Potential and Sampling Based RRT Star for Real-Time Dynamic Motion Planning Accounting for Momentum in Cost Function

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11307)


Path planning is an extremely important step in every robotics related activity today. In this paper, we present an approach to a real-time path planner which makes use of concepts from the random sampling of the Rapidly-exploring random tree and potential fields. It revises the cost function to incorporate the dynamics of the obstacles in the environment. Not only the path generated is significantly different but also it is much more optimal and rigid to breakdowns and features faster replanning. This variant of the Real-Time RRT* incorporates artificial potential field with a revised cost function.


Path planning Robotics RRT Potential energy Wavefront Doppler effect 



We thank Manjunath Bhatt (, Rahul Kumar ( and Shubham Maddhashiya ( for assisting us in this project and supporting us as and when required.


  1. 1.
    Bhushan, M., Agarwal, S., Gaurav, A.K., Nirala, M.K., Sinha, S., et al.: KgpKubs 2018 team description paper. In: RoboCup 2018 (2018)Google Scholar
  2. 2.
    Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artif. Intell. 90(1–2), 279–298 (1997)zbMATHGoogle Scholar
  3. 3.
    Dolgov, D., Thrun, S., Montemerlo, M., Diebel, J.: Practical search techniques in path planning for autonomous driving. In: Proceedings of the First International Symposium on Search Techniques in Artificial Intelligence and Robotics (STAIR-08) (2008)Google Scholar
  4. 4.
    Fox, D., Burgard, W., Thrun, S.: The dynamic window approach to collision avoidance. IEEE Robot. Autom. Mag. 4, 23–33 (1997)CrossRefGoogle Scholar
  5. 5.
    Karaman, S., Frazzoli, E.: Incremental Sampling-based Algorithms for Optimal Motion Planning. Robotics: Science and Systems. arXiv preprint:1005.0416 (2010)Google Scholar
  6. 6.
    Kim, J., Ostrowski, J.P.: Motion planning of aerial robot using rapidly-exploring random trees with dynamic constraints. In: IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), vol. 2, pp. 2200–2205 (2003)Google Scholar
  7. 7.
    Kunigahalli, R., Russell, J.S.: Visibility graph approach to detailed path planning in CNC concrete placement. In: Proceedings of the 11th ISARC, pp. 141–147 (1994)CrossRefGoogle Scholar
  8. 8.
    LaValle, S.M.: Rapidly-exploring random trees: A new tool for path planning. Report No. TR 98–11. Computer Science Department, Iowa State University (1998)Google Scholar
  9. 9.
    Naderi, K., Rajamki, J., Hmlinen, P.: RT-RRT*: a real-time path planning algorithm based on RRT*. In: 8th ACM SIGGRAPH Conference on Motion in Games (MIG 2015), pp. 113–118 (2015)Google Scholar
  10. 10.
    Nguyen, K.D., Ng, T.C., Chen, I.M.: On algorithms for planning S-curve motion profiles. Int. J. Adv. Robot. Syst. 5(1), 99–106 (2008)CrossRefGoogle Scholar
  11. 11.
    Qixin, C., Yanwen, H., Jingliang, Z.: An evolutionary artificial potential field algorithm for dynamic path planning of mobile robot. In: International Conference on Intelligent Robots and Systems, pp. 3331–3336 (2006)Google Scholar
  12. 12.
    Qureshi, A.H., et al.: Potential guided directional-RRT* for accelerated motion planning in cluttered environments. In: IEEE International Conference on Mechatronics and Automation, Takamatsu, pp. 519–524 (2013)Google Scholar
  13. 13.
    Sinha, S., Nirala, M.K., Ghosh, S., Ghosh, S.K.: Hybrid path planner for efficient navigation in urban road networks through analysis of trajectory traces. In: 24th International Conference on Pattern Recognition (2018, in Press)Google Scholar
  14. 14.
    Tan, C., Ma, S., Dai, Y., Qian, Y.: Barzilai-Borwein step size for stochastic gradient descent. arXiv preprint:1605.04131 (2016)Google Scholar
  15. 15.
    Vadakkepat, P., Lee, T.H., Xin, L.: Application of evolutionary artificial potential field in robot soccer system. In: Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 5, pp. 2781–2785 (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations