Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-05-01T03:34:43.326Z Has data issue: false hasContentIssue false

One Way to Fill All the Concave Region in Grid-Based Map

Published online by Cambridge University Press:  10 September 2020

ZiYing Zhang
Affiliation:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China
Xu Yang
Affiliation:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China
Dong Xu*
Affiliation:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China
Ke Geng
Affiliation:
College of Computer Science and Technology, HeiLongJiang Institution of Technology, Harbin, China
YuLong Meng
Affiliation:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China
GuangSheng Feng
Affiliation:
College of Computer Science and Technology, Harbin Engineering University, Harbin, China
*
*Corresponding author. E-mail: xudong@hrbeu.edu.cn

Summary

The search space of the path planning problem can greatly affect the running time and memory consumption, for example, the concave obstacle in grid-based map usually leads to the invalid search space. In this paper, the filling container algorithm is proposed to alleviate the concave area problem in 2D map space, which is inspired from the scenario of pouring water into a cup. With this method, concave areas can be largely excluded by scanning the map repeatedly. And the effectiveness has been proved in our experiments.

Type
Articles
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Hart, P. E., Nilsson, N. J. and Raphael, B., “A Formal Basis for the Heuristic Determination of Minimum Cost Paths,” IEEE Trans. Syst. Sci. Cybern. 4(2), 2829 (1972).Google Scholar
Kuffner, J. J. and Lavalle, S. M., “RRT-Connect: An Efficient Approach to Single-Query Path Planning,” IEEE International Conference on Robotics and Automation 4, 9951001 (2002).Google Scholar
Amatoc, N. M. and Dale, L. K., “OBPRM: An Obstacle-Based PRM for 3D Workspaces,” Found of Robotics (1998).Google Scholar
Rimon, E. and Koditschek, D. E., “Exact robot navigation using artificial potential functions,” IEEE Trans. Robot. Autom. 8(5), 501518 (1992).CrossRefGoogle Scholar
Hoy, M., Matveev, A. S. and Savkin, A. V., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey,” Robotica 33(3), 463497 (2015).CrossRefGoogle Scholar
Thrun, S., “Learning metric-topological maps for indoor mobile robot navigation,” Artif. Intell. 99(1), 2171 (1998).CrossRefGoogle Scholar
Carsten, J., Rankin, A., Ferguson, D. and Stentz, A., “Global Path Planning on Board the Mars Exploration Rovers,” IEEE Aerospace Conference (2007) pp. 111.Google Scholar
Wang, M. and Liu, J. N. K., “Fuzzy logic-based real-time robot navigation in unknown environment with dead ends,” Robot. Auto. Syst. 56(7), 625643 (2008).CrossRefGoogle Scholar
Berg, M. D., Kreveld, M. V., Overmars, M. and Schwarzkopf, O., Computational Geometry: Algorithms and Applications (Springer Publishing Company, Incorporated, 2000).CrossRefGoogle Scholar
Chan, R. H. T., Tam, P. K. S. and Leung, D. N. K., “Robot Navigation in Unknown Terrains via Multi-resolution Grid Maps,” 1991 International Conference on Industrial Electronics, Control and Instrumentation (1991) pp. 11381143.Google Scholar
Einhorn, E., Schröter, C. and Gross, H., “Finding the Adequate Resolution for Grid Mapping - Cell Sizes Locally Adapting on-the-Fly,” 2011 IEEE International Conference on Robotics and Automation (2011) pp. 18431848.Google Scholar
Chen, Y., Shuai, W. and Chen, X., “A Probabilistic, Variable-Resolution and Effective Quadtree Representation for Mapping of Large Environments,” 2015 International Conference on Advanced Robotics (ICAR) (2015) pp. 605610.Google Scholar
Kambhampati, S. and Davis, L. S., “Multiresolution path planning for mobile robots,” IEEE J. Robot. Autom. 3(2), 135145 (1986).CrossRefGoogle Scholar
Finkel, R. A. and Bentley, J. L., “Quad trees a data structure for retrieval on composite keys,” Acta Informatica 1(2), 19 (1974).CrossRefGoogle Scholar
Liu, M., Colas, F. and Oth, L., “Incremental topological segmentation for semi-structured environments using discretized GVG,” Auto. Robots 38(2), 143160 (2015).CrossRefGoogle Scholar
Tsardoulias, E. G., Serafi, A. T., Panourgia, M. N., Papazoglou, A. and Petrou, L., “Construction of minimized topological graphs on occupancy grid maps based on GVD and sensor coverage information,” J. Intell. Robot. Syst. 75(3), 457474 (2013).CrossRefGoogle Scholar
Jain, A. K., Murty, M. N. and Flynn, P. J., “Data clustering: A review,” ACM Comput. Surv. 31(3), 264323 (1999).CrossRefGoogle Scholar
Zhou, Y., Yu, S., Sun, R., Sun, Y. and Sun, L., “Topological Segmentation for Indoor Environments from Grid Maps Using an Improved NJW Algorithm,” 2017 IEEE International Conference on Information and Automation (ICIA) (2017) pp. 142147.Google Scholar
Liu, M., Colas, F. and Siegwart, R., “Regional Topological Segmentation Based on Mutual Information Graphs,” 2011 IEEE International Conference on Robotics and Automation (2011) pp. 32693274.Google Scholar
Kaleci, B., Senler, Ç. M., Parlaktuna, O. and Gürel, U., “Constructing Topological Map from Metric Map Using Spectral Clustering,” 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI) (2015) pp. 139145.Google Scholar
Kallmann, M., “Shortest Paths with Arbitrary Clearance from Navigation Meshes,” Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (2010) pp. 159168.Google Scholar
Barber, C., Dobkin, D. P. and Huhdanpaa, H., “The quickhull algorithm for convex hulls,” ACM Trans. Math. Software 22(4), 469483 (1996).Google Scholar
Graham, R. L., “A reevaluation of an efficient algorithm for determining the convex hull of a finite planar set,” Inf. Process. Lett. 7(1), 5355 (1978).Google Scholar
Preparata, F. P. and Hong, S. J., “Convex hulls of finite sets of points in two and three dimensions,” Commun. ACM 20(2), 8793 (1977).CrossRefGoogle Scholar
Park, M. G. and Lee, M. C., “Experimental Evaluation of Robot Path Planning by Artificial Potential Field Approach with Simulated Annealing,” Proceedings of the 41st SICE Annual Conference. SICE 2002 4, pp. 21902195, (2002).Google Scholar
Lee, M. C. and Park, M. G., “Artificial Potential Field Based Path Planning for Mobile Robots Using a Virtual Obstacle Concept,” Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003). 2, 735740 (2003).Google Scholar
Zhang, C., Hu, C., Feng, J., Liu, Z., Zhou, Y. and Zhang, Z., “A self-heuristic ant-based method for path planning of unmanned aerial vehicle in complex 3-D space with dense U-type obstacles,” IEEE Access. 7, 150775150791 (2019).CrossRefGoogle Scholar
Chen, H., Ji, Y. and Niu, L., “Reinforcement learning path planning algorithm based on obstacle area expansion strategy,” Intell. Serv. Robot. 13(2), 289297 (2020).CrossRefGoogle Scholar
Grisetti, G., Stachniss, C. and Burgard, W., “Improved techniques for grid mapping with Rao-Blackwellized particle filters,” IEEE Trans. Robot. 23(2), 3446 (2007).CrossRefGoogle Scholar