Skip to main content

Automatic Generation of Random Step Environment Models for Gazebo Simulator

  • 433 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 324)


Negotiating rough terrain obstacles is a key task of a mobile terrestrial robot. One of the most popular standards in rough terrain modeling is the NIST Random Step Environment (RSE) or Random Stepfield, which allows constructing a broad variety of debris-like environments. This paper proposes a virtual RSE models’ automatic generator LIRS-RSEGen for the Gazebo simulator. We analyzed typical for RSEs obstacles and structures, determined parameters that are required in order to generate a RSE and implemented a generator, which constructs RSE Gazebo worlds that could be further edited or directly used within the Gazebo. Constructed by our generator worlds were validated in the Gazebo using virtual models of TurtleBot3 wheeled robot and Servosila Engineer crawler robot.


  • USAR
  • UGV
  • Modelling
  • Gazebo
  • Random step environment

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-86294-7_36
  • Chapter length: 13 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   189.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-86294-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   249.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.


  1. 1.

  2. 2.

    LIRS-RSEGen, GitLab,


  1. Abbyasov, B., Lavrenov, R., Zakiev, A., Yakovlev, K., Svinin, M., Magid, E.: Automatic tool for gazebo world construction: from a grayscale image to a 3D solid model. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 7226–7232. IEEE (2020)

    Google Scholar 

  2. Amsters, R., Slaets, P.: Turtlebot 3 as a robotics education platform. In: International Conference on Robotics in Education (RiE), pp. 170–181. Springer (2019).

  3. Borisov, A., Kuznetsov, S., Mamaev, I., Tenenev, V.: Describing the motion of a body with an elliptical cross section in a viscous uncompressible fluid by model equations reconstructed from data processing. Tech. Phys. Lett. 42(9), 886–890 (2016)

    CrossRef  Google Scholar 

  4. OSR Foundation: Gazebo official site (2021).

  5. Jacoff, A., Downs, A., Virts, A., Messina, E.: Stepfield pallets: repeatable terrain for evaluating robot mobility. In: Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems, pp. 29–34 (2008)

    Google Scholar 

  6. Jacoff, A., et al.: Using competitions to advance the development of standard test methods for response robots. In: Proceedings of the Workshop on Performance Metrics for Intelligent Systems, pp. 182–189 (2012)

    Google Scholar 

  7. Jakobi, N., Husbands, P., Harvey, I.: Noise and the reality gap: the use of simulation in evolutionary robotics. In: European Conference on Artificial Life, pp. 704–720. Springer (1995).

  8. Lavrenov, R., Zakiev, A.: Tool for 3D gazebo map construction from arbitrary images and laser scans. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 256–261. IEEE (2017)

    Google Scholar 

  9. Magid, E., Tsubouchi, T.: Static balance for rescue robot navigation: discretizing rotational motion within random step environment. In: International Conference on Simulation, Modeling, and Programming for Autonomous Robots, pp. 423–435. Springer (2010).

  10. Malov, D., Edemskii, A., Saveliev, A.: Proactive localization system as a part of a cyberphysical smart environment. In: 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), pp. 1–5. IEEE (2019)

    Google Scholar 

  11. Moskvin, I., Lavrenov, R.: Modeling tracks and controller for servosila engineer robot. In: Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”, pp. 411–422. Springer (2020).

  12. Moskvin, I., Lavrenov, R., Magid, E., Svinin, M.: Modelling a crawler robot using wheels as pseudo-tracks: model complexity vs performance. In: 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA), pp. 1–5. IEEE (2020)

    Google Scholar 

  13. Oliphant, T.E.: A guide to NumPy, vol. 1. Trelgol Publishing USA (2006)

    Google Scholar 

  14. Pepper, C., Balakirsky, S., Scrapper, C.: Robot simulation physics validation. In: Proceedings of the 2007 Workshop on Performance Metrics for Intelligent Systems, pp. 97–104 (2007)

    Google Scholar 

  15. Quigley, M., et al.: Ros: an open-source robot operating system. In: ICRA Workshop on Open Source Software, vol. 3, p. 5. Kobe, Japan (2009)

    Google Scholar 

  16. Safin, R., Lavrenov, R., Martínez-García, E.A.: Evaluation of visual slam methods in usar applications using ros/gazebo simulation. In: Proceedings of 15th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”, pp. 371–382. Springer (2021).

  17. Shabalina, K., Sagitov, A., Su, K.L., Hsia, K.H., Magid, E.: Avrora unior car-like robot in gazebo environment. In: International Conference on Artificial Life and Robotics, pp. 116–119 (2019)

    Google Scholar 

  18. Sheh, R., Kadous, M., Sammut, C., Hengst, B.: Extracting terrain features from range images for autonomous random stepfield traversal. In: IEEE International Workshop on Safety, Security and Rescue Robotics, Rome (2007)

    Google Scholar 

  19. Sheh, R., Hengst, B., Sammut, C.: Behavioural cloning for driving robots over rough terrain. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 732–737. IEEE (2011)

    Google Scholar 

  20. Sheh, R., et al.: Advancing the state of urban search and rescue robotics through the robocuprescue robot league competition. In: Field and service robotics, pp. 127–142. Springer (2014).

  21. Simakov, N., Lavrenov, R., Zakiev, A., Safin, R., Martínez-García, E.A.: Modeling usar maps for the collection of information on the state of the environment. In: 2019 12th International Conference on Developments in eSystems Engineering (DeSE), pp. 918–923. IEEE (2019)

    Google Scholar 

  22. Timperley, C.S., Afzal, A., Katz, D.S., Hernandez, J.M., Le Goues, C.: Crashing simulated planes is cheap: can simulation detect robotics bugs early? In: 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST), pp. 331–342. IEEE (2018)

    Google Scholar 

  23. Willman, J.: Overview of pyqt5. In: Modern PyQt, pp. 1–42. Springer (2021).

  24. Yakovlev, K., Baskin, E., Hramoin, I.: Grid-based angle-constrained path planning. In: Joint German/Austrian Conference on Artificial Intelligence (Künstliche Intelligenz), pp. 208–221. Springer (2015).

Download references


This work was supported by the Russian Foundation for Basic Research (RFBR), project ID 19–58-70002. The third and forth authors acknowledge the support of the Japan Science and Technology Agency, the JST Strategic International Collaborative Research Program, Project No. 18065977.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Evgeni Magid .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Gabdrahmanov, R., Tsoy, T., Bai, Y., Svinin, M., Magid, E. (2022). Automatic Generation of Random Step Environment Models for Gazebo Simulator. In: Chugo, D., Tokhi, M.O., Silva, M.F., Nakamura, T., Goher, K. (eds) Robotics for Sustainable Future. CLAWAR 2021. Lecture Notes in Networks and Systems, vol 324. Springer, Cham.

Download citation