Optimal Path Planning Method for Unmanned Surface Vehicles Based on Improved Shark-Inspired Algorithm

Liang, Jingrun; Liu, Lisang

doi:10.3390/jmse11071386

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…In addition, nature-based algorithms can also provide better performance than conventional approaches because nature algorithms involve the learning and optimization scenario. Some studies have been conducted that use nature-based algorithms for USV path planning, such as the improved biological-inspired neural network [17], trajectory-cell-based algorithm [18], bacterial foraging optimization algorithm [19], improved shark-inspired algorithm [20], and plant growth algorithms [21]. Physics-based algorithms also provide methods like the artificial potential field approach [22].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm

Hamid,

Dharmawan,

Nambo

2023

ASI

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Unmanned surface vehicles (USVs) are experiencing significant development across various fields due to extensive research, enabling these devices to offer substantial benefits. One kind of research that has been developed to produce better USVs is path planning. Despite numerous research efforts employing conventional algorithms, deep reinforcement learning, and evolutionary algorithms, USV path planning research consistently faces the challenge of effectively addressing issues within dynamic surface environments where USVs navigate. This study aims to solve USV dynamic environmental problems, as well as convergence problems in evolutionary algorithms. This research proposes a neuronal genetic algorithm that utilizes neural network input for processing with a genetic operator. The modifications in this research were implemented by incorporating a partially exponential-based fitness function into the neuronal genetic algorithm. We also implemented an inverse time variable to the fitness function. These two modifications produce faster convergence. Based on the experimental results, which were compared to those of the basic neural-network-based genetic algorithms, the proposed method can produce faster convergent solutions for USV path planning with competitive performance for total distance and time traveled in both static and dynamic environments.

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Section: Related Workmentioning

confidence: 99%

Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm

Hamid,

Dharmawan,

Nambo

2023

ASI

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“…Ref. [24] used circle chaotic mapping, adaptive weight factor, and the simplex method to improve the initial solution and spatial search efficiency and accelerate the convergence of the algorithm. Optimal path information planned by the improved WSO is put into the DWA to enhance the USV's navigation performance.…”

Section: Introductionmentioning

confidence: 99%

Unmanned Vessel Collision Avoidance Algorithm by Dynamic Window Approach Based on COLREGs Considering the Effects of the Wind and Wave

Yuan,

Tong,

et al. 2023

JMSE

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In recent years, the rapid development of artificial intelligence algorithms has promoted the intelligent transformation of the ship industry; unmanned surface vessels (USVs) have become a widely used representative product. The dynamic window approach (DWA) is an effective robotic collision avoidance algorithm; however, there are deficiencies in its application to the ship field. First, the DWA algorithm does not consider International Regulations for Preventing Collisions at Sea (COLREGs), which must be met for ship collision avoidance to ensure the navigational safety of the USV and other ships. Second, the DWA algorithm does not consider the influence of wind and waves on the collision avoidance of USVs in actual navigational environments. Reasonable use of windy and wavy environments not only improves navigational safety but also saves navigational time and fuel consumption, thereby improving the economy. Therefore, this paper proposes an improvement algorithm by DWA referred to as utility DWA (UDWA) based on COLREGs considering the sailing environment. The velocity sampling area was improved by dividing the priority, and the velocity function in the objective function was enhanced to convert the effect of wind and waves on the USVs into a change in velocity. The simulation results showed that the UDWA algorithm optimized the distance to the obstacle ship by 43.25%, 31.36%, and 67.81% in a head-on situation, crossing situation, and overtaking situation, respectively, compared to the COLREGs-compliant DWA algorithm, which considers the COLREGs. The improved algorithm not only follows the COLREGs but also has better flexibility in emergency collision avoidance and can safely and economically navigate and complete collision avoidance in windy and wavy environments.

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“…Underwater acoustic guidance refers to the application of acoustic equipment such as long baseline (LBL) systems, short baseline (SBL) systems and ultra-short baseline (USBL) systems for guidance. The advantage of acoustic guidance is that it works over long distances, up to 3 km, and can be omnidirectional [23,24]. The disadvantage is that the positioning accuracy is low, the real-time performance is poor and it is easily exposed [25,26].…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustically Guided Docking Method Based on Multi-Stage Planning

Xu,

Yang,

Bai

et al. 2023

JMSE

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Autonomous underwater vehicles (AUVs) are important in areas such as underwater scientific research and underwater resource collection. However, AUVs suffer from data portability and energy portability problems due to their physical size limitation. In this work, an acoustic guidance method for underwater docking is proposed to solve the problem of persistent underwater operation. A funnel docking station and an autonomous remotely operated vehicle (ARV) are used as the platform for designing the guidance algorithms. First, the underwater docking guidance is divided into three stages: a long-range approach stage, a mid-range adjustment stage and a short-range docking stage. Second, the relevant guidance strategy is designed for each stage to improve the docking performance. Third, a correction method based on an ultra-short baseline (USBL) system is proposed for the ARV’s estimate of the depth, relative position and orientation angle of the docking station. To verify the feasibility of the docking guidance method, in this work, tests were performed on a lake and in a shallow sea. The success rate of autonomous navigation docking on the lake was 4 out of 7. The success rate of acoustic guidance docking on the lake and in the shallow sea were 11 out of 14 and 6 out of 8, respectively. The experimental results show the effectiveness of the docking guidance method in lakes and shallow seas.

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Optimal Path Planning Method for Unmanned Surface Vehicles Based on Improved Shark-Inspired Algorithm

Cited by 12 publications

References 33 publications

Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm

Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm

Unmanned Vessel Collision Avoidance Algorithm by Dynamic Window Approach Based on COLREGs Considering the Effects of the Wind and Wave

Underwater Acoustically Guided Docking Method Based on Multi-Stage Planning

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