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Modeling dynamic scenarios for local sensor-based motion planning

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Abstract

This paper addresses the modeling of the static and dynamic parts of the scenario and how to use this information with a sensor-based motion planning system. The contribution in the modeling aspect is a formulation of the detection and tracking of mobile objects and the mapping of the static structure in such a way that the nature (static/dynamic) of the observations is included in the estimation process. The algorithm provides a set of filters tracking the moving objects and a local map of the static structure constructed on line. In addition, this paper discusses how this modeling module is integrated in a real sensor-based motion planning system taking advantage selectively of the dynamic and static information. The experimental results confirm that the complete navigation system is able to move a vehicle in unknown and dynamic scenarios. Furthermore, the system overcomes many of the limitations of previous systems associated to the ability to distinguish the nature of the parts of the scenario.

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References

  • Andrade-Cetto, J., & Sanfeliu, A. (2002). Concurrent map building and localization on indoor dynamic environments. International Journal on Pattern Recognition and Artificial Intelligence, 16, 361–374.

    Article  Google Scholar 

  • Arkin, R. C. (1999). Behavior-based robotics. Cambridge: MIT Press.

    Google Scholar 

  • Bar-Shalom, Y., & Fortmann, T. E. (1988). Mathematics in science and engineering. Tracking and data association. New York: Academic Press.

    MATH  Google Scholar 

  • Bar-Shalom, Y., Li, X. R., & Kirubarajan, T. (2001). Estimation with applications to tracking and navigation. New York: Wiley.

    Google Scholar 

  • Bengtsson, O., & Baerveldt, A.-J. (1999). Localization in changing environments by matching laser range scans. In EURobot (pp. 169–176).

  • Besl, P. J., & McKay, N. D. (1992). A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239–256.

    Article  Google Scholar 

  • Biber, P., & Duckett, T. (2005). Dynamic maps for long-term operation of mobile service robots. In Robotics: science and systems, 8–10 June 2005.

  • Biber, P., & Strafler, W. (2003). The normal distributions transform: a new approach to laser scan matching. In IEEE international conference on intelligent robots and systems, Las Vegas, USA.

  • Biswas, R., Limketkai, B., Sanner, S., & Thrun, S. (2002). Towards object mapping in dynamic environments with mobile robots. In Proceedings of the conference on intelligent robots and systems (IROS), Lausanne, Switzerland.

  • Blackman, S., & Popoli, R. (1999). Design and analysis of modern tracking systems. Norwood: Artech House.

    MATH  Google Scholar 

  • Brock, O., & Khatib, O. (1999). High-speed navigation using the global dynamic window approach. In IEEE international conference on robotics and automation (pp. 341–346), Detroit, MI.

  • Canny, J., & Reif, J. (1987). New lower bound techniques for robot motion planning problems. In Proceedings of the 27th annual IEEE symposium on the foundations of computer science (pp. 49–60).

  • Castellanos, J. A., & Tardós, J. D. (1999). Mobile robot localization and map building: a multisensor fusion approach. Boston: Kluwer Academic.

    Google Scholar 

  • Cheeseman, P., & Smith, P. (1986). On the representation and estimation of spatial uncertainty. International Journal of Robotics, 5, 56–68.

    Article  Google Scholar 

  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Part B, 39, 1–38.

    MATH  MathSciNet  Google Scholar 

  • Doucet, A., Godsill, S. J., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208.

    Article  Google Scholar 

  • Elfes, A. (1989). Occupancy grids: a probabilistic framework for robot perception. PhD thesis.

  • Fiorini, P., & Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles. International Journal of Robotic Research, 17(7), 760–772.

    Article  Google Scholar 

  • Foisy, A., Hayward, V., & Aubry, S. (1990). The use of awareness in collision prediction. In IEEE international conference on robotics and automation.

  • Fraichard, T., & Asama, H. (2004). Inevitable collision states. A step towards safer robots? Advanced Robotics, 18(10), 1001–1024.

    Article  Google Scholar 

  • Gutmann, J.-S., & Konolige, K. (1999). Incremental mapping of large cyclic environments. In Conference on intelligent robots and applications (CIRA), Monterey, CA.

  • Hähnel, D. (2004). Mapping with mobile robots. PhD thesis, University of Freiburg.

  • Hähnel, D., Schulz, D., & Burgard, W. (2002). Map building with mobile robots in populated environments. In Proceedings  of the IEEE/RSJ international conference on intelligent robots and systems.

  • Hähnel, D., Triebel, R., Burgard, W., & Thrun, S. (2003). Map building with mobile robots in dynamic environments. In Proceedings  of the IEEE international conference on robotics and automation (ICRA).

  • Jensen, B., & Siegwart, R. (2004). Scan alignment with probabilistic distance metric. In Proceedings of the IEEE-RSJ international conference on intelligent robots and systems, Sendai, Japan.

  • Koenig, S., & Likhachev, M. (2002). Improved fast replanning for robot navigation in unknown terrain. In International conference on robotics and automation, Washington, USA.

  • Lu, F., & Milios, E. (1997a). Globally consistent range scan alignment for environment mapping. Autonomous Robots, 4, 333–349.

    Article  Google Scholar 

  • Lu, F., & Milios, E. (1997b). Robot pose estimation in unknown environments by matching 2D range scans. Intelligent and Robotic Systems, 18, 249–275.

    Article  Google Scholar 

  • Martinez-Cantin, R., de Freitas, N., & Castellanos, J. A. (2007). Analysis of particle methods for simultaneous robot localization and mapping and a new algorithm: Marginal-slam. In Proceedings of the IEEE international conference on robotics and automation (ICRA), Rome, Italy.

  • McLachlan, G., & Krishnan, T. (1997). Wiley series in probability and statistics. The EM algorithm and extensions. New York: Wiley.

    MATH  Google Scholar 

  • Minguez, J., & Montano, L. (2002). Robot navigation in very complex dense and cluttered indoor/outdoor environments. In 15th IFAC world congress, Barcelona, Spain.

  • Minguez, J., & Montano, L. (2004). Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios. IEEE Transactions on Robotics and Automation, 20(1), 45–59.

    Article  Google Scholar 

  • Minguez, J., & Montano, L. (2005). Sensor-based robot motion generation in unknown, dynamic and troublesome scenarios. Robotics and Autonomous Systems, 52(4), 290–311.

    Article  Google Scholar 

  • Minguez, J., Montano, L., & Santos-Victor, J. (2006a). Abstracting the vehicle shape and kinematic constraints from the obstacle avoidance methods. Autonomous Robots, 20, 43–59.

    Article  Google Scholar 

  • Minguez, J., Montesano, L., & Lamiraux, F. (2006b). Metric-based iterative closest point scan matching for sensor displacement estimation. IEEE Transactions on Robotics, 22(5), 1047–1054.

    Article  Google Scholar 

  • Modayil, J., & Kuipers, B. (2004a). Bootstrap learning for object discovery. In IEEE/RSJ international conference on intelligent robots and systems (IROS-04), Sendai, Japan.

  • Modayil, J., & Kuipers, B. (2004b). Towards bootstrap learning for object discovery. In AAAI-2004 workshop on anchoring symbols to sensor data.

  • Montesano, L. (2006). Detection and tracking of moving objects from a mobile platform. application to navigation and multi-robot localization. PhD thesis, Universidad de Zaragoza.

  • Montesano, L., Minguez, J., & Montano, L. (2005). Probabilistic scan matching for motion estimation in unstructured environments. In IEEE international conference on intelligent robots and systems (IROS).

  • Montesano, L., Minguez, J., & Montano, L. (2006). Lessons learned in integration for sensor-based robot navigation systems. International Journal of Advanced Robotic Systems, 3(1), 85–91.

    Google Scholar 

  • Moravec, H. P., & Elfes, A. (1985). High resolution maps from wide angle sonar. In IEEE international conference on robotics and automation (pp. 116–121), March 1985.

  • Murphy, K. (2002). Dynamic Bayesian networks: representation, inference and learning. PhD thesis, UC Berkeley, Computer Science Division.

  • Owen, E., & Montano, L. (2005). Motion planning in dynamic environments using the velocity space. In Proceedings of the IEEE-RSJ international conference on intelligent robots and systems, Edmonton, AB (CA), August 2005.

  • Philipsen, R., & Siegwart, R. (2003). Smooth and efficient obstacle avoidance for a tour guide robot. In IEEE international conference on robotics and automation, Taipei, Taiwan.

  • Ratering, S., & Gini, M. (1993). Robot navigation in a known environment with unknown moving obstacles. In International conference on robotics and automation (pp. 25–30), Atlanta, USA.

  • Schlegel, C. (2004). Navigation and execution for mobile robots in dynamic environments—an integrated approach. PhD thesis, University of Ulm.

  • Schulz, D., Burgard, W., Fox, D., & Cremers, A. (2001). Tracking multiple moving targets with a mobile robot using particle filters and statistical data association. In IEEE international conference on robotics and automation, Seoul, Korea.

  • Schulz, D., Burgard, W., Fox, D., & Cremers, A. B. (2003). People tracking with a mobile robot using sample-based joint probabilistic data association filters. International Journal of Robotics Research (IJRR), 22(2), 99–116.

    Article  Google Scholar 

  • Stachniss, C., & Burgard, W. (2002). An integrated approach to goal-directed obstacle avoidance under dynamic constraints for dynamic environments. In IEEE-RSJ international conference on intelligent robots and systems (pp. 508–513), Switzerland.

  • Stachniss, C., & Burgard, W. (2005). Mobile robot mapping and localization in non-static environments. In Proceedings  of the national conference on artificial intelligence (AAAI), Pittsburgh, PA, USA.

  • Thrun, S., Burgard, W., & Fox, D. (2000). A real time algorithm for mobile robot mapping with applications to multi-robot and 3d mapping. In IEEE international conference on robotics and automation, San Francisco, USA.

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Ulrich, I., & Borenstein, J. (2000). VFH*: local obstacle avoidance with look-ahead verification. In IEEE international conference on robotics and automation (pp. 2505–2511), San Francisco, USA.

  • Wang, C., & Thorpe, C. (2002). Simultaneous localization and mapping with detection and tracking of moving objects. In IEEE international conference on robotics and automation, Washington, USA.

  • Wang, C.-C., Thorpe, C., & Thrun, S. (2003). Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas. In Proceedings of the IEEE international conference on robotics and automation (ICRA).

  • Wang, C., Thorpe, C., Thrun, S., Hebert, M., & Durrant-Whyte, H. (2007). Simultaneous localization, mapping and moving object tracking. International Journal of Robotics Research, 26(9), 889–916.

    Article  Google Scholar 

  • Wolf, D. F., & Sukhatme, G. S. (2005). Mobile robot simultaneous localization and mapping in dynamic environments. Autonomous Robots, 19(1), 53–65.

    Article  Google Scholar 

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Authors and Affiliations

  1. Instituto de Sistemas e Robotica, Instituto Superior Tecnico, Lisboa, Portugal

    Luis Montesano

  2. Dpto de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Zaragoza, Spain

    Javier Minguez & Luis Montano

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  1. Luis Montesano
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  2. Javier Minguez
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  3. Luis Montano
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Correspondence to Luis Montesano.

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Montesano, L., Minguez, J. & Montano, L. Modeling dynamic scenarios for local sensor-based motion planning. Auton Robot 25, 231–251 (2008). https://doi.org/10.1007/s10514-008-9092-9

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