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Theory and experiments in SmartNav rover navigation

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Abstract

This paper describes theoretical and experimental results using the SmartNav rule-free fuzzy rover navigation system. SmartNav divides the terrain perceived by the rover into a number of circular sectors, and evaluates each sector using goal and safety preference factors to differentiate between preferred and unpreferred terrain sectors. The goal-preference factor is used to make sector evaluation based on the sector orientation relative to the designated goal position. The safety-preference factors are used to make sector evaluations on the basis of the sector local and regional terrain hazards. Three methods are developed to blend the three sector evaluations in order to find the effective preference factor for each sector. Two sector selection methods are then described in which the sector preference factors are used to find the heading command for the rover. The rover speed command is also computed based on the goal distance and safety-preference factor of the chosen sector. The above navigation steps are continuously repeated throughout the rover motion. Experimental results are presented to demonstrate the navigational capabilities of SmartNav using a commercial Pioneer 2AT rover traversing a simulated Martian terrain at the JPL Mini Mars Yard.

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Notes

  1. The reason for using a non-zero value for α is to penalize, but not to completely eliminate, a sector based on its goal misalignment.

  2. There are different implementations of the fuzzy intersection (AND) operation. For instance, using the product implementation of AND, we obtain \(f_s = f_l*f_r\), in which case \(f_s \leq {\rm Min}\{f_l, f_r\}\).

  3. This method is conceptually similar to a defuzzification method in fuzzy logic (Pfluger et al., 1992).

References

  • Gennery, D.B. 1999. Traversability analysis and path planning for a planetary rover. Autonomous Robots, 6: 131–146.

    Google Scholar 

  • Goldberg, S.B., Maimone, M.W., and Matthies, L.H. 2002. Stereo vision and rover navigation software for planetary exploration. In Proc. IEEE Aerospace Conf., Big Sky.

  • Guven, M.K. and Passino, K.M. 2001. Avoiding exponential parameter growth in fuzzy systems. IEEE Trans. on Fuzzy Systems, 9(1): 194–199.

    Google Scholar 

  • Hagras, H., et al. 2002. Online learning and adaptation of autonomous mobile robots for sustainable agriculture. Autonomous Robots, 13(1): 37–52.

    Google Scholar 

  • Howard, A. and Seraji, H. 2001a. An intelligent terrain-based navigation system for planetary rovers. IEEE Robotics and Automation Magazine, 8(4): 9–17.

  • Howard, A. and Seraji, H. 2001b. Vision-based terrain characterization and traversability assessment. Journal of Robotic Systems, 18(10): 577–587.

  • Lacroix, S., Chatila, R., Fleury, S., Herrb, M., and Simeon, T. 1994. Autonomous navigation in outdoor environment: Adaptive approach and experiment. In Proc. IEEE Int. Conf. Robotics and Automation, San Diego, CA, pp. 426–432.

  • Langer, D., Rosenblatt, J. K., and Hebert, M. 1994. A behavior-based system for off-road navigation. IEEE Transactions on Robotics and Automation, 10: 776–783.

    Google Scholar 

  • Martin-Alvarez, A., Volpe, R., Hayati, S., and Petras, R. 1999. Fuzzy reactive piloting for continuous driving of long range autonomous planetary micro-rovers. In Proc. IEEE Aerospace Conference, Aspen.

  • Ojeda, L., Reina, G., and Borenstein, J. 2004. Experimental results from FLEXnav: An expert rule-based dead-reckoning system for Mars rovers. In Proc. IEEE Aerospace Conference, Big Sky.

  • Passino, K.M. and Yurkovich, S. 1997. Fuzzy Control. Addison-Wesley Publishing Company, CA.

  • Pfluger, N., Yen, J., and Langari, R. 1992. A defuzzification strategy for a fuzzy logic controller employing prohibitive information in command formulation. In Proc. IEEE Inter. Conf. on Fuzzy Systems, San Diego, pp. 717–723.

  • Saffiotti, A. 1997. The uses of fuzzy logic in autonomous robot navigation. Journal of Soft Computing, 1(4): 180–197.

    Google Scholar 

  • Seraji, H. 1999. Traversability Index: A new concept for planetary rovers. In Proc. IEEE Inter. Conf. on Robotics and Automation, vol. 3, pp. 2006–2013, Detroit.

  • Seraji, H. 2000. Fuzzy Traversability Index: A new concept for terrain-based navigation. Journal of Robotic Systems, 17(2): 75–91.

    Google Scholar 

  • Seraji, H. 2003. New traversability indices and traversability grid for integrated sensor/map-based navigation. Journal of Robotic Systems, 20(3): 121–134.

    Google Scholar 

  • Seraji, H. 2005. SmartNav: A rule-free fuzzy approach to rover navigation. Journal of Robotic Systems, 22(12): 795–808.

    Google Scholar 

  • Seraji, H. and Howard, A. 2002. Behavior-based robot navigation on challenging terrain: A fuzzy logic approach. IEEE Transactions on Robotics and Automation, 18(3): 308–321.

    Google Scholar 

  • Shirkhodaie, A., Amrani, R., and Tunstel, E. 2004. Visual terrain mapping for traversable path planning of mobile robots. In Proc. SPIE Conf.

  • Simmons, R., et al. 1995. Experience with rover navigation for Lunar-like terrains. In Proc. IEEE/RSJ Inter. Conf. on Intelligent Robots and Systems (IROS), Pittsburgh, pp. 441–446.

  • Singh, S., et al. 2000. Recent progress in local and global traversability for planetary rovers. In Proc. IEEE Inter. Conf. on Robotics and Autonomous, San Francisco, CA, vol. 2, pp. 1194–1200.

  • Tunstel, E. 1995. Coordination of distributed fuzzy behaviors in mobile robot control. In Proc. IEEE Inter. Conf. Systems, Man, and Cybernetics, Vancouver, Canada, pp. 4009–4014.

  • Ye, C. and Borenstein, J. 2004. T-Transformation: Traversability analysis for navigation on rugged terrain. In Proceedings of the Defense and Security Symposium, Unmanned Ground Vehicle Technology VI (OR54), Orlando, FL.

  • Werger, B. 2000. Ayllu: Distributed port-arbitrated behavior-based control. In Distributed Autonomous Robotic Systems 4, L. Parker, G. Bekey, and J. Barhen (eds.), Springer, pp. 25–34.

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Acknowledgement

The research described in this paper was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration

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Seraji, H., Werger, B. Theory and experiments in SmartNav rover navigation. Auton Robot 22, 165–182 (2007). https://doi.org/10.1007/s10514-006-9011-x

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Navigation