Towards Cooperative Multi-robot Belief Space Planning in Unknown Environments

Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 2)


We investigate the problem of cooperative multi-robot planning in unknown environments, which is important in numerous applications in robotics. The research community has been actively developing belief space planning approaches that account for the different sources of uncertainty within planning, recently also considering uncertainty in the environment observed by planning time. We further advance the state of the art by reasoning about future observations of environments that are unknown at planning time. The key idea is to incorporate within the belief indirect multi-robot constraints that correspond to these future observations. Such a formulation facilitates a framework for active collaborative state estimation while operating in unknown environments. In particular, it can be used to identify best robot actions or trajectories among given candidates generated by existing motion planning approaches, or to refine nominal trajectories into locally optimal trajectories using direct trajectory optimization techniques. We demonstrate our approach in a multi-robot autonomous navigation scenario and show that modeling future multi-robot interaction within the belief allows to determine robot trajectories that yield significantly improved estimation accuracy.


  1. 1.
    Bry, A., Roy, N.: Rapidly-exploring random belief trees for motion planning under uncertainty. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 723–730 (2011)Google Scholar
  2. 2.
    Burgard, W., Moors, M., Stachniss, C., Schneider, F.: Coordinated Multi-robot Exploration. IEEE Trans. Robot. (2005)Google Scholar
  3. 3.
    Carlone, L., Kaouk Ng, M., Du, J., Bona, B., Indri, M.: Rao-Blackwellized particle filters multi robot SLAM with unknown initial correspondences and limited communication. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 243–249 (2010)Google Scholar
  4. 4.
    Chaves, S.M., Kim, A., Eustice, R.M.: Opportunistic sampling-based planning for active visual slam. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3073–3080. IEEE, New York (2014)Google Scholar
  5. 5.
    He, R., Prentice, S., Roy, N.: Planning in information space for a quadrotor helicopter in a GPS-denied environment. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1814–1820 (2008)Google Scholar
  6. 6.
    Hollinger, G.A., Sukhatme, G.S.: Sampling-based robotic information gathering algorithms. Int. J. Robot. Res. 1271–1287 (2014)Google Scholar
  7. 7.
    Indelman, V.: Towards multi-robot active collaborative state estimation via belief space planning. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015)Google Scholar
  8. 8.
    Indelman, V., Carlone, L., Dellaert, F.: Towards planning in generalized belief space. In: The 16th International Symposium on Robotics Research. Singapore (2013)Google Scholar
  9. 9.
    Indelman, V., Carlone, L., Dellaert, F.: Planning in the continuous domain: a generalized belief space approach for autonomous navigation in unknown environments. Int. J. Robot. Res. 34(7), 849–882 (2015)CrossRefGoogle Scholar
  10. 10.
    Indelman, V., Gurfil, P., Rivlin, E., Rotstein, H.: Distributed vision-aided cooperative localization and navigation based on three-view geometry. Robot. Auton. Syst. 60(6), 822–840 (2012)CrossRefGoogle Scholar
  11. 11.
    Indelman, V., Nelson, E., Michael, N., Dellaert, F.: Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)Google Scholar
  12. 12.
    Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)CrossRefMATHGoogle Scholar
  13. 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)CrossRefGoogle Scholar
  14. 14.
    Kurniawati, H., Hsu, D., Lee, W.S.: Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces. In: Robotics: Science and Systems (RSS), vol. 2008 (2008)Google Scholar
  15. 15.
    LaValle, S.M., Kuffner, J.J.: Randomized kinodynamic planning. Intl. J. Robot. Res. 20(5), 378–400 (2001)CrossRefGoogle Scholar
  16. 16.
    Levine, D., Luders, B., How, J.P.: Information-theoretic motion planning for constrained sensor networks. J. Aerosp. Inf. Syst. 10(10), 476–496 (2013)Google Scholar
  17. 17.
    Papadimitriou, C., Tsitsiklis, J.: The complexity of markov decision processes. Math. Oper. Res. 12(3), 441–450 (1987)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Patil, S., Kahn, G., Laskey, M., Schulman, J., Goldberg, K., Abbeel, P.: Scaling up gaussian belief space planning through covariance-free trajectory optimization and automatic differentiation. In: International Workshop on the Algorithmic Foundations of Robotics (2014)Google Scholar
  19. 19.
    Pineau, J., Gordon, G.J., Thrun, S.: Anytime point-based approximations for large pomdps. J. Artif. Intell. Res. 27, 335–380 (2006)MATHGoogle Scholar
  20. 20.
    Platt, R., Tedrake, R., Kaelbling, L.P., Lozano-Pérez, T.: Belief space planning assuming maximum likelihood observations. In: Robotics: Science and Systems (RSS), pp. 587–593 (2010)Google Scholar
  21. 21.
    Prentice, S., Roy, N.: The belief roadmap: efficient planning in belief space by factoring the covariance. Int. J. Robot. Res. (2009)Google Scholar
  22. 22.
    Roumeliotis, S.I., Bekey, G.A.: Distributed multi-robot localization. IEEE Trans. Robot. Autom. (2002)Google Scholar
  23. 23.
    Stachniss, C., Grisetti, G., Burgard, W.: Information gain-based exploration using rao-blackwellized particle filters. In: Robotics: Science and Systems (RSS), pp. 65–72 (2005)Google Scholar
  24. 24.
    Valencia, R., Morta, M., Andrade-Cetto, J., Porta, J.M.: Planning reliable paths with pose SLAM. IEEE Trans. Robot. (2013)Google Scholar
  25. 25.
    Van Den Berg, J., Patil, S., Alterovitz, R.: Motion planning under uncertainty using iterative local optimization in belief space. Int. J. Robot. Res. 31(11), 1263–1278 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Aerospace EngineeringTechnion - Israel Institute of TechnologyHaifaIsrael

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