Journal of Intelligent & Robotic Systems

, Volume 84, Issue 1–4, pp 745–758 | Cite as

A Reactive Method for Collision Avoidance in Industrial Environments

  • D. AlejoEmail author
  • J. A. Cobano
  • G. Heredia
  • A. Ollero


This paper presents a reactive method for collision avoidance with multiple aerial vehicles that has been applied in real time considering industrial environments. The proposed method is based on the 3D-Optimal Reciprocal Collision Avoidance algorithm. The main contribution of the proposed method is that it takes into consideration 3D modeled static obstacles. Therefore, it has been successfully applied in realistic industrial environments with the presence of complex static obstacles. Considerations of dynamic constraints of the aerial vehicles have been added. The algorithm has been integrated in ROS framework and tested in simulation. Several simulations with up to eight aerial vehicles have been performed, including long endurance cooperative missions. Finally, the second main contribution consists of the evaluation of several real experiments with up to four aerial vehicles which have been carried out in the testbed of the Center for Advanced Technologies (CATEC) facilities. The aerial vehicles flew in the presence of static obstacles and avoided potential collisions by modifying the planned trajectories in real-time.


Collision Avoidance Real-time Reactive Cooperative Experimental Safety Reciprocal Quadrotor Multi-UAS 


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  1. 1.
    Merino, L., Caballero, F., de Dios, J.M., Maza, I., Ollero, A.: An unmanned aircraft system for automatic forest fire monitoring and measurement. J. Intell. Robot. Syst. 65(1), 533–548 (2012). [Online]. Available. doi: 10.1007/s10846-011-9560-x CrossRefGoogle Scholar
  2. 2.
    Beard, R. W., McLain, T., Nelson, D., Kingston, D., Johanson, D.: Decentralized cooperative aerial surveillance using fixed-wing miniature UAVs. Proc. IEEE 94(7), 1306–1324 (2006)CrossRefGoogle Scholar
  3. 3.
    Ollero, A: Aerial robotics cooperative assembly system (ARCAS): first results. In: Aerial Physically Acting Robots (AIRPHARO) Workshop, IROS 2012, Vilamoura, Portugal (2012)Google Scholar
  4. 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 International Conference Robotics and Automation (ICRA), Karlsruhe, Germany (2013)Google Scholar
  5. 5.
    Lavalle, S.M., Kuffnerm, J.J. Jr.: Rapidly-exploring random trees: progress and prospects. In: Algorithmic and Computational Robotics: New Directions, pp. 293–308 (2000)Google Scholar
  6. 6.
    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 the IEEE International Robotics and Automation (ICRA) Conference, pp. 4429–4434 (2011)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    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
  9. 9.
    Pontani, M., Conway, B. A.: Particle swarm optimization applied to space trajectories. J. Guid. Control. Dyn. 33, 1429–1441 (2010)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Hwang, I., Tomlin, C. J.: C.: Protocol-based conflict resolution for air traffic control. Air Traffic Control Quarterly 15(1) (2007)Google Scholar
  12. 12.
    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 (2009)Google Scholar
  13. 13.
    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. [Online]. Available: (2012)
  14. 14.
    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)CrossRefGoogle Scholar
  15. 15.
    Lalish, E., Morgansen, K.A.: In: Proceedings of the 47nd IEEE Conference on Decisionn and Control, CancunGoogle Scholar
  16. 16.
    Roussos, G., Chaloulos, G., Kyriakopoulos, K., Lygeros, J.: In: Proceedings of the 47nd IEEE Conference on Decisionn and Control, Cancun.Google Scholar
  17. 17.
    van den Berg, J. P., Lin, M., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation, pp. 1928–1935. In: ICRA. IEEE. [Online]. Available: (2008)
  18. 18.
    van den Berg, J., Guy, S. J., Lin, M. C., Manocha, D.: Reciprocal n-body collision avoidance. In: International Symposium on Robotics Research (2009)Google Scholar
  19. 19.
    Alonso-Mora, J., Breitenmoser, A., Beardsley, P., Siegwart, P.: In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Saint PaulGoogle Scholar
  20. 20.
    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17, 760–772 (1998)CrossRefGoogle Scholar
  21. 21.
    Alonso-Mora, J., Rufli, M., Siegwart, P., Beardsley, P.: In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), KarlsruheGoogle Scholar
  22. 22.
    Meyer, J.: Hector quadrotor ros package website, Accessed 5 Feb 2014. [Online]. Available: (2014)
  23. 23.
    van den Berg, J., Snape, J., Guy, S., Manocha, D.: Reciprocal collision avoidance with acceleration-velocity obstacles. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 3475–3482 (2011)Google Scholar
  24. 24.
    Larsen, E., Gottschalk, S., Manocha, D.: Proximity query package website. [Online]. Available: (2014). Accessed 5 Feb 2014
  25. 25.
    Ghosh, M., Amato, N.M., Lu, Y., Lien, J.-M.: Fast approximate convex decomposition using relative concavity. Computer-Aided Design, in press 2012, also appear in Proceedings of Symposium on Solid and Physical Modeling, Dijon, France (2012)Google Scholar
  26. 26.
    Alonso-Mora, J., Breitenmoser, A., Rufli, M., Beardsley, P., Siegwart, R.: Optimal reciprocal collision avoidance for multiple non-holonomic robots. In: Martinoli, A., Mondada, F. (eds.) Proceedings of the 10Th International Symposium on Distributed Autonomous Robotic Systems (DARS). Springer Press, Berlin (2010)Google Scholar
  27. 27.
    Alejo, D., Cobano, J., Heredia, G., Ollero, A.: Optimal reciprocal collision avoidance with mobile and static obstacles for multi-uav systems. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1259–1266 (2014)Google Scholar

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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Robotics, Vision and Control Group Engineering SchoolUniversity of SevilleSevilleSpain

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