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Robotic Path Planning Based on a Triangular Mesh Map

  • Robot and Applications
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International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

Aiming at the problem of robot path planning in complex maps, an algorithm of robot path planning based on triangular grid graph is proposed. Firstly, a weighted undirected loop graph and a feasible domain of nodes are obtained by discretizing the triangular mesh map. Next, the Dijkstra search algorithm is applied to find the feasible shortest path from an initial to a final configuration. Finally, The Douglas-Peucker algorithm is applied to remove duplicate and redundant nodes in the feasible path, and the waypoint are extracted. The final path is a curve that is obtained by connecting the several extracted waypoint. The proposed algorithm is tested for various maps. Compared with probabilistic roadmap method, the experimental results show that the proposed method can overcome the shortcomings of the random sampling method. Furthermore, the experimental result of triangular mesh map method used in two labyrinth maps show that the triangular mesh map method can solve the robot path planning problem in complex map very well, and it is an excellent algorithm for robot path planning.

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References

  1. Z. Pan, D. Wang, H. Deng, and K. Li, “A virtual spring method for the multi-robot path planning and formation control,” International Journal of Control, Automation and Systems, vol. 17, no. 5, pp. 1272–1282, 2019.

    Article  Google Scholar 

  2. J. Lee, “Heterogeneous-ants-based path planner for global path planning of mobile robot applications,” International Journal of Control, Automation and Systems, vol. 15, no. 4, pp. 1754–1769, 2017.

    Article  Google Scholar 

  3. M. Elbanhawi and M. Simic, “Sampling-based robot motion planning: a review,” IEEE Access, vol. 2, pp. 56–77, Feb. 2014.

    Article  Google Scholar 

  4. T. Maekawa, T. Noda, S. Tamura, T. Ozaki, and K.-I. Machida, “Curvature continuous path generation for autonomous vehicle using B-spline curves,” Comput-Aided Des, vol. 42, no. 4, pp. 350–359, Apr. 2010.

    Article  Google Scholar 

  5. P. Bhattacharya and M. L. Gavrilova, “Roadmap-based path planning - using the voronoi diagram for a clearancebased shortest path,” IEEE Robotics & Automation Magazine, vol. 15, no. 2, pp. 58–66, Jun. 2008.

    Article  Google Scholar 

  6. G. E. Jan, C. Sun, W. C. Tsai and T. Lin, “An O(nlogn) shortest path algorithm based on delaunay triangulation,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 2, pp. 660–666, Apr. 2014.

    Article  Google Scholar 

  7. M. Elbanhawi, M. Simic, and R. Jazar, “Autonomous mobile robot path planning: an adaptive roadmap approach,” Appl. Mech. Mater., vols. 373–375, pp. 246–254, Jan. 2013.

    Article  Google Scholar 

  8. R. V. Cowlagi and P. Tsiotras, “Multiresolution motion planning for autonomous agents via wavelet-based cell decompositions,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 42, no. 5, pp. 1455–1469, Oct. 2012.

    Article  Google Scholar 

  9. L. S. C. Pun-Cheng, M. Y. F. Tang, and I. K. L. Cheung, “Exact cell decomposition on base map features for optimal path finding,” International Journal of Geographical Information Science, vol. 21, no. 2, pp. 175–185, Jan. 2007.

    Article  Google Scholar 

  10. Y. Rasekhipour, A. Khajepour, S. Chen, and B. Litkouhi, “A potential field-based model predictive path-planning controller for autonomous road vehicles,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 5, pp. 1255–1267, May 2017.

    Article  Google Scholar 

  11. S. S. Ge and Y. J. Cui, “Dynamic motion planning for mobile robots using potential field method,” Autonomous Robots, vol. 13, no. 3, pp. 207–222, Nov. 2002.

    Article  Google Scholar 

  12. M. A. P. Garcia, O. Montiel, O. Castillo, R. Sepúlveda, and P. Melin, “Path planning for autonomous mobile robot navigation with ant colony optimization and fuzzy cost function evaluation,” Appl. Soft Comput., vol. 9, no. 3, pp. 1102–1110, Jun. 2009.

    Article  Google Scholar 

  13. Q. Zou, M. Cong, D. Liu, and Y. Du, “Robotic path planning based on episodic-cognitive map,” International Journal of Control, Automation and Systems, vol. 17, no. 5, pp. 1304–1313, 2019.

    Article  Google Scholar 

  14. J. Zhang, Y. Zhang, and Y. Zhou, “Path planning of mobile robot based on hybrid multi-objective bare bones particle swarm optimization with differential evolution,” IEEE Access, vol. 6, pp. 44542–44555, Aug. 2018.

    Article  Google Scholar 

  15. C. C. Tsai, H. C. Huang, and C. K. Chan, “Parallel elite genetic algorithm and its application to global path planning for autonomous robot navigation,” IEEE Transactions on Industrial Electronics, vol. 58, no. 10, pp. 4813–4821, Nov. 2004.

    Article  Google Scholar 

  16. S. Chakravorty and S. Kumar, “Generalized samplingbased motion planners,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 41, no. 3, pp. 855–866, Jun. 2011.

    Article  Google Scholar 

  17. J. D. Marble and K. E. Bekris, “Asymptotically nearoptimal planning with probabilistic roadmap spanners,” IEEE Transactions on Robotics, vol. 29, no. 2, pp. 432–444, Apr. 2013.

    Article  Google Scholar 

  18. J. Kim and S. H. Woo, “Reference test maps for path planning algorithm test,” International Journal of Control, Automation and Systems, vol. 16, no. 1, pp. 397–401, 2018.

    Article  Google Scholar 

  19. Y. Zhao, S. L. Ho, and W. N. Fu, “A novel adaptive mesh triangular meshes method for nonlinear magnetic field analysis,” IEEE Transactions on Magnetics, vol. 49, no. 5, pp. 1777–1780, May 2013.

    Article  Google Scholar 

  20. F. Ledoux and J. Shepherd, “Topological modifications of hexah-edral meshes via sheet operations: a theoretical study,” Engineering with Computers, vol. 26, no. 4, pp. 433–447, Aug. 2010.

    Article  Google Scholar 

  21. H. Nguyen-Xuan, L. V. Tran, C. H. Thai, and T. Nguyen-Thoi, “Analysis of functionally graded plates by an efficient triangular meshes method with node-based strain smoothing,” Thin-Walled Structures, vol. 54, pp. 1–18, May 2012.

    Article  Google Scholar 

  22. H. Zhang, G. Zhao, and X. Ma, “Adaptive generation of hexahedral element mesh using an improved grid-based method,” Computer-Aided Design, vol. 39, no. 10, pp. 914–928, Oct. 2007.

    Article  Google Scholar 

  23. K. E. Kakosimos and M. J. Assael, “An efficient 3D mesh generator based on geometry decomposition,” Computers and Structures, vol. 87, no. 1-2, pp. 27–38, Jan. 2008.

    Article  Google Scholar 

  24. D. Engwirda, Locally-optimal Delaunay-refinement and Optimisation-based Mesh Generation, Ph.D. Thesis, School of Mathematics and Statistics, The University of Sydney, September 2014.

    Google Scholar 

  25. J. Liu, S. Xing, and L. Shen, “Lattice-reduction-aided breadth-first tree searching algorithm for MIMO detection,” IEEE Communications Letters, vol. 21, no. 4, pp. 845–848, Apr. 2017.

    Article  Google Scholar 

  26. H. Qian, X. Wang, K. Kang, and W. Xiang, “A depthfirst ml decoding algorithm for tail-biting trellises,” IEEE Transactions on Vehicular Technology, vol. 64, no. 8, pp. 3339–3346, Aug. 2015.

    Article  Google Scholar 

  27. R. Kala, A. Shukla, and R. Tiwari, “Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning,” Artificial Intelligence Review, vol. 33, no. 4, pp. 307–327, Apr. 2010.

    Article  Google Scholar 

  28. F. M. Mena, R. H. Ucan, V. U. Cetina, and F. M. Ramirez, “Web service composition using the bidirectional Dijkstra algorithm,” IEEE Latin America Transactions, vol. 14, no. 5, pp. 2522–2528, May 2016.

    Article  Google Scholar 

  29. D. H. Douglas and T. K. Peucker, “Algorithms for the reduction of the number of points required to represent a digitized line or its caricature,” Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 10, no. 2, pp. 112–122, Dec. 1973.

    Article  Google Scholar 

  30. K. Ebisch, “A correction to the Douglas-Peucker line generalization algorithm,” Computers & Geosciences, vol. 28, no. 8, pp. 995–997, Oct. 2002.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Mechanics and Optoelectronic physics, Anhui University of Science and Technology, Anhui, China

    Yanbin Liu

  2. Department of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, China

    Yuanyuan Jiang

Authors
  1. Yanbin Liu
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  2. Yuanyuan Jiang
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Correspondence to Yuanyuan Jiang.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Augie Widyotriatmo under the direction of Editor Myo Taeg Kim. The author acknowledges the support provided by Anhui Natural Science Foundation (Nos.1708085QF135 and J2017A077), Young Talents Foundation of Anhui Province (No. gxyqZD2016082), Foreign Visiting Project of Outstanding, Young Talents in Anhui (No. gxfx2017025) and National Natural Science Foundation of China (No.51604011).

Yanbin Liu received his Ph.D. degree from Harbin Institute of Technology, Harbin, China, in 2011. His research interests include nonlinear dynamics and control, optimal path planning, signal processing, and fault diagnosis of electromechanical system.

Yuanyuan Jiang received her Ph.D. degree from the Nanjing University of Aeronautics and Astronautics (NUAA). She is currently a Professor with the Department of Electrical and Information Engineering, Anhui University of Science and Technology. Her current research interests include signal processing and PHM of electronic systems.

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Liu, Y., Jiang, Y. Robotic Path Planning Based on a Triangular Mesh Map. Int. J. Control Autom. Syst. 18, 2658–2666 (2020). https://doi.org/10.1007/s12555-019-0396-z

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  • DOI: https://doi.org/10.1007/s12555-019-0396-z

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