Skip to main content
SpringerLink
Log in

Safety assessment of robot trajectories for navigation in uncertain and dynamic environments

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper presents a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects. For safety assessment, the overall collision probability is used to rank candidate trajectories by considering the probability of colliding with known objects as well as the estimated collision probability beyond the planning horizon. In addition, we introduce a safety assessment cost metric, the probabilistic collision cost, which considers the relative speeds and masses of multiple moving objects in which the robot may possibly collide with. The collision probabilities with other objects are estimated by probabilistic reasoning about their future motion trajectories as well as the ability of the robot to avoid them. The results are integrated into a navigation framework that generates and selects trajectories that strive to maximize safety while minimizing the time to reach a goal location. An example implementation of the proposed framework is applied to simulation scenarios, that explores some of the inherent computational trade-offs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alonso-Mora, J., Breitenmoser, A., Rufli, M., Beardsley, P., & Siegwart, R. (2010). Optimal reciprocal collision avoidance for multiple non-holonomic robots. In A. Martinoli & F. Mondada (Eds.), Proc. of the 10th international symposium on distributed autonomous robotic systems (DARS). Berlin: Springer.

    Google Scholar 

  • Althoff, D., Althoff, M., Wollherr, D., & Buss, M. (2010). Probabilistic collision state checker for crowded environments. In Proc. of the IEEE int. conference on robotics and automation.

    Google Scholar 

  • Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003). An introduction to mcmc for machine learning. Machine Learning, 50, 5–43. doi:10.1023/A:1020281327116.

    Article  MATH  Google Scholar 

  • Bauer, A., Klasing, K., Lidoris, G., Mühlbauer, Q., Rohrmüller, F., Sosnowski, S., Xu, T., Kühnlenz, K., Wollherr, D., & Buss, M. (2009). The autonomous city explorer: towards natural human-robot interaction in urban environments. International Journal of Social Robotics, 1(2), 127–140.

    Article  Google Scholar 

  • Bautin, A., Martinez-Gomez, L., & Fraichard, T. (2010). Inevitable collision states: a probabilistic perspective. In Proc. of the IEEE int. conference on robotics and automation.

    Google Scholar 

  • Bennewitz, M. (2004). Mobile robot navigation in dynamic environments. Ph.D. thesis, University of Freiburg, Department of Computer Science.

  • Broadhurst, A., Baker, S., & Kanade, T. (2005). Monte Carlo road safety reasoning. In Proc. of the IEEE intelligent vehicle symposium.

    Google Scholar 

  • Cappe, O., Godsill, S., & Moulines, E. (2007). An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE, 95(5), 899–924. doi:10.1109/JPROC.2007.893250.

    Article  Google Scholar 

  • Chan, N., Kuffner, J. J., & Zucker, M. (2008). Improved motion planning speed and safety using regions of inevitable collision. In 17th CISM-IFToMM symposium on robot design, dynamics, and control.

    Google Scholar 

  • Danielsson, S., Petersson, L., & Eidehall, A. (2007). Monte Carlo based threat assessment: analysis and improvements. In Proc. of the IEEE conference on intelligent vehicles symposium.

    Google Scholar 

  • Eidehall, A., & Petersson, L. (2008). Statistical threat assessment for general road scenes using Monte Carlo sampling. IEEE Transactions on Intelligent Transportation Systems, 9, 137–147.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Fraichard, T. (1999). Trajectory planning in a dynamic workspace: a ‘state-time space’ approach. Advanced Robotics, 13, 75–94.

    Article  Google Scholar 

  • Fraichard, T. (2007). A short paper about motion safety. In Proc. of the IEEE international conference on robotics and automation (pp. 1140–1145).

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  • Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings. Part F. Radar and Signal Processing, 140(2), 107–113.

    Article  Google Scholar 

  • Gottschalk, S., Lin, M. C., & Manocha, D. (1996). Obb-tree: a hierarchical structure for rapid interference detection. In Proc. on ACM Siggraph 96.

    Google Scholar 

  • Gross, H. M., Boehme, H. J., Schroeter, C., Mueller, S., Koenig, A., Martin, C., Merten, M., & Bley, A. (2008). Shopbot: progress in developing an interactive mobile shopping assistant for everyday use. In Proc. int. conf. on systems, man and cybernetics (SMC).

    Google Scholar 

  • Handschin, J., & Mayne, D. (1969). Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering. International Journal of Control, 9, 547–559.

    Article  MathSciNet  MATH  Google Scholar 

  • Helbing, D., & Molnar, P. (1995). Social force model for pedestrian dynamics. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 51, 4282–4286.

    Article  Google Scholar 

  • Jansson, J. (2005). Collision avoidance theory with application to automotive collision mitigation. Ph.D. thesis, Linköping University.

  • Jiang, R., Tian, X., Xie, L., & Chen, Y. (2008). A robot collision avoidance scheme based on the moving obstacle motion prediction. In Proc. of int. colloquium on computing, communication, control, and management (Vol. 2, pp. 341–345). doi:10.1109/CCCM.2008.347.

    Chapter  Google Scholar 

  • Kluge, B., & Prassler, E. (2004). Reflective navigation: Individual behaviors and group behaviors. In Proc. of the IEEE int. conference on robotics and automation (pp. 4172–4177).

    Google Scholar 

  • Kluge, B., & Prassler, E. (2006). Recursive probabilistic velocity obstacles for reflective navigation. In S. Yuta, H. Asama, E. Prassler, T. Tsubouchi, & S. Thrun (Eds.), Springer tracts in advanced robotics: Vol. 24. Field and service robotics (pp. 71–79). Berlin: Springer.

    Chapter  Google Scholar 

  • Lambert, A., Gruyer, D., Pierre, G. S., & Ndjeng, A. N. (2008). Collision probability assessment for speed control. In ITSC (pp. 1043–1048).

    Google Scholar 

  • Latombe, J. C. (1991). Robot motion planning. Norwell: Kluwer Academic.

    Book  Google Scholar 

  • Liu, J. S., & Chen, R. (1998). Sequential Monte Carlo methods for dynamic systems. Journal of the American Statistical Association, 93, 1032–1044.

    Article  MathSciNet  MATH  Google Scholar 

  • Petti, S., & Fraichard, T. (2005). Safe motion planning in dynamic environments. In Proc. of the IEEE int. conference on intelligent robots and systems.

    Google Scholar 

  • Philippsen, R. (2004). Motion planning and obstacle avoidance for mobile robots in highly cluttered dynamic environments. Ph.D. thesis, ETH Zürich, Institute of Robotics and Intelligent Systems.

  • Prassler, E., Scholz, J., & Strobel, M. (1998). Maid: mobility assistance for elderly and disabled people. In Proc. of the 24th annual conference of industrial electronics society.

    Google Scholar 

  • Reif, J., & Sharir, M. (1994). Motion planning in the presence of moving obstacles. Journal of the ACM, 41, 764–790.

    Article  MATH  Google Scholar 

  • Rubinstein, R. Y., & Kroese, D. P. (1981). Simulation and the Monte Carlo method. New York: Wiley-Interscience.

    Book  MATH  Google Scholar 

  • Snape, J., van den Berg, J., Guy, S. J., & Manocha, D. (2010). Smooth and collision-free navigation for multiple robots under differential-drive constraints. In Proc. of the IEEE international conference on intelligent robots and systems.

    Google Scholar 

  • Sud, A., Gayle, R., Andersen, E., Guy, S., Lin, M., & Manocha, D. (2007). Real-time navigation of independent agents using adaptive roadmaps. In ACM symposium on virtual reality software and technology.

    Google Scholar 

  • Thompson, S., Horiuchi, T., & Kagami, S. (2009). A probabilistic model of human motion and navigation intent for mobile robot path planning. In Proc. of int. conference on autonomous robots and agents (pp. 663–668), doi:10.1109/ICARA.2000.4803931.

    Google Scholar 

  • Urmson, C., Baker, C., Dolan, J. M., Rybski, P., Salesky, B., Whittaker, W. R. L., Ferguson, D., & Darms, M. (2009). Autonomous driving in traffic: Boss and the urban challenge. AI Magazine, 30, 17–29.

    Google Scholar 

  • van den Berg, J. (2007). Path planning in dynamic environments. Ph.D. thesis, Utrecht University, Utrecht, The Netherlands.

  • van den Berg, J., Patil, S., Sewall, J., Manocha, D., & Lin, M. (2008). Interactive navigation of individual agents in crowded environments. In Symposium on interactive 3D graphics and games (I3D).

    Google Scholar 

  • van den Berg, J., Guy, S. J., Lin, M. C., & Manocha, D. (2009). Reciprocal n-body collision avoidance. In International symposium on robotics research.

    Google Scholar 

  • Vasquez, D., Fraichard, T., & Laugier, C. (2009). Incremental learning of statistical motion patterns with growing hidden Markov models. IEEE Transactions on Intelligent Transportation Systems, 10, 403–416.

    Article  Google Scholar 

  • Trautman, P., & Krause, A. (2010). Unfreezing the robot: Navigation in dense, interacting crowds. In Proc. of the IEEE int. conference on intelligent robots and systems.

    Google Scholar 

  • Ziebart, B. D., Ratliff, N., Gallagher, G., Mertz, C., Peterson, K., Martial Hebert, J. A. B., Dey, A. K., & Srinivasa, S. (2009). Planning-based prediction for pedestrians. In IEEE int. conf. on intelligent robots and systems.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Automatic Control Engineering (LSR), Technische Universität München, München, Germany

    Daniel Althoff, Dirk Wollherr & Martin Buss

  2. The Robotics Institute, Carnegie Mellon University, Pittsburg, USA

    James J. Kuffner

Authors
  1. Daniel Althoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James J. Kuffner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dirk Wollherr
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Martin Buss
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniel Althoff.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Althoff, D., Kuffner, J.J., Wollherr, D. et al. Safety assessment of robot trajectories for navigation in uncertain and dynamic environments. Auton Robot 32, 285–302 (2012). https://doi.org/10.1007/s10514-011-9257-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-011-9257-9

Keywords

Search

Navigation