Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment

Autonomous underwater vehicle (AUV) path planning in complex marine environments meets many chanllenges, such as many influencing factors, complex models and the performance of the optimization algorithm to be improved. To find a path with minimum cost of the actual environmental threats, navigation...

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Vydáno v:	Journal of marine science and engineering Ročník 10; číslo 11; s. 1695
Hlavní autoři:	Li, Bin, Mao, Jianlin, Yin, Shuyi, Fu, Lixia, Wang, Yan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2022
Témata:	Adaptive algorithms Algorithms Altitude Autonomous underwater vehicles AUV path planning Convergence Energy consumption Environmental impact Flow velocity Food Foraging behavior global optimality improved sparrow search algorithm Jumping Marine environment Methods Minimum cost Mutation Navigation Ocean currents Optimization Optimization algorithms Path planning Search algorithms Search methods Threats time-varying currents Underwater robots Underwater vehicles
ISSN:	2077-1312, 2077-1312
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Abstract	Autonomous underwater vehicle (AUV) path planning in complex marine environments meets many chanllenges, such as many influencing factors, complex models and the performance of the optimization algorithm to be improved. To find a path with minimum cost of the actual environmental threats, navigation height changes and the minimum energy consumption of the AUV, an improved sparrow search algorithm is designed under the impact of the time-varying characteristics of the current in the complex marine environment on the AUV and the physical constraints of the AUV movement. In the proposed algorithm, an adaptive weight factor balance strategy is introduced into the position update of the discoverer to improve the convergence speed and search ability. In the follower position, the variable spiral search strategy is improved based on the position update formula to enhance the local jumping ability of the algorithm. After the position update, Levy flying strategy is added, and Cauchy–Gaussian mutation is performed on the optimal individual to increase the population diversity, which improves the anti-stagnation ability of the algorithm. Finally, the AUV global path with minimum cost in the sense of multi-objective weighting is obtained. The simulation results show that the proposed algorithm can plan a path with less cost and quickly converge to an optimized point, and finally it can meet the global path planning requirements of AUV when navigating in complex sea areas.
AbstractList	Autonomous underwater vehicle (AUV) path planning in complex marine environments meets many chanllenges, such as many influencing factors, complex models and the performance of the optimization algorithm to be improved. To find a path with minimum cost of the actual environmental threats, navigation height changes and the minimum energy consumption of the AUV, an improved sparrow search algorithm is designed under the impact of the time-varying characteristics of the current in the complex marine environment on the AUV and the physical constraints of the AUV movement. In the proposed algorithm, an adaptive weight factor balance strategy is introduced into the position update of the discoverer to improve the convergence speed and search ability. In the follower position, the variable spiral search strategy is improved based on the position update formula to enhance the local jumping ability of the algorithm. After the position update, Levy flying strategy is added, and Cauchy–Gaussian mutation is performed on the optimal individual to increase the population diversity, which improves the anti-stagnation ability of the algorithm. Finally, the AUV global path with minimum cost in the sense of multi-objective weighting is obtained. The simulation results show that the proposed algorithm can plan a path with less cost and quickly converge to an optimized point, and finally it can meet the global path planning requirements of AUV when navigating in complex sea areas.
Author	Li, Bin Yin, Shuyi Mao, Jianlin Fu, Lixia Wang, Yan
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SubjectTerms	Adaptive algorithms Algorithms Altitude Autonomous underwater vehicles AUV path planning Convergence Energy consumption Environmental impact Flow velocity Food Foraging behavior global optimality improved sparrow search algorithm Jumping Marine environment Methods Minimum cost Mutation Navigation Ocean currents Optimization Optimization algorithms Path planning Search algorithms Search methods Threats time-varying currents Underwater robots Underwater vehicles
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Title	Path Planning of Multi-Objective Underwater Robot Based on Improved Sparrow Search Algorithm in Complex Marine Environment
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