[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

Human–robot planning and learning for marine data collection

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

We propose an integrated learning and planning framework that leverages knowledge from a human user along with prior information about the environment to generate trajectories for scientific data collection in marine environments. The proposed framework combines principles from probabilistic planning with nonparametric uncertainty modeling to refine trajectories for execution by autonomous vehicles. These trajectories are informed by a utility function learned from the human operator’s implicit preferences using a modified coactive learning algorithm. The resulting techniques allow for user-specified trajectories to be modified for reduced risk of collision and increased reliability. We test our approach in two marine monitoring domains and show that the proposed framework mimics human-planned trajectories while also reducing the risk of operation. This work provides insight into the tools necessary for combining human input with vehicle navigation to provide persistent autonomy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abbeel, P., Coates, A., & Ng, A. Y. (2010). Autonomous helicopter aerobatics through apprenticeship learning. The International Journal of Robotics Research, 29(13), 1608–1639.

    Article  Google Scholar 

  • Abbeel, P., & Ng, A. Y. (2004). Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the International Conference on Machine Learning, pp. 1–8.

  • Alvarez, M., & Lawrence, N. (2011). Computationally efficient convolved multiple output gaussian processes. The Journal of Machine Learning Research, 12, 1459–1500.

    MathSciNet  MATH  Google Scholar 

  • Applegate, D. L., Bixby, R. E., Chvatal, V., & Cook, W. J. (2006). The traveling salesman problem: A computational study. Princeton: Princeton University Press. doi:10.1109/ICICTA.2009.96.

    MATH  Google Scholar 

  • Bryson, M., Reid, A., Ramos, F., & Sukkarieh, S. (2010). Airborne vision-based mapping and classification of large farmland environments. Journal of Field Robotics, 27(5), 632–655.

    Article  Google Scholar 

  • Cao, N., Low, K. H., & Dolan, J. M. (2013). Multi-robot informative path planning for active sensing of environmental phenomena: A tale of two algorithms. In Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems, pp. 7–14.

  • Das, J., Maughan, T., McCann, M., Godin, M., O’Reilly, T., Messie, M., Bahr, F., Gomes, K., Py, F., Bellingham, J. G., Sukhatme, G. S., & Rajan, K. (2011). Towards mixed-initiative, multi-robot field experiments: Design, deployment, and lessons learned. In IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3132–3139.

  • Das, J., Rajany, K., Frolovy, S., Pyy, F., Ryany, J., Caron, D. A., & Sukhatme, G. S. (2010). Towards marine bloom trajectory prediction for AUV mission planning. In IEEE International Conference on Robotics and Automation, pp. 4784–4790.

  • Ellison, R., & Cook, M. (2009). Cost-effective automated water quality monitoring systems providing high-resolution data in near real-time. In: World Environmental and Water Resources Congress, pp. 1–10.

  • Goetschalckx, R., Fern, A., & Tadepalli, P. (2014). Coactive learning for locally optimal problem solving. In AAAI Conference on Artificial Intelligence, pp. 1824–1830.

  • Hoang, T. N., Low, K. H., Jaillet, P., & Kankanhalli, M. (2014). Nonmyopic epsilon-Bayes-optimal active learning of Gaussian processes.

  • Hollinger, G. A., Englot, B., Hover, F. S., Mitra, U., & Sukhatme, G. S. (2012). Active planning for underwater inspection and the benefit of adaptivity. The International Journal of Robotics Research, 32(1), 3–18.

    Article  Google Scholar 

  • Hollinger, G. A., Pereira, A. A., & Sukhatme, G. S. (2013). Learning uncertainty models for reliable operation of autonomous underwater vehicles. In IEEE International Conference on Robotics and Automation, pp. 5593–5599.

  • Hollinger, G. A., & Sukhatme, G. S. (2014). Sampling-based robotic information gathering algorithms. The International Journal of Robotics Research, 33(9), 1271–1287.

    Article  Google Scholar 

  • Hollinger, G. A., & Sukhatme, G. S. (2014). Trajectory learning for human-robot scientific data collection. In IEEE International Conference on Robotics and Automation, pp. 6600–6605.

  • Jain, A., Wojcik, B., Joachims, T., & Saxena, A. (2013). Learning trajectory preferences for manipulators via iterative improvement. In Advances in Neural Information Processing Systems, pp. 575–583.

  • Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30, 846–894.

    Article  MATH  Google Scholar 

  • Kim, Y. H., Shell, D. A., Ho, C., & Saripalli, S. (2013). Spatial Interpolation for robotic sampling: Uncertainty with two models of variance. International Symposium on Experimental Robotics, 88, 759–774.

    Article  Google Scholar 

  • Krause, A., & Guestrin, C. (2011). Submodularity and its applications in optimized information gathering. ACM Transactions on Intelligent Systems and Technology, 2(4), 32:1–32:20.

    Article  Google Scholar 

  • Latombe, J. C. (1991). Robot motion planning. Boston, ma: Kluwer Academic Publishers.

    Book  MATH  Google Scholar 

  • LaValle, S. M. (2006). Planning algorithms. Cambridge: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Lermusiaux, P. F. (2006). Uncertainty estimation and prediction for interdisciplinary ocean dynamics. Journal of Computational Physics, 217(1), 176–199.

    Article  MathSciNet  MATH  Google Scholar 

  • Li, P., Chao, Y., Vu, Q., Li, Z., Farrara, J., Zhang, H., & Wang, X. (2006). OurOcean—an integrated solution to Ocean monitoring and forecasting. In: OCEANS, pp. 1–6.

  • Low, K. H., Dolan, J. M., & Khosla, P. K. (2009). Information-theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing. In Proceedings of the International Conference on Automated Planning and Scheduling, pp. 233–240.

  • Mora, A., Ho, C., & Saripalli, S. (2013). Analysis of adaptive sampling techniques for underwater vehicles. Autonomous Robots, 35(2), 111–122.

    Article  Google Scholar 

  • Nooner, S. L., & Chadwick, W. W. (2009). Volcanic inflation measured in the caldera of axial seamount: Implications for magma supply and future eruptions. Geochemistry, Geophysics, Geosystems, 10(2), 1–14.

    Article  Google Scholar 

  • Pereira, A. A., Binney, J., Hollinger, G. A., & Sukhatme, G. S. (2013). Risk-aware path planning for autonomous underwater vehicles using predictive ocean models. Journal of Field Robotics, 30(5), 741–762.

    Article  Google Scholar 

  • Raman, K., Joachims, T., Shivaswamy, P., & Schnabel, T. (2013). Stable coactive learning via perturbation. Proceedings of the International Conference on Machine Learning, 28, 837–845.

    Google Scholar 

  • Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. Cambridge: The MIT Press.

    MATH  Google Scholar 

  • Ratliff, N. D. (2009). Learning to search: Structured prediction techniques for imitation learning. Ph.D. Thesis, Carnegie Mellon University.

  • Reif, J. H. (1979). Complexity of the mover’s problem and generalizations. In Symposium on Foundations of Computer Science, pp. 421–427.

  • Sattar, J., & Dudek, G. (2011). Towards quantitative modeling of task confirmations in human-robot dialog. In IEEE International Conference on Robotics and Automation, pp. 1957–1963.

  • Shchepetkin, A. F., & McWilliams, J. C. (2005). The regional oceanic modeling system (ROMS): A split-explicit, free-surface, topography-following-coordinate oceanic model. Ocean Modelling, 9(4), 347–404.

    Article  Google Scholar 

  • Shivaswamy, P., & Joachims, T. (2012). Online structured prediction via coactive learning. Proceedings of the International Conference on Machine Learning pp. 1–8.

  • Silver, D., Bagnell, J. A., & Stentz, A. (2010). Learning from demonstration for autonomous navigation in complex unstructured terrain. The International Journal of Robotics Research, 29(12), 1565–1592.

    Article  Google Scholar 

  • Singh, A., Krause, A., Guestrin, C., Kaiser, W. J., & Batalin, M. A. (2007). Efficient planning of informative paths for multiple robots. International Joint Conference on Artificial Intelligence, 7, 2204–2211.

    Google Scholar 

  • Singh, A., Krause, A., & Kaiser, W. J. (2009). Nonmyopic adaptive informative path planning for multiple robots. International Joint Conference on Artificial Intelligence pp. 1843–1850.

  • Smith, R., Das, J., Heidarsson, H., Pereira, A., Arrichiello, F., Cetnic, I., et al. (2010). USC CINAPS builds bridges: Observing and monitoring the Southern California bight. IEEE Robotics & Automation Magazine, 17(1), 20–30.

    Article  Google Scholar 

  • Somers, T., & Hollinger, G. A. (2014). Coactive learning with a human expert for robotic monitoring. Workshop on Robotic Monitoring at Robotics Science and Systems, 2014, 1–2.

    Google Scholar 

  • Somers, T., & Hollinger, G. A. (2015). Coactive learning with a human expert for robotic information gathering. In IEEE International Conference on Robotics and Automation, pp. 559–564.

  • Thompson, D. R., Chien, S., Yi Chao, Li, P., Cahill, B., Levin, J., Schofield, O., Balasuriya, A., Petillo, S., Arrott, M., & Meisinger, M. (2010). Spatiotemporal path planning in strong, dynamic, uncertain currents. In IEEE International Conference on Robotics and Automation, pp. 4778–4783.

  • Willmott, C. J., Ackleson, S. G., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., et al. (1985). Statistics for the evaluation and comparison of models. Journal of Geophysical Research, 90(C5), 8995–9005.

    Article  Google Scholar 

  • Yamamoto, J. K. (2000). An alternative measure of the reliability of ordinary Kriging estimates. Mathematical Geology, 32(4), 489–509.

    Article  MathSciNet  MATH  Google Scholar 

  • Yamamoto, J. K., & Monteiro, M. (2008). Properties and applications of the interpolation variance associated with ordinary kriging estimates. In: International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pp. 70–77.

Download references

Acknowledgments

The authors thank Robby Goetschalckx, Alan Fern, and Prasad Tadepalli from Oregon State University for their insightful comments. Further thanks go to Gaurav Sukhatme from the University of Southern California for providing access to the Ecomapper vehicle in the field experiments. This work was supported in part by the following Grants: NSF IIS-1317815.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thane Somers.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Somers, T., Hollinger, G.A. Human–robot planning and learning for marine data collection. Auton Robot 40, 1123–1137 (2016). https://doi.org/10.1007/s10514-015-9502-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-015-9502-8

Keywords

Navigation