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Cornered Quadtrees/Octrees and Multiple Gateways Between Each Two Nodes; A Structure for Path Planning in 2D and 3D Environments

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3D Research

Abstract

In this paper, modified versions of quadtree/octree, as structures used in path planning, are proposed which we call them cornered quadtree/octree. Also a new method of creating paths in quadtrees/octrees, once quadrants/octants to be passed are determined, is proposed both to improve traveled distance and path smoothness. In proposed modified versions of quadtree/octree, four corner cells of quadrants and eight corner voxels of octants are also considered as nodes of the graph to be searched for finding the shortest path. This causes better quadrant/octant selection during graph search relative to simple quadtrees and octrees. On the other hand, after that all quadrants/octants are determined, multiple gateways are nominated between each two selected nodes and path is constructed by passing through the gateway which its selection leads in shorter and smoother path. Proposed structures in this paper alongside the utilized path construction approach, creates better paths in terms of path length than those created if simple trees are used, somehow equal to the quality of the achieved paths by framed trees, meanwhile interestingly, consumed time and memory in our proposed method are closer to the used time and memory if simple trees are used.

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References

  1. Huang, H. M., Pavek, K., Novak, B., Albus, J., & Messin, E. (2005). A framework for autonomy levels for unmanned systems (ALFUS). In Proceedings of AUVSI Unmanned Systems 2005.

  2. Pehlivanoglu, Y. V. (2012). A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV. Aerospace Science and Technology, 16(1), 47–55.

    Article  Google Scholar 

  3. Yahja, A., Stentz, A., Singh, S., & Brumitt, B. L. (1998). Framed-quadtree path planning for mobile robots operating in sparse environments. In Proceedings of IEEE International Conference on Robotics and Automation, 1998. (Vol. 1, pp. 650–655). IEEE.

  4. Finkel, R. A., & Bentley, J. L. (1974). Quad trees a data structure for retrieval on composite keys. Acta Informatica, 4(1), 1–9.

    Article  MATH  Google Scholar 

  5. Meagher, D. J. (1980). Octree encoding: A new technique for the representation, manipulation and display of arbitrary 3-d objects by computer. Electrical and Systems Engineering Department Rensseiaer Polytechnic Institute Image Processing Laboratory.

  6. Chen, D. Z., Szczerba, R. J., & Uhran, J. J. (1995). Using framed-octrees to find conditional shortest paths in an unknown 3-d environment. Informe Técnico, 95–9.

  7. Chen, D. Z., Szczerba, R. J., & Uhran J. J. Jr. (1995). Using framed-quadtrees to find conditional shortest paths in an unknown 2-D environment (Vol. 95, No. 2). Technical Report.

  8. Chen, D. Z., Szczerba, R. J., & Uhran J. J. Jr. (1995). Planning conditional shortest paths through an unknown environment: A framed-quadtree approach. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems 95. ‘Human Robot Interaction and Cooperative Robots, 1995 (Vol. 3, pp. 33–38). IEEE.

  9. Latombe, J. C. (2012). Robot motion planning (Vol. 124). Berlin: Springer Science & Business Media.

    Google Scholar 

  10. Lozano-Pérez, T., & Wesley, M. A. (1979). An algorithm for planning collision-free paths among polyhedral obstacles. Communications of the ACM, 22(10), 560–570.

    Article  Google Scholar 

  11. Goerzen, C., Kong, Z., & Mettler, B. (2010). A survey of motion planning algorithms from the perspective of autonomous UAV guidance. Journal of Intelligent and Robotic Systems, 57(1–4), 65–100.

    Article  MATH  Google Scholar 

  12. Howlet, J. K., Schulein, G., & Mansur, M. H. (2004). A practical approach to obstacle field route planning for unmanned rotorcraft.

  13. Yahja, A., Singh, S., & Stentz, A. (2000). An efficient on-line path planner for outdoor mobile robots. Robotics and Autonomous systems, 32(2), 129–143.

    Article  Google Scholar 

  14. Ghosh, S., Halder, A., & Sinha, M. (2011). Micro air vehicle path planning in fuzzy quadtree framework. Applied Soft Computing, 11(8), 4859–4865.

    Article  Google Scholar 

  15. Vörös, J. (2001). Low-cost implementation of distance maps for path planning using matrix quadtrees and octrees. Robotics and Computer-Integrated Manufacturing, 17(6), 447–459.

    Article  Google Scholar 

  16. Vörös, J. (1998). Using extended quadtrees in robot path planning. In Proceedings of the Seventh Workshop on Robotics in Alpe-Adria-Danube Region, Smolenice (pp. 451–456).

  17. Guanglei, Z., & Heming, J. (2013). 3D path planning of AUV based on improved ant colony optimization. In 32nd Chinese Control Conference (CCC), 2013 (pp. 5017–5022). IEEE.

  18. Xu, S., Honegger, D., Pollefeys, M., & Heng, L. (2015). Real-time 3D navigation for autonomous vision-guided MAVs. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015 (pp. 53–59). IEEE.

  19. Zhang, Q., Ma, J., & Xie, W. (2012). A framed-quadtree based on reversed d* path planning approach for intelligent mobile robot. Journal of Computers, 7(2), 464–469.

    Google Scholar 

  20. Zhang, Q., Ma, J., & Liu, Q. (2012). Path planning based quadtree representation for mobile robot using hybrid-simulated annealing and ant colony optimization algorithm. In 10th World Congress on Intelligent Control and Automation (WCICA), 2012 (pp. 2537–2542). IEEE.

  21. Colombo, A., Fontanelli, D., Legay, A., Palopoli, L., & Sedwards, S. (2015). Efficient customisable dynamic motion planning for assistive robots in complex human environments. Journal of Ambient Intelligence and Smart Environments, 7, 617–633.

    Article  Google Scholar 

  22. Abbadi, A., & Přenosil, V. (2015). Safe path planning using cell decomposition approximation. Distance Learning, Simulation and Communication, 2015, 8.

    Google Scholar 

  23. Hernandez, J. D., Vidal, E., Vallicrosa, G., Galceran, E., & Carreras, M. (2015). Online path planning for autonomous underwater vehicles in unknown environments. In IEEE International Conference on Robotics and Automation (ICRA), 2015 (pp. 1152–1157). IEEE.

  24. Hornung, A., Wurm, K. M., Bennewitz, M., Stachniss, C., & Burgard, W. (2013). OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots, 34(3), 189–206.

    Article  Google Scholar 

  25. Omar, F. S., Islam, M. N., & Haron, H. (2015). Shortest path planning for single manipulator in 2D environment of deformable objects. Jurnal Teknologi, 75(2), 33–37.

    Article  Google Scholar 

  26. Zelinsky, A. (1992). A mobile robot exploration algorithm. IEEE Transactions on Robotics and Automation, 8(6), 707–717.

    Article  Google Scholar 

  27. De Berg, M., Van Kreveld, M., Overmars, M., & Schwarzkopf, O. C. (2000). Computational geometry (pp. 1–17). Berlin: Springer.

    Book  MATH  Google Scholar 

  28. Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271.

    Article  MathSciNet  MATH  Google Scholar 

  29. Hart, P. E., Nilsson, N. J., & Raphael, B. (1968). A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100–107.

    Article  Google Scholar 

  30. Namdari, M. H., Hejazi, S. R., & Palhang, M. (2015). MCPN, Octree Neighbor Finding During Tree Model Construction Using Parental Neighboring Rule. 3D. Research, 6(3), 1–15.

    Google Scholar 

  31. Stentz, A. (1994). Optimal and efficient path planning for partially-known environments. In IEEE International Conference on Robotics and Automation, 1994. Proceedings (pp. 3310–3317). IEEE.

  32. Koenig, S., & Likhachev, M. (2005). Fast replanning for navigation in unknown terrain. IEEE Transactions on Robotics, 21(3), 354–363.

    Article  Google Scholar 

  33. Daniel, K., Nash, A., Koenig, S., & Felner, A. (2010). Theta*: Any-angle path planning on grids. Journal of Artificial Intelligence Research, 39, 533–579.

    MathSciNet  MATH  Google Scholar 

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Correspondence to Mohammad Hasan Namdari.

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Namdari, M.H., Hejazi, S.R. & Palhang, M. Cornered Quadtrees/Octrees and Multiple Gateways Between Each Two Nodes; A Structure for Path Planning in 2D and 3D Environments. 3D Res 7, 14 (2016). https://doi.org/10.1007/s13319-016-0092-9

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  • DOI: https://doi.org/10.1007/s13319-016-0092-9

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