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Open AccessArticle

An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle

1
College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
2
Transport College, Chongqing Jiaotong University, Chongqing 400074, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(11), 2640; https://doi.org/10.3390/s19112640
Received: 9 April 2019 / Revised: 27 May 2019 / Accepted: 5 June 2019 / Published: 11 June 2019
(This article belongs to the Special Issue Multi-Sensor Fusion and Data Analysis)
The genetic algorithm (GA) is an effective method to solve the path-planning problem and help realize the autonomous navigation for and control of unmanned surface vehicles. In order to overcome the inherent shortcomings of conventional GA such as population premature and slow convergence speed, this paper proposes the strategy of increasing the number of offsprings by using the multi-domain inversion. Meanwhile, a second fitness evaluation was conducted to eliminate undesirable offsprings and reserve the most advantageous individuals. The improvement could help enhance the capability of local search effectively and increase the probability of generating excellent individuals. Monte-Carlo simulations for five examples from the library for the travelling salesman problem were first conducted to assess the effectiveness of algorithms. Furthermore, the improved algorithms were applied to the navigation, guidance, and control system of an unmanned surface vehicle in a real maritime environment. Comparative study reveals that the algorithm with multi-domain inversion is superior with a desirable balance between the path length and time-cost, and has a shorter optimal path, a faster convergence speed, and better robustness than the others. View Full-Text
Keywords: genetic algorithm; unmanned surface vehicle; path planning; multi-domain inversion; Monte-Carlo simulation genetic algorithm; unmanned surface vehicle; path planning; multi-domain inversion; Monte-Carlo simulation
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MDPI and ACS Style

Xin, J.; Zhong, J.; Yang, F.; Cui, Y.; Sheng, J. An Improved Genetic Algorithm for Path-Planning of Unmanned Surface Vehicle. Sensors 2019, 19, 2640.

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