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Topological path planning for autonomous information gathering

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Abstract

In this paper, we present two novel algorithms for information space topological planning that identify topological features in an information field and use them to plan maximally informative paths for a robot in an information gathering task. These features provide a way to rapidly incorporate global context into the informative path planning process by partitioning the state space or the path space of a robot. Our first algorithm, hierarchical hotspot information gathering, uses a topological state space partitioning by constructing a high-level map of information hotspots. We then solve a global scheduling problem over the topological graph, the solution of which is then used for path planning by a set of local greedy coverage planners within each hotspot. Our second algorithm, Topology-Aware Self Organizing Maps, extends the Self Organizing Map algorithm to discover prominent topological features in the information function. These features are used to perform a topological path space decomposition to provide a Stochastic Gradient Ascent optimization algorithm with topologically diverse initialization, improving its performance. In simulated trials and field experiments, we compare the tradeoffs of these two approaches and show that our methods that leverage topological features of the information field consistently perform competitively or better than methods that do not exploit these features, while requiring less computation time.

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Notes

  1. The Monterey Bay ROMS model output is provided by the Cooperative Ocean Prediction System (COPS), and is available through their website at http://west.rssoffice.com/ca_roms_nowcast_300m.

References

  • Aurenhammer, F. (1991). Voronoi diagrams—A survey of a fundamental geometric data structure. ACM Computing Surveys (CSUR), 23(3), 345–405.

    Article  Google Scholar 

  • Basener, W. F. (2006). Topology and its applications. New York: Wiley.

    Book  Google Scholar 

  • Benoit-Bird, K. J., Welch, T. P., Waluk, C. M., Barth, J. A., Wangen, I., McGill, P., et al. (2018). Equipping an underwater glider with a new echosounder to explore ocean ecosystems. Limnology and Oceanography: Methods, 16(11), 734–749.

    Google Scholar 

  • Best, G. (2019). Planning algorithms for multi-robot active perception. PhD thesis, University of Sydney.

  • Bhattacharya, S., Ghrist, R., & Kumar, V. (2015). Persistent homology for path planning in uncertain environments. IEEE Transactions on Robotics, 31(3), 578–590.

    Article  Google Scholar 

  • Bhattacharya, S., Kumar, V., & Likhachev, M. (2010). Search-based path planning with homotopy class constraints. In Proceedings of the AAAI conference on artificial intelligence, Atlanta, Georgia, pp. 1230–1237.

  • Bhattacharya, S., Likhachev, M., & Kumar, V. (2012). Topological constraints in search-based robot path planning. Autonomous Robots, 33(3), 273–290.

    Article  Google Scholar 

  • Binney, J., & Sukhatme, G. S. (2012). Branch and bound for informative path planning. In Proceedings of the IEEE international conference on robotics and automation, Minneapolis, Minnesota, pp. 2147–2154.

  • Bormann, R., Jordan, F., Li, W., Hampp, J., & Hägele, M. (2016). Room segmentation: Survey, implementation, and analysis. In Proceedings of the IEEE international conference on robotics and automation, Stockholm, Sweden, pp. 1019–1026.

  • Brunskill, E., Kollar, T., & Roy, N. (2007). Topological mapping using spectral clustering and classification. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, San Diego, California, pp. 3491–3496.

  • Charrow, B., Kahn, G., Patil, S., Liu, S., Goldberg, K., Abbeel, P., Michael, N., & Kumar, V. (2015). Information-theoretic planning with trajectory optimization for dense 3d mapping. In Proceedings of robotics: Science and systems, Rome, Italy, Vol. 11.

  • Edelsbrunner, H., Letscher, D., & Zomorodian, A. (2000). Topological persistence and simplification. In Proceedings 41st annual symposium on foundations of computer science, IEEE, pp. 454–463.

  • Edelsbrunner, H., Morozov, D., & Pascucci, V. (2006). Persistence-sensitive simplification functions on 2-manifolds. In Proceedings of the twenty-second annual symposium on computational geometry, pp. 127–134.

  • Everett, H, I. I. I. (1963). Generalized Lagrange multiplier method for solving problems of optimum allocation of resources. Operations Research, 11(3), 399–417.

    Article  MathSciNet  Google Scholar 

  • Faigl, J., & Hollinger, G. A. (2017). Autonomous data collection using a self-organizing map. IEEE Transactions on Neural Networks and Learning Systems, 29(5), 1703–1715.

    Article  MathSciNet  Google Scholar 

  • Faigl, J., Kulich, M., Vonásek, V., & Přeučil, L. (2011). An application of the self-organizing map in the non-Euclidean traveling salesman problem. Neurocomputing, 74(5), 671–679.

    Article  Google Scholar 

  • Faigl, J., Pěnička, R., & Best, G. (2016). Self-organizing map-based solution for the orienteering problem with neighborhoods. In Proceedings of the IEEE international conference on systems, man, and cybernetics, Budapest, Hungary, pp. 1315–1321.

  • Friedman, S., Pasula, H., & Fox, D. (2007). Voronoi random fields: Extracting topological structure of indoor environments via place labeling. In Proceedings of the international joint conference on artificial intelligence, Vol. 7, Hyderabad, India, pp. 2109–2114.

  • 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 

  • Huyer, A. (1983). Coastal upwelling in the California current system. Progress in Oceanography, 12(3), 259–284.

    Article  Google Scholar 

  • Ji, G., Shen, H.-W., & Wenger, R. (2003). Volume tracking using higher dimensional iso surfacing. In Proceedings of the IEEE computer society visualization conference, Seattle, Washington, pp. 28–36.

  • Jones, D., & Hollinger, G. A. (2017). Planning energy-efficient trajectories in strong disturbances. IEEE Robotics and Automation Letters, 2(4), 2080–2087.

    Article  Google Scholar 

  • Jones, D., Kuhlman, M. J., Sofge, D. A., Gupta, S. K., & Hollinger, G. A. (2018). Stochastic optimization for autonomous vehicles with limited control authority. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Madrid, Spain, pp. 2395–2401.

  • Kalakrishnan, M., Chitta, S., Theodorou, E., Pastor, P., & Schaal, S. (2011). STOMP: Stochastic trajectory optimization for motion planning. In Proceedings of the IEEE international conference on robotics and automation, Shanghai, China, pp. 4569–4574.

  • Kim, S., Bhattacharya, S., Ghrist, R., & Kumar, V. (2013). Topological exploration of unknown and partially known environments. In 2013 IEEE/RSJ international conference on intelligent robots and systems, Tokyo, Japan, pp. 3851–3858.

  • Kiwiel, K. C. (2001). Convergence and efficiency of subgradient methods for quasiconvex minimization. Mathematical Programming, 90(1), 1–25.

    Article  MathSciNet  Google Scholar 

  • Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464–1480.

    Article  Google Scholar 

  • Kostavelis, I., Charalampous, K., Gasteratos, A., & Tsotsos, J. K. (2016). Robot navigation via spatial and temporal coherent semantic maps. Engineering Applications of Artificial Intelligence, 48, 173–187.

    Article  Google Scholar 

  • Kularatne, D., Bhattacharya, S., & Hsieh, M. A. (2018). Going with the flow: A graph based approach to optimal path planning in general flows. Autonomous Robots, 42(7), 1369–1387.

    Article  Google Scholar 

  • LaValle, S. M. (1998). Rapidly-exploring random trees: A new tool for path planning. Princeton: Citeseer.

    Google Scholar 

  • Lukasczyk, J., Maciejewski, R., Garth, C., & Hagen, H. (2015). Understanding hotspots: A topological visual analytics approach. In Proceedings of the SIGSPATIAL international conference on advances in geographic information systems, ACM, Seattle, Washington, pp. 36–46.

  • Luperto, M., & Amigoni, F. (2019). Predicting the global structure of indoor environments: A constructive machine learning approach. Autonomous Robots, 43(4), 813–835.

    Article  Google Scholar 

  • McCammon, S., & Hollinger, G. A. (2017). Planning and executing optimal non-entangling paths for tethered underwater vehicles. In Proceedings of the IEEE international conference on robotics and automation, Singapore, pp. 3040–3046.

  • McCammon, S., & Hollinger, G. A. (2018). Topological hotspot identification for informative path planning with a marine robot. In Proceedings of the IEEE international conference on robotics and automation, Brisbane, Australia, pp. 4865–4872.

  • McCammon, S., Jones, D., & Hollinger, G. A. (2020). Topology-aware self organizing maps for robotic information gathering. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Las Vegas, Nevada (Virtual), pp. 1717–1724.

  • McNamara, T. P. (1986). Mental representations of spatial relations. Cognitive Psychology, 18(1), 87–121.

    Article  Google Scholar 

  • Michini, M., Hsieh, M. A., Forgoston, E., & Schwartz, I. B. (2014). Robotic tracking of coherent structures in flows. IEEE Transactions on Robotics, 30(3), 593–603.

    Article  Google Scholar 

  • Oßwald, S., Bennewitz, M., Burgard, W., & Stachniss, C. (2016). Speeding-up robot exploration by exploiting background information. IEEE Robotics and Automation Letters, 1(2), 716–723.

    Article  Google Scholar 

  • Petres, C., et al. (2007). Path planning for autonomous underwater vehicles. IEEE Transactions on Robotics, 23(2), 331–341.

    Article  MathSciNet  Google Scholar 

  • Platypus, L. L. C. (2014). The Lutra Prop.

  • Pokorny, F. T., Hawasly, M., & Ramamoorthy, S. (2016). Topological trajectory classification with filtrations of simplicial complexes and persistent homology. The International Journal of Robotics Research, 35(1–3), 204–223.

    Article  Google Scholar 

  • Popović, M., Hitz, G., Nieto, J., Sa, I., Siegwart, R., & Galceran, E. (2017). Online informative path planning for active classification using UAVS. In Proceedings of the IEEE international conference on robotics and automation, Singapore, pp. 5753–5758.

  • Rinne, H. (2008). The Weibull distribution: A handbook. Boca Raton: CRC Press.

    Book  Google Scholar 

  • Saroya, M., Best, G., & Hollinger, G. A. (2020). Online exploration of tunnel networks leveraging topological cnn-based world predictions. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Las Vegas, Nevada (Virtual).

  • Sethian, J. A. (1999). Level set methods and fast marching methods: Evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science (Vol. 3). Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  • 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 

  • Singh, A., Krause, A., Guestrin, C., Kaiser, W. J., & Batalin, M. A. (2007). Efficient planning of informative paths for multiple robots. In Proceedings of the international joint conference on artificial intelligence, Vol. 7, pp. 2204–2211.

  • Somhom, S., Modares, A., & Enkawa, T. (1997). A self-organising model for the travelling salesman problem. Journal of the Operational Research Society, 48(9), 919–928.

    Article  Google Scholar 

  • The GUDHI Project. (2014). GUDHI user and reference manual.

  • Thrun, S. (1998). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99(1), 21–71.

    Article  Google Scholar 

  • Topp, E. A., & Christensen, H. I. (2006). Topological modelling for human augmented mapping. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Beijing, China, pp. 2257–2263.

  • Yu, J., Aslam, J., Karaman, S., & Rus, D. (2015). Anytime planning of optimal schedules for a mobile sensing robot. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, Hamburg, Germany, pp. 5279–5286.

  • Yu, J., Schwager, M., & Rus, D. (2016). Correlated orienteering problem and its application to persistent monitoring tasks. IEEE Transactions on Robotics, 32(5), 1106–1118.

  • Zivkovic, Z., Bakker, B., & Krose, B. (2006). Hierarchical map building and planning based on graph partitioning. In Proceedings of the IEEE international conference on robotics and automation, Orlando, Florida, pp. 803–809.

  • Zomorodian, A., & Carlsson, G. (2005). Computing persistent homology. Discrete & Computational Geometry, 33(2), 249–274.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Seth McCammon

  2. Oregon State University, Corvallis, OR, USA

    Seth McCammon & Geoffrey A. Hollinger

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  1. Seth McCammon
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  2. Geoffrey A. Hollinger
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Correspondence to Seth McCammon.

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This work is funded in part by NSF Grant IIS-1723924 and NSF Grant IIS-1845227.

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McCammon, S., Hollinger, G.A. Topological path planning for autonomous information gathering. Auton Robot 45, 821–842 (2021). https://doi.org/10.1007/s10514-021-10012-x

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