Robust Collision Avoidance for Autonomous Mobile Robots in Unknown Environments

  • Muhannad Mujahed
  • Dirk Fischer
  • Bärbel Mertsching
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9776)

Abstract

This paper presents a new collision avoidance method for mobile robots operating in unknown cluttered environments. The proposed method computes the steering angle based on the location of all obstacles surrounding the robot, not just the closest one. Hence, our technique is capable of generating smooth robot trajectories, particularly for unstructured environments. Moreover, the stability of the robot’s motion is improved by providing a smoother bridge between avoiding obstacles and approaching the goal. Oscillations occurring in narrow corridors are reduced by considering the distribution of obstacles to both sides of the direction of motion. Simulation and experimental results are presented to demonstrate the performance of the proposed approach.

References

  1. 1.
    Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. 5, 90–98 (1986)CrossRefGoogle Scholar
  2. 2.
    Rezaee, H., Abdollahi, F.: Adaptive artificial potential field approach for obstacle avoidance of unmanned aircrafts. In: 2012 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (Kachsiung), pp. 1–6, July 2012Google Scholar
  3. 3.
    Ren, J., McIsaac, K.A., Patel, R.V.: Modified newtons method applied to potential field based navigation for nonholonomic robots in dynamic environments. Robotica 26, 117–127 (2008)Google Scholar
  4. 4.
    Panagou, D.: Motion planning and collision avoidance using navigation vector fields. In: 2014 IEEE International Conference on Robotics and Automation, (Hong Kong, China), pp. 2513–2518, May 2014Google Scholar
  5. 5.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Aut. Mag. 4(1), 23–33 (1997)CrossRefGoogle Scholar
  6. 6.
    Seder, M., Petrovic, I.: Dynamic window based approach to mobile robot motion control in the presence of moving obstacles. In: IEEE International Conference on Robotics and Automation (ICRA), (Roma, Italy), pp. 1986–1991, April 2007Google Scholar
  7. 7.
    Simmons, R.: The curvature-velocity method for local obstacle avoidance. In: IEEE International Conference on Robotics and Automation (ICRA), (Minnosota, USA), pp. 3375–3382, April 1996Google Scholar
  8. 8.
    Shi, C., Wang, Y., Yang, J.: A local obstacle avoidance method for mobile robots in partially known environment. Robot. Auton. Syst. 58, 425–434 (2010)CrossRefGoogle Scholar
  9. 9.
    Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Rob. Res. 17, 760–772 (1998)CrossRefGoogle Scholar
  10. 10.
    Wu, A., How, J.P.: Guaranteed infinite horizon avoidance of unpredictable, dynamically constrained obstacles. Aut. Rob. 32(3), 227–242 (2012)CrossRefGoogle Scholar
  11. 11.
    Fraichard, T., Asama, H.: Inevitable collision states - a step towards safer robots? Adv. Robot. 18(10), 1001–1024 (2004)CrossRefGoogle Scholar
  12. 12.
    Lawitzky, A., Nicklas, A., Wollherr, D., Buss, M.: Determining states of inevitable collision using reachability analysis. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (USA), pp. 4142–4147, September 2014Google Scholar
  13. 13.
    Berg, J., Lin, M.C., Manocha, D.: Reciprocal velocity obstacles for real-time multi-agent navigation. In: IEEE International Conference on Robotics and Automation (ICRA), (Pasadena, CA), pp. 1928–1935, May 2008Google Scholar
  14. 14.
    Bareiss, D., Berg, J.: Generalized reciprocal collision avoidance. Int. J. Robot. Res. 34, 1501–1514 (2015)CrossRefGoogle Scholar
  15. 15.
    Jin, J., Kim, Y., Wee, S., Gans, N.: Decentralized cooperative mean approach to collision avoidance for nonholonomic mobile robots. In: IEEE International Conference on Robotics and Automation, (USA), pp. 35–41, May 2015Google Scholar
  16. 16.
    Shiller, Z., Sharma, S.: High speed on-line motion planning in cluttered environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Vilamoura, Portugal), pp. 596–601, October 2012Google Scholar
  17. 17.
    Minguez, J., Montano, L.: Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios. IEEE Trans. Robot. Autom. 20(1), 45–59 (2004)CrossRefGoogle Scholar
  18. 18.
    Minguez, J., Osuna, J., Montano, L.: A “divide and conquer” strategy based on situations to achieve reactive collision avoidance in troublesome scenarios. In: IEEE International Conference on Robotics and Automation, pp. 3855–3862 (2004)Google Scholar
  19. 19.
    Durham, J.W., Bullo, F.: Smooth nearness-diagram navigation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Nice, France), pp. 690–695, September 2008Google Scholar
  20. 20.
    Mujahed, M., Fischer, D., Mertsching, B., Jaddu, H.: Closest Gap based (CG) reactive obstacle avoidance navigation for highly cluttered environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (Taipei, Taiwan), pp. 1805–1812, October 2010Google Scholar
  21. 21.
    Mujahed, M., Jaddu, H., Fischer, D., Mertsching, B.: Tangential closest Gap based (TCG) reactive obstacle avoidance navigation for cluttered environments. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), (Linköping, Sweden), pp. 1–6, October 2013Google Scholar
  22. 22.
    Ferreira, A., Pereira, F.G., Vassallo, R.F., Sarcinelli-Filho, M., Bastos-Filho, T.F.: An approach to avoid obstacles in mobile robot navigation: the tangential escape. SBA. Sociedade Brasileira de Automatica 19, 395–405 (2008)CrossRefGoogle Scholar
  23. 23.
    Mujahed, M., Fischer, D., Mertsching, B.: Safe Gap based (SG) reactive navigation for mobile robots. In: European Conference on Mobile Robots (ECMR), (Barcelona, Spain), pp. 325–330, June 2013Google Scholar
  24. 24.
    Mujahed, M., Fischer, D., Mertsching, B.: Smooth reactive collision avoidance in difficult environments. In: IEEE Conference on Robotics and Biomimetics (ROBIO), (Zhuhai, China), pp. 1471–1476, December 2015Google Scholar
  25. 25.
    Quigley, M., Conley, K., Gerkey, B.P., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: ROS: an open-source robot operating system. In: ICRA Workshop on Open Source Software (2009)Google Scholar
  26. 26.
    Munoz, N., Valencia, J., Londono, N.: Evaluation of navigation of an autonomous mobile robot. In: Proceedings of International Workshop on Performance Metrics for Intelligent Systems Workshop (PerMIS), pp. 15–21 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Muhannad Mujahed
    • 1
  • Dirk Fischer
    • 1
  • Bärbel Mertsching
    • 1
  1. 1.GET LabUniversity of PaderbornPaderbornGermany

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