Development of a conflict detection and resolution methodololy used in the operational flight 4D-trajectory planning
https://doi.org/10.51955/2312-1327_2024_2_77
Abstract
Conflict detection and resolution is one of the key tasks in ensuring the safety and efficiency of air transport. In Trajectory Based Operation (TBO), aircraft are given greater flexibility in planning trajectories along the route and greater responsibility for self-separation from each other, so the pilot will need assistance to safely and efficiently perform the task of decentralized conflict resolution during the en-route flight. In this work, we develop a method for identifying and resolving conflict situations in cruising phase based on four-dimensional grid nodes (4D-grid) and the A-star shortest path search algorithm (A* for short) to form an optimal four-dimensional trajectory (4D-trajectory) bypass all airspace obstacles. This new approach helps to avoid false warnings about potential conflicts due to the ability to early detect them and accurately determine the distance from aircraft to areas of dangerous proximity (prohibited zones (PZ), zones of bad weather, other aircraft) and then autonomously form a time-spatial trajectory to bypass them. In order to demonstrate the effectiveness of the proposed method, we conduct three experiments in different airspace conditions (with and without the areas of dangerous proximity). The results of the experiments prove that potential dangerous proximities of aircraft in flight are effectively identified and resolved using the proposed methodology.
About the Authors
Thi Linh Phuong NguyenRussian Federation
Nguyen Thi Linh Phuong, Ph. D. Student; teacher-researcher
4, Volokolamskoe shosse, Moscow, 125993
104 Nguyen Van Troi, Ward 8, Phu Nhuan District, Ho Chi Minh City, Vietnam
E. S. Neretin
Russian Federation
Evgeny S. Neretin, Candidate of Technical Sciences, Associated Professor
4, Volokolamskoe shosse, Moscow, 125993
Nhu Man Nguyen
Russian Federation
Nguyen Nhu Man, Candidate of Technical Sciences
4, Volokolamskoe shosse, Moscow, 125993
References
1. Acevedo J. J., Castaño Á. R., Andrade-Pineda J. L., Ollero A. (2019). A 4D grid-based approach for efficient conflict detection in large-scale multi-UAV scenarios. 2019 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED UAS). Cranfield, UK, 2019. pp. 18-23. doi: 10.1109/REDUAS47371.2019.8999724.
2. Alonso-Ayuso A., Escudero L. F., Martin-Campo F. J. (2016). Multiobjective optimization for aircraft conflict resolution: A metaheuristic approach. European Journal of Operational Research. 248(2): 691-702.
3. Architecture of National Airspace System (NAS). Concepts for Future NAS Operations. Department of Transportation, FAA, USA. 1996.
4. Bilimoria K. D. (2000). A Geometric Optimization Approach to Aircraft Conflict Resolution. AIAA Guidance, Navigation and Control Conf. Denver, 2000.
5. Bilimoria K. D., Lee H. Q., Mao Z. H. [et al]. (2000). Comparison of centralized and decentralized conflict resolution strategies for multiple-aircraft problems. AIAA Guidance, Navigation, and Control Conf. Denver, 2000.
6. Degtyarev O. V., Orlov V. S., Puchkov B. V. (2010). Development of on-board algorithms for detection and decentralized resolution of dangerous approaches in the air based on the method of potential fields. Theory and control Systems. 5: 93. (in Russian)
7. Eby M. S. (1994). A Self-Organizational Approach for Resolving Air Traffic Conflicts. The Lincoln Laboratory Journal. 7(2): 239-254.
8. Frazzoli E., Mao Z. H., Oh J. H. [at al.]. (2001). Resolution of Conflicts Involving Many Aircraft via Semi-definite Programming. Journal Guidance, Control and Dynamics. 24(1): 79-86. DOI 10.2514/2.4678.
9. Gong H., Zhou X. (2013). Flight short-term collision detection based on control tower simulation system. Computer. Technology and Development. 23(4): 151-154.
10. Henk A. P. Blom, Bakker G. J. (2015). Safety evaluation of advanced self-separation under very high en route traffic demand. Journal of Aerospace Information Systems. 12(6): 413-427.
11. Hernández-Romero E., Valenzuela A., Rivas D. (2019). A probabilistic approach to measure aircraft conflict severity considering wind forecast uncertainty. Aerospace Science and Technology. 86: 401–414. doi: 10.1016/j.ast.2019.01.024.
12. Hoekstra J. M. M., Gent M., Ruigrok M. (1998). Conceptual Design of Free Flight with Airborne Separation Assurance. AIAA Guidance, Navigation, and Control Conf. Boston, 1998.
13. ICAO 2002. Doc 9674 – World Geodetic System — 1984 (WGS-84) Manual. Second edition. 2002.
14. ICAO 2012. Doc 9574 – Manual on Implementation of a 300 m (1000 ft) Vertical Separation Minimum Between FL 290 and FL 410 Inclusive. Third edition. 2012.
15. ICAO 2016. Doc 4444 – Procedures for Air Navigation Services/Air Traffic Management (PANS/ATM). 16th Edition. 2016.
16. Isaev V. K., Zolotukhin V. V. (2009). Some tasks of 2D aircraft maneuvering in order to ensure vortex safety. Bulletin of MAI. 16(7): 1. (in Russian)
17. Jardin M. (2005). Grid-Based Strategic Air Traffic Conflict Detection. AIAA Guidance, Navigation, and Control Conference and Exhibit. 2005. doi:10.2514/6.2005-5826.
18. Kuchar J. K., Yang L. C. (2000). A Review of Conflict Detection and Resolution Modeling Methods. IEEE Trans, on Intelligent Transportation Systems. 1(4): 179-189.
19. Kumkov S. I., Pyatko S. G. (2013). The task of detecting and resolving conflict situations in an automated air traffic control system. Scientific Bulletin of the Research Institute of Aeronautics. 12: 35-46. (in Russian)
20. Liu Y., Xiang J., Luo Z., Jin W. (2017). Short-term conflict detection algorithm for free flight in low-altitude airspace. Journal of Beijing University of Aeronautics and Astronautics. 43(9): 1873-1881. DOI 10.13700/j.bh.1001-5965.2016.0687.
21. Mondoloni S., Conway S. (2001). An Airborne Conflict Resolution Approach Using a Genetic Algorithm. AIAA Guidance, Navigation, and Control Conf. Montreal, 2001.
22. Neretin E. S., Phuong N. T. L., Quan N. N. H. (2022). An Analysis of Human Interaction and Weather Effects on Aircraft Trajectory Prediction via Artificial Intellegence. 2022 XIX Technical Scientific Conference on Aviation Dedicated to the Memory of N.E. Zhukovsky (TSCZh). Moscow, 2022. pp. 85-89. doi: 10.1109/TSCZh55469.2022.9802458.
23. Nogami J., Nakasuka S., Hori K. (1998). Real-time Decision Support for Air Traffic Management, Utilizing Concept Learning. AIAA Guidance, Navigation, and Control Conf. Boston, 1998.
24. Petrov N. A. (2014). Development of a universal algorithm for resolving conflict situations in airspace during the flight of a mainline aircraft. Scientific Bulletin of MSTU GA. 205: 129-136. (in Russian)
25. Wallace E., Kelly I. (2000). Advances in Force Field Conflicts Resolution Algorithms. AIAA Guidance, Navigation and Control Conf. Denver, 2000.
26. Wanke C., Greenbaum D. (2007). Incremental, Probabilistic Decision Making for En Route Traffic Management. Air Traffic Control Quarterly. 15(4): 299-319.
27. Zeghal K. (1998). A Review of Different Approaches based on Force Fields for Airborne Conflict Resolution. AIAA Guidance, Navigation and Control Conf. Boston, 1998.
Review
For citations:
Nguyen T., Neretin E.S., Nguyen N. Development of a conflict detection and resolution methodololy used in the operational flight 4D-trajectory planning. Crede Experto: transport, society, education, language. 2024;(2):77-95. (In Russ.) https://doi.org/10.51955/2312-1327_2024_2_77
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