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Autonomous exploration with online learning of traversable yet visually rigid obstacles

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Abstract

This paper concerns online learning of terrain properties combining haptic perception with exteroceptive sensing to reason about forces needed to pass through terrains that visually appear as untraversable obstacles. Terrain learning is studied within the context of autonomous exploration. We propose predicting the traversability of potentially obstructing terrains by active perception to establish a connection between the observed geometric environment model and deliberately sampled forces to pass through the terrain using a haptic sensor that probes the terrain in front of the robot. The developed solution uses a Gaussian Process regressor in online learning and force prediction. The robot is navigated by following the information gain to improve traversability and spatial models. The proposed approach has been experimentally verified in fully autonomous exploration with a multi-legged walking robot. The robot is navigated through visually looking obstacles and explores “hidden” areas while following the expected information gain to explore the terrain properties of the mission area.

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References

  • Baleia, J., Santana, P., & Barata, J. (2015). On exploiting haptic cues for self-supervised learning of depth-based robot navigation affordances. Journal of Intelligent & Robotic Systems, 80(3–4), 455–474. https://doi.org/10.1007/s10846-015-0184-4.

    Article  Google Scholar 

  • Bayer, J., & Faigl, J. (2019). On autonomous spatial exploration with small hexapod walking robot using tracking camera intel realsense T265. In European conference on mobile robots (ECMR) (pp. 1–6). https://doi.org/10.1109/ECMR.2019.8870968

  • Bayer, J., & Faigl, J. (2020). Speeded up elevation map for exploration of large-scale subterranean environments. In 2019 modelling and simulation for autonomous systems (MESAS) (pp. 190–202). https://doi.org/10.1007/978-3-030-43890-6_15

  • Belter, D., Wietrzykowski, J., & Skrzypczyński, P. (2019). Employing natural terrain semantics in motion planning for a multi-legged robot. Journal of Intelligent & Robotic Systems, 93(3), 723–743. https://doi.org/10.1007/s10846-018-0865-x.

    Article  Google Scholar 

  • Biber, P., & Duckett, T. (2005). Dynamic maps for long-term operation of mobile service robots. In Robotics: Science and systems (RSS). https://doi.org/10.15607/RSS.2005.I.003

  • Bourgault, F., Makarenko, A. A., Williams, S. B., Grocholsky, B., & Durrant-Whyte, H. F. (2002). Information based adaptive robotic exploration. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 540–545). https://doi.org/10.1109/IRDS.2002.1041446

  • Bradley, D. M., Chang, J. K., Silver, D., Powers, M., Herman, H., Rander, P., & Stentz, A. (2015). Scene understanding for a high-mobility walking robot. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1144–1151). https://doi.org/10.1109/IROS.2015.7353514

  • Brown, D., & Webster, G. (2010). Now a stationary research platform, nasa’s mars rover spirit starts a new chapter in red planet scientific studies. NASA Press Release

  • Brunner, M., Brüggemann, B., & Schulz, D. (2013). Rough terrain motion planning for actuated, tracked robots. In International conference on agents and artificial intelligence (ICAART) (pp. 40–61). Springer. https://doi.org/10.1007/978-3-662-44440-5_3

  • Burgard, W., Cremers, A. B., Fox, D., Hähnel, D., Lakemeyer, G., Schulz, D., et al. (1999). Experiences with an interactive museum tour-guide robot. Artificial Intelligence, 114(1), 3–55. https://doi.org/10.1016/S0004-3702(99)00070-3.

    Article  MATH  Google Scholar 

  • Carrillo, H., Dames, P., Kumar, V., & Castellanos, J. A. (2018). Autonomous robotic exploration using a utility function based on Rényi’s general theory of entropy. Autonomous Robots, 42(2), 235–256. https://doi.org/10.1007/s10514-017-9662-9.

    Article  Google Scholar 

  • Cunningham, C., Nesnas, I. A., & Whittaker, W. L. (2019). Improving slip prediction on Mars using thermal inertia measurements. Autonomous Robots, 43(2), 503–521. https://doi.org/10.1007/s10514-018-9796-4.

    Article  Google Scholar 

  • Faigl, J., & Čížek, P. (2019). Adaptive locomotion control of hexapod walking robot for traversing rough terrains with position feedback only. Robotics and Autonomous Systems, 116, 136–147. https://doi.org/10.1016/j.robot.2019.03.008.

    Article  Google Scholar 

  • Faigl, J., & Kulich, M. (2013). On determination of goal candidates in frontier-based multi-robot exploration. In European conference on mobile robots (ECMR) (pp. 210–215). https://doi.org/10.1109/ECMR.2013.6698844

  • Faigl, J., & Kulich, M. (2015). On benchmarking of frontier-based multi-robot exploration strategies. In European conference on mobile robots (ECMR) (pp. 1–8). https://doi.org/10.1109/ECMR.2015.7324183

  • Giguere, P., & Dudek, G. (2008). Clustering sensor data for terrain identification using a windowless algorithm. In Robotics: Science and systems (RSS). https://doi.org/10.15607/RSS.2008.IV.004

  • Gu, J., Cao, Q., & Huang, Y. (2008). Rapid traversability assesment in 2.5 d grid based map on rough terrain. International Journal of Advanced Robotic Systems. https://doi.org/10.5772/6233.

    Article  Google Scholar 

  • Halodová, L., Dvořáková, E., Majer, F., Vintr, T., Mozos, O. M., Dayoub, F., & Krajník, T. (2019). Predictive and adaptive maps for long-term visual navigation in changing environments. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 7033–7039). https://doi.org/10.1109/IROS40897.2019.8967994

  • Homberger, T., Bjelonic, M., Kottege, N., & Borges, P. V. K. (2016). Terrain-dependant control of hexapod robots using vision. In International symposium on experimental robotics (ISER) (pp. 92–102). Springer. https://doi.org/10.1007/978-3-319-50115-4_9

  • Homberger, T., Wellhausen, L., Fankhauser, P., & Hutter, M. (2019). Support surface estimation for legged robots. In IEEE international conference on robotics and automation (ICRA) (pp. 8470–8476). https://doi.org/10.1109/ICRA.2019.8793646

  • Jadidi, M. G., Miro, J. V., & Dissanayake, G. (2018). Gaussian processes autonomous mapping and exploration for range-sensing mobile robots. Autonomous Robots, 42(2), 273–290. https://doi.org/10.1007/s10514-017-9668-3.

    Article  Google Scholar 

  • Kahn, G., Abbeel, P., & Levine, S. (2021). BADGR: An autonomous self-supervised learning-based navigation system. Robotics and Automation Letters, 6(2), 1312–1319. https://doi.org/10.1109/LRA.2021.3057023.

    Article  Google Scholar 

  • Karolj, V., Viseras, A., Merino, L., & Shutin, D. (2020). An integrated strategy for autonomous exploration of spatial processes in unknown environments. Sensors, 20(13), 3663.

    Article  Google Scholar 

  • Kottege, N., Parkinson, C., Moghadam, P., Elfes, A., & Singh, S. P. N. (2015). Energetics-informed hexapod gait transitions across terrains. In IEEE international conference on robotics and automation (ICRA) (pp. 5140–5147). https://doi.org/10.1109/ICRA.2015.7139915

  • Kragh, M., Jørgensen, R. N., & Pedersen, H. (2015). Object detection and terrain classification in agricultural fields using 3D lidar data. In International conference on computer vision systems (ICVS) (pp. 188–197). https://doi.org/10.1007/978-3-319-20904-3_18

  • Krüsi, P., Bosse, M., & Siegwart, R. (2016). Driving on point clouds: Motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments. Journal of Field Robotics, 34(5), 940–984. https://doi.org/10.1002/rob.21700.

    Article  Google Scholar 

  • Lalonde, J. F., Vandapel, N., Huber, D. F., & Hebert, M. (2006). Natural terrain classification using three-dimensional ladar data for ground robot mobility. Journal of Field Robotics, 23(10), 839–861. https://doi.org/10.1002/rob.20134.

    Article  Google Scholar 

  • Luo, W., & Sycara, K. (2018). Adaptive sampling and online learning in multi-robot sensor coverage with mixture of Gaussian processes. In IEEE international conference on robotics and automation (ICRA) (pp. 6359–6364). https://doi.org/10.1109/ICRA.2018.8460473

  • Makarenko, A. A., Williams, S. B., Bourgault, F., & Durrant-Whyte, H. F. (2002). An experiment in integrated exploration. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (vol. 1, pp. 534–539). https://doi.org/10.1109/IRDS.2002.1041445

  • McGhee, R. B., & Frank, A. A. (1968). On the stability properties of quadruped creeping gaits. Mathematical Biosciences, 3, 331–351. https://doi.org/10.1016/0025-5564(68)90090-4.

    Article  MATH  Google Scholar 

  • Moravec, H., & Elfes, A. (1985). High resolution maps from wide angle sonar. IEEE International Conference on Robotics and Automation (ICRA), 2, 116–121. https://doi.org/10.1109/ROBOT.1985.1087316.

    Article  Google Scholar 

  • O’Meadhra, C., Tabib, W., & Michael, N. (2019). Variable resolution occupancy mapping using gaussian mixture models. Robotics and Automation Letters, 4(2), 2015–2022. https://doi.org/10.1109/LRA.2018.2889348.

    Article  Google Scholar 

  • Otsu, K., Ono, M., Fuchs, T. J., Baldwin, I., & Kubota, T. (2016). Autonomous terrain classification with co- and self-training approach. Robotics and Automation Letters, 1(2), 1–6. https://doi.org/10.1109/LRA.2016.2525040.

    Article  Google Scholar 

  • Papadakis, P. (2013). Terrain traversability analysis methods for unmanned ground vehicles: A survey. Engineering Applications of Artificial Intelligence, 26(4), 1373–1385. https://doi.org/10.1016/j.engappai.2013.01.006.

    Article  Google Scholar 

  • Pearson, M. J., Dora, S., Struckmeier, O., Knowles, T. C., Mitchinson, B., Tiwari, K., et al. (2021). Multimodal representation learning for place recognition using deep Hebbian predictive coding. Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2021.732023.

    Article  Google Scholar 

  • Petrou, Z., Manakos, I., Stathaki, T., Mücher, C., & Adamo, M. (2015). Discrimination of vegetation height categories with passive satellite sensor imagery using texture analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(4), 1442–1455. https://doi.org/10.1109/JSTARS.2015.2409131.

    Article  Google Scholar 

  • Prágr, M., Čížek, P., & Faigl, J. (2018a). Incremental learning of traversability cost for aerial reconnaissance support to ground units. In Modelling and simulation for autonomous systems (MESAS). Springer. https://doi.org/10.1007/978-3-030-14984-0_30

  • Prágr, M., Čížek, P., & Faigl, J. (2018b). Cost of transport estimation for legged robot based on terrain features inference from aerial scan. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1745–1750). https://doi.org/10.1109/IROS.2018.8593374

  • Prágr, M., Čížek, P., Bayer, J., & Faigl, J. (2019). Online incremental learning of the terrain traversal cost in autonomous exploration. In Robotics: Science and systems (RSS). https://doi.org/10.15607/RSS.2019.XV.040

  • Prágr, M., & Faigl, J. (2019). Benchmarking incremental regressors in traversal cost assessment. In International conference on artificial neural networks (ICANN) (pp. 685–697). https://doi.org/10.1007/978-3-030-30487-4_52

  • Prágr, M., Váňa, P., & Faigl, J. (2020). Aerial reconnaissance and ground robot terrain learning in traversal cost assessment. In 2019 modelling and simulation for autonomous systems (MESAS) (pp. 3–10). https://doi.org/10.1007/978-3-030-43890-6_1

  • Quigley, M., Conley, K., Gerkey, B. P., Faust, J., Foote, T., Leibs, J., Wheeler, R., & Ng, A. Y. (2009). ROS: An open-source robot operating system. In ICRA workshop on open source software (pp. 1–6)

  • Ramos, F., & Ott, L. (2016). Hilbert maps: Scalable continuous occupancy mapping with stochastic gradient descent. International Journal of Robotics Research, 35(14), 1717–1730. https://doi.org/10.1177/0278364916684382.

    Article  Google Scholar 

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

  • Rényi, A. (1961). On measures of entropy and information. In Berkeley symposium on mathematical statistics and probability (vol. 1, pp. 547–561). https://projecteuclid.org/euclid.bsmsp/1200512181

  • Rothrock, B., Kennedy, R., Cunningham, C., Papon, J., Heverly, M., & Ono, M. (2016). SPOC: Deep learning-based terrain classification for mars rover missions. In AIAA SPACE 2016, American Institute of aeronautics and astronautics. https://doi.org/10.2514/6.2016-5539

  • Ruiz, A. V., & Olariu, C. (2015). A general algorithm for exploration with Gaussian processes in complex, unknown environments. In IEEE international conference on robotics and automation (ICRA) (pp. 3388–3393). https://doi.org/10.1109/ICRA.2015.7139667

  • Singh, A., Krause, A., Guestrin, C., Kaiser, W., & Batalin, M. (2007). Efficient planning of informative paths for multiple robots. In International joint conference on artifical intelligence (pp. 2204–2211). https://doi.org/10.5555/1625275.1625631

  • Sofman, B., Lin, E., Bagnell, J. A., Cole, J., Vandapel, N., & Stentz, A. (2006). Improving robot navigation through self-supervised online learning. Journal of Field Robotics, 23(11–12), 1059–1075. https://doi.org/10.1002/rob.20169.

    Article  Google Scholar 

  • Srinivas, N., Krause, A., Kakade, S., & Seeger, M. (2010). Gaussian process optimization in the bandit setting: no regret and experimental design. In Intl. Conf. international conference on machine learning (ICML). Haifa, Israel (pp. 1015–1022). https://icml.cc/conferences/2010/papers/422.pdf

  • Stachniss, C., Grisetti, G., & Burgard, W. (2005). Information gain-based exploration using rao-blackwellized particle filters. In Robotics: Science and systems (RSS), Robotics: Science and systems foundation. https://doi.org/10.15607/RSS.2005.I.009

  • Stelzer, A., Hirschmüller, H., & Görner, M. (2012). Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain. International Journal of Robotics Research, 31(4), 381–402. https://doi.org/10.1177/0278364911435161.

    Article  Google Scholar 

  • Tabib, W., Goel, K., Yao, J., Dabhi, M., Boirum, C., & Michael, N. (2019). Real-time information-theoretic exploration with Gaussian mixture model maps. In Robotics: Science and systems (RSS) (vol. 15). https://doi.org/10.15607/RSS.2019.XV.061

  • Tiwari, K., Xiao, X., Malik, A., & Chong, N. Y. (2019). A unified framework for operational range estimation of mobile robots operating on a single discharge to avoid complete immobilization. Mechatronics, 57, 173–187. https://doi.org/10.1016/j.mechatronics.2018.12.006.

    Article  Google Scholar 

  • Tucker, V. A. (1975). The energetic cost of moving about: Walking and running are extremely inefficient forms of locomotion: Much greater efficiency is achieved by birds, fish-and bicyclists. American Scientist, 63(4), 413–419.

    Google Scholar 

  • Ünsalan, C., & Boyer, K. L. (2004). Linearized vegetation indices based on a formal statistical framework. Transactions on Geoscience and Remote Sensing, 42(7), 1575–1585. https://doi.org/10.1109/TGRS.2004.826787.

    Article  Google Scholar 

  • Vasudevan, S., Ramos, F., Nettleton, E., Durrant-Whyte, H., & Blair, A. (2009). Gaussian Process modeling of large scale terrain. In IEEE international conference on robotics and automation (ICRA) (pp. 1047–1053). IEEE. https://doi.org/10.1002/rob.20309

  • Yamauchi, B. (1997). A frontier-based approach for autonomous exploration. In CIRA (pp. 146–151). IEEE. https://doi.org/10.1109/CIRA.1997.613851

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  1. Department of Computer Science, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic

    Miloš Prágr, Jan Bayer & Jan Faigl

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  1. Miloš Prágr
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  2. Jan Bayer
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Correspondence to Miloš Prágr.

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The presented work has been supported by the Czech Science Foundation (GAČR) under research projects No. 18-18858S, No. 19-20238S and No. 20-29531S.

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Prágr, M., Bayer, J. & Faigl, J. Autonomous exploration with online learning of traversable yet visually rigid obstacles. Auton Robot 47, 161–180 (2023). https://doi.org/10.1007/s10514-022-10075-4

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