GA-based path planning for multiple AUVs
GA-based path planning for multiple AUVs
In this paper, the genetic algorithm (GA), which is a simulation of Darwinian evolution and an efficient way for large-scale optimization subject to non-linear constraints, is applied to find economical and safe routes for a swarm of AUVs to revisit an area with waypoints and obstacles which are known a priori. The algorithm can be divided into three phases: (1) waypoint assignment: allocating the waypoints to individual AUVs; (2) route optimization: minimizing the total journey of the vehicles and (3) route validation: checking whether there exist stationary and/or moving collisions. A case study for three AUVs to survey a given area is also presented to verify the algorithm.
1180-1185
Wu, X.
b6ec2d37-5357-42db-a350-a73969909549
Feng, Z.
72a3bad8-4857-4dca-a5f7-f18b1ffaff63
Zhu, J.
24933408-2227-4c23-acf1-ea2c7857a952
Allen, R.
956a918f-278c-48ef-8e19-65aa463f199a
July 2007
Wu, X.
b6ec2d37-5357-42db-a350-a73969909549
Feng, Z.
72a3bad8-4857-4dca-a5f7-f18b1ffaff63
Zhu, J.
24933408-2227-4c23-acf1-ea2c7857a952
Allen, R.
956a918f-278c-48ef-8e19-65aa463f199a
Wu, X., Feng, Z., Zhu, J. and Allen, R.
(2007)
GA-based path planning for multiple AUVs.
International Journal of Control, 80 (7), .
(doi:10.1080/00207170601145289).
Abstract
In this paper, the genetic algorithm (GA), which is a simulation of Darwinian evolution and an efficient way for large-scale optimization subject to non-linear constraints, is applied to find economical and safe routes for a swarm of AUVs to revisit an area with waypoints and obstacles which are known a priori. The algorithm can be divided into three phases: (1) waypoint assignment: allocating the waypoints to individual AUVs; (2) route optimization: minimizing the total journey of the vehicles and (3) route validation: checking whether there exist stationary and/or moving collisions. A case study for three AUVs to survey a given area is also presented to verify the algorithm.
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Published date: July 2007
Additional Information:
Special Issue on Navigation, Guidance and Control of Uninhabited Underwater Vehicles
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Local EPrints ID: 49575
URI: http://eprints.soton.ac.uk/id/eprint/49575
ISSN: 0020-3270
PURE UUID: e1c8d1de-6053-475b-b3c8-83d21eb33bca
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Date deposited: 15 Nov 2007
Last modified: 15 Mar 2024 09:57
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Author:
X. Wu
Author:
Z. Feng
Author:
J. Zhu
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