Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-25T04:18:38.595Z Has data issue: false hasContentIssue false
  • English
  • Français

A global path planning method for mobile robot based on a three-dimensional-like map

Published online by Cambridge University Press:  07 October 2013

Yaonan Wang  and
Wenming Cao
Yaonan Wang
Affiliation:
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
Wenming Cao*
Affiliation:
College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a novel global path planning method for mobile robots. An improved grid map, called three-dimensional-like map, is developed to represent the global workspace area. The new environment model includes not only contour information of obstacles but also artificial height information. Based on this new model, a simple but efficient obstacle avoidance algorithm is developed to solve robot path planning problems in static environment. The proposed algorithm only requires simple distance calculations and several comparison operations. In addition, unlike other algorithms, the proposed algorithm only needs to deal with some obstacles instead of all. The research results show that this method is computationally efficient and can be used to find an optimal or near optimal path.

Keywords

Three-dimensional-like mapGlobal static environmentObstacle avoidance algorithm
Type
Articles
Information
Robotica , Volume 32 , Issue 4 , July 2014 , pp. 611 - 624
Copyright
Copyright © Cambridge University Press 2013 

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

1.Lozano-Pérez, T. and Wesley, M. A., “An algorithm for planning collision-free paths among polyhedral obstacles,” Commun. ACM 22, 560–570 (1979).CrossRefGoogle Scholar
2.Leven, D. and Sharir, M., “Planning a purely translational motion for a convex object in two-dimensional space using generalized Voronoi diagrams,” Discrete Comput. Geom. 2, 9–31 (1987).CrossRefGoogle Scholar
3.Bhattacharya, P. and Gavrilova, M. L., “Roadmap-based path planning-using the Voronoi diagram for a clearance-based shortest path,” IEEE Robot. Autom. Mag., 15, 58–66 (2008).CrossRefGoogle Scholar
4.Brooks, R. A., “Solving the find-path problem by good representation of free space,” IEEE Trans. Syst. Man Cybern. 13, 190–197 (1983).CrossRefGoogle Scholar
5.Arney, T., “An Efficient Solution to Autonomous Path Planning by Approximate Cell Decomposition,” Proceedings of the 3rd International Conference on Information and Automation for Sustainability, Melbourne, VIC (4–6, 2007) pp. 88–93.Google Scholar
6.Alexopoulos, C. and Griffin, P. M., “Path planning for a mobile robot,” IEEE Trans. Syst. Man Cybern. 22, 318–322 (1992).CrossRefGoogle Scholar
7.Yahja, A., Singh, S. and Stentz, A., “An efficient on-line path planner for outdoor mobile robots,” Robot. Auton. Syst. 32, 129–143 (2000).CrossRefGoogle Scholar
8.Khatib, O., “Real-time obstacle avoidance for manipulators and mobile robots,” Int. J. Robot. Res. 5, 90–98 (1986).CrossRefGoogle Scholar
9.Glasius, R., Komoda, A. and Gielen, S. C. A. M., “Neural network dynamics for path planning and obstacle avoidance,” Neural Netw. 8, 125–133 (1995).CrossRefGoogle Scholar
10.Zhang, P., Lu, T. and Song, L., “Soccer robot path planning based on the artificial potential field approach with simulated annealing,” Robotica 22, 563–566 (2004).CrossRefGoogle Scholar
11.Brand, M., Masuda, M., Wehner, N. and Yu, X., “Ant Colony Optimization Algorithm for Robot Path Planning,” Proceedings of the 2010 International Conference On Computer Design and Applications, vol. 3 (2010) pp. 436–440.Google Scholar
12.Tsourveloudis, N., Valavanis, K. P. and Hebert, T., “Autonomous vehicle navigation utilizing electrostatic potentialfields and fuzzy logic,” IEEE Trans. Robot. Autom. 17, 490–497 (2001).CrossRefGoogle Scholar
13.Taharwa, A., Sheta, A. and Weshah, M. A., “A mobile robot path planning using genetic algorithm in static environment,” J. Comput. Sci. 4, 341–344 (2008).CrossRefGoogle Scholar
14.Buniyamin, N., Sariff, N., Wan Ngah, W. A. J. and Mohamda, Z., “Robot global path planning overview and a variation of ant colony system algorithm,” Int. J. Math. Comput. Simul. 1, 9–16 (2011).Google Scholar
15.Tan, G. Z., He, H. and Sloman, A., “Ant colony system algorithm for real-time globally optimal path planning of mobile robots,” Acta Autom. Sin. 33 (3), 279–285 (2007).CrossRefGoogle Scholar
16.Chen, X., Kong, Y., Fang, X. and Wu, Q., “A fast two-stage ACO algorithm for robotic path planning,” Neural Comput. Appl. 22 (2), 313–319 (2013).CrossRefGoogle Scholar
17.Wang, Y. and Lane, D. M., “Sub-sea vehicle path planning using nonlinear programming and constructive solid geometry,” IEEE Proc. D Control Theory Appl. 144 (2), 143–152 (1997).CrossRefGoogle Scholar
18.Wang, Y. and Lane, D. M., “Solving a generalized constrained optimization problem with both logic AND and OR relationships by a mathematical transformation and its application to robot path planning,” IEEE Trans. Syst. Man Cybern. Appl. Rev. 30 (4), 525–536 (2000).CrossRefGoogle Scholar
19.Wang, Y., Liu, H., Li, M., Wang, Q., Zhou, J. and Cartmell, M., “A real-time path planning approach without the computation of Cspace obstacles,” Robotica 22 (2), 173–187 (2004).CrossRefGoogle Scholar
20.Wang, Y., Cartmell, M., Tao, Q. and Liu, H., “A generalized real-time obstacle avoidance method without the cspace calculation,” J. Comput. Sci. Technol. 20 (6), 774–787 (2005).CrossRefGoogle Scholar
21.Wang, Y., Lane, D. M. and Falconer, G. J., “Two novel approaches for unmanned underwater vehicle path planning: constrained optimization and semi-infinite constrained optimization,” Robotica 18, 123–142 (2000).CrossRefGoogle Scholar