test Results Table 1: Test results of applying the algorithm onto input graphs (congestion areas) with specified parameters Test Results 24-28 Semantic Cities @ AAAI 2012

25 Conclusion  one of the fundamental challenges in the current air traffic management and especially in free flight method is detection and resolution of conflicts.  in our approach, we mapped conflict detection and resolution problem to graph coloring problem, then use imperialist competitive algorithm to solve GCP.  Our proposed model provides an efficient and reliable solution for solving conflicts in air traffic management  in conflict resolution process we tried to select routes with lowest cost when the aircrafts’ redirected from main route, therefore, we have least delay in flights and the minimum consumption of resources (e.g. in fuel consumption).  our proposed model use multiple strategies for the resolution of conflicts. Conclusion 25-28 Semantic Cities @ AAAI 2012

26 References 1. Federal Aviation Regulations and Aeronautical Information Manual, 2010 edition. 2009 ASA, Inc. Newcastle, Washington. 2. Rong, J., Geng, Sh., Valasek, J., R. Ioerger, T. 2002. Air traffic conflict negotiation and resolution using an onboard multi agent system. Digital Avionics systems Conf. Irvine, CA. 3. Kuchar, J., and Yang, L. C. 2000. A Review of CD&R Modeling Methods. IEEE Transactions on Intelligent Transportation Systems, Vol. 1, No. 4, December. 4. Agogino, A., and Tumer, K. 2008. Adaptive Management of Air Traffic Flow: A Multiagent Coordination Approach. Proceedings of the Twenty-Third AAAI Conf. on Artificial Intelligence. 5. Agogino, A., and Tumer, K. 2009. Improving air traffic management with a learning multiagent system. IEEE Intell. Syst., vol. 24, no. 1, pp. 18–21, Jan/Feb. 6. Atashpaz-Gargari, E., Lucas, C. 2007. Imperialist Competitive Algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, 4661–4667. 7. Eby, M. S. and III, W. E. K. 1999. Free flight separation assurance using distributed algorithms. In Proceedings of aerospace conference. Aspen, Co. 8. Archibald k., Hill c., Jepsen a., Stirling c., and Frost l. 2008. A satisfying approach to aircraft conflict resolution. IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 38, no. References 26-28 Semantic Cities @ AAAI 2012

27 References 9. Agogino, A. K., and Tumer, K. 2009. Learning indirect actions in complex domains: Action suggestions for air traffic control. Advances in Complex Systems, 12, 493–512. 10. Whalen, S. 2002. Graph Coloring with Genetic Algorithms. http://mat.gsia.cmu.edu/COLOR/color.html. 11. Garey, M.R. and Johnson, D.S. 1999. Computers and intractability: a guide to the theory of NP-completeness, W.H. Freeman and Company, New York. 12. De Werra D. 1990. Heuristics for Graph Coloring. Computing Suppl. 7, pp. 191-208. 13. Goldberg, D. E. 1980. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, MA. 14. Valkanas, G., Natsiavas,P., Bassiliades,N. 2009. A collision detection and resolution multi agent approach using utility functions, Fourth Balkan Conf. in Informatics. 15. Chao, W. and Jing, G. 2009. Analysis of Air Traffic Flow Control through Agent-Based Modelling and Simulation. International Conf. on Computer Modelling and Simulation, IEEE. 16. Jensen T.R., Toft B. 1995. Graph Coloring Problems, Wiley interscience Series in Discrete Mathematics and Optimization. References 27-28 Semantic Cities @ AAAI 2012

28 The End & Thanks T hanks for your attention !


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