GA path planning for AUV to avoid moving obstacles based on forward looking sonar

Zong-hu, Chang; Tang, Zhaodong; Cai, Hansheng; Shi, Xiaohan; Bian, Xinqian

doi:10.1109/icmlc.2005.1527181

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…The modification involved the introduction of iterations consisting of additional runs of the algorithm with different initial conditions and the operator based on the random immigrants' mechanism, which sets the level of randomness of the developing population. Reference [72] discussed the framework of the collision-avoidance system based on the GA. The simulation test proved the proper functioning of the method for static and dynamic obstacles.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

Kot

2022

Electronics

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The rapid technological development of computing power and system operations today allows for increasingly advanced algorithm implementation, as well as path planning in real time. The objective of this article is to provide a structured review of simulations and practical implementations of collision-avoidance and path-planning algorithms in autonomous underwater vehicles (AUVs). The novelty of the review paper is to consider not only the results of numerical research but also the newest results of verifying collision-avoidance and path-planning algorithms in real applications together with a comparison of the difficulties encountered during simulations and their practical implementation. Analysing the last 20 years of AUV development, it can be seen that experiments in a real environment are dominated by classical methods. In the case of simulation studies, artificial intelligence (AI) methods are used as often as classical methods. In simulation studies, the APF approach is most often used among classical methods, whereas among AI algorithms reinforcement learning and fuzzy logic methods are used. For real applications, the most used approach is reactive behaviors, and AI algorithms are rarely used in real implementations. Finally, this article provides a general summary, future works, and a discussion of the limitations that inhibit the further development in this field.

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Section: Genetic Algorithmmentioning

confidence: 99%

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

Kot

2022

Electronics

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“…FL algorithms are most often combined with specific rules of behaviour that allow for avoiding both static and dynamic obstacles [33]- [35]. GA and PSO algorithms belong to the group of optimization algorithms that lead to solutions close to optimal without the need to analyse the entire map [36], [37]. However, the closer to optimal results, the higher the computational cost required, leading to longer calculations.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Collision Avoidance Systems With Local Path Planning for Autonomous Underwater Vehicles

Kot,

Szymak,

Piskur

et al. 2024

IEEE Access

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This article presents an extensive comparison of collision avoidance systems with local path planning based on a mathematical model of an autonomous underwater vehicle (AUV) controlled by the trajectory tracking control system. In the study, six methods such as improved artificial potential field (APF), D* algorithm, dynamic course adjustment (DCA), genetic algorithm (GA), particle swarm optimization (PSO) and rapidly-exploring random tree (RRT) were tested, the efficiency of which was verified on 10 test maps with various types of obstacles' configurations and shapes with taking into account real-time decision-making ability. Both obstacle detection system and collision avoidance systems were extensively fine-tuned to achieve a compromise between collision avoidance efficiency and minimization of computation time. Based on the prepared simulation framework and environment, the trajectory completion percentage, average trajectory deviation, trajectory energy consumption, computation time and percentage of collision trials were assessed. The best results in terms of AUV safety were achieved by the D* and DCA methods. Slightly worse results were obtained by GA and PSO. Considering real-time decision-making ability, the best results were obtained by DCA and APF methods. Based on a detailed analysis of the results, it can be concluded that the DCA method can be used in quick-react emergency systems, and D*, GA and PSO methods are reasonable to use in medium-range local collision avoidance systems.

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“…The current means of AUV underwater path planning mainly include the ant colony algorithm [ 5 , 6 ], fuzzy algorithm [ 7 ], genetic algorithm [ 8 , 9 ], algorithms based on neural networks [ 10 ], and algorithms based on the artificial potential field theory [ 11 , 12 ]. Zhang et al [ 13 ] used a combination of the ant colony algorithm and forward sonar to accomplish two-dimensional obstacle avoidance. Dong et al [ 14 ] implemented a path-planning method for AUVs, achieving a smoother generated path.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

Xing

Wang

Ding

et al. 2023

Sensors

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With the development of ocean exploration technology, the exploration of the ocean has become a hot research field involving the use of autonomous underwater vehicles (AUVs). In complex underwater environments, the fast, safe, and smooth arrival of target points is key for AUVs to conduct underwater exploration missions. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. In this paper, we propose a method to improve the local minimum in the artificial potential field (APF) to make AUVs out of the local minimum by constructing a traction force. The improved artificial potential field (IAPF) method is combined with DRL for path planning while optimizing the reward function in the DRL algorithm and using the generated path to optimize the future path. By comparing our results with the experimental data of various algorithms, we found that the proposed method has positive effects and advantages in path planning. It is an efficient and safe path-planning method with obvious potential in underwater navigation devices.

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GA path planning for AUV to avoid moving obstacles based on forward looking sonar

Cited by 11 publications

References 2 publications

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

Review of Collision Avoidance and Path Planning Algorithms Used in Autonomous Underwater Vehicles

A Comparative Study of Different Collision Avoidance Systems With Local Path Planning for Autonomous Underwater Vehicles

Improved Artificial Potential Field Algorithm Assisted by Multisource Data for AUV Path Planning

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