Mevlut Uzun
ABSTRACT
The future flight deck will need additional avionics and operational procedures which involve adaptive algorithms and advanced decision support tools, considering the transformation of redefining existing roles in ATM technologies. These tools should also meet the requirements of the future flight operations defined in NextGen and SESAR 2020+ visions. Following this line of thought, the main purpose is to develop a theoretical framework for both in-tactical 4D trajectory planning and collision avoidance problems of an aircraft equipped with automated tools, which in turn can almost achieve free flight. The proposed approach in 4D trajectory planning involves recent algorithmic advances in both probabilistic and deterministic methods, in order to fully benefit from the powerful and useful capabilities of both approaches. Furthermore, we have constructed an aircraft performance model based on BADA 4 so as to generate trajectories. The multi-modal approach for the aircraft model is utilized to overcome the problem of the complex optimal trajectory generation via reducing the dimension. The sampling-based algorithm embeds this local trajectory generation procedure and guarantees asymptotic optimality under certain conditions. Moreover, a cross-entropy method has been integrated to enable highly efficient sampling. We have also formulated the collision avoidance problem as a perfect-information zero-sum differential game, as well as introduced a sampling-based procedure integrating sampling-based motion planning algorithms to generate feasible approximate solutions. Finally, different scenarios have been simulated, and pilot interactions with the integrated B737-800 Flight Deck Testbed deployed in ITU ARC have been demonstrated.
- S. Altus. Effective Flight plans can Help Airlines Economize. Boeing Aero, 2009.Google Scholar
- T. Basar and G. J. Olsder. Dynamic Noncooperative Game Theory, volume 23. SIAM, 1999.Google Scholar
- B. Burns and O. Brock. Toward optimal configuration space sampling. In Robotics: Science and Systems, pages 105--112. Citeseer, 2005.Google ScholarCross Ref
- M. A. Christodoulou and S. G. Kodaxakis. Automatic commercial aircraft-collision avoidance in free flight: the three-dimensional problem. Intelligent Transportation Systems, IEEE Transactions on, 7(2):242--249, 2006. Google ScholarDigital Library
- J. P. Chryssanthacopoulos. Accounting for state uncertainty in collision avoidance. Journal of Guidance, 2011.Google ScholarCross Ref
- J. Denny and N. M. Amato. Toggle prm: Simultaneous mapping of c-free and c-obstacle-a study in 2d. In Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on, pages 2632--2639. IEEE, 2011.Google ScholarCross Ref
- Dever, C, Mettler, B, Feron, E, Popovic, J, and McConley, M. Nonlinear Trajectory Generation for Autonomous Vehicles via Parameterized Maneuver Classes. Journal of Guidance Control and Dynamics, 29:289--302, 2006.Google ScholarCross Ref
- EUROCONTROL. User Manual for the Base of Aircraft Data (BADA) Family 4. EEC TechnicalScientific Report No. -, Apr. 2014.Google Scholar
- E. Frazzoli, M. A. Dahleh, and E. Feron. Maneuver-Based Motion Planning for Nonlinear Systems With Symmetries. IEEE Transactions on Robotics, 21(6):1077--1091, Nov. 2005. Google ScholarDigital Library
- E. Frazzoli and S. Karaman. Incremental sampling-based algorithms for optimal motion planning. Int. Journal of Robotics Research, 2010.Google Scholar
- D. Hsu, R. Kindel, J.-C. Latombe, and S. Rock. Randomized Kinodynamic Motion Planning with Moving Obstacles. International Journal of Robotics Research, 21(2):233--255, Dec. 2002.Google Scholar
- D. Hsu, G. Sánchez-Ante, and Z. Sun. Hybrid prm sampling with a cost-sensitive adaptive strategy. In Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, pages 3874--3880. IEEE, 2005.Google ScholarCross Ref
- M. Kalisiak and M. van de Panne. Faster motion planning using learned local viability models. In Robotics and Automation, 2007 IEEE International Conference on, pages 2700--2705. IEEE, 2007.Google ScholarCross Ref
- S. Karaman and E. Frazzoli. Incremental Sampling-based Algorithms for a class of Pursuit-Evasion Games. Ninth International Workshop on the Algorithmic Foundations of Robotics, pages 1--16, Oct. 2010.Google ScholarCross Ref
- S. Karaman and E. Frazzoli. Optimal kinodynamic motion planning using incremental sampling-based methods. In Decision and Control (CDC), 2010 49th IEEE Conference on, pages 7681--7687. IEEE, 2010.Google ScholarCross Ref
- S. Karaman and E. Frazzoli. Incremental Sampling-Based Algorithms for a Class of Pursuit-Evasion Games. In Algorithmic Foundations of Robotics IX, pages 71--87. Springer Berlin Heidelberg, Berlin, Heidelberg, Jan. 2011.Google Scholar
- S. Karaman, M. R. Walter, A. Perez, E. Frazzoli, and S. Teller. Anytime Motion Planning using the RRT*. In Robotics and Automation (ICRA), 2011 IEEE International Conference on, pages 1478--1483. IEEE, 2011.Google ScholarCross Ref
- M. Kobilarov. Cross-entropy motion planning. The International Journal of Robotics Research, 31(7):855--871, may 2012. Google ScholarDigital Library
- M. J. Kochenderfer and J. P. Chryssanthacopoulos. Partially-controlled markov decision processes for collision avoidance systems. In International Conference on Agents and Artificial Intelligence, Rome, Italy, 2011.Google Scholar
- M. J. Kochenderfer and J. P. Chryssanthacopoulos. Robust Airborne Collision Avoidance Through Dynamic Programming. Massachusetts Institute of Technology, Lincoln Laboratory, Project Report ATC-371, 2011.Google Scholar
- E. Koyuncu and G. Inalhan. Exploiting Delayed and Imperfect Information for Generating Approximate UAV Target Interception Strategy. Journal of Intelligent and Robotic Systems, 69(1--4):313--329, 2013. Google ScholarDigital Library
- E. Koyuncu, N. K. Ure, and G. Inalhan. Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs. JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 57(1--4):143--170, 2010. Google ScholarDigital Library
- J. Kuchar. A review of conflict detection and resolution modeling methods. IEEE Transactions on Intelligent Transportation Systems, 1  4, 2000. Google ScholarDigital Library
- J. J. Kuffner and S. M. LaValle. RRT-Connect: An Efficient Approach to Single-Query Path Planning. In Proc. of IEEE International Conference on Robotics and Automation (ICRA), volume 2, pages 995--1001, 2000.Google ScholarCross Ref
- S. LaValle and J. Kuffner. Randomized kinodynamic planning. Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on, 1:473--479 vol.1, Apr. 1999.Google ScholarCross Ref
- S. M. LaValle. On the Relationship between Classical Grid Search and Probabilistic Roadmaps. The International Journal of Robotics Research, 23(7--8):673--692, Aug. 2004.Google ScholarCross Ref
- S. M. LaValle and J. J. Kuffner. Randomized Kinodynamic Planning. page 7, Mar. 1999.Google Scholar
- Y. Li and K. E. Bekris. Balancing state-space coverage in planning with dynamics. In Robotics and Automation (ICRA), 2010 IEEE International Conference on, pages 3246--3253. IEEE, 2010.Google Scholar
- S. Lindeman and S. M. LaValle. Current Issues in Sampling-Based Motion Planning, page 10. Springer Berlin Heidelberg, 2005.Google Scholar
- S. R. Lindemann and S. M. LaValle. Steps toward derandomizing RRTs. In Proceedings of the Fourth International Workshop on Robot Motion and Control, RoMoCo'04, 2004.Google ScholarCross Ref
- B. D. Luders, S. Karaman, and J. P. How. Robust sampling-based motion planning with asymptotic optimality guarantees. AIAA Guidance, 2013.Google ScholarCross Ref
- A. Nuic, C. Poinsot, M. Iagaru, E. Gallo, F. A. Navarro, and C. Querejeta. Advanced Aircraft Performance Modeling for ATM: Enhancements to the Bada Model. In Digital Avionics Systems Conference, 2005. DASC 2005. The 24th, pages 2--2.B.4--1. IEEE, 2005.Google ScholarCross Ref
- R. Parasuraman, T. B. Sheridan, and C. D. Wickens. A model for types and levels of human interaction with automation. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 30(3):286--297, May 2000. Google ScholarDigital Library
- A. Richards and J. P. How. Aircraft trajectory planning with collision avoidance using mixed integer linear programming. In American Control Conference, 2002. Proceedings of the 2002, pages 1936--1941 vol.3, 2002.Google ScholarCross Ref
- RTCA SC-147. Minimum Operational Performance Standards for Traffic Alert and Collision Avoidance System II (TCAS II). In Technical Report RTCA/DO-185B Change 2, 2013.Google Scholar
- I. Şucan and L. E. Kavraki. A Sampling-Based Tree Planner for Systems With Complex Dynamics. IEEE Transactions on Robotics, 28(1):116--131, 2000. Google ScholarDigital Library
- C. Tomlin, G. J. Pappas, and S. Sastry. Conflict Resolution for Air Traffic Management: A Study in Multi-Agent Hybrid Systems. IEEE Transactions on Automatic Control, 43, 1998.Google Scholar
- L. F. Winder. Hazard Avoidance Alerting with Markov Decision Processes, 2004.Google Scholar
- T. Wolf and M. Kochenderfer. Aircraft Collision Avoidance Using Monte Carlo Real-Time Belief Space Search. Journal of Intelligent and Robotics Systems, 64:277--298, 2011. Google ScholarDigital Library
Recommendations
Bi-level Flight Path Planning of UAV Formations with Collision Avoidance
This paper deals with the problem of generating 3D flight paths for a swarm of cooperating Unmanned Aerial Vechicles (UAVs) flying in a formation having a prespecified shape, in the presence of polygonal obstacles, no-fly zones and other non cooperative ...
A 4D Augmented Flight Management System Based on Flight Planning and Trajectory Generation Merging
Towards Autonomous Robotic SystemsAbstractIn recent decades, the helicopter has become a safe and suitable solution for future traffic, both in congested megacities and in isolated places as well as in mountainous environment where the terrain can be dangerous. Adding the third dimension ...
Collaborative Intent Exchange Based Flight Management System with Airborne Collision Avoidance for UAS
This paper presents a Flight Management System (FMS) with multi-level autonomy modes that meet the requirements of future flight operations for unmanned aerial systems (UAS). It is envisioned that the future of airspace will become highly heterogeneous ...
Comments