Skip to main content
Log in

Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles

  • Published:
Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper presents a new approach to guaranteeing collision avoidance with respect to moving obstacles that have constrained dynamics but move unpredictably. Velocity Obstacles have been used previously to plan trajectories that avoid collisions with obstacles under the assumption that the trajectories of the objects are either known or can be accurately predicted ahead of time. However, for real systems this predicted trajectory will typically only be accurate over short time-horizons. To achieve safety over longer time periods, this paper instead considers the set of all reachable points by an obstacle assuming that the dynamics fit the unicycle model, which has known constant forward speed and a maximum turn rate (sometimes called the Dubins car model). This paper extends the Velocity Obstacle formulation by using reachability sets in place of a single “known” trajectory to find matching constraints in velocity space, called Velocity Obstacle Sets. The Velocity Obstacle Set for each obstacle is equivalent to the union of all velocity obstacles corresponding to any dynamically feasible future trajectory, given the obstacle’s current state. This region remains bounded as the time horizon is increased to infinity, and by choosing control inputs that lie outside of these Velocity Obstacle Sets, it is guaranteed that the host agent can always actively avoid collisions with the obstacles, even without knowing their exact future trajectories. Furthermore it is proven that, subject to certain initial conditions, an iterative planner under these constraints guarantees safety for all time. Such an iterative planner is implemented and demonstrated in simulation.

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

Access this article

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

  • Bekris, K. E. (2010). Avoiding inevitable collision states: Safety and computational efficiency in replanning with sampling-based algorithms. In Workshop on “guaranteeing safe navigation in dynamic environments”, international conference on robotics and automation (ICRA-10), Anchorage, AK, May 2010.

    Google Scholar 

  • Borowko, P., Rzymowski, W., & Stachura, A. (1988). Evasion from many pursuers in the simple motion case. Journal of Mathematical Analysis and Applications, 135(1), 75–80.

    Article  MathSciNet  MATH  Google Scholar 

  • Chitsaz, H., & LaValle, S. (2007). Time-optimal paths for a Dubins airplane. In 46th IEEE conference on decision and control (pp. 2379–2384). New York: IEEE.

    Chapter  Google Scholar 

  • Chodun, W. (1989). Differential games of evasion with many pursuers. Journal of Mathematical Analysis and Applications, 142(2), 370–389.

    Article  MathSciNet  MATH  Google Scholar 

  • Cockayne, E. J., & Hall, G. W. C. (1975). Plane motion of a particle subject to curvature constraints. SIAM Journal on Control, 13, 197.

    Article  MathSciNet  MATH  Google Scholar 

  • Dubins, L. (1957). On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematics, 79(3), 497–516.

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  • Frazzoli, E., Dahleh, M., & Feron, E. (2002). Real-time motion planning for agile autonomous vehicles. Journal of Guidance, Control, and Dynamics, 25(1), 116–129.

    Article  Google Scholar 

  • Friachard, T., & Bouraine, S. (2011). Provably safe navigation for mobile robots with limited field-of-views in dynamic environments. In Workshop on “guaranteeing motion safety for robots”, robotics: science and systems conference (RSS-11), Los Angeles, CA, June 2011.

    Google Scholar 

  • Gal, O., Shiller, Z., & Rimon, E. (2009). Efficient and safe on-line motion planning in dynamic environments. In IEEE international conference on robotics and automation (ICRA) (pp. 88–93).

    Chapter  Google Scholar 

  • Kuwata, Y., Fiore, G. A., Teo, J., Frazzoli, E., & How, J. P. (2008). Motion planning for urban driving using RRT. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1681–1686). 22–26 Septembet 2008.

    Google Scholar 

  • Large, F., Laugier, C., & Shiller, Z. (2005). Navigation among moving obstacles using the NLVO: Principles and applications to intelligent vehicles. Autonomous Robots, 19(2), 159–171.

    Article  Google Scholar 

  • L’Esperance, B., Kazemi, M., & Gupta, K. (2011). Analyzing safety for mobile robots in partially known dynamic indoor environments. In Workshop on “guaranteeing motion safety for robots” at the robotics: science and systems conference (RSS-11), Los Angeles, CA, June 2011. http://safety2011.inrialpes.fr/.

    Google Scholar 

  • Mitchell, I., Bayen, A., & Tomlin, C. (2005). A time-dependent Hamilton-Jacobi formulation of reachable sets for continuous dynamic games. IEEE Transactions on Automatic Control, 50(7), 947–957.

    Article  MathSciNet  Google Scholar 

  • Pashkov, A., & Sinitsyn, A. (1995). Construction of the value function in a pursuit-evasion game with three pursuers and one evader. Journal of Applied Mathematics and Mechanics, 59(6), 941–949.

    Article  MathSciNet  MATH  Google Scholar 

  • Petti, S., & Fraichard, T. (2005). Safe motion planning in dynamic environments. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 2210–2215). New York: IEEE.

    Google Scholar 

  • Qu, Z., Wang, J., & Plaisted, C. (2004). A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles. IEEE Transactions on Robotics, 20(6), 978–993.

    Article  Google Scholar 

  • Rzymowski, W. (1986). Evasion along each trajectory in differential games with many pursuers. Journal of Differential Equations, 62(3), 334–356.

    Article  MathSciNet  MATH  Google Scholar 

  • Schouwenaars, T., How, J., & Feron, E. (2004). Receding horizon path planning with implicit safety guarantees. In Proceedings of the 2004 American control conference (Vol. 6, pp. 5576–5581). New York: IEEE.

    Google Scholar 

  • Shiller, Z., Large, F., & Sekhavat, S. (2005). Motion planning in dynamic environments: Obstacles moving along arbitrary trajectories. In IEEE international conference on robotics and automation (vol. 4, pp. 3716–3721). New York: IEEE.

    Google Scholar 

  • Sinitsyn, A. V. (1993). Construction of the value function in a game of approach with several pursuers. Journal of Applied Mathematics and Mechanics, 57(1), 59–65.

    Article  MathSciNet  Google Scholar 

  • Tomlin, C., Pappas, G., & Sastry, S. (1998). Conflict resolution for air traffic management: a study in multiagent hybrid systems. IEEE Transactions on Automatic Control, 43, 509–521.

    Article  MathSciNet  MATH  Google Scholar 

  • Van Den Berg, J., Ferguson, D., & Kuffner, J. (2006). Anytime path planning and replanning in dynamic environments. In IEEE international conference on robotics and automation (ICRA) (pp. 2366–2371).

    Google Scholar 

  • Van den Berg, J., Lin, M., & Manocha, D. (2008). Reciprocal velocity obstacles for real-time multi-agent navigation. In IEEE international conference on robotics and automation (ICRA) (pp. 1928–1935).

    Chapter  Google Scholar 

  • Vatcha, R., & Xiao, J. (2009). Perceiving guaranteed continuously collision-free robot trajectories in an unknown and unpredictable environment. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1433–1438). New York: IEEE.

    Google Scholar 

  • Vendittelli, M., Laumond, J., & Nissoux, C. (1999). Obstacle distance for car-like robots. IEEE Transactions on Robotics and Automation, 15(4), 678–691.

    Article  Google Scholar 

  • Wilkie, D., van den Berg, J., & Manocha, D. (2009). Generalized velocity obstacles. In IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 5573–5578). New York: IEEE.

    Google Scholar 

  • Wu, A. (2011). Guaranteed avoidance of unpredictable, dynamically constrained obstacles using velocity obstacle sets. Master’s thesis, MIT Department of Aeronautics and Astronautics, May 2011. Available online at http://acl.mit.edu/papers/WuSM.pdf.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Albert Wu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wu, A., How, J.P. Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles. Auton Robot 32, 227–242 (2012). https://doi.org/10.1007/s10514-011-9266-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-011-9266-8

Keywords

Navigation