LAPSE:2018.0545
Published Article
LAPSE:2018.0545
Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization
Dong Xu, Luo Yu, Zhiyu Lv, Jiahuang Zhang, Di Fan, Wei Dai
September 21, 2018
The traditional leader-follower formation algorithm can realize the formation of multiply robotic fishes, but fails to consider the energy consumption during the formation. In this paper, the energy optimized leader-follower formation algorithm has been investigated to solve this problem. Considering that the acceleration of robotic fish is tightly linked to the motion state and energy consumption, we optimize the corresponding control parameters of the acceleration to reduce energy consumption during the formation via particle swarm algorithm. The whole process has been presented as follows: firstly we realize the formation on the base of the kinematic model with leader-follower formation algorithm; then the energy consumption on the base of dynamical model are derived; finally we seek the optimal control parameters based on the particle swarm optimization (PSO) algorithm. The dynamics simulation of the energy optimization scheme is conducted to verify the functionality of the proposed energy optimized leader-follower formation algorithm via MATLAB. The optimized results demonstrate that the proposed approach, reducing energy consumption during the formation, is superior to the traditional leader-follower formation algorithm and can reduce energy consumption during the formation. The novelty of the work is that we can reduce the energy consumption during the process of formation by considering the energy consumption, which is a gap in the current research field.
Keywords
energy consumption, leader-follower formation flocking, parameter optimization, robotic fish
Suggested Citation
Xu D, Yu L, Lv Z, Zhang J, Fan D, Dai W. Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization. (2018). LAPSE:2018.0545
Author Affiliations
Xu D: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Yu L: School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
Lv Z: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Zhang J: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Fan D: Department of Computer Science, USC Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90089, USA
Dai W: School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China [ORCID]
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Journal Name
Energies
Volume
11
Issue
8
Article Number
E2023
Year
2018
Publication Date
2018-08-03
Published Version
ISSN
1996-1073
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Original Submission
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PII: en11082023, Publication Type: Journal Article
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LAPSE:2018.0545
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doi:10.3390/en11082023
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Sep 21, 2018
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Sep 21, 2018
 
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Calvin Tsay
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