Advanced search
1 file | 1.96 MB Add to list

Scheduled maintenance: Publication downloads temporarily unavailable.

Due to maintenance publication downloads will not be available on:

  • Wednesday, March 27, 17:00 – 21:00
  • Thursday, March 28, 17:00 – 21:00

Exports of lists, FWO and BOF information will remain available.

For any questions, please contact biblio@ugent.be. Apologies for any inconveniences, and thank you for your understanding.

A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization

(2017) APPLIED SOFT COMPUTING. 59. p.68-76
Author
Organization
Abstract
In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach. (C) 2017 Elsevier B.V. All rights reserved.
Keywords
PSO, Multi-objective optimization, Pareto front, Constraints optimization, Mobile robot, Optimal path planning

Downloads

  • (...).pdf
    • full text
    • |
    • UGent only
    • |
    • PDF
    • |
    • 1.96 MB

Citation

Please use this url to cite or link to this publication:

MLA
Mac Thi, Thoa, et al. “A Hierarchical Global Path Planning Approach for Mobile Robots Based on Multi-Objective Particle Swarm Optimization.” APPLIED SOFT COMPUTING, vol. 59, 2017, pp. 68–76, doi:10.1016/j.asoc.2017.05.012.
APA
Mac Thi, T., Copot, C., Duc Trung Tran, D. T. T., & De Keyser, R. (2017). A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. APPLIED SOFT COMPUTING, 59, 68–76. https://doi.org/10.1016/j.asoc.2017.05.012
Chicago author-date
Mac Thi, Thoa, Cosmin Copot, Duc Trung Tran Duc Trung Tran, and Robain De Keyser. 2017. “A Hierarchical Global Path Planning Approach for Mobile Robots Based on Multi-Objective Particle Swarm Optimization.” APPLIED SOFT COMPUTING 59: 68–76. https://doi.org/10.1016/j.asoc.2017.05.012.
Chicago author-date (all authors)
Mac Thi, Thoa, Cosmin Copot, Duc Trung Tran Duc Trung Tran, and Robain De Keyser. 2017. “A Hierarchical Global Path Planning Approach for Mobile Robots Based on Multi-Objective Particle Swarm Optimization.” APPLIED SOFT COMPUTING 59: 68–76. doi:10.1016/j.asoc.2017.05.012.
Vancouver
1.
Mac Thi T, Copot C, Duc Trung Tran DTT, De Keyser R. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. APPLIED SOFT COMPUTING. 2017;59:68–76.
IEEE
[1]
T. Mac Thi, C. Copot, D. T. T. Duc Trung Tran, and R. De Keyser, “A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization,” APPLIED SOFT COMPUTING, vol. 59, pp. 68–76, 2017.
@article{8535198,
  abstract     = {{In this paper, a novel hierarchical global path planning approach for mobile robots in a cluttered environment is proposed. This approach has a three-level structure to obtain a feasible, safe and optimal path. In the first level, the triangular decomposition method is used to quickly establish a geometric free configuration space of the robot. In the second level, Dijkstra's algorithm is applied to find a collision-free path used as input reference for the next level. Lastly, a proposed particle swarm optimization called constrained multi-objective particle swarm optimization with an accelerated update methodology based on Pareto dominance principle is employed to generate the global optimal path with the focus on minimizing the path length and maximizing the path smoothness. The contribution of this work consists in: (i) The development of a novel optimal hierarchical global path planning approach for mobile robots moving in a cluttered environment; (ii) The development of proposed particle swarm optimization with an accelerated update methodology based on Pareto dominance principle to solve robot path planning problems; (iii) Providing optimal global robot paths in terms of the path length and the path smoothness taking into account the physical robot system limitations with computational efficiency. Simulation results in various types of environments are conducted in order to illustrate the superiority of the hierarchical approach. (C) 2017 Elsevier B.V. All rights reserved.}},
  author       = {{Mac Thi, Thoa and Copot, Cosmin and Duc Trung Tran, Duc Trung Tran and De Keyser, Robain}},
  issn         = {{1568-4946}},
  journal      = {{APPLIED SOFT COMPUTING}},
  keywords     = {{PSO,Multi-objective optimization,Pareto front,Constraints optimization,Mobile robot,Optimal path planning}},
  language     = {{eng}},
  pages        = {{68--76}},
  title        = {{A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization}},
  url          = {{http://doi.org/10.1016/j.asoc.2017.05.012}},
  volume       = {{59}},
  year         = {{2017}},
}

Altmetric
View in Altmetric
Web of Science
Times cited: