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An adaptive multi-spline refinement algorithm in simulation based sailboat trajectory optimization using onboard multi-core computer systems

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Języki publikacji
EN
Abstrakty
EN
A new dynamic programming based parallel algorithm adapted to on-board heterogeneous computers for simulation based trajectory optimization is studied in the context of “high-performance sailing”. The algorithm uses a new discrete space of continuously differentiable functions called the multi-splines as its search space representation. A basic version of the algorithm is presented in detail (pseudo-code, time and space complexity, search space auto-adaptation properties). Possible extensions of the basic algorithm are also described. The presented experimental results show that contemporary heterogeneous on-board computers can be effectively used for solving simulation based trajectory optimization problems. These computers can be considered micro high performance computing (HPC) platforms—they offer high performance while remaining energy and cost efficient. The simulation based approach can potentially give highly accurate results since the mathematical model that the simulator is built upon may be as complex as required. The approach described is applicable to many trajectory optimization problems due to its black-box represented performance measure and use of OpenCL.
Rocznik
Strony
351--365
Opis fizyczny
Bibliogr. 42 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Computer Science, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
Bibliografia
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  • [14] Dębski, R. (2014b). High-performance simulation-based algorithms for an alpine ski racer’s trajectory optimization in heterogeneous computer systems, International Journal of Applied Mathematics and Computer Science 24(3): 551–566, DOI: 10.2478/amcs-2014-0040.
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  • [21] Li, H. and Lü, M. (2014). A three dimensional route planning method based on improved ant colony optimization algorithm, Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 32(4): 563–568.
  • [22] Marchaj, C. (2004). Sailing Theory: Aerodynamics of the Sail, Alma-Press, Warsaw, (in Polish).
  • [23] Park, C., Pan, J. and Manocha, D. (2013). Real-time optimization-based planning in dynamic environments using GPUs, 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, pp. 4090–4097.
  • [24] Pêtres, C., Romero-Ramirez, M.-A. and Plumet, F. (2011). Reactive path planning for autonomous sailboat, 2011 15th International Conference on Advanced Robotics (ICAR), Tallinn, Estonia, pp. 112–117.
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  • [37] Ćurković, P., Jerbić, B. and Stipančić, T. (2009). Swarm-based approach to path planning using honey-bees mating algorithm and art neural network, in Z. Gosiewska and Z. Kulesza (Eds.), Mechatronic Systems and Materials III, Solid State Phenomena, Vol. 147, Trans Tech Publications, Pfaffikon, pp. 74–79.
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  • [42] Zhou, S., Zhu, G., Li, H.,Wang, Y. and Liu, X. (2011). Real-time route planning for uav based on weather threat, 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE), Nanijng, China, pp. 2342–2345.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-dc3bcfb7-f38e-463a-b86f-fce35a3327ae
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