Advertisement

Probabilistic Boundary Coverage for Unknown Target Fields with Large Perception Uncertainty and Limited Sensing Range

  • Binbin Li
  • Dezhen SongEmail author
Conference paper
Part of the Springer Proceedings in Advanced Robotics book series (SPAR, volume 10)

Abstract

We introduce a new type of probabilistic boundary coverage problem where a robot has to enclose unknown target fields (UTFs) with large perception uncertainty and limited sensing range. When the robot gets closer to UTF and accumulates sufficient sensory readings, it employs Gaussian processes (GPs) as a local belief function to approximate field boundary distribution in an ellipse-shaped local region. The local belief function allows us to predict UTF boundary trends and establish an adjacent ellipse for further exploration. The process is governed by a depth-first search process until UTF is approximately enclosed by connected ellipses when the boundary coverage process ends. We formally prove that our boundary coverage process guarantees the enclosure above a given coverage ratio with a preset probability threshold. We have implemented our algorithm and tested it under different field types in simulation.

Notes

Acknowledgements

Thanks for C. Chou, H. Cheng, S. Yeh, A. Kingery, A. Angert, H. Li, and T. Sun for their inputs and Y. Sun, M. Jin, D. Wang, and Y. Yu for their contributions to the NetBot Laboratory, Texas A&M University.

References

  1. 1.
    Acar, E.U., Choset, H., Lee, J.Y.: Sensor-based coverage with extended range detectors. IEEE Trans. Robot. 22(1), 189–198 (2006)CrossRefGoogle Scholar
  2. 2.
    Bekris, K., Shome, R., Krontiris, A., Dobson, A.: Reducing roadmap size for network transmission in support of cloud automation. IEEE Robot. Autom. Mag. (2016)Google Scholar
  3. 3.
    Bryan, B., Nichol, R.C., Genovese, C.R., Schneider, J., Miller, C.J., Wasserman, L.: Active learning for identifying function threshold boundaries. In: Advances in Neural Information Processing Systems, pp. 163–170 (2006)Google Scholar
  4. 4.
    Chen, J., Low, K.H., Yao, Y., Jaillet, P.: Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems. IEEE Trans. Autom. Sci. Eng. 12(3), 901–921 (2015)CrossRefGoogle Scholar
  5. 5.
    Chung, T.H., Hollinger, G.A., Isler, V.: Search and pursuit-evasion in mobile robotics. Auton. Robots 31(4), 299 (2011)CrossRefGoogle Scholar
  6. 6.
    Fink, J., Hsieh, M.A., Kumar, V.: Multi-robot manipulation via caging in environments with obstacles. In: IEEE International Conference on Robotics and Automation, pp. 1471–1476 (2008)Google Scholar
  7. 7.
    Ivan, V., Vijayakumar, S.: Space-time area coverage control for robot motion synthesis. In: International Conference on Advanced Robotics (ICAR), pp. 207–212. IEEE (2015)Google Scholar
  8. 8.
    Jadaliha, M., Xu, Y., Choi, J., Johnson, N., Li, W.: Gaussian process regression for sensor networks under localization uncertainty. IEEE Trans. Signal Process. 61(2), 223–237 (2013).  https://doi.org/10.1109/TSP.2012.2223695MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Jing, W., Newman, W.: Improving robotic assembly performance through autonomous exploration. IEEE International Conference on Robotics and Automation, p. 3303 (2002)Google Scholar
  10. 10.
    Kim, C.Y., Song, D., Xu, Y., Yi, J., Wu, X.: Cooperative search of multiple unknown transient radio sources using multiple paired mobile robots. IEEE Trans. Robot. 30(5), 1161–1173 (2014)CrossRefGoogle Scholar
  11. 11.
    Kim, C.Y., Song, D., Yi, J., Wu, X.: Decentralized searching of multiple unknown and transient radio sources with paired robots. Engineering 1(1), 058–065 (2015)CrossRefGoogle Scholar
  12. 12.
    Low, K.H., Chen, J., Dolan, J.M., Chien, S., Thompson, D.R.: Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems-Volume 1, International Foundation for Autonomous Agents and Multiagent Systems, pp. 105–112 (2012)Google Scholar
  13. 13.
    Maeda, Y., Kodera, N., Egawa, T.: Caging-based grasping by a robot hand with rigid and soft parts. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 5150–5155 (2012)Google Scholar
  14. 14.
    Mannadiar, R., Rekleitis, I.: Optimal coverage of a known arbitrary environment. In: IEEE International Conference on Robotics and Automation, pp. 5525–5530 (2010)Google Scholar
  15. 15.
    Marchant, R., Ramos, F.: Bayesian optimisation for intelligent environmental monitoring. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2242–2249 (2012)Google Scholar
  16. 16.
    Miller, L.M., Silverman, Y., MacIver, M.A., Murphey, T.D.: Ergodic exploration of distributed information. IEEE Trans. Robot. 32(1), 36–52 (2016)CrossRefGoogle Scholar
  17. 17.
    Paull, L., Saeedi, S., Seto, M., Li, H.: Sensor-driven online coverage planning for autonomous underwater vehicles. IEEE/ASME Trans. Mechatron. 18(6), 1827–1838 (2013)CrossRefGoogle Scholar
  18. 18.
    Pereira, G.A., Campos, M.F., Kumar, V.: Decentralized algorithms for multi-robot manipulation via caging. Int. J. Robot. Res. 23(7–8), 783–795 (2004)CrossRefGoogle Scholar
  19. 19.
    Pipattanasomporn, P., Makapunyo, T., Sudsang, A.: Multifinger caging using dispersion constraints. IEEE Trans. Robot. 32(4), 1033–1041 (2016)CrossRefGoogle Scholar
  20. 20.
    Plonski, P.A., Vander Hook, J., Isler, V.: Environment and solar map construction for solar-powered mobile systems. IEEE Trans. Robot. 32(1), 70–82 (2016)CrossRefGoogle Scholar
  21. 21.
    Rasmussen, C.E.: Gaussian Processes for Machine Learning. The MIT Press (2006)Google Scholar
  22. 22.
    Rodner, E., Freytag, A., Bodesheim, P., Denzler, J.: Large-scale Gaussian process classification with flexible adaptive histogram kernels. In: European Conference on Computer Vision, pp. 85–98. Springer (2012)Google Scholar
  23. 23.
    Rodriguez, A., Mason, M.T., Ferry, S.: From caging to grasping. Int. J. Robot. Res. 31(7), 886–900 (2012)CrossRefGoogle Scholar
  24. 24.
    Shnaps, I., Rimon, E.: Online coverage by a tethered autonomous mobile robot in planar unknown environments. IEEE Trans. Robot. 30(4), 966–974 (2014)CrossRefGoogle Scholar
  25. 25.
    Song, D., Kim, C.Y., Yi, J.: On the time to search for an intermittent signal source under a limited sensing range. IEEE Trans. Robot. 27(2), 313–323 (2011)CrossRefGoogle Scholar
  26. 26.
    Song, D., Kim, C.Y., Yi, J.: Simultaneous localization of multiple unknown and transient radio sources using a mobile robot. IEEE Trans. Robot. 28(3), 668–680 (2012).  https://doi.org/10.1109/TRO.2012.2183069CrossRefGoogle Scholar
  27. 27.
    Vasudevan, S., Ramos, F., Nettleton, E., Durrant-Whyte, H.: Gaussian process modeling of large-scale terrain. J. Field Robot. 26(10), 812–840 (2009).  https://doi.org/10.1002/rob.20309CrossRefzbMATHGoogle Scholar
  28. 28.
    Vongmasa, P., Sudsang, A.: Coverage diameters of polygons. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4036–4041. IEEE (2006)Google Scholar
  29. 29.
    Wan, W., Fukui, R.: Efficient planar caging test using space mapping. IEEE Trans. Autom. Sci. Eng. (2016)Google Scholar
  30. 30.
    Xu, L., Stentz, A.: An efficient algorithm for environmental coverage with multiple robots. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4950–4955 (2011)Google Scholar
  31. 31.
    Yang, K., Keat Gan, S., Sukkarieh, S.: A gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV. Adv. Robot. 27(6), 431–443 (2013).  https://doi.org/10.1080/01691864.2013.756386CrossRefGoogle Scholar
  32. 32.
    Sk, Yun, Rus, D.: Distributed coverage with mobile robots on a graph: locational optimization and equal-mass partitioning. Robotica 32(02), 257–277 (2014)CrossRefGoogle Scholar
  33. 33.
    Zarubin, D., Pokorny, F.T., Toussaint, M., Kragic, D.: Caging complex objects with geodesic balls. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2999–3006. IEEE (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA

Personalised recommendations