Autonomous Robots

, Volume 42, Issue 2, pp 353–373 | Cite as

Cooperative multi-robot belief space planning for autonomous navigation in unknown environments

  • Vadim IndelmanEmail author
Part of the following topical collections:
  1. Active Perception


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 paths using direct trajectory optimization techniques. We demonstrate our approach in a multi-robot autonomous navigation scenario and consider its applicability for autonomous navigation in unknown obstacle-free and obstacle-populated environments. Results indicate that modeling future multi-robot interaction within the belief allows to determine robot actions (paths) that yield significantly improved estimation accuracy.


Multi-robot belief space planning Active SLAM Active perception 



This work was partially supported by the Technion Autonomous Systems Program.


  1. Agha-Mohammadi, A.-A., Chakravorty, S., & Amato, N. M. (2014). Firm: Sampling-based feedback motion planning under motion uncertainty and imperfect measurements. The International Journal of Robotics Research., 33(2), 268–304.CrossRefGoogle Scholar
  2. Amato, C., Konidaris, G. D., Cruz, G., Maynor, C. A., How, J. P., & Kaelbling, L. P. (2014). Planning for decentralized control of multiple robots under uncertainty. arXiv preprint arXiv:1402.2871.
  3. Atanasov, N., Le Ny, J., Daniilidis, K., & Pappas, G. J. (2015). Decentralized active information acquisition: Theory and application to multi-robot slam. In IEEE international conference on robotics and automation (ICRA).Google Scholar
  4. Bry, A., & Roy, N. (2011). Rapidly-exploring random belief trees for motion planning under uncertainty. In IEEE international conference on robotics and automation (ICRA), pp. 723–730.Google Scholar
  5. Burgard, W., Moors, M., Stachniss, C., & Schneider, F. (2005). Coordinated multi-robot exploration. IEEE Transactions on Robotics., 21(3), 376–386.CrossRefGoogle Scholar
  6. Carlone, L., Kaouk Ng, M., Du, J., Bona, B., & Indri, M. (2010). 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. doi: 10.1109/ROBOT.2010.5509307.
  7. Chaves, S. M., Kim, A., & Eustice, R. M. (2014). Opportunistic sampling-based planning for active visual slam. In IEEE/RSJ interantional conference on intelligent robots and systems (IROS), IEEE, pp. 3073–3080.Google Scholar
  8. Dellaert, F. (September 2012). Factor graphs and GTSAM: A hands-on introduction. Technical Report GT-RIM-CP&R-2012-002, Georgia Institute of Technology.Google Scholar
  9. Eustice, R. M., Singh, H., & Leonard, J. J. (2006). Exactly sparse delayed-state filters for view-based SLAM. IEEE Transactions on Robotics, 22(6), 1100–1114.CrossRefGoogle Scholar
  10. Hartley, R. I., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed.). Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  11. He, R., Prentice, S., & Roy, N. (2008). 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.Google Scholar
  12. Hollinger, G. A., & Sukhatme, G. S. (2014). Sampling-based robotic information gathering algorithms. International Journal of Robotics Research, 33(9), 1271–1287.CrossRefGoogle Scholar
  13. Indelman, V. (September 2015a). Towards multi-robot active collaborative state estimation via belief space planning. In IEEE/RSJ international conference on intelligent robots and systems (IROS).Google Scholar
  14. Indelman, V. (September 2015b). Towards cooperative multi-robot belief space planning in unknown environments. In Proceedings of the international symposium of robotics research (ISRR).Google Scholar
  15. Indelman, V., & Dellaert, F. (2015). Incremental light bundle adjustment: Probabilistic analysis and application to robotic navigation. In New development in robot vision, cognitive systems monographs (Vol. 23, pp. 111–136). Springer: Berlin Heidelberg. ISBN 978-3-662-43858-9. doi: 10.1007/978-3-662-43859-6_7.
  16. Indelman, V., Gurfil, P., Rivlin, E., & Rotstein, H. (2012). Distributed vision-aided cooperative localization and navigation based on three-view geometry. Robotics and Autonomous Systems, 60(6), 822–840.CrossRefGoogle Scholar
  17. Indelman, V., Carlone, L., & Dellaert, F. (December 2013). Towards planning in generalized belief space. In The 16th international symposium on robotics research, Singapore.Google Scholar
  18. Indelman, V., Nelson, E., Michael, N., & Dellaert, F. (2014). 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).Google Scholar
  19. Indelman, V., Carlone, L., & Dellaert, F. (2015a). Planning in the continuous domain: A generalized belief space approach for autonomous navigation in unknown environments. Intnernational Journal of Robotics Research, 34(7), 849–882.CrossRefGoogle Scholar
  20. Indelman, V., Roberts, R., & Dellaert, F. (2015b). Incremental light bundle adjustment for structure from motion and robotics. Robotics and Autonomous Systems, 70, 63–82.CrossRefGoogle Scholar
  21. Indelman, V., Nelson, E., Dong, J., Michael, N., & Dellaert, F. (2016). Incremental distributed inference from arbitrary poses and unknown data association: Using collaborating robots to establish a common reference. IEEE Control Systems Magazine (CSM), Special Issue on Distributed Control and Estimation for Robotic Vehicle Networks, 36(2), 41–74.MathSciNetGoogle Scholar
  22. Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J., & Dellaert, F. (2012). iSAM2: Incremental smoothing and mapping using the Bayes tree. International Journal of Robotics Research, 31, 217–236.CrossRefGoogle Scholar
  23. Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. International Journal of Robotics Research, 30(7), 846–894.CrossRefzbMATHGoogle Scholar
  24. Kavraki, L. E., Svestka, P., Latombe, J.-C., & Overmars, M. H. (1996). Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions on Robotics and Automation, 12(4), 566–580.CrossRefGoogle Scholar
  25. Kim, A., & Eustice, R. M. (2014). Active visual slam for robotic area coverage: Theory and experiment. International Journal of Robotics Research., 34(4–5), 457–475.Google Scholar
  26. Koenig, Sven, & Likhachev, Maxim. (2005). Fast replanning for navigation in unknown terrain. IEEE Transactions on Robotics, 21(3), 354–363.CrossRefGoogle Scholar
  27. Kurniawati, H., Hsu, D., & Lee, W. S. (2008). Sarsop: Efficient point-based pomdp planning by approximating optimally reachable belief spaces. In Robotics: Science and systems (RSS) (Vol. 2008).Google Scholar
  28. LaValle, S. M., & Kuffner, J. J. (2001). Randomized kinodynamic planning. International Journal of Robotics Research, 20(5), 378–400.CrossRefGoogle Scholar
  29. Levine, D., Luders, B., & How, J. P. (2013). Information-theoretic motion planning for constrained sensor networks. Journal of Aerospace Information Systems, 10(10), 476–496.CrossRefGoogle Scholar
  30. Lu, F., & Milios, E. (Apr 1997). Globally consistent range scan alignment for environment mapping. Autonomous robots, pp. 333–349.Google Scholar
  31. Papadimitriou, C., & Tsitsiklis, J. (1987). The complexity of markov decision processes. Mathematics of Operations Research, 12(3), 441–450.MathSciNetCrossRefzbMATHGoogle Scholar
  32. Pathak, S., Thomas, A., Feniger, A., & Indelman, V. (2016a). Robust active perception via data-association aware belief space planning. arXiv preprint: arXiv.1606.05124.
  33. Pathak, S., Thomas, A., Feniger, A., & Indelman, V. (September 2016b). Da-bsp: Towards data association aware belief space planning for robust active perception. In Europian conference on AI (ECAI). Accepted.Google Scholar
  34. Patil, S., Kahn, G., Laskey, M., Schulman, J., Goldberg, K., & Abbeel, P. (2014). Scaling up gaussian belief space planning through covariance-free trajectory optimization and automatic differentiation. In International workshop on the algorithmic foundations of robotics.Google Scholar
  35. Pineau, J., Gordon, G. J., & Thrun, S. (2006). Anytime point-based approximations for large pomdps. Journal of Artificial Intelligence Research, 27, 335–380.zbMATHGoogle Scholar
  36. Platt, R., Tedrake, R., Kaelbling, L. P., & Lozano-Pérez, T. (2010). Belief space planning assuming maximum likelihood observations. Robotics: science and systems (RSS) (pp. 587–593). Spain: Zaragoza.Google Scholar
  37. Prentice, S., & Roy, N. (2009). The belief roadmap: Efficient planning in belief space by factoring the covariance. International Journal of Robotics Research., 28(11–12), 1448–1465.CrossRefGoogle Scholar
  38. Regev, T., & Indelman, V. (2016). Multi-robot decentralized belief space planning in unknown environments via efficient re-evaluation of impacted paths. In IEEE/RSJ international conference on intelligent robots and systems (IROS), accepted.Google Scholar
  39. Ross, Stéphane, Pineau, Joelle, Paquet, Sébastien, & Chaib-Draa, Brahim. (2008). Online planning algorithms for pomdps. Journal of Artificial Intelligence Research, 32, 663–704.MathSciNetzbMATHGoogle Scholar
  40. Roumeliotis, S. I., & Bekey, G. A. (August 2002). Distributed multi-robot localization. In IEEE transactions on robotics and automation.Google Scholar
  41. Silver, David, & Veness, Joel (2010). Monte-carlo planning in large pomdps. In Advances in neural information processing systems (NIPS), pp. 2164–2172.Google Scholar
  42. Stachniss, C., Grisetti, G., & Burgard, W. (2005). Information gain-based exploration using rao-blackwellized particle filters. In Robotics: science and Systems (RSS), pp. 65–72.Google Scholar
  43. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics. Cambridge: The MIT press.zbMATHGoogle Scholar
  44. Valencia, R., Morta, M., Andrade-Cetto, J., & Porta, J. M. (2013). Planning reliable paths with pose SLAM. IEEE Transactions on Robotics, 29(4), 1050–1059.CrossRefGoogle Scholar
  45. Van Den Berg, J., Patil, S., & Alterovitz, R. (2012). Motion planning under uncertainty using iterative local optimization in belief space. International Journal of Robotics Research, 31(11), 1263–1278.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

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

Personalised recommendations