Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

SpringerLink
Log in

Wheeled vehicles’ velocity updating by navigating on outdoor terrains

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

This paper addresses the velocity control of wheeled vehicles regarding the terrain features, beyond detection and avoidance of the obstacles as most current works do. Terrain appearance average is used to enable the wheeled vehicle to adapt velocity such that, as speedy as possible, it safely navigates. The vehicle velocity adaptation imitates the human beings’ driving behavior regarding the terrain features: humans use a quick and imprecise estimation of the terrain features but enough to drive–navigate without sliding or falling. A fuzzy neural network sets the vehicle velocity according to average estimations of terrain roughness. The terrain textures are modeled by the principal components that are enough to use pattern recognition for navigation purpose. One set of tests is executed using a small, wheeled robot, which adjusts velocity while navigating on surfaces such as ground, ground with grass, and stones paving. The other tests are done using images of roads of ground, concrete, asphalt, and loose stones, which are video filmed from a real car driven at less than 60 km/h of velocity; by applying the present approach, the required time/distance ratio to smoothly velocity change is granted.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Bajracharya M, Maimone MW, Helmick D (2008) Autonomy for mars rovers: past, present, and future. Computer 41:44–50

    Article  Google Scholar 

  2. Brooks CA, Iagnemma K (2005) Vibration-based terrain classification for planetary exploration rovers. IEEE T Robot 21:1185–1191

    Article  Google Scholar 

  3. Brooks CA, Iagnemma K (2009) Visual detection of novel terrain via two-class classification. In: Proceedings of the 2009 ACM symposium on applied computing, pp 1145–1150

  4. Brooks CA, Iagnemma K, Dubowsky S (2006) Visual wheel sinkage measurement for planetary rover mobility characterization. Auton Robot 21:55–64

    Article  Google Scholar 

  5. Carsten J, Rankin A, Ferguson D, Stentz A (2007) Global path planning on board the mars exploration rovers. In: IEEE aerospace conference, pp 1–11

  6. Columbia Utrecht Reflectance and Texture Database (2010) http://ww1.cs.columbia.edu/CAVE//software/curet/

  7. Dai X, Zhang H, Shi Y (2007) Autonomous navigation for wheeled mobile robots-A survey. In: Second international conference on innovative computing, information and control, pp 2207–2210

  8. Iagnemma K, Dubowsky S (2004) Mobile robot in rough terrain. Springer Tracts in Advanced Robotics

  9. Ishigami G, Nagatani K, Yoshida K (2007) Path planning for planetary exploration rovers and its evaluation based on wheel slip dynamics. In: IEEE international conference on robotics and automation, pp 2361–2366

  10. Kahraman F, Stegmann MB (2006) towards illumination-invariant localization of faces using active appearance models. In: 7th Nordic signal processing symposium, p 4

  11. Kelly A, Stentz A (1998) Rough terrain autonomous mobility–part 2: an active vision, predictive control approach. Auton Robot 5:163–198

    Article  Google Scholar 

  12. Konolige K, Agrawal M, Blas MR, Bolles RC, Gerkey B, Solà J, Sundaresan A (2009) Mapping, navigation, and learning for off-road traversal. J Field Robot 26:88–113

    Article  MATH  Google Scholar 

  13. Kramer J, Scheutz M (2007) Development environments for autonomous mobile robots: a survey. Auton Robot 22:101–132

    Article  Google Scholar 

  14. Lambert A, Gruyer D, Pierre GS, Ndjeng AN (2008) Collision probability assessment for speed control. In: 11th IEEE conference on intelligent transportation systems, pp 1043–1048

  15. Langer D, Rosenblatt JK, Herbert M (1994) A behavior-based system for off-road navigation. IEEE T Robot Autom 10:776–783

    Article  Google Scholar 

  16. Larson AC, Voyles RM, Demir GK (2005) Terrain classification using weakly-structured vehicle/terrain interaction. Auton Robot 19:41–52

    Article  Google Scholar 

  17. Leonardis A, Bischof H (2000) Robust recognition using eigenimages. Comput Vis Image Und 78:99–118

    Article  Google Scholar 

  18. Liao S, Chung ACS (2007) Texture classification by using advanced local binary patterns and spatial distribution of dominant patterns. In: IEEE international conference on acoustics, speech and signal processing, pp 1221–1224

  19. Murase H, Nayar SK (1995) Visual learning and recognition of 3-D objects from appearance. Int J Comput Vision 14:5–24

    Article  Google Scholar 

  20. Pereira GAS, Pimenta LCA, Chaimowicz L, Fonseca AF, de Almeida DSC, Correa LQ, Mesquita RC, Campos FM (2009) Robot navigation in multi-terrain outdoor environments. Int J Robot Res 28:685–700

    Article  Google Scholar 

  21. Pietikäinen M, Nurmela T, Mäenpää T, Turtinen M (2004) View-based recognition of real-world textures. Pattern Recog 37:313–323

    Article  MATH  Google Scholar 

  22. Ray LE (2008) Autonomous terrain parameter estimation for wheeled vehicles. In: Proceedings of the SPIE—the international society for optical engineering, pp 6962–6974

  23. ROBOTIS Co., LTD (2009) http://www.robotis.com

  24. Rojas R (1996) Neural networks: a systematic introduction. Springer, Berlin

    Google Scholar 

  25. Seraji H, Howard A (2002) Behavior-based robot navigation on challenging terrain: a fuzzy logic approach. IEEE T Robot Autom 18:308–321

    Article  Google Scholar 

  26. Seraji H, Werger B (2007) Theory and experiments in SmartNav rover navigation. Auton Robot 22:165–182

    Article  Google Scholar 

  27. Seeni A, Schäfer B, Rebele B, Tolyarenko N (2008) Robot mobility concepts for extraterrestrial surface exploration. In: IEEE aerospace conference, pp 1–14

  28. Selekwa MF, Dunlap DD, Shi D, Collins EG (2008) Robot navigation in very cluttered environments by preference-based fuzzy behaviors. Robot Auton Syst 56:231–246

    Article  Google Scholar 

  29. Sun Z, Bebis G, Miller R (2006) On-road vehicle detection: a review. IEEE T Pattern Anal 28:694–711

    Article  Google Scholar 

  30. Tsai DM, Tseng CF (1999) Surface roughness classification for castings. Pattern Recog 32:389–405

    Article  Google Scholar 

  31. Tu KY, Baltes J (2006) Fuzzy potential energy for a map approach to robot navigation. Robot Auton Syst 54:574–589

    Article  Google Scholar 

  32. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cognitive Neurosci 3:71–86

    Article  Google Scholar 

  33. Ward CC, Iagnemma K (2008) A dynamic-model-based wheel slip detector for mobile robots on outdoors terrain. IEEE T Robot 24:821–831

    Article  Google Scholar 

  34. Ward K, Zelinsky A (2000) Acquiring mobile robot behaviors by learning trajectory velocities. Auton Robot 9:113–133

    Article  Google Scholar 

  35. Zhu AM, Yang SX (2007) Neurofuzzy-based approach to mobile robot navigation in unknown environments. IEEE T Syst Man Cy B 37:610–621

    Article  Google Scholar 

Download references

Acknowledgments

Farid García thanks the financial support from Mexican CONACyT, Consejo Nacional de Ciencia y Tecnología, PhD scholarship number 207029.

Author information

Authors and Affiliations

  1. Department of Computing, Centre of Research and Advanced Studies, IPN, Av. Instituto Politécnico Nacional 2508, 07360, San Pedro Zacatenco, Mexico DF, Mexico

    Matías Alvarado & Farid García

Authors
  1. Matías Alvarado
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farid García
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matías Alvarado.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alvarado, M., García, F. Wheeled vehicles’ velocity updating by navigating on outdoor terrains. Neural Comput & Applic 20, 1097–1109 (2011). https://doi.org/10.1007/s00521-010-0429-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0429-x

Keywords

Search

Navigation