Skip to main content
SpringerLink
Log in

Gap Based Elastic Trees as a Novel Approach for Fast and Reliable Obstacle Avoidance for UGVs

  • Regular paper
  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper presents a novel approach to improve the online implementation performance of existing obstacle avoidance and path planning methods for environments with no maps. The Gap Based Elastic Trees (GET) algorithm proposed in this paper, combines some features of popular algorithms in path planning, obstacle avoidance, and path smoothing, and outperforms popular obstacle avoidance approaches designed for cluttered environments, also paving the way for the use of path planners in real-time applications. The design steps of GET consists of the following steps: gap calculation, smooth path generation and trajectory planning. Even though the GET method is an obstacle avoidance method, to demonstrate its efficiency we compared with offline version of base planner (RRT) of GET and a well-known shortest path algorithm (A*). The results demonstrate that, in most cases, the GET method guides the robot on almost the shortest routes with no requirement of an environment map. The GET algorithm also outperforms the obstacle avoidance methods with proven success in cluttered environments in terms of speed and safety. Further analysis was done to see the performance of the GET method in real world scenarios. These tests both validated simulation results and revealed that GET algorithm has more smooth trajectories than the most successful alternative. Overall, the improved performance of GET can be attributed to its predictive feature, and capability to plan ahead. Additionally, GET method can be adapted both robotic and autonomous car navigation tasks.

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

Code Availability

We shared all implementations (SND, CG, TCG and GET methods) publicly at https://github.com/aykut3416/get_oa_ros.

References

  1. Fiorini, P., Shiller, Z.: Motion planning in dynamic environments using velocity obstacles. Int. J. Robot. Res. 17(7), 760–772 (1998)

    Article  Google Scholar 

  2. Minguez, J., Montano, L.: Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios. IEEE Trans. Robot. Autom. 20(1), 45–59 (2004)

    Article  Google Scholar 

  3. Kavraki, L., Svestka, P., Overmars, M.H.: Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces. vol. 1994. Unknown Publisher (1994)

  4. LaValle, S.M.: Rapidly-exploring random trees: A new tool for path planning (1998)

  5. LaValle, S.M., Kuffner Jr, J.J.: Rapidly-exploring random trees: Progress and prospects (2000)

  6. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011)

    Article  MATH  Google Scholar 

  7. Karaman, S., Walter, M.R., Perez, A., Frazzoli, E., Teller, S.: Anytime motion planning using the RRT. In: 2011 IEEE International Conference on Robotics and Automation, pp 1478–1483. IEEE (2011)

  8. Otte, M., Frazzoli, E., RRT, X.: Real-time motion planning/replanning for environments with unpredictable obstacles. In: Algorithmic Foundations of Robotics XI, pp 461–478. Springer (2015)

  9. Luders, B., Kothari, M., How, J.: Chance constrained RRT for probabilistic robustness to environmental uncertainty. In: AIAA Guidance, Navigation, and Control Conference, p 8160 (2010)

  10. Kuwata, Y., Teo, J., Fiore, G., Karaman, S., Frazzoli, E., How, J.P.: Real-time motion planning with applications to autonomous urban driving. IEEE Trans. Control Syst. Technol. 17(5), 1105–1118 (2009)

    Article  Google Scholar 

  11. Li, Y., Littlefield, Z., Bekris, K.E.: Asymptotically optimal sampling-based kinodynamic planning. Int. J. Robot. Res. 35(5), 528–564 (2016)

    Article  Google Scholar 

  12. Allen, R., Pavone, M.: A real-time framework for kinodynamic planning with application to quadrotor obstacle avoidance. In: AIAA Guidance, Navigation, and Control Conference, p 1374 (2016)

  13. Giusti, A., Guzzi, J., Cireşan, D.C., He, F.L., Rodríguez, J.P., Fontana, F., et al.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1 (2), 661–667 (2015)

    Article  Google Scholar 

  14. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al.: End to end learning for self-driving cars. arXiv:160407316 (2016)

  15. Loquercio, A., Maqueda, A.I., Del-Blanco, C.R., Scaramuzza, D.: Dronet: Learning to fly by driving. IEEE Robot. Autom. Lett. 3(2), 1088–1095 (2018)

    Article  Google Scholar 

  16. Tai, L., Li, S., Liu, M.: A deep-network solution towards model-less obstacle avoidance. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 2759–2764. IEEE (2016)

  17. Tai, L., Paolo, G., Liu, M.: Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 31–36. IEEE (2017)

  18. Xie, L., Wang, S., Rosa, S., Markham, A., Trigoni, N.: Learning with training wheels: speeding up training with a simple controller for deep reinforcement learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp 6276–6283. IEEE (2018)

  19. Fan, T., Long, P., Liu, W., Pan, J.: Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios. Int. J. Robot. Res. 39(7), 856–892 (2020)

    Article  Google Scholar 

  20. Simmons, R.: The curvature-velocity method for local obstacle avoidance. In: Proceedings of IEEE International Conference on Robotics and Automation, vol. 4, pp 3375–3382. IEEE (1996)

  21. Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4(1), 23–33 (1997)

    Article  Google Scholar 

  22. Brock, O., Khatib, O.: High-speed navigation using the global dynamic window approach. In: Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No. 99CH36288C), vol. 1, pp 341–346. IEEE (1999)

  23. Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: Autonomous Robot Vehicles, pp 396–404. Springer (1986)

  24. Borenstein, J., Koren, Y.: The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans. Robot. Autom. 7(3), 278–288 (1991)

    Article  Google Scholar 

  25. Ulrich, I., Borenstein, J.: VFH+: Reliable obstacle avoidance for fast mobile robots. In: Proceedings. 1998 IEEE international conference on robotics and automation (Cat. No. 98CH36146), vol. 2, pp 1572–1577. IEEE (1998)

  26. Minguez, J.: The obstacle-restriction method for robot obstacle avoidance in difficult environments. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 2284–2290. IEEE (2005)

  27. Sezer, V., Gokasan, M.: A novel obstacle avoidance algorithm: “Follow the Gap Method”. Robot. Autonom. Syst. 60(9), 1123–1134 (2012)

    Article  Google Scholar 

  28. Durham, J.W., Bullo, F.: Smooth nearness-diagram navigation. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 690–695. IEEE (2008)

  29. Mujahad, M., Fischer, D., Mertsching, B., Jaddu, H.: Closest Gap based (CG) reactive obstacle avoidance navigation for highly cluttered environments. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 1805–1812. IEEE (2010)

  30. Mujahed, M., Jaddu, H., Fischer, D., Mertsching, B.: Tangential closest gap based (TCG) reactive obstacle avoidance navigation for cluttered environments. In: 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pp 1–6, IEEE (2013)

  31. Mujahed, M., Fischer, D., Mertsching, B.: Tangential Gap Flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments. Robot. Autonom. Syst. 84, 15–30 (2016)

    Article  Google Scholar 

  32. Ester, M., Kriegel, H.P., Sander, J., Xu, X., et al.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp 226–231 (1996)

  33. Çakmak, E., Tekin, S., Özdemir, A., Boǧosyan, S.: Gap based novel approach for safe and fast obstacle avoidance for autonomous platforms. In: 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), pp 1392–1397. IEEE (2020)

Download references

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Mechatronics Engineering Department, Istanbul Technical University, Istanbul, 34469, Turkey

    Aykut Özdemir

  2. Control and Automation Engineering Department, Istanbul Technical University, Istanbul, 34469, Turkey

    Seta O. Bogosyan

Authors
  1. Aykut Özdemir
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Seta O. Bogosyan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study conception and design. This work is a part of PhD thesis of Aykut Özdemir, and carried out under Seta O. Bogosyan’s supervision. Implementation of the work was done by Aykut Özdemir, Seta O. Bogosyan has supported this work with her innovative ideas and suggestions. The first draft of the manuscript was written by Aykut Özdemir and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Aykut Özdemir.

Ethics declarations

Ethics approval

Not applicable

Consent to participate

Not applicable

Consent for Publication

Not applicable

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Özdemir, A., Bogosyan, S.O. Gap Based Elastic Trees as a Novel Approach for Fast and Reliable Obstacle Avoidance for UGVs. J Intell Robot Syst 107, 9 (2023). https://doi.org/10.1007/s10846-022-01792-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-022-01792-0

Keywords

Search

Navigation