A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization
A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization
This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm
called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the
effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.
Double-layer ant colony optimization (DLACO), path planning, trajectory optimization, piecewise Bspline curve
Yang, Hui
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Qi, Jie
9bb92311-6fa1-48ed-9740-fc70874541eb
Miao, Yongchun
78b1e611-927c-4786-8bd2-817f06612253
Sun, Haixin
7af149b8-4f08-4068-8662-964388b2715d
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Yang, Hui
0b7fae32-c6f2-4b91-ae18-9b1e139ade5f
Qi, Jie
9bb92311-6fa1-48ed-9740-fc70874541eb
Miao, Yongchun
78b1e611-927c-4786-8bd2-817f06612253
Sun, Haixin
7af149b8-4f08-4068-8662-964388b2715d
Li, Jianghui
9c589194-00fa-4d42-abaf-53a32789cc5e
Yang, Hui, Qi, Jie, Miao, Yongchun, Sun, Haixin and Li, Jianghui
(2018)
A new robot navigation algorithm based on a double-layer ant algorithm and trajectory optimization.
IEEE Transactions on Industrial Electronics.
(doi:10.1109/TIE.2018.2886798).
Abstract
This paper presents an efficient double-layer ant colony algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms which run independently and successively. First, a parallel elite ant colony optimization (PEACO) method is proposed to generate an initial collision-free path in a complex map, and then we apply a path improvement algorithm
called turning point optimization algorithm (TPOA), in which the initial path is optimized in terms of length, smoothness and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the
effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.
Text
ALL_18-TIE-2089
- Accepted Manuscript
More information
Accepted/In Press date: 2 December 2018
e-pub ahead of print date: 24 December 2018
Keywords:
Double-layer ant colony optimization (DLACO), path planning, trajectory optimization, piecewise Bspline curve
Identifiers
Local EPrints ID: 426683
URI: http://eprints.soton.ac.uk/id/eprint/426683
ISSN: 0278-0046
PURE UUID: e28b8ce8-6ca0-4584-a453-51a7f92aa322
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Date deposited: 10 Dec 2018 17:31
Last modified: 16 Mar 2024 07:22
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Contributors
Author:
Hui Yang
Author:
Jie Qi
Author:
Yongchun Miao
Author:
Haixin Sun
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