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

A trajectory generation method for mobile robot based on iterative extension-like process

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

In this paper, we propose a trajectory generation method for mobile robot based on iterative extension-like process. Due to use mobile robots in the real world, trajectory generation must be done depending on the faced situation on each occasion. Proposed method enables online iterative trajectory extension process based on a low-order polynomial curve named as trajectory segment. The waypoints on the existing trajectory segment and a waypoint designated every fixed interval are the constraints to trigger the trajectory extension. For maintaining the smooth continuity of the trajectory, the velocity state must be sustained at the connecting point. Resultantly, the trajectory segments are organized into a single smooth trajectory.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Explore related subjects

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

References

  1. Sakamoto N, Okugawa M (2012) Human following control of porter robot with velocity vectors. In: Proceedings of ASME 2012 5th annual dynamic systems and control conference joint with the JSME 2012 11th motion and vibration conference, pp 817–821

  2. Tachi S, Tanie K, Komoriya K, Abe M (1985) MELDOG: the guide dog robot. IEEE Trans Biomed Eng 32(7):256–270

    Google Scholar 

  3. Karreman DE , van Dijk EMAG, Evers V (2012) Using the visitor experiences for mapping the possibilities of implementing a robotic guide in outdoor sites. In: Proceedings of the 21st IEEE international symposium on robot and human interactive communication, pp 1059–1065

  4. Zacharie M (2010) Security guard robot detecting human using Gaussian distribution histogram method. J Comput Sci 6(10):1144–1150

    Article  Google Scholar 

  5. Lee H-T, Lin W-C, Huang C-H (2011) Indoor surveillance security robot with a self-propelled patrolling vehicle. J Robot Vol 2011. Article ID 197105. doi:10.1155/2011/197105

  6. Kelly A, Nagy B (2003) Reactive nonholonomic trajectory generation via parametric optimal control. Int J Robot Res 22(7–8):583–601

    Article  Google Scholar 

  7. Maekawa T, Noda T, Tamura Ozaki T, Machida K (2010) Curvature continuous path generation for autonomous vehicle using B-spline curves. Comput Aided Des 42:350–359

    Article  Google Scholar 

  8. Bezier P (1986) Courbes et surfaces. Mathmatiques et CAO, Hermes, Paris

    MATH  Google Scholar 

  9. Jolly KG, Kumar RS, Vijayakumar R (2009) A Bezier curve based path planning in a multi-agent robot soccer system without violating the acceleration limits. Robot Auton Syst 57:23–33

    Article  Google Scholar 

  10. Ma L, Yang J, Zhang M (2012) A two-level path planning method for on-road autonomous driving. In: Proceedings of international conference on intelligent system design and engineering application, pp 661–664

  11. Kawabata K, Ma L, Xue J, Zheng N (2015) A path generation for automated vehicle based on bezier curve and via-points. Robot Auton Syst 74:243–252

    Article  Google Scholar 

  12. Choi JW, Curry RE, Elkaim GH (2010) Continuous curvature path generation based on bezier curves for autonomous vehicles. IAENG J Appl Math 40(2):IJAM_40_2_07

  13. Zhou F, Song B, Tian G (2011) Bezier curve based smooth path planning for mobile robot. J Inf Comput Sci 8(12):2441–2450

    Google Scholar 

  14. LaValle SM, Kuffner JJ (2001) Randomized kinodynamic planning. Int J Robot Res 20(5):378–400

    Article  Google Scholar 

  15. Melchior NA, Simmons R (2007) Particle RRT for path planning with uncertainty. In: Proceedings of 2007 IEEE international conference on robotics and automation, pp 1617–1624

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

    Article  Google Scholar 

  17. Yang K, Moon S, Yoo S, Kang J, Doh NL, Kim NB, Joo S (2014) Spline-based RRT path planner for non-holonomic robots. J Intell Robot Syst 73:763–782

    Article  Google Scholar 

  18. Karaman S, Walter MR, Perez A, Frazzoli E, Teller S (2011) Anytime motion planning using the RRT\(^{\ast }\). In: Proceedings of IEEE international conference on robotics and automation, pp 1478–1483

  19. Paromtchik I, Asama H (2000) A motion generation approach for an omnidirectional vehicle. In: Proceedings of international conference on robotics and automation, pp 1213–1218

  20. Kawabata K, Xue J, Ma L, Zheng N (2014) A sequential path extension method for mobile robot. Proc IEEE TENCON 2014:075

    Google Scholar 

  21. Kawabata K, Xue J, Ma L, Yokota S, Mitsukura Y, Zheng N (2015) Iterative polynomial-based trajectory extension for mobile robot. In: Proceedings of 2015 IEEE international conference on advanced intelligent mechatronics, pp 255–260

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kuniaki Kawabata.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kawabata, K. A trajectory generation method for mobile robot based on iterative extension-like process. Artif Life Robotics 21, 500–509 (2016). https://doi.org/10.1007/s10015-016-0305-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-016-0305-6

Keywords