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Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains

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Abstract

A probabilistic stable motion planning strategy applicable to reconfigurable robots is presented in this paper. The methodology derives a novel statistical stability criterion from the cumulative distribution of a tip-over metric. The measure is dynamically updated with imprecise terrain information, localization and robot kinematics to plan safety-constrained paths which simultaneously allow the widest possible visibility of the surroundings by simultaneously assuming highest feasible vantage robot configurations. The proposed probabilistic stability metric allows more conservative poses through areas with higher levels of uncertainty, while avoiding unnecessary caution in poses assumed at well-known terrain sections. The implementation with the well known grid based A* algorithm and also a sampling based RRT planner are presented. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging elevation terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and the other from a publicly available dataset of a quasi-outdoor rover testing facility.

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References

  • Berg, J. V. D., Abbeel, P., & Goldberg, K. (2011). Lqg-mp: Optimized path planning for robots with motion uncertainty and imperfect state information. The International Journal of Robotics Research, 30(7), 895–913.

    Article  Google Scholar 

  • Besseron, G., Grand, C., Amar, F.B., & Bidaud, P. (2008). Decoupled control of the high mobility robot hylos based on a dynamic stability margin. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 22–23). Nice, France.

  • Brooks, A., Makarenko, A., Williams, S., & Durrant-Whyte, H. (2006). Parametric POMDPs for planning in continuous state spaces. Robotics and Autonomous Systems, 54(11), 887–897.

    Article  Google Scholar 

  • Candido, S., Davidson, J., & Hutchinson, S. (2010). Exploiting domain knowledge in planning for uncertain robot systems modeled as pomdps. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 3596–3603). Alaska, USA.

  • Coelho, P., & Nunes, U. (2005). Path-following control of mobile robots in presence of uncertainties. IEEE Transactions on Robotics, 21(2), 252–261.

    Article  Google Scholar 

  • Freitas, G., Gleizer, G., Lizarralde, F., & Hsu, L. (2010). Kinematic reconfigurability control for an environmental mobile robot operating in the amazon rain forest. Journal of Field Robotics, 27(2), 197–216.

    Google Scholar 

  • Gill, R., Kulic, D., & Nielsen, C. (2013). Robust path following for robot manipulators. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 3412–3418). Tokyo, Japan.

  • Greenberg, M. (1998). Advanced engineering mathematics, 2/E. New Delhi: Pearson Education India.

    Google Scholar 

  • Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.

    Article  Google Scholar 

  • Iagnemma, K., & Dubowsky, S. (2004). Mobile robot in rough terrain (Vol. 12). Berlin: Springer Tracts in Advanced Robotics.

    Google Scholar 

  • Iagnemma, K., Rzepniewski, A., Dubowsky, S., & Schenker, P. (2003). Control of robotic vehicles with actively articulated suspensions in rough terrain. Autonomous Robots, 14(1), 5–16.

    Article  MATH  Google Scholar 

  • Julier, S., & Uhlmann, J. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92(3), 401–422.

    Article  Google Scholar 

  • Keiji, N., Seiga, K., Yoshito, O., Kazuki, O., Kazuya, Y., Satoshi, T., et al. (2013). Emergency response to the nuclear accident at the fukushima daiichi nuclear power plants using mobile rescue robots. Journal of Field Robotics, 30(1), 44–63.

    Article  Google Scholar 

  • LaValle, S. (1998). Rapidly-exploring random trees: A new tool for path planning. Technical report, Iowa State University, Dept. of Computer Science.

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

    Book  MATH  Google Scholar 

  • Liang, D., Zongquan, D., Haibo, G., Junlong, G., Dapeng, Z., & Karl, I. (2013). Experimental study and analysis of the wheels’ steering mechanics for planetary exploration wheeled mobile robots moving on deformable terrain. The International Journal of Robotics Research, 32(6), 712–743.

    Article  Google Scholar 

  • Liangjun, Z., & Dinesh, M. (2008). An efficient retraction-based rrt planner. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 3743–3750). Pasadena, USA.

  • Liu, Y., & Liu, G. (2010). Interaction analysis and online tip-over avoidance for a reconfigurable tracked mobile modular manipulator negotiating slopes. IEEE/ASME Transactions on Mechatronics, 15(4), 623–635.

    Article  Google Scholar 

  • Matthijs, S., & Nikos, V. (2005). Perseus: Randomized point-based value iteration for pomdps. Journal of Artificial Intelligence Research, 24, 195–220.

    Google Scholar 

  • Norouzi, M., Miro, J.V., & Dissanayake, G. (2013a). Planning stable and efficient paths for articulated mobile robots on challenging terrains. In Proceedings of Australasian Conference on Robotics and Automation (p. 10). Sydney, Australia: UNSW.

  • Norouzi, M., Miro, J.V., & Dissanayake, G. (2013b). A statistical approach for uncertain stability analysis of mobile robots. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 191–196). Karlsruhe, Germany.

  • Norouzi, M., Miro, J.V., Dissanayake, G., & Vidal-Calleja, T. (2014). Path planning with stability uncertainty for articulated mobile vehicles in challenging environments. In IEEE/RSJ Proceedings of International Conference on Intelligent Robots and Systems (pp. 1748–1753). Chicago, Illinois, USA.

  • Papadopoulos, E., & Rey, D. (1996). A new measure of tipover stability margin for mobile manipulators. In Proceedings of IEEE International Conference on Robotics and Automation (vol. 4, pp. 3111–3116). Minneapolis, USA.

  • Papadopoulos, E., & Rey, D. (2000). The force angle measure of tipover stability margin for mobile manipulatiors. Vehicle System Dynamics, 33(1), 29–48.

    Article  Google Scholar 

  • Parks, P. (1966). Liapunov redesign of model reference adaptive control systems. IEEE Transactions on Automatic Control, 11(3), 362–367.

    Article  Google Scholar 

  • Roan, P., Burmeister, A., Rahimi, A., Holz, K., & Hooper, D. (2010). Real-world validation of three tipover algorithms for mobile robots. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 4431–4436). Anchorage, Alaska, USA.

  • Rubinstein, & Reuven, (1981). Simulation and the Monte Carlo method. New York: Wiley.

    Book  MATH  Google Scholar 

  • Santosh, H., Heijden, V. D., Wam, G., Frits, V. E., Alfred, S., & Cajo, T. B. (2014). Laser range finder model for autonomous navigation of a robot in a maize field using a particle filter. Computers and Electronics in Agriculture, 100, 41–50.

    Article  Google Scholar 

  • Sebastian, T., Wolfram, B., & Dieter, F. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • SeungBeum, S., JunHo, C., ChangHyun, C., YeonSub, J., Seung-Yeup, H., & Sungchul, K. (2014). Mine detecting robot system. In Field and Service Robotics (pp. 97–109). Springer.

  • Siegwart, R., & Nourbakhsh, I. R. (2004). Introduction to autonomous mobile robotos. Cambridge: The MIT press.

    Google Scholar 

  • Smith, R. (2005). Open dynamics engine. http://www.ode.org/. Accessed 2011.

  • Toit, N. D., & Burdick, J. (2012). Robot motion planning in dynamic, uncertain environments. IEEE Transactions on Robotics, 28(1), 101–115.

    Article  Google Scholar 

  • Tong, C. H., Gingras, D., Larose, K., Barfoot, T. D., & Dupuis, E. (2013). The canadian planetary emulation terrain 3D mapping dataset. The International Journal of Robotics Research, 32(4), 389–395.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering and IT, University of Technology, Sydney (UTS), 15 Broadway, Ultimo, NSW, 2007, Australia

    Mohammad Norouzi, Jaime Valls Miro & Gamini Dissanayake

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  1. Mohammad Norouzi
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  2. Jaime Valls Miro
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  3. Gamini Dissanayake
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Correspondence to Mohammad Norouzi.

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Norouzi, M., Valls Miro, J. & Dissanayake, G. Probabilistic stable motion planning with stability uncertainty for articulated vehicles on challenging terrains. Auton Robot 40, 361–381 (2016). https://doi.org/10.1007/s10514-015-9474-8

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  • DOI: https://doi.org/10.1007/s10514-015-9474-8

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