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Collision-Free 4D Trajectory Planning in Unmanned Aerial Vehicles for Assembly and Structure Construction

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Abstract

This paper presents a new system for assembly and structure construction with multiple Unmanned Aerial Vehicles (UAVs) which automatically identifies conflicts among them. The system proposes the most effective solution considering the available computation time. After detecting conflicts between UAVs, the system resolves them cooperatively using a collision-free 4D trajectory planning algorithm based on a simple one-at-a-time strategy to quickly compute a feasible but non-optimal initial solution and a stochastic optimization technique named Particle Swarm Optimization (PSO) to improve the initial solution. An anytime approach using PSO is applied. It yields trajectories whose quality improves when available computation time increases. Thus, the method could be applied in real-time depending on the available computation time. The method has been validated with simulations in scenarios with multiple UAVs in a common workspace and experiment in an indoor testbed.

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References

  1. Beard, R.W., McLain, T.W., Nelson, D.B., Kingston, D., Johanson, D.: Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proc. IEEE 94(7), 1306–1324 (2006)

    Article  Google Scholar 

  2. Cobano, J.A., Martínez-de Dios, J.R., Conde, R., Sánchez-Matamoros, J.M., Ollero, A.: Data retrieving from heterogeneous wireless sensor network nodes using UAVs. J. Intell. Robot. Syst. 60(1), 133–151 (2010). Online available: http://www.springerlink.com/index/10.1007/s10846-010-9414-y

    Article  MATH  Google Scholar 

  3. Ollero, A.: Aerial robotics cooperative assembly system (ARCAS): first results. In: Aerial Physically Acting Robots (AIRPHARO) Workshop, IROS 2012. Vilamoura, Portugal, 7–12 Oct 2012

  4. Jimenez-Cano, A.E., Martin, J., Heredia, G., Cano, R., Ollero, A.: Control of an aerial robot with multi-link arm for assembly tasks. In: IEEE Int. Conf. Robotics and Automation (ICRA). Karlsruhe, Germany, 6–10 May 2013

  5. LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge (2006). Available at http://planning.cs.uiuc.edu/

    Book  MATH  Google Scholar 

  6. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Rob. Res. 5(1), 90–98 (1986). Online available: doi:10.1177/027836498600500106

    Article  MathSciNet  Google Scholar 

  7. Stentz, A., Mellon, I.C.: Optimal and efficient path planning for unknown and dynamic environments. Int. J. Robot. Autom. 10, 89–100 (1993)

    Google Scholar 

  8. Lavalle, S.M., Kuffner, J.J. Jr.: Rapidly-exploring random trees: progress and prospects. In: Algorithmic and Computational Robotics: New Directions, pp. 293–308 (2000)

  9. Cobano, J.A., Conde, R., Alejo, D., Ollero, A.: Path planning based on genetic algorithms and the monte-carlo method to avoid aerial vehicle collisions under uncertainties. In: Proc. IEEE Int Robotics and Automation (ICRA) Conf, pp. 4429–4434 (2011)

  10. Vivona, R., Karr, D., Roscoe, D.: Pattern-based genetic algorithm for airborne conflict resolution. In: AIAA Guidance, Navigation and Control Conference and Exhibit. Keystone, Colorado (2006)

  11. Durand, N., Alliot, J.: Optimization resolution of en route conflicts. In: First USA/Europe Air Traffic Management Research and Development Seminar (ATM1997). Saclay (France), 17–19 June 1997

  12. Pontani, M., Conway, B.A.: Particle swarm optimization applied to space trajectories. J. Guidance Control Dyn. 33, 1429–1441 (2010)

    Article  Google Scholar 

  13. Pohl, A.J., Lamont, G.B.: Multi-objective UAV mission planning using evolutionary computation. In: Winter Simulation Conference, pp. 1268–1279 (2008). Available online, doi:10.1109/WSC.2008.4736199

  14. Gilmore, J.F.: Autonomous vehicle planning analysis methodology. In: AIAAA Guidance Navigation Control Conference, pp. 2000–4370 (1991)

  15. Szczerba, R.J.: Threat netting for real-time, intelligent route planners. In: IEEE Symp. Inf., Decis. Control, pp. 377–382 (1999)

  16. Kuchar, J.K., Yang, L.C.: A review of conflict detection and resolution modeling methods. IEEE Trans. Intell. Transp. Syst. 1, 179–189 (2000)

    Article  Google Scholar 

  17. Cobano, J.A., Alejo, D., Ollero, A., Viguria, A.: Efficient conflict resolution method in air traffic management based on the speed assignment. In: Proceedings of the 2nd International Conference on Application and Theory of Automation in Command and Control Systems, ser. ATACCS ’12, pp. 54–61. IRIT Press, Toulouse, France (2012). Online available: http://dl.acm.org/citation.cfm?id=2325676.2325684

  18. Vela, A., Solak, S., Singhose, W., Clarke, J.-P.: A mixed integer program for flight-level assignment and speed control for conflict resolution. In: Proceedings of the 48th IEEE Conference on Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference CDC/CCC 2009, pp. 5219 –5226 (2009)

  19. Erzberger, H.: Automated conflict resolution for air traffic control. In: Proceeding International Congress Aeronautical Sciences, pp. 179–189 (2006)

  20. Richards, A., How, J.P.: Aircraft trajectory planning with collision avoidance using mixed integer linear programming. In: In Proc. ACC, pp. 1936–1941 (2002)

  21. Pallottino, L., Feron, E., Bicchi, A.: Conflict resolution problems for air traffic management systems solved with mixed integer programming. IEEE Trans. Intell. Transp. Syst. 3(1), 3–11 (2002)

    Article  Google Scholar 

  22. Hwang, I., Tomlin, C.J.: Protocol-based conflict resolution for air traffic control. Air Traffic Control Quarterly 15(1), (2007)

  23. Durand, N., Alliot, J.: Ant colony optimization for air traffic conflict resolution. In: Proceedings of the Eighth USA/Europe Air Traffic Management Research and Development Seminar (ATM2009). Napa, CA, USA (2009)

  24. Masci, P., Tedeschi, A.: Modelling and evaluation of a game-theory approach for airborne conflict resolution in omnet+ +. In: Second International Conference on Dependability (2009)

  25. Parsopoulos, K.E., Vrahatis, M.N.: Recent approaches to global optimization problems through particle swarm optimization. Nat. Comput Springer 1, 235–306 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  26. Conde, R., Alejo, D., Cobano, J.A., Viguria, A., Ollero, A.: Conflict detection and resolution method for cooperating unmanned aerial vehicles. J. Intell. Robot. Syst. 65, 495–505 (2012). doi:10.1007/s10846-011-9564-6

    Article  Google Scholar 

  27. Alejo, D., Cobano, J.A., Heredia, G., Ollero, A.: Particle swarm optimization for collision-free 4d trajectory planning in unmanned aerial vehicles. In: Proceedings of the 1st International Conference on Application and Theory of Automation in Command and Control Systems, pp. 298–307. Atlanta, USA, 28–31 May 2013

  28. Alejo, D., Díaz-Báñez, J.M., Cobano, J.A., Pérez-Lantero, P., Ollero, A.: The velocity assignment problem for conflict resolution with multiple aerial vehicles sharing airspace. J. Intell. Robot. Syst. 69(1–4), 331–346 (2013)

    Article  Google Scholar 

  29. Pallotino, L., Scordio, V.G., Bicchi, A., Frazzoli, E.: Decentralized cooperative policy for conflict resolution in multi-vehicle systems. IEEE Trans. Robot. 23(6), 1170–1183 (2007)

    Article  Google Scholar 

  30. Kennedy, J., Eberhat, R.: Particle swarm optimization. IEEE Int. Conf. Neural Netw. 4, 1942–1948 (1995)

    Google Scholar 

  31. Pedersen, M.E.H.: Good parameters for particle swarm optimization. Hvass Laboratories, Technical Report no. HL1001 (2010)

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Authors and Affiliations

  1. Robotics, Vision and Control Group Engineering School, University of Seville, 41092, Seville, Spain

    D. Alejo, J. A. Cobano, G. Heredia & A. Ollero

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  1. D. Alejo
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  2. J. A. Cobano
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  3. G. Heredia
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  4. A. Ollero
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Correspondence to J. A. Cobano.

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Alejo, D., Cobano, J.A., Heredia, G. et al. Collision-Free 4D Trajectory Planning in Unmanned Aerial Vehicles for Assembly and Structure Construction. J Intell Robot Syst 73, 783–795 (2014). https://doi.org/10.1007/s10846-013-9948-x

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