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Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

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Cooperative Robots and Sensor Networks 2015

Part of the book series: Studies in Computational Intelligence ((SCI,volume 604))

Abstract

This chapter discusses the problem of trajectory planning for WSN (Wireless Sensor Network) data retrieving deployed in remote areas with a cooperative system of UAVs (Unmanned Aerial Vehicles). Three different path planners are presented in order to autonomously guide the UAVs during the mission. The missions are given by a set of waypoints which define WSN collection zones and each UAV should pass through them to collect the data while avoiding passing over forbidden areas and collisions between UAVs. The proposed UAV trajectory planners are based on Genetics Algorithm (GA), RRT (Rapidly-exploring Random Trees) and RRT* (Optimal Rapidly-exploring Random Trees). Simulations and experiments have been carried out in the airfield of Utrera (Seville, Spain). These results are compared in order to measure the performance of the proposed planners.

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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)

    Google Scholar 

  2. 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

    Google Scholar 

  3. Merino, L., Caballero, F., Martinez de Dios, J.R., Maza, I., Ollero, A.: An unmanned aircraft system for automatic forest fire monitoring and measurement. J. Intell. Robot. Syst. 65(1–4), 533–548 (2012)

    Article  Google Scholar 

  4. 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)

    Google Scholar 

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

    Google Scholar 

  6. Szczerba, R.J.: Threat netting for real-time, intelligent route planners. In: IEEE Symposium Information, Decision Control, pp. 377–382 (1999)

    Google Scholar 

  7. Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., De Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. Trans. Sys. Man Cyber Part C 39(2), 133–155 (2009). http://dx.doi.org/10.1109/TSMCC.2008.2007252

  8. Li, Y., Ang, K., Chong, G., Feng, W., Tan, K., Kashiwagi, H.: Cautocsdevolutionary search and optimisation enabled computer automated control system design. Int. J. Autom. Comput. 1(1), 76–88 (2007)

    Article  Google Scholar 

  9. Chang Wook Ahn, R.S.R.: A genetic algorithm for shortest path routing, problem and the sizing of populations. IEEE Trans. Evol. Comput. 6(6), 566–579 (2012)

    Google Scholar 

  10. 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: Proceedings of IEEE International Robotics and Automation (ICRA) Conference, pp. 4429–4434 (2011)

    Google Scholar 

  11. Lavalle, S.M.: Rapidly-exploring random trees: a new tool for path planning. In: Computer Science Department, Iowa State University. Technical Report TR 98–11 (1998)

    Google Scholar 

  12. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30, 1–76 (2011)

    Article  Google Scholar 

  13. Pignaton, C.P.T.L.E., Morado, A.: Middleware support in unmanned aerial vehicles and wireless sensor networks for surveillance applications. Stud. Comput. Intell. 237, 289–296 (2009)

    Article  Google Scholar 

  14. Mitchell, H.L.P.D., Qiu, J., Grace, D.: Use of aerial platforms for energy efficient medium access control in wireless sensor networks. Comput. Commun. 33(4), 500–512 (2010)

    Article  Google Scholar 

  15. Teh, S.K., Mejias, L., Corke, P., Hu, W.: Experiments in integrating autonomous uninhabited aerial vehicles (uavs) and wireless sensor networks. In: 2008 Australasian Conference on Robotics and Automation (ACRA 08). The Australian Robotics and Automation Association Inc., Canberra (2008). http://eprints.qut.edu.au/15536/

  16. Valente, J., Sanz, D., Barrientos, A., Cerro, J., Ribeiro, A., Rossi, C.: An air-ground wireless sensor network for crop monitoring. Sensors 11(6), 6088–6108 (2011). http://www.mdpi.com/1424-8220/11/6/6088

  17. Martinez-de Dios, J., Lferd, K., de San Bernab, A., Nez, G., Torres-Gonzlez, A., Ollero, A.: Cooperation between uas and wireless sensor networks for efficient data collection in large environments. J. Intell. Robot. Syst. 70(1–4), 491–508 (2013). http://dx.doi.org/10.1007/s10846-012-9733-2

  18. Reif, J., Sharir, M.: Motion planning in the presence of moving obstacles. J. ACM 41(4), 764–790 (1994)

    Article  MATH  Google Scholar 

  19. 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 

  20. Goerzen, C., Kong, Z., Mettler, B.: A survey of motion planning algorithms from the perspective of autonomous uav guidance. J. Intell. Robot. Syst. 57(1–4), 65–100 (2010)

    Article  MATH  Google Scholar 

  21. Prasanna, H.M., Ghosey, D., Bhat, M.S., Bhattacharyya, C., Umakant, J.: Interpolation-aware trajectory optimization for a hypersonic vehicle using nonlinear programming. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, San Francisco, USA, Aug 2005

    Google Scholar 

  22. 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, pp. 5219–5226, Dec 2009

    Google Scholar 

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

    Article  Google Scholar 

  24. Bauso, D., Giarre, L., Pesenti, R.: Multiple uav cooperative path planning via neuro-dynamic programming. In: 43rd IEEE Conference on Decision and Control, pp. 1087–1092. Nassau, Bahamas, Dec 2004

    Google Scholar 

  25. Geiger, B.: Unmanned aerial vehicle trajectory planning with direct methods. Ph.D. dissertation, The Pennsylvania State University, Pennsylvania, USA (2009)

    Google Scholar 

  26. Vera, S., Cobano, J.A., Heredia, G., Ollero, A.: An hp-adaptative pseudospectral method for collision avoidance with multiple uavs in real-time applications. In: IEEE International Conference Robotics and Automation (ICRA), pp. 4717–4722. Hong-Kong, China, 31 May–7 June 2014

    Google Scholar 

  27. Spall, J.C.: Introduction to Stochastic Search and Optimization, 1st edn. Wiley, New York (2003)

    Google Scholar 

  28. Chakrabarty, A., Langelaan, J.W.: Flight path planning for uav atmospheric energy harvesting using heuristic search. In: AIAA Guidance, Navigation and Controls Conference, Toronto, Canada, Aug 2010

    Google Scholar 

  29. Lamont, G.B., Slear, J., Melendez, K.: Uav swarm mission planning and routing using multi-objective evolutionary algorithms. In: IEEE Symposium on Computational Intelligence in Multicriteria Decision Making, pp. 10–20. Honolulu, Hawai, USA, 1–5 April 2007

    Google Scholar 

  30. 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 

  31. Alejo, D., Cobano, J.A., Heredia, G., Ollero, A.: Collision-free 4d trajectory planning in unmanned aerial vehicles for assembly and structure construction. J. Intell. Robot. Syst. 73, 783–795 (2014)

    Article  Google Scholar 

  32. 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)

    Google Scholar 

  33. Xue, E.M., y Atkins, M.: Terminal area trajectory optimization using simulated annealing. In: 44th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, Jan 2006

    Google Scholar 

  34. 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)

    Google Scholar 

  35. Alejo, D., Conde, R., Cobano, J., Ollero, A.: Multi-UAV collision avoidance with separation assurance under uncertainties. In: IEEE International Conference on Mechatronics, ICM 2009, pp. 1–6, April (2009)

    Google Scholar 

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

  37. Akgun, B., Stilman, M.: Sampling heuristics for optimal motion planning in high dimensions. In: International Conferences on Intelligent Robots and Systems (IROS2011), pp. 2640–2645. San Francisco, USA, 25–30 Sept 2011

    Google Scholar 

  38. Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. Syst. Sci. Cybern., IEEE Trans. 4(2), 100–107 (1968)

    Article  Google Scholar 

  39. Şucan, I.A., Moll, M., Kavraki, L.E.: The open motion planning library. IEEE Robot. Autom. Mag. 19(4), 72–82, Dec 2012. http://ompl.kavrakilab.org

  40. Otte, M., Correll, N.: 1 c-forest: parallel shortest-path planning with super linear speedup. IEEE Robot 29(3) 798–806 June 2013

    Google Scholar 

  41. Yang, K., Sukkarieh, S.: Planning continuous curvature paths for uavs amongst obstacles. In: Australasian Conference on Robotics Automation, Canberra, Australia (2008)

    Google Scholar 

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Acknowledgments

This work was supported by the European Commission FP7 ICT Programme under the Project PLANET (European Commission FP7-257649-ICT-2009-5) and the RANCOM Project (P11-TIC-7066) funded by the Junta de Andalucía (Spain). David Alejo is granted with a FPU Spanish fellowship from the Ministerio de Educación, Cultura y Deporte (Spain).

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Alejo, D., Cobano, J.A., Heredia, G., Martínez-de Dios, J.R., Ollero, A. (2015). Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs. In: Koubâa, A., Martínez-de Dios, J. (eds) Cooperative Robots and Sensor Networks 2015. Studies in Computational Intelligence, vol 604. Springer, Cham. https://doi.org/10.1007/978-3-319-18299-5_3

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  • DOI: https://doi.org/10.1007/978-3-319-18299-5_3

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