Humanoids’ abilities to navigate stairs and uneven terrain make them well-suited for disaster response efforts. However, humanoid navigation in such environments is currently limited by the capabilities of navigation planners. Such planners typically consider only footstep locations, but planning with palm contacts may be necessary to cross a gap, avoid an obstacle, or maintain balance. However, considering palm contacts greatly increases the branching factor of the search, leading to impractical planning times for large environments. Planning a contact transition sequence in a large environment is important because it verifies that the robot will be able to reach a given goal. In previous work we explored using library-based methods to address difficult navigation planning problems requiring palm contacts, but such methods are not efficient when navigating an easy-to-traverse part of the environment. To maximize planning efficiency, we would like to use discrete planners when an area is easy to traverse and switch to the library-based method only when traversal becomes difficult. Thus, in this paper we present a method that (1) Plans a torso guiding path which accounts for the difficulty of traversing the environment as predicted by learned regressors; and (2) Decomposes the guiding path into a set of segments, each of which is assigned a motion mode (i.e. a set of feet and hands to use) and a planning method. Easily-traversable segments are assigned a discrete-search planner, while other segments are assigned a library-based method that fits existing motion plans to the environment near the given segment. Our results suggest that the proposed approach greatly outperforms standard discrete planning in success rate and planning time. We also show an application of the method to a real robot in a mock disaster scenario.
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Baudouin, L., Perrin, N., Moulard, T., Lamiraux, F., Stasse, O., & Yoshida, E. (2011). Real-time replanning using 3d environment for humanoid robot. In IEEE-RAS international conference on humanoid robots (humanoids).
Brandao, M., Fallon, M., & Havoutis, I. (2019). Multi-controller multi-objective locomotion planning for legged robots. In 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS)
Caron, S., Pham, Q., & Nakamura, Y. (2015). Leveraging cone double description for multi-contact stability of humanoids with applications to statics and dynamics. In Robotics: Science and systems (RSS).
Chestnutt, J., Kuffner, J., Nishiwaki, K., & Kagami, S. (2003). Planning biped navigation strategies in complex environments. In IEEE-RAS international conference on humanoid robots (Humanoids).
Chilian, A., & Hirschmuller, H. (2009). Stereo camera based navigation of mobile robots on rough terrain. In IEEE/RSJ international conference on intelligent robots and systems (IROS).
Chung, S., & Khatib, O. (2015). Contact-consistent elastic strips for multi-contact locomotion planning of humanoid robots. In IEEE international conference on robotics and automation (ICRA).
Cunningham, C., Whittaker, W. L., & Nesnas, I. A. (2017). Improving slip prediction on mars using thermal inertia measurements. In Robotics: Science and systems (RSS).
Deits, R., Tedrake, R. (2014). Footstep planning on uneven terrain with mixed-integer convex optimization. In IEEE-RAS international conference on humanoid robots (humanoids).
Diankov, R. (2010). Automated construction of robotic manipulation programs. PhD thesis, Carnegie Mellon University.
Dornbush, A., Vijayakumar, K., Bardapurkar, S., Islam, F., Ito, M., & Likhachev, M. (2018). A single-planner approach to multi-modal humanoid mobility. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 4334–4341).
Escande, A., Kheddar, A., Miossec, S., & Garsault, S. (2009). Planning support contact-points for acyclic motions and experiments on HRP-2. Experimental Robotics, 2, 293–302.
Fang, Z., Yang, S., Jain, S., Dubey, G., Roth, S., Maeta, S., et al. (2017). Robust autonomous flight in constrained and visually degraded shipboard environments. Journal of Field Robotics, 34(1), 25–52. https://doi.org/10.1002/rob.21670.
Fernbach, P., Tonneau, S., & Taïx, M. (2018) CROC: Convex resolution of centroidal dynamics trajectories to provide a feasibility criterion for the multi contact planning problem. In IEEE/RSJ international conference on intelligent robots and systems (IROS).
Fernbach, P., Tonneau, S., Stasse, O., Carpentier, J., & Taïx, M. (2020). C-CROC: Continuous and convex resolution of centroidal dynamic trajectories for legged robots in multicontact scenarios. IEEE Transactions on Robotics, 36(3), 676–691. https://doi.org/10.1109/TRO.2020.2964787.
Grey, M. X., Liu, C.K., & Ames, A. D. (2016). Traversing environments using possibility graphs with multiple action types. arXiv e-prints
Grey, M. X., Ames, A. D., Liu, C. K. (2017). Footstep and motion planning in semi-unstructured environments using randomized possibility graphs. In: IEEE International Conference on Robotics and Automation (ICRA).
Griffin, R. J., Wiedebach, G., McCrory, S., Bertrand, S., Lee, I., & Pratt, J. (2019). Footstep planning for autonomous walking over rough terrain. In: 2019 IEEE-RAS 19th international conference on humanoid robots (humanoids) (pp. 9–16). https://doi.org/10.1109/Humanoids43949.2019.9035046.
Hornung, A., Dornbush, A., Likhachev, M., & Bennewitz, M. (2012). Anytime search-based footstep planning with suboptimality bounds. In: IEEE-RAS international conference on humanoid robots (humanoids).
Kanoun, O., Yoshida, E., & Laumond, J. P. (2009). An optimization formulation for footsteps planning. In IEEE-RAS international conference on humanoid robots (humanoids).
Knabe, C., Seminatore, J., Webb, J., Hopkins, M., Furukawa, T., Leonessa, A., & Lattimer, B. (2015). Design of a series elastic humanoid for the DARPA robotics challenge. In IEEE-RAS international conference on humanoid robots (humanoids).
Kuffner, J., Nishiwaki, K., Kagami, S., Inaba, M., & Inoue, H. (2001). Footstep planning among obstacles for biped robots. In: IEEE/RSJ international conference on intelligent robots and systems (IROS).
Kumagai, I., Morisawa, M., Benallegue, M., & Kanehiro, F. (2019). Bipedal locomotion planning for a humanoid robot supported by arm contacts based on geometrical feasibility. In IEEE-RAS international conference on humanoid robots (humanoids).
Lin, Y., & Berenson, D. (2016). Using previous experience for humanoid navigation planning. In IEEE-RAS international conference on humanoid robots (humanoids).
Lin, Y., & Berenson, D. (2017). Humanoid navigation in uneven terrain using learned estimates of traversability. In: IEEE-RAS international conference on humanoid robots (humanoids).
Lin, Y., & Berenson, D. (2018). Humanoid navigation planning in large unstructured environments using traversability-based segmentation. In IEEE/RSJ international conference on intelligent robots and systems (IROS).
Lin, Y., Ponton, B., Righetti, L., & Berenson, D. (2019). Efficient humanoid contact planning using learned centroidal dynamics prediction. In: International conference on robotics and automation (ICRA).
Maier, D., Lutz, C., & Bennewitz, M. (2013) Integrated perception, mapping, and footstep planning for humanoid navigation among 3d obstacles. In: 2013 IEEE/RSJ international conference on intelligent robots and systems (pp. 2658–2664). https://doi.org/10.1109/IROS.2013.6696731.
Michel, P., Chestnutt, J., Kuffner, J., & Kanade, T. (2005). Vision-guided humanoid footstep planning for dynamic environments. In IEEE-RAS international conference on humanoid robots (humanoids).
Scherer, S., Rehder, J., Achar, S., Cover, H., Chambers, A., Nuske, S., & Singh, S. (2012). River mapping from a flying robot: State estimation, river detection, and obstacle mapping. Autonomous Robots, 33(1–2), 189–214. https://doi.org/10.1007/s10514-012-9293-0.
Shneier, M., Chang, T., Hong, T., Shackleford, W., Bostelman, R., & Albus, J. S. (2008). Learning traversability models for autonomous mobile vehicles. Autonomous Robots, 24(1), 69–86.
Suger, B., Steder, B., & Burgard, W. (2015). Traversability analysis for mobile robots in outdoor environments: A semi-supervised learning approach based on 3d-lidar data. In IEEE international conference on robotics and automation (ICRA).
Tonneau, S., Prete, A. D., Pettré, J., Park, C., Manocha, D., & Mansard, N. (2018). An efficient acyclic contact planner for multiped robots. IEEE Transactions on Robotics, 34(3), 586–601.
van den Berg, J., Shah, R., Huang, A., & Goldberg, K. (2011). Anytime nonparametric A*. In AAAI.
Wellhausen, L., Dosovitskiy, A., Ranftl, R., Walas, K., Cadena, C., & Hutter, M. (2019). Where should I walk? Predicting terrain properties from images via self-supervised learning. IEEE Robotics and Automation Letters, 4(2), 1509–1516. https://doi.org/10.1109/LRA.2019.2895390.
Wermelinger, M., Fankhauser, P., Diethelm, R., Krüsi, P., Siegwart, R., & Hutter, M. (2016). Navigation planning for legged robots in challenging terrain. In IEEE/RSJ international conference on intelligent robots and systems (IROS).
Funding was provided by Office of Naval Research (US) (N00014-17-1-2050).
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Lin, YC., Berenson, D. Long-horizon humanoid navigation planning using traversability estimates and previous experience. Auton Robot 45, 937–956 (2021). https://doi.org/10.1007/s10514-021-09996-3
- Motion planning
- Humanoid robots
- Multi contact locomotion planning