Skip to main content

Advertisement

Springer Nature Link
Log in

A Real-Time 3D Path Planning Solution for Collision-Free Navigation of Multirotor Aerial Robots in Dynamic Environments

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Deliberative capabilities are essential for intelligent aerial robotic applications in modern life such as package delivery and surveillance. This paper presents a real-time 3D path planning solution for multirotor aerial robots to obtain a feasible, optimal and collision-free path in complex dynamic environments. High-level geometric primitives are employed to compactly represent the situation, which includes self-situation of the robot and situation of the obstacles in the environment. A probabilistic graph is utilized to sample the admissible space without taking into account the existing obstacles. Whenever a planning query is received, the generated probabilistic graph is then explored by an A⋆ discrete search algorithm with an artificial field map as cost function in order to obtain a raw optimal collision-free path, which is subsequently shortened. Realistic simulations in V-REP simulator have been created to validate the proposed path planning solution, integrating it into a fully autonomous multirotor aerial robotic system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. (2017) ROS navigation stack. http://wiki.ros.org/navigation

  2. Aguiar, A P, Hespanha, J P: Trajectory-tracking and path-following of underactuated autonomous vehicles with parametric modeling uncertainty. IEEE Trans. Autom. Control 52(8), 1362–1379 (2007). https://doi.org/10.1109/TAC.2007.902731

    Article  MathSciNet  MATH  Google Scholar 

  3. Arantes, M d S, Arantes, J d S, Toledo, C F M, Williams, BC: A hybrid multi-population genetic algorithm for UAV path planning. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference, pp 853–860. ACM (2016)

  4. Bavle, H, Sanchez-Lopez, J L, Rodriguez-Ramos, A, Sampedro, C, Campoy, P: A flight altitude estimator for multirotor UAVS in dynamic and unstructured indoor environments. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp 1044–1051 (2017), https://doi.org/10.1109/ICUAS.2017.7991467

  5. Bohlin, R, Kavraki, L E: Path planning using lazy prm. In: Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065), vol. 1, pp 521–528 (2000), https://doi.org/10.1109/ROBOT.2000.844107

  6. Chen, YB, Luo, Gc, Mei, Ys, Yu, Jq, Su, Xl: UAV path planning using artificial potential field method updated by optimal control theory. Int. J. Syst. Sci. 47(6), 1407–1420 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  7. Dechter, R, Pearl, J: Generalized best-first search strategies and the optimality of a*. J. ACM 32(3), 505–536 (1985). https://doi.org/10.1145/3828.3830

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  9. Holland, J H: Adaptation in Natural and Artificial Systems: an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press (1992)

  10. Hossain, M A, Ferdous, I: Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique. Robot. Auton. Syst. 64, 137–141 (2015)

    Article  Google Scholar 

  11. Hwang, Y K, Ahuja, N: A potential field approach to path planning. IEEE Trans. Robot. Autom. 8(1), 23–32 (1992). https://doi.org/10.1109/70.127236

    Article  Google Scholar 

  12. Kassim, A A, Kumar, B V: A neural network architecture for path planning. In: International Joint Conference on Neural Networks, 1992. IJCNN, vol. 2, pp 787–792. IEEE (1992)

  13. Kavraki, L E, Svestka, P, Latombe, J C, Overmars, M H: Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Trans. Robot. Autom. 12(4), 566–580 (1996). https://doi.org/10.1109/70.508439

    Article  Google Scholar 

  14. Kennedy, J: Particle swarm optimization. In: Encyclopedia of machine learning, pp 760–766. Springer (2011)

  15. Lacasa, L, Luque, B, Ballesteros, F, Luque, J, Nuno, J C: From time series to complex networks: the visibility graph. Proc. Natl. Acad. Sci. 105(13), 4972–4975 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. LaValle, SM: Rapidly-Exploring Random Trees: a New Tool for Path Planning. Citeseer (1998)

  17. Lin, Y, Saripalli, S: Sampling-based path planning for UAV collision avoidance. IEEE Trans. Intell. Transp. Syst. (2017)

  18. Liu, S, Atanasov, N, Mohta, K, Kumar, V: Search-based motion planning for quadrotors using linear quadratic minimum time control. arXiv:170905401 (2017)

  19. Molina, M, Diaz-Moreno, A, Palacios, D, Suarez-Fernandez, R A, Sanchez-Lopez, J L, Sampedro, C, Bavle, H, Campoy, P: Specifying complex missions for aerial robotics in dynamic environments. In: International Micro Air Vehicle Conference and Competition, IMAV, p 2016, Beijing (2016)

  20. Molina, M, Suarez-Fernandez, R A, Sampedro, C, Sanchez-Lopez, J L, Campoy, P: Tml: a language to specify aerial robotic missions for the framework aerostack. Int. J. Intell. Comput. Cybern. 10(4), 491–512 (2017). https://doi.org/10.1108/IJICC-03-2017-0025

    Article  Google Scholar 

  21. Narayanan, V, Phillips, M, Likhachev, M: Anytime safe interval path planning for dynamic environments. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 4708–4715. IEEE (2012)

  22. Nieuwenhuisen, M, Droeschel, D, Beul, M, Behnke, S: Autonomous navigation for micro aerial vehicles in complex gnss-denied environments. J. Intell. Robot. Syst. 84(1–4), 199–216 (2016)

    Article  Google Scholar 

  23. Olivares-Mendez, M, Kannan, S, Voos, H: Vision based fuzzy control autonomous landing with UAVS: from V-REP to real experiments. In: 2015 23th Mediterranean Conference on Control and Automation (MED), pp 14–21 (2015), https://doi.org/10.1109/MED.2015.7158723

  24. Pestana, J, Mellado-Bataller, I, Sanchez-Lopez, J L, Fu, C, Mondragón, I F, Campoy, P: A general purpose configurable controller for indoors and outdoors gps-denied navigation for multirotor unmanned aerial vehicles. J. Intell. Robot. Syst. 73(1), 387–400 (2014). https://doi.org/10.1007/s10846-013-9953-0

    Article  Google Scholar 

  25. Pestana, J, Sanchez-Lopez, J L, de la Puente, P, Carrio, A, Campoy, P: A vision-based quadrotor swarm for the participation in the 2013 international micro air vehicle competition. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp 617–622 (2014), https://doi.org/10.1109/ICUAS.2014.6842305

  26. Pestana, J, Collumeau, J F, Suarez-Fernandez, R, Campoy, P, Molina, M: A vision based aerial robot solution for the mission 7 of the international aerial robotics competition. In: 2015 International Conference on Unmanned Aircraft Systems (ICUAS), pp 1391–1400 (2015), https://doi.org/10.1109/ICUAS.2015.7152435

  27. Pestana, J, Sanchez-Lopez, J L, de la Puente, P, Carrio, A, Campoy, P: A vision-based quadrotor multi-robot solution for the indoor autonomy challenge of the 2013 international micro air vehicle competition. J. Intell. Robot. Syst. 84(1), 601–620 (2016). https://doi.org/10.1007/s10846-015-0304-1

    Article  Google Scholar 

  28. Quigley, M, Conley, K, Gerkey, B, Faust, J, Foote, T, Leibs, J, Wheeler, R, Ng, A Y: Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p 5 (2009)

  29. Richter, C, Bry, A, Roy, N: Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments, pp 649–666. Springer International Publishing, Cham (2016)

    Google Scholar 

  30. Rohmer, E, Singh, S P N, Freese, M: V-rep: a versatile and scalable robot simulation framework. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 1321–1326 (2013), https://doi.org/10.1109/IROS.2013.6696520

  31. Sampedro, C, Bavle, H, Sanchez-Lopez, J L, Suárez Fernández, R A, Rodríguez-Ramos, A, Molina, M, Campoy, P: A flexible and dynamic mission planning architecture for UAV swarm coordination. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp 355–363 (2016), https://doi.org/10.1109/ICUAS.2016.7502669

  32. Sanchez-Lopez, J L, Pestana, J, de la Puente, P, Suarez-Fernandez, R, Campoy, P: A system for the design and development of vision-based multi-robot quadrotor swarms. In: International Conference on Unmanned Aircraft Systems (ICUAS), vol. 2014, pp 640–648 (2014), https://doi.org/10.1109/ICUAS.2014.6842308

  33. Sanchez-Lopez, J L, Pestana, J, de la Puente, P, Campoy, P: A reliable open-source system architecture for the fast designing and prototyping of autonomous multi-UAV systems: simulation and experimentation. J. Intell. Robot. Syst. 84(1), 779–797 (2016). https://doi.org/10.1007/s10846-015-0288-x

    Article  Google Scholar 

  34. Sanchez-Lopez, J L, Suárez Fernández, R A, Bavle, H, Sampedro, C, Molina, M, Pestana, J, Campoy, P: Aerostack: an architecture and open-source software framework for aerial robotics. In: International Conference on Unmanned Aircraft Systems (ICUAS), pp 332–341 (2016), https://doi.org/10.1109/ICUAS.2016.7502591

  35. Sanchez-Lopez, J L, Arellano-Quintana, V, Tognon, M, Campoy, P, Franchi, A: Visual marker based multi-sensor fusion state estimation. In: Proceedings of the 20th IFAC World Congress (2017)

  36. Sanchez-Lopez, J L, Molina, M, Bavle, H, Sampedro, C, Suárez Fernández, R A, Campoy, P: A multi-layered component-based approach for the development of aerial robotic systems: the aerostack framework. J. Intell. Robot. Syst. https://doi.org/10.1007/s10846-017-0551-4 (2017)

  37. Sanchez-Lopez, J L, Pestana, J, Campoy, P: A robust real-time path planner for the collision-free navigation of multirotor aerial robots in dynamic environments. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp 316–325. https://doi.org/10.1109/ICUAS.2017.7991354 (2017)

  38. Song, G, Miller, S, Amato, N M: Customizing prm roadmaps at query time. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164), vol 2., pp. 1500–1505 https://doi.org/10.1109/ROBOT.2001.932823 (2001)

  39. Stentz A: Optimal and efficient path planning for partially-known environments. In: Proceedings of the 1994 IEEE International Conference on Robotics and Automation. Proceedings, vol. 4, pp 3310–3317 (1994), https://doi.org/10.1109/ROBOT.1994.351061

  40. Suárez Fernández, R A, Sanchez-Lopez, J L, Sampedro, C, Bavle, H, Molina, M, Campoy, P: Natural user interfaces for human-drone multi-modal interaction. In: International Conference on Unmanned Aircraft Systems (ICUAS), vol. 2016, pp 1013–1022 (2016), https://doi.org/10.1109/ICUAS.2016.7502665

  41. Tisdale, J, Kim, Z, Hedrick, J K: Autonomous UAV path planning and estimation. IEEE Robot. Autom. Mag. 16(2) (2009)

  42. Wurm, K M, Hornung, A, Bennewitz, M, Stachniss, C, Burgard, W: Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems. In: Proceedings of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation, vol 2 (2010)

  43. Yan, F, Liu, Y S, Xiao, J Z: Path planning in complex 3d environments using a probabilistic roadmap method. Int. J. Autom. Comput. 10(6), 525–533 (2013)

    Article  Google Scholar 

  44. Yang, L, Qi, J, Song, D, Xiao, J, Han, J, Xia, Y: Survey of robot 3d path planning algorithms. J. Control Sci. Eng. 2006, 5 (2016)

  45. Yao, P, Wang, H, Liu, C: 3-d dynamic path planning for UAV based on interfered fluid flow. In: Guidance, Navigation and Control Conference (CGNCC), IEEE Chinese, pp 997–1002. IEEE (2014)

  46. Yue, R, Xiao, J, Wang, S, Joseph, S L: Modeling and path planning of the city-climber robot part ii: 3d path planning using mixed integer linear programming. In: 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp 2391–2396. IEEE (2009)

Download references

Acknowledgments

This work was supported by the “Fonds National de la Recherche” (FNR), Luxembourg, under the project C15/15/10484117 (BEST-RPAS).

Author information

Authors and Affiliations

  1. Automation and Robotics Research Group (ARG), Interdisciplinary Center for Security, Reliability and Trust (SnT), University of Luxembourg, 29, avenue J. F. Kenedy, 1855, Luxembourg, Luxembourg

    Jose Luis Sanchez-Lopez, Min Wang, Miguel A. Olivares-Mendez & Holger Voos

  2. Center for Automation and Robotics (CAR), CSIC-UPM, Calle Jose Gutierrez Abascal 2, 28024, Madrid, Spain

    Jose Luis Sanchez-Lopez

  3. Department of Artificial Intelligence, Technical University of Madrid (UPM), Campus de Montegancedo S/N 28660 Boadilla del Monte, Madrid, Spain

    Martin Molina

  4. Computer Vision and Aerial Robotics (CVAR), Technical University of Madrid (UPM), Madrid, Spain

    Jose Luis Sanchez-Lopez & Martin Molina

Authors
  1. Jose Luis Sanchez-Lopez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Min Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Miguel A. Olivares-Mendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Molina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Holger Voos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jose Luis Sanchez-Lopez.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sanchez-Lopez, J.L., Wang, M., Olivares-Mendez, M.A. et al. A Real-Time 3D Path Planning Solution for Collision-Free Navigation of Multirotor Aerial Robots in Dynamic Environments. J Intell Robot Syst 93, 33–53 (2019). https://doi.org/10.1007/s10846-018-0809-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-018-0809-5

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy