Abstract
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages. Environments are GPS-denied and perceptually-degraded with variable lighting from dark to lit and obscurants (dust, fog, smoke). Lack of prior maps and degraded communication eliminates the possibility of prior or off-board computation or operator intervention. This necessitates real-time on-board computation using noisy sensor data. To address these challenges, we propose a resilient architecture that exploits redundancy and heterogeneity in sensing modalities. Further resilience is achieved by triggering recovery behaviors upon failure. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots, as a part of Team CoSTAR’s effort to the DARPA Subterranean Challenge, where the team won 2nd and 1st place in the Tunnel and Urban Circuits, respectively.
R. Thakker, N. Alatur, D.D. Fan and J. Tordesillas—Equally contributed.
N. Alatur and J. Tordesillas—Work done while at the JPL, Caltech.
©2020, California Institute of Technology. All Rights Reserved.
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References
Haruyama, J., Morota, T., Kobayashi, S., Sawai, S., Lucey, P.G., Shirao, M., Nishino, M.N.: Lunar holes and lava tubes as resources for lunar science and exploration. In: Moon, pp. 139–163. Springer, Heidelberg (2012)
Murphy, R.R.: Disaster Robotics. MIT Press, Cambridge (2014)
Papadakis, P.: Terrain traversability analysis methods for unmanned ground vehicles: a survey. Eng. Appl. Artif. Intell. 26(4), 1373–1385 (2013)
Otsu, K., Matheron, G., Ghosh, S., Toupet, O., Ono, M.: Fast approximate clearance evaluation for rovers with articulated suspension systems. J. Field Robot. 37(5), 768–785 (2019)
Krüsi, P., Furgale, P., Bosse, M., Siegwart, R.: Driving on point clouds: motion planning, trajectory optimization, and terrain assessment in generic nonplanar environments. J. Field Robot. 34(5), 940–984 (2017)
Florence, P., Carter, J., Tedrake, R.: Integrated perception and control at high speed: evaluating collision avoidance maneuvers without maps. In: Workshop on the Algorithmic Foundations of Robotics (WAFR) (2016)
Florence, P.R., Carter, J., Ware, J., Tedrake, R.: NanoMap: fast, uncertainty-aware proximity queries with lazy search over local 3D data. In: IEEE International Conference on Robotics and Automation (ICRA)
Brunner, M., Fiolka, T., Schulz, D., Schlick, C.M.: Design and comparative evaluation of an iterative contact point estimation method for static stability estimation of mobile actively reconfigurable robots. Robot. Auton. Syst. 63(P1), 89–107 (2015)
Brunner, M., Brüggemann, B., Schulz, D.: Autonomously traversing obstacles: metrics for path planning of reconfigurable robots on rough terrain. In: International Conference on Informatics in Control, Automation and Robotics (ICINCO), vol. 2, pp. 58–69 (2012)
Ma, Y., Shiller, Z.: Pose Estimation of Vehicles Over Uneven Terrain (2019)
Iagnemma, K., Shimoda, S., Shiller, Z.: Near-optimal navigation of high speed mobile robots on uneven terrain. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 4098–4103 (2008)
Fankhauser, P., Bloesch, M., Hutter, M.: Probabilistic terrain mapping for mobile robots with uncertain localization. IEEE Robot. Autom. Lett. 3(4), 3019–3026 (2018)
Goldberg, S.B., Maimone, M.W., Matthies, L.: Stereo vision and rover navigation software for planetary exploration. IEEE Aerosp. Conf. 5, 2025–2036 (2002)
Ebadi, K., Chang, Y., Palieri, M., Stephens, A., Hatteland, A., Heiden, E., Thakur, A., Morrell, B., Carlone, L., Agha-Mohammadi, A.: LAMP: Large-scale autonomous mapping and positioning for exploration of perceptually-degraded subterranean environments. In: IEEE International Conference on Robotics and Automation, pp. 80–86 (2020)
Santamaria, A., Thakker, R., Fan, D.D., Morrell, B., Agha, A.: Towards resilient autonomous navigation of drones. In: International Symposium on Robotics Research (2019)
Agha-Mohammadi, A., Chakravorty, S., Amato, N.: FIRM: sampling-based feedback motion planning under motion uncertainty and imperfect measurements. Int. J. Robot. Res. 33(2), 268–304 (2014)
Bouman, A., Ginting, M.F., Alatur, N., Palieri, M., Fan, D.D., Touma, T., Pailevanian, T., Kim, S.-K., Otsu, K., Burdick, J., Agha-Mohammadi, A.: Autonomous spot: long-range autonomous exploration of extreme environments with legged locomotion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1057–1064 (2020)
Ebadi, K., Chang, Y., Palieri, M., Stephens, A., Hatteland, A., Heiden, E., Thakur, A., Funabiki, N., Morrell, B., Wood, S., et al.: LAMP: large-scale autonomous mapping and positioning for exploration of perceptually-degraded subterranean environments. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 80–86. IEEE (2020)
Kramer, A., Stahoviak, C., Santamaria-Navarro, A., Agha-Mohammadi, A.-A., Heckman, C.: Radar-inertial ego-velocity estimation for visually degraded environments. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 5739–5746. IEEE (2020)
Tagliabue, A., Tordesillas, J., Cai, X., Angel Santamaria-Navarro, A., How, J.P., Carlone, L., Agha-Mohammadi, A.-A.: Lion: Lidar-inertial observability-aware navigator for vision-denied environments. In: International Symposium on Experimental Robotics (2020)
Agha-Mohammadi, A.-A., Heiden, E., Hausman, K., Sukhatme, G.: Confidence-rich grid mapping. Int. J. Robot. Res. 38(12–13), 1352–1374 (2017)
Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Auton. Robots 34(3), 189–206 (2013)
Otsu, K., Tepsuporn, S., Thakker, R., Vaquero, T.S., Edlund, J.A., Walsh, W., Miles, G., Heywood, T., Wolf, M.T., Agha-Mohammadi, A.-A.: Supervised autonomy for communication-degraded subterranean exploration by a robot team. In: IEEE Aerospace Conference, pp. 1–9 (2020)
Himmelsbach, M., Hundelshausen, F.V., Wuensche, H.-J.: Fast segmentation of 3D point clouds for ground vehicles. In: Intelligent Vehicles Symposium (IV), 2010 IEEE, pp. 560–565. IEEE (2010)
Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)
Rosmann, C., Hoffmann, F., Bertram, T.: Kinodynamic trajectory optimization and control for car-like robots, pp. 5681–5686 (2017)
Acknowledgements
The authors would like to thank Anushri Dixit and Joel Burdick for support with the Ackermann robot and members of Team CoSTAR for their hardware and testing support. The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA).
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Thakker, R. et al. (2021). Autonomous Off-Road Navigation over Extreme Terrains with Perceptually-Challenging Conditions. In: Siciliano, B., Laschi, C., Khatib, O. (eds) Experimental Robotics. ISER 2020. Springer Proceedings in Advanced Robotics, vol 19. Springer, Cham. https://doi.org/10.1007/978-3-030-71151-1_15
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