Abstract
NASA and the international aviation community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and civil tilt rotors. However, there is significant concern over the impact of noise on the communities surrounding the transportation facilities. One way to address the rotorcraft noise problem is by exploiting powerful search techniques coming from artificial intelligence to design low-noise flight profiles that can be then validated though field tests. This article investigates the use of discrete heuristic search methods to design low-noise approach trajectories for rotorcraft. Our work builds on a long research tradition in trajectory optimization using either numerical methods or discrete search. Novel features of our approach include the use of a discrete search space with a resolution that can be varied, and the coupling of search with a robust simulator to evaluate candidates. The article includes a systematic comparison of different search techniques; in particular, in the experiments, we are able to do a trade study that compares complete search algorithms such as A* with faster but approximate methods such as local search.
- E. Atkins and M. Xue. 2004. Noise-sensitive final approach trajectory optimization for runway-independent aircraft. Journal of Aerospace Computation, Information and Communication 1 (2004), 269--287.Google ScholarCross Ref
- J. T. Betts. 1998. Survey of numerical methods for trajectory optimization. Journal of Guidance, Control and Dynamics 21, 2 (1998), 193--207.Google ScholarCross Ref
- A. Booker, J. E. Dennis, P. D. Frank, D. B. Serafini, V. Torczon, and M. W. Trosset. 1998. A Rigorous Framework for Optimization of Expensive Functions by Surrogates. Technical Report 98-47. National Aeronautics and Space Administration. Google ScholarDigital Library
- A. E. Bryson and Y.-C. Ho. 1975. Applied Optimal Control. Hemisphere Publishing Corp., New York.Google Scholar
- P. Cheng and S. M. Lavalle. 2002. Resolution complete rapidly-exploring random trees. In Proceedings of the IEEE International Conference on Robotics and Automation. 267--272.Google Scholar
- D. A. Conner, C. L. Burley, and C. D. Smith. 2006. Flight acoustic testing and data acquisition for the rotor noise model (RNM). In Proceedings of the 62nd Annual Forum of the American Helicopter Society. 1--17.Google Scholar
- F. Fahroo and M. Ross. 2007. A perspective on methods for trajectory optimization. In AIAA/AAS Astrodynamics Specialist Conference and Exhibit, Monterey, California.Google Scholar
- Federal Aviation Administration. 2007. Environmental Desk Reference for Airport Actions. Technical Report.Google Scholar
- D. Ferguson and A. (T.) Stentz. 2005. The Field D* Algorithm for Improved Path Planning and Replanning in Uniform and Non-Uniform Cost Environments. Technical Report CMU-RI-TR-05-19. Robotics Institute, Pittsburgh, Pennsylvania.Google Scholar
- G. Goplan, M. Xue, E. Atkins, and F. H. Schmitz. 2003. Longitudinal-plane simultaneous non-interfering approach trajectory design for noise minimization. In Proceedings of the 59th AHS International Forum and Technology Display. 1--18.Google Scholar
- H. H. Hoos and T. Stutzle. 2004. Stochastic Local Search: Foundations and Applications. Elsevier - Morgan Kaufmann. Google ScholarDigital Library
- H. J. Horn. 1965. Application of an Iterative Guidance Mode to a Lunar Landing. National Aeronautics and Space Administration.Google Scholar
- L. E. Kavraki, M. N. Kolountzakis, and J.-C. Latombe. 1998. Analysis of probabilistic roadmaps for path planning. IEEE Trans. Robotics and Automation 14, 1 (1998), 166--171.Google ScholarCross Ref
- S. M. LaValle. 2006. Planning Algorithms. Cambridge University Press, Cambridge, UK. http://planning.cs.uiuc.edu/. Google ScholarDigital Library
- S. M. LaValle, M. S. Branicky, and S. R. Lindemann. 2004. On the relationship between classical grid search and probabilistic roadmaps. International Journal of Robotics Research 23, 7-8 (2004), 673--692.Google ScholarCross Ref
- O. J. Mengshoel. 2008. Understanding the role of noise in stochastic local search: Analysis and experiments. Artificial Intelligence 172, 8-9 (2008), 955--990. Google ScholarDigital Library
- R. A. Morris, M. Donini, K. B. Venable, and M. Johnson. 2013. Designing quiet rotorcraft landing trajectories with probabilistic road maps. In Proceedings of the Scheduling and Planning Applications Workshop (SPARK’13). Rome, Italy.Google Scholar
- R. A. Morris, K. B. Venable, and J. Lindsay. 2012a. Automated design of noise-minimal, safe rotorcraft trajectories. In Proceedings of the 68th American Helicopter Society Annual Forum & Technology Display.Google Scholar
- R. A. Morris, K. B. Venable, and J. Lindsay. 2012b. Simulation to support local search in trajectory optimization planning. In Proceedings of the IEEE 2012 Aerospace Conference.Google Scholar
- R. A. Morris, K. B. Venable, M. Pegoraro, and J. Lindsay. 2012c. Local search for designing noise-minimal rotorcraft approach trajectories. In Proceedings of the Twenty-Fourth Conference on Innovative Applications of Artificial Intelligence (IAAI'12). Google ScholarDigital Library
- Federal Interagency Committee on Noise. 1992. 1992 Federal Interagency Commitee on Noise (FICON) Report-Federal Agency Review of Selected Airport Noise Analysis Issues. Technical Report.Google Scholar
- S. L. Padula, C. L. Burley, D. D. Boyd Jr., and M. A. Marcolini. 2009. Design of Quiet Rotorcraft Approach Trajectories. Technical Report NASA/TM-215771. Langley Research Center.Google Scholar
- J. Page, C. Wilmer, and K. J. Plotkin. 2007. Rotorcraft Noise Model Technical Reference and User Manual (Version 7). Technical Report WR 07-04. Wyle Laboratories for NASA Langley Research Center.Google Scholar
- P. O. Pettersson and P. Doherty. 2006. Probabilistic roadmap based path planning for an autonomous unmanned helicopter. Journal of Intelligent and Fuzzy Systems 17, 4 (2006), 395--405. Google ScholarDigital Library
- B. W.-C. Sim, F. H. Schmitz, and G. Gopalan. 2002. Flight-path management/control methodology to reduce helicopter blade-vortex interaction noise. Journal of Aircraft 39, 2 (2002), 193--205.Google ScholarCross Ref
- K. B. Venable, R. A. Morris, M. Johnson, A. Mousavi, and N. Oza. 2014. A machine learning surrogate for rotorcraft noise optimization. In Proceedings of the Scheduling and Planning Applications Workshop (SPARK’14).Google Scholar
- M. Xue. 2006. Real-Time Terminal Area Trajectory Planning for Runway Independent Aircraft. Ph.D. Dissertation. University of Maryland. Google ScholarDigital Library
- M. Xue and E. M. Atkins. 2006a. Noise-minimum runway-independent aircraft approach design for baltimore-washington international airport. Journal of Aircraft, American Institute of Aeronautics and Astronautics (AIAA) 43, 1 (2006), 39--51.Google Scholar
- M. Xue and E. M. Atkins. 2006b. Terminal area trajectory optimization using simulated annealing. In Proceedings of the 44th AIAA Aerospace Sciences Meeting and Exhibit. AIAA, Reno, Nevada.Google Scholar
Index Terms
- Designing Noise-Minimal Rotorcraft Approach Trajectories
Recommendations
Local search for designing noise-minimal rotorcraft approach trajectories
AAAI'12: Proceedings of the Twenty-Sixth AAAI Conference on Artificial IntelligenceNASA and the international community are investing in the development of a commercial transportation infrastructure that includes the increased use of rotorcraft, specifically helicopters and civil tilt rotors. However, there is significant concern over ...
Comments