Parallel and distributed computing for UAVs trajectory planning

  • Domenico Pascarella
  • Salvatore  Venticinque
  • Rocco  Aversa
  • Massimiliano Mattei
  • Luciano Blasi
Original Research


The problem of generating optimal flight trajectories for an unmanned aerial vehicle in the presence of no-fly zones is computationally expensive. It is usually solved offline, at least for those parts which cannot satisfy real time constraints. An example is the core paths graph algorithm, which discretizes the operational flight scenario with a finite dimensional grid of positions-directions pairs. A weighted and oriented graph is then defined, wherein the nodes are the earlier mentioned grid points and the arcs represent minimum length trajectories compliant with obstacle avoidance constraints. This paper investigates the exploitation of two parallel programming techniques to reduce the lead time of the core paths graph algorithm. The former employs some parallelization techniques for multi-core and/or multi-processor platforms. The latter is targeted to a distributed fleet of unmanned aerial vehicles. Here the statement of the problem and preliminary development are discussed. A two-dimensional scenario is analysed by way of example to show the applicability and the effectiveness of the approaches.


UAV Trajectory planning Core paths graph Parallel computing 


  1. Anderson E, Beard R, McLain T (2005) Real-time dynamic trajectory smoothing for unmanned air vehicles. IEEE Trans Control Syst Technol 13(3):471–477CrossRefGoogle Scholar
  2. Asseo SJ (1998) In-flight replanning of penetration routes to avoid threats zones of circular shapes. In: Proceedings of Aerospace and Electronics Conference (NAECON 1998), IEEE, pp 383–391Google Scholar
  3. Betts JT (1998) Survey of numerical methods for trajectory optimization. J Guid Control Dyn 21:193–207MATHCrossRefGoogle Scholar
  4. Blasi L, Barbato S, Mattei M (2013) A particle swarm approach for flight path optimization in a constrained environment. Aerosp Sci Technol 26(1):128–137CrossRefGoogle Scholar
  5. Borrelli F, Subramanian D, Raghunathan AU, Biegler LT (2006) MILP and NLP techniques for centralized trajectory planning of multiple unmanned air vehicles. In: Proceedings of 2006 American Control Conference, IEEEGoogle Scholar
  6. Cen Y, Wang L, Zhang H (2007) Real-time obstacle avoidance strategy for mobile robot based on improved coordinating potential field with genetic algorithm. In: Proceedings of the 16th IEEE International Conference on Control Applications, IEEE, pp 415–419Google Scholar
  7. Chitsaz H, LaValle SM (2007) Time-optimal paths for a Dubins airplane. In: Proceedings of the 46th IEEE Conference on Decision and Control (CDC), IEEE, pp 2379–2384Google Scholar
  8. Dever C, Mettler B, Feron E, Popovic J, McConley M (2006) Nonlinear trajectory generation for autonomous vehicles via parameterized maneuver classes. J Guid Control Dyn 29:289–302CrossRefGoogle Scholar
  9. Faied M, Mostafa A, Girard A (2010) Vehicle routing problem instances: application to multi-UAV mission planning. In: Proceedings of AIAA Guidance, Navigation and Control Conference, AIAAGoogle Scholar
  10. Ficco M, Avolio G, Battaglia L, Manetti V (2014) Hybrid Simulation of distributed large-scale critical infrastructures. In: Proceedings of 2014 International Conference on Intelligent Networking and Collaborative Systems (INCoS), IEEE, pp 616–621Google Scholar
  11. Frazzoli E, Dahleh M, Feron E (2002) Nonlinear trajectory generation for autonomous vehicles via parameterized maneuver classes. J Guid Control Dyn 25:116–129CrossRefGoogle Scholar
  12. Gertz EM, Wright SJ (2001) OOQP user guide. Technical Memorandum ANL/MCS-TM-252, Argonne National Laboratory. Mathematics and Computer Science DivisionGoogle Scholar
  13. Gigante G, Gargiulo F, Ficco M (2015) A semantic driven approach for requirements verification. Intelligent distributed computing VIII, Springer International Publishing, Studies in computational intelligence 570:427–436Google Scholar
  14. Gross D, Rasmussen S, Chandler P, Feitshans G (2006) Cooperative operations in urban terrain (COUNTER). In: Proceedings of Society of Photo-Optical Instrumentation Engineers Conference, vol 6249Google Scholar
  15. Hargraves CR, Paris SW (1987) Direct trajectory optimization using nonlinear programming and collocation. J Guid Control Dyn 10:338–342MATHCrossRefGoogle Scholar
  16. Hu XB, Wu SF, Jiang J (2004) On-line free-flight path optimization based on improved genetic algorithms. Eng Appl Artif Intell 17(8):897–907CrossRefGoogle Scholar
  17. Hwang YK, Ahuja N (1992) A potential field approach to path planning. IEEE Trans Robot Autom 8:23–32CrossRefGoogle Scholar
  18. Ingham LA (2008) Considerations for a roadmap for the operations of unmanned aerial vehicles (UAV) in South African airspace. PhD thesis, Universiteit Stellenbosch UniversityGoogle Scholar
  19. Israel M (2011) A UAV-based roe deer fawn detection system. Int Arch Photogramm Remote Sens XXXVIII:1–5Google Scholar
  20. Kider JT, Henderson M, Likhachev M, Safonova A (2010) High-dimensional planning on the GPU. In: Proceedings of 2010 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 2515–2522Google Scholar
  21. Kopřiva V, Šišlák D, Pěchouček M (2012) Towards parallel real-time trajectory planning. In: Advances on practical applications of agents and multi-agent systems: 10th international conference on practical applications of agents and multi-agent systems. Advances in Intelligent and Soft Computing, vol 155. Springer, Berlin, Heidelberg, pp 99–108CrossRefGoogle Scholar
  22. Lei L, Wang H, Wu Q (2006) Improved genetic algorithms based path planning of mobile robot under unknown environment. In: Proceedings of IEEE International Conference on Mechatronics and Automation, IEEE, pp 1728–1732Google Scholar
  23. Li S, Sun X, Xu Y (2006) Particle swarm optimization for route planning of unmanned aerial vehicles. In: Proceedings of 2006 IEEE International Conference on Information Acquisition, IEEE, pp 1213–1218Google Scholar
  24. Mattei M, Blasi L (2010) Smooth flight trajectory planning in the presence of no-fly zones and obstacles. J Guid Control Dyn 33(2):454–462CrossRefGoogle Scholar
  25. Meier L, Tanskanen P, Fraundorfer F, Pollefeys M (2011) PIXHAWK: A system for autonomous flight using onboard computer vision. In: Proceedings of 2011 IEEE International Conference on Robotics and Automation (ICRA), IEEE, pp 2992–2997Google Scholar
  26. Monteiro RDC, Adler I, Resende MGC (1990) A polynomial-time primal-dual affine scaling algorithm for linear and convex quadratic programming and its power series extension. Math Oper Res 15(2):191–214MATHMathSciNetCrossRefGoogle Scholar
  27. Murray RM (2007) Resent research in cooperative control of multi-vehicle systems. J Dyn Syst Meas Control 129(5):571–583CrossRefGoogle Scholar
  28. OpenMP (2013) OpenMP application program interface, version 4.0. OpenMP Architecture Review BoardGoogle Scholar
  29. Pan J, Lauterbach C, Manocha D (2010) g-planner: real-time motion planning and global navigation using GPUs. In: Proceedings of the National Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence (AAAI), vol 2, pp 1245–1251Google Scholar
  30. Pascarella D, Venticinque S, Aversa R (2013) Agent-based design for UAV mission planning. In: Proceedings of the 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC-2013), IEEE, pp 76–83Google Scholar
  31. Pascarella D, Venticinque S, Aversa R, Mattei M, Blasi L (2014) A parallel and a distributed implementation of the core paths graph algorithm. In: Intelligent distributed computing VIII, Springer International Publishing, Studies in Computational Intelligence 570:417–426Google Scholar
  32. Quaritsch M, Kruggl K, Wischounig-Strucl D, Bhattacharya S, Shah M, Rinner B (2010) Networked UAVs as aerial sensor network for disaster management applications. Elektrotech Informationstechnik 127(3):56–63CrossRefGoogle Scholar
  33. Raja P, Pugazhenhi S (2009) Path planning for mobile robots in dynamic environments using particle swarm optimization. In: Proceedings of 2009 International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom’09), IEEE, pp 401–405Google Scholar
  34. Russ M, Stütz P (2012) Airborne sensor and perception management: a conceptual approach for surveillance UAS. In: Proceedings of 2012 15th International Conference on Information Fusion (FUSION), IEEE, pp 2444–2451Google Scholar
  35. Saska M, Macas M, Preucil L, Lhotska L (2006) Robot path planning using particle swarm optimization of ferguson splines. In: Proceedings of 2006 IEEE Conference on Emerging Technologies and Factory Automation (ETFA06), IEEE, pp 833–839Google Scholar
  36. Schouwenaars T, Feron E, How J (2004) Receding horizon path planning with implicit safety guarantees. In: Proceedings of 2004 American Control Conference, IEEE, vol 6, pp 5576–5581Google Scholar
  37. Schumacher C, Chandler PR, Patcher M, Patcher LS (2007) Optimization of air vehicles operations using mixed-integer linear programming. J Oper Res Soc 58:516–527MATHCrossRefGoogle Scholar
  38. Shima T, Rasmussen S (2009) UAV cooperative decision and control: challenges and practical approaches. Society for Industrial and Applied Mathematics, PhiladelphiaCrossRefGoogle Scholar
  39. Vaidyanathan R, Hocaoglu C, Prince TS, Quinn RD (2001) Evolutionary path planning for autonomous air vehicle using multiresolution path representation. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS), IEEE, vol 1, pp 69–76Google Scholar
  40. Yang HI, Zhao YJ (2004) Trajectory planning for autonomous aerospace vehicles amid known obstacles and conflicts. J Guid Control Dyn 27:997–1008CrossRefGoogle Scholar
  41. Yokoyama N, Suzuki S (2005) Modified genetic algorithm for constrained trajectory optimization. J Guid Control Dyn 28:139–144CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Domenico Pascarella
    • 1
  • Salvatore  Venticinque
    • 2
  • Rocco  Aversa
    • 2
  • Massimiliano Mattei
    • 2
  • Luciano Blasi
    • 2
  1. 1.Soft Computing LaboratoryCIRA (Italian Aerospace Research Centre)CapuaItaly
  2. 2.Department of Industrial and Information EngineeringSecond University of NaplesAversaItaly

Personalised recommendations