Ship predictive collision avoidance method based on an improved beetle antennae search algorithm
TL;DR: A predictive collision avoidance method based on an improved beetle antennae search (BAS) algorithm for underactuated surface vessels is proposed, and an improved BAS algorithm is proposed to enhance the optimization performance of the original BAS algorithm under the known constraints, which is applied to solve the predictive collisions avoidance problem.
About: This article is published in Ocean Engineering.The article was published on 2019-11-15. It has received 52 citations till now. The article focuses on the topics: Collision avoidance & Collision.
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TL;DR: Wang et al. as mentioned in this paper proposed a hybrid approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics to predict the number of the COVID-19 cases.
167 citations
TL;DR: This paper proposes enhancements to Beetle Antennae search algorithm to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule.
Abstract: In this paper, we propose enhancements to Beetle Antennae search ( BAS ) algorithm, called BAS-ADAM, to smoothen the convergence behavior and avoid trapping in local-minima for a highly non-convex objective function. We achieve this by adaptively adjusting the step-size in each iteration using the adaptive moment estimation ( ADAM ) update rule. The proposed algorithm also increases the convergence rate in a narrow valley. A key feature of the ADAM update rule is the ability to adjust the step-size for each dimension separately instead of using the same step-size. Since ADAM is traditionally used with gradient-based optimization algorithms, therefore we first propose a gradient estimation model without the need to differentiate the objective function. Resultantly, it demonstrates excellent performance and fast convergence rate in searching for the optimum of non-convex functions. The efficiency of the proposed algorithm was tested on three different benchmark problems, including the training of a high-dimensional neural network. The performance is compared with particle swarm optimizer ( PSO ) and the original BAS algorithm.
133 citations
TL;DR: The results show that the improved DRL can achieve autonomous path planning, and it has good convergence speed and stability.
Abstract: Deep reinforcement learning (DRL) has excellent performance in continuous control problems and it is widely used in path planning and other fields. An autonomous path planning model based on DRL is proposed to realize the intelligent path planning of unmanned ships in the unknown environment. The model utilizes the deep deterministic policy gradient (DDPG) algorithm, through the continuous interaction with the environment and the use of historical experience data; the agent learns the optimal action strategy in a simulation environment. The navigation rules and the ship's encounter situation are transformed into a navigation restricted area, so as to achieve the purpose of planned path safety in order to ensure the validity and accuracy of the model. Ship data provided by ship automatic identification system (AIS) are used to train this path planning model. Subsequently, the improved DRL is obtained by combining DDPG with the artificial potential field. Finally, the path planning model is integrated into the electronic chart platform for experiments. Through the establishment of comparative experiments, the results show that the improved model can achieve autonomous path planning, and it has good convergence speed and stability.
115 citations
Cites methods from "Ship predictive collision avoidance..."
...[27] proposed a predictive collision avoidance method for under-actuated surface ships based on the improved beetle antenna search (BAS) method....
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TL;DR: A path planning strategy unified with a collision avoidance function based on deep reinforcement learning (DRL) is proposed, and it is shown that the enhanced DRL can effectively realize autonomous collision avoidance path planning.
95 citations
TL;DR: The proposed method may assist with the identification of critical scenarios in various voyages not currently accounted for by existing accident databases, the definition of commonly agreed risk criteria to set off alarms, and the estimation of risk profile over the life cycle of fleet operations.
94 citations
Cites methods from "Ship predictive collision avoidance..."
...033, respectively, which are determined by quantifying the difficulty of ship avoidance in various conflicts using the navigation simulator [78,96]....
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References
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TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Abstract: Although the problem of determining the minimum cost path through a graph arises naturally in a number of interesting applications, there has been no underlying theory to guide the development of efficient search procedures. Moreover, there is no adequate conceptual framework within which the various ad hoc search strategies proposed to date can be compared. This paper describes how heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching and demonstrates an optimality property of a class of search strategies.
10,366 citations
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TL;DR: The proposed beetle antennae search algorithm (BAS) imitates the function of antennae and the random walking mechanism of beetles in nature, and then two main steps of detecting and searching are implemented.
Abstract: Meta-heuristic algorithms have become very popular because of powerful performance on the optimization problem. A new algorithm called beetle antennae search algorithm (BAS) is proposed in the paper inspired by the searching behavior of longhorn beetles. The BAS algorithm imitates the function of antennae and the random walking mechanism of beetles in nature, and then two main steps of detecting and searching are implemented. Finally, the algorithm is benchmarked on 2 well-known test functions, in which the numerical results validate the efficacy of the proposed BAS algorithm.
276 citations
25 Apr 2018
TL;DR: In this paper, a new algorithm called beetle antennae search algorithm (BAS) is proposed in the paper inspired by the searching behavior of longhorn beetles, which imitates the function of antennae and the random walking mechanism of beetles in nature.
Abstract: Meta-heuristic algorithms have become very popular because of powerful performance on the optimization problem. A new algorithm called beetle antennae search algorithm (BAS) is proposed in the paper inspired by the searching behavior of long-horn beetles. The BAS algorithm imitates the function of antennae and the random walking mechanism of beetles in nature, and then two main steps of detecting and searching are implemented. Finally, the algorithm is benchmarked on 2 well-known test functions, in which the numerical results validate the efficacy of the proposed BAS algorithm.
227 citations
TL;DR: Even after embargo period expires, authors' right to distribute as green open access is conditional on the green openAccess version including a DOI link, and on thegreen open access version being distributed under the Creative Commons CC-BY-NC-ND licence.
169 citations
TL;DR: In this paper, a systematic and critical review of the newer ship domain models and related research is presented, which discusses multiple differences in approach to ship domain concept: from definitions and safety criteria, through research methodologies and factors taken into account, to sometimes largely different results obtained by various authors.
167 citations