Autonomous Robots

, Volume 39, Issue 1, pp 101–121 | Cite as

Collision avoidance for aerial vehicles in multi-agent scenarios

  • Javier Alonso-Mora
  • Tobias Naegeli
  • Roland Siegwart
  • Paul Beardsley
Article

Abstract

This article describes an investigation of local motion planning, or collision avoidance, for a set of decision-making agents navigating in 3D space. The method is applicable to agents which are heterogeneous in size, dynamics and aggressiveness. It builds on the concept of velocity obstacles (VO), which characterizes the set of trajectories that lead to a collision between interacting agents. Motion continuity constraints are satisfied by using a trajectory tracking controller and constraining the set of available local trajectories in an optimization. Collision-free motion is obtained by selecting a feasible trajectory from the VO’s complement, where reciprocity can also be encoded. Three algorithms for local motion planning are presented—(1) a centralized convex optimization in which a joint quadratic cost function is minimized subject to linear and quadratic constraints, (2) a distributed convex optimization derived from (1), and (3) a centralized non-convex optimization with binary variables in which the global optimum can be found, albeit at higher computational cost. A complete system integration is described and results are presented in experiments with up to four physical quadrotors flying in close proximity, and in experiments with two quadrotors avoiding a human.

Keywords

Collision avoidance Reciprocal  Aerial vehicle  Quadrotor Multi-robot Multi-agent  Motion planning Dynamic environment 

Supplementary material

Supplementary material 1 (mp4 14075 KB)

References

  1. Alonso-Mora, J., Breitenmoser, A., Beardsley, P., & Siegwart, R. (2012a). Reciprocal collision avoidance for multiple car-like robots. In 2012 IEEE International conference on robotics and automation (ICRA) (pp. 360–366).Google Scholar
  2. Alonso-Mora, J., Schoch, M., Breitenmoser, A., Siegwart, R., & Beardsley, P. (2012b). Object and animation display with multiple aerial vehicles. In 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1078–1083).Google Scholar
  3. Alonso-Mora, J., Rufli, M., Siegwart, R., & Beardsley, P. (2013). Collision avoidance for multiple agents with joint utility maximization. ICRA, 2013, 1–6.Google Scholar
  4. Augugliaro, F., Schoellig, A. P., & D’Andrea, R. (2012). Generation of collision-free trajectories for a quadrocopter fleet: A sequential convex programming approach. In IEEE/RSJ international conference on intelligent robots and systems (pp. 1–6).Google Scholar
  5. Bareiss, D., & van den Berg, J. (2013). Reciprocal collision avoidance for robots with linear dynamics using LQR-obstacles. In IEEE international conference robotics and automation.Google Scholar
  6. Fiorini, P., & Shillert, Z. (1998). Motion planning in dynamic environments using velocity obstacles. International Journal of Robotics Research, 17(7), 760–772.CrossRefGoogle Scholar
  7. Fox, D., Burgard, W., & Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4(1), 23–33.CrossRefGoogle Scholar
  8. Frazzoli, E., Dahleh, M. A., & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control, and Dynamics, 25(1), 116–129.CrossRefGoogle Scholar
  9. Guy, S. J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., & Manocha, D. (2010). Pledestrians: A least-effort approach to crowd simulation. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics symposium on computer animation (pp. 119–128).Google Scholar
  10. Hoffmann, G. M., & Tomlin, C. J. (2008). Decentralized cooperative collision avoidance for acceleration constrained vehicles. In 47th IEEE conference on decision and control (CDC) (pp. 4357–4363).Google Scholar
  11. Hoffmann, G. M., Waslander, S. L., & Tomlin, C. J. (2008). Quadrotor helicopter trajectory tracking control. In AIAA guidance, navigation and control conference and exhibit (pp. 1–14).Google Scholar
  12. Knepper, R. A., & Mason, M. T. (2012). Real-time informed path sampling for motion planning search. The International Journal of Robotics Research, 31, 1231–1250.Google Scholar
  13. Kumar, V., & Michael, N. (2012). Opportunities and challenges with autonomous micro aerial vehicles. The International Journal of Robotics Research, 31, 1279–1291.CrossRefGoogle Scholar
  14. Kushleyev, A., Mellinger, D., & Kumar, V. (2012). Towards a swarm of agile micro quadrotors. In Robotics: Science and systems (RSS).Google Scholar
  15. Kuwata, Y., & How, J. P. (2007). Robust cooperative decentralized trajectory optimization using receding horizon MILP. In Proceedings of the 2007 American control conference (pp. 11–13).Google Scholar
  16. Lee, T., Leoky, M., & McClamroch, N. H. (2010). Geometric tracking control of a quadrotor UAV on SE (3). In 49th IEEE conference on decision and control (CDC), 2010 (pp. 5420–5425).Google Scholar
  17. Lupashin, S., Schöllig, A., Hehn, M., & D’Andrea, R. (2011). The Flying Machine Arena as of 2010. In: 2011 IEEE international conference on robotics and automation (ICRA) (pp. 2970–2971).Google Scholar
  18. Mahony, R., Kumar, V., & Corke, P. (2012). Multirotor aerial vehicles: modeling, estimation, and control of quadrotor. IEEE Robotics & Automation Magazine, 19(3), 20–32.CrossRefGoogle Scholar
  19. Mcfadyen, A., Corke, P., & Mejias, L. (2012). Rotorcraft collision avoidance using spherical image-based visual servoing and single point features. In: 2012 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE (pp. 1199–1205).Google Scholar
  20. Mellinger, D., & Kumar, V. (2011). Minimum snap trajectory generation and control for quadrotors. In 2011 IEEE international conference on robotics and automation (ICRA) (pp. 2520–2525).Google Scholar
  21. Mellinger, D., Kushleyev, A., & Kumar, V. (2012). Mixed-integer quadratic program trajectory generation for heterogeneous quadrotor teams. In: 2012 IEEE international conference on robotics and automation (ICRA) (pp. 477–483).Google Scholar
  22. Michael, N., Mellinger, D., Lindsey, Q., & Kumar, V. (2010). The GRASP multiple micro-UAV testbed. IEEE Robotics & Automation Magazine, 17(3), 56–65.CrossRefGoogle Scholar
  23. Ogren, P., Fiorelli, E., & Leonard, N. E. (2004). Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment. IEEE Transactions on Automatic Control, 49(8), 1292–1302.CrossRefMathSciNetGoogle Scholar
  24. Pivtoraiko, M., & Kelly, A. (2005). Generating near minimal spanning control sets for constrained motion planning in discrete state spaces. In 2005 IEEE/RSJ international conference on intelligent robots and systems, 2005 (IROS 2005) (pp. 3231–3237).Google Scholar
  25. Raghunathan, A. U., Gopal, V., Subramanian, D., Biegler, L. T., & Samad, T. (2004). Dynamic optimization strategies for three-dimensional conflict resolution of multiple aircraft. Journal of Guidance, Control, and Dynamics, 27(4), 586–594.CrossRefGoogle Scholar
  26. Rufli, M., Alonso-Mora, J., & Siegwart, R. (2013). Reciprocal collision avoidance with motion continuity constraints. IEEE Transactions on Robotics, 29, 899–912.CrossRefGoogle Scholar
  27. Schwager, M., Julian, B. J., Angermann, M., & Rus, D. (2011). Eyes in the sky: Decentralized control for the deployment of robotic camera networks. Proceedings of the IEEE, 99(9), 1541–1561.CrossRefGoogle Scholar
  28. Shim, D. H., Kim, H. J., & Sastry, S. (2003). Decentralized nonlinear model predictive control of multiple flying robots. In Proceedings of the 42nd IEEE conference on decision and control, 2003 (pp. 3621–3626).Google Scholar
  29. van den Berg, J., Guy, S. J., Lin, M., & Manocha, D. (2009). Reciprocal n-body collision avoidance. In International symposium on robotics research (ISRR).Google Scholar
  30. Van Nieuwstadt, M. J., & Murray, R. M. (1997). Real time trajectory generation for differentially flat systems. International Journal of Robust and Nonlinear Control, 8, 995–1020.CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Javier Alonso-Mora
    • 1
  • Tobias Naegeli
    • 2
  • Roland Siegwart
    • 2
  • Paul Beardsley
    • 3
  1. 1.ETH Zurich and Disney Research ZurichZurichSwitzerland
  2. 2.ETH ZurichZurichSwitzerland
  3. 3.Disney Research ZurichZurichSwitzerland

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