Skip to main content

Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model

  • Conference paper
  • First Online:
New Technologies, Development and Application IV (NT 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 233))

  • 1074 Accesses

Abstract

The recent development of faster and more accurate deep learning models has enabled researchers to utilize the potential of deep learning in robotics. Convolutional neural networks used for the process of semantic segmentation are being applied to improve the traditional robotic tasks by adding an additional level of intelligence, through the execution of context-aware tasks. Having that in mind, visual servoing can now be performed in a completely new manner, by exploiting only semantic and geometric knowledge about the environment. To carry out visual servoing, the mathematical model of the error between the images generated at the current and the desired mobile robot pose (i.e. position and orientation) in the image space needs to be adequately defined. In this paper, we propose the novel mathematical model for the weighted fitness function evaluation, which is utilized for the image registration process within the visual servoing framework. By weighting the classes by their importance in the desired image, the convergence domain of the initial error in the visual servoing process can be greatly extended. The experimental evaluation is carried out on the mobile robot RAICO (Robot with Artificial Intelligence based COgnition), where it is shown that weighted fitness function enables more robust intelligent visual servoing systems with a lower possibility of failure, easier real-world implementation, and feasible object driven navigation.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Mitić, M., Miljković, Z.: Neural network learning from demonstration and epipolar geometry for visual control of a nonholonomic mobile robot. Soft Comput. 18(5), 1011–1025 (2014)

    Article  Google Scholar 

  2. Sünderhauf, N., Brock, O., Scheirer, W., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., Corke, P.: The limits and potentials of deep learning for robotics. Int. J. Robot. Res. 37(4–5), 405–420 (2018)

    Article  Google Scholar 

  3. Bateux, Q., Marchand, E., Leitner, J., Chaumette, F.: Training deep neural networks for visual servoing. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1–8 (2018)

    Google Scholar 

  4. Sadeghi, F., Toshev, A., Jang, E., Levine, S.: Sim2Real viewpoint invariant visual servoing by recurrent control. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4691–4699 (2018)

    Google Scholar 

  5. Petrović, M., Mystkowski, A., Jokić, A., Đokić, L., Miljković, Z.: Deep Learning-based Algorithm for Mobile Robot Control in Textureless Environment. In: IEEE International Conference Mechatronic Systems and Materials (MSM), pp. 1–4 (2020)

    Google Scholar 

  6. Đokić, L., Jokić, A., Petrović, M., Miljković, Z.: Biologically inspired optimization methods for image registration in visual servoing of a mobile robot. In: 7th International Conference on Electrical, Electronics and Computing Engineering (IcETRAN 2020), 715–720 (2020)

    Google Scholar 

  7. Indraswari, R., Kurita, T., Arifin, A.Z., Suciati, N., Astuti, E.R.: Multi-projection deep learning network for segmentation of 3D medical images. Pattern Recogn. Lett. 125, 791–797 (2019)

    Article  Google Scholar 

  8. Goli, P.: A new perceptually weighted cost function in deep neural network based speech enhancement systems. Hearing Balance Commun. 17(3), 191–196 (2019)

    Article  Google Scholar 

  9. Cao, F., Che, S., Zhao, J.: A weighted hybrid training algorithm of neural networks for robust data regression. Int. J. Mach. Intell. Sensory Signal Process. 2(1), 51–66 (2017)

    Google Scholar 

Download references

Acknowledgment

This work has been financially supported by the Ministry of Education, Science and Technological Development of the Serbian Government, through the project “Integrated research in macro, micro, and nano mechanical engineering – Deep learning of intelligent manufacturing systems in production engineering” (contract No. 451-03-9/2021-14/200105), by the Science Fund of the Republic of Serbia, grant No. 6523109, AI - MISSION4.0, 2020–2022, and by the Polish National Agency for Academic Exchange through the project: “Biologically inspired optimization algorithms for control and scheduling of intelligent robotic systems”, Grant No. PPN/ULM/2019/1/00354/U/00001.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aleksandar Jokić .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jokić, A., Petrović, M., Kulesza, Z., Miljković, Z. (2021). Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model. In: Karabegović, I. (eds) New Technologies, Development and Application IV. NT 2021. Lecture Notes in Networks and Systems, vol 233. Springer, Cham. https://doi.org/10.1007/978-3-030-75275-0_82

Download citation

Publish with us

Policies and ethics