Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

Xie, Shuguo; Chu, Xiumin; Zheng, Mao; Liu, Chenguang

doi:10.1016/j.oceaneng.2019.106542

Cited by 75 publications

(48 citation statements)

References 48 publications

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“…Similarly as in Wu et al (2020) and Xie, Chu, Zheng, and Liu (2019) , by running empirical simulations, it can be concluded that some components of the original BAS metaheuristics could be enhanced. The main drawbacks of the basic BAS refer to the premature convergence with implications that the search process may be trapped in local optimums.…”

Section: Proposed Hybrid Machine Learning Methodsmentioning

confidence: 86%

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Živković

Bačanin

Venkatachalam

et al. 2021

Sustainable Cities and Society

272

105

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Section: Proposed Hybrid Machine Learning Methodsmentioning

confidence: 86%

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

Živković

Bačanin

Venkatachalam

et al. 2021

Sustainable Cities and Society

272

105

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“…The result shows that the algorithm is superior with a desirable balance between the path length and time-cost, and it has a shorter optimal path, a faster convergence speed, and better robustness. Xie, S et al [27] proposed a predictive collision avoidance method for under-actuated surface ships based on the improved beetle antenna search (BAS) method. A predictive optimization strategy for real-time collision avoidance is established and COLREGS is used as a constraint condition while considering the minimization of safety and economic cost.…”

Section: Introductionmentioning

confidence: 99%

An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning

Guo

Zhang

Zheng

et al. 2020

Sensors

153

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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.

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“…The beetle antennae search (BAS) algorithm is a new type of computational intelligence algorithms which is applied in scientific and engineering applications, e.g., geomechanics analysis [22], network training [23], controller parameter tuning in servo system [24], unmanned aerial vehicle path planning [25], route planning [26], pattern classification [23], power system [27], ship predictive collision avoidance [28], bridge sensor placement [29], and investment portfolio problems [30]. Because of the excellent nonlinear optimization ability, BAS can be regarded as a potential efficient optimizer in robot applications.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

Cheng

et al. 2020

IEEE Access

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Redundant manipulators play important roles in many industrial and service applications by assisting people fulfill heavy and repetitive jobs. However, redundant manipulators are coupled highlynonlinear systems which exert difficulty of redundancy resolution computation. Conventional methods such as pseudo-inverse-based approaches obtain the resolved joint angles from joint velocity level, which may bring about more computational cost and may neglect joint velocity limits. In this work, a motion planning method based on beetle antennae search algorithm (BAS) is proposed for motion planning of redundant manipulators with the variable joint velocity limit. Such proposed work does not need to resolve the velocity kinematics equation as the conventional methods do, and the proposed method can directly deal with the forward kinematics equation to resolve the desired joint angles. The simulation and experiment on the five-link planar manipulator and the Kuka industrial manipulator system demonstrate the efficiency of the proposed method for motion planning of redundant manipulator, and reveal the reliable performance of the BAS algorithm as compared with genetic algorithm (GA), particle swarm optimization (PSO), firefly algorithm(FA) and quantum behaved particle swarm algorithm(QPSO) methods.

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Ship predictive collision avoidance method based on an improved beetle antennae search algorithm

Cited by 75 publications

References 48 publications

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

COVID-19 cases prediction by using hybrid machine learning and beetle antennae search approach

An Autonomous Path Planning Model for Unmanned Ships Based on Deep Reinforcement Learning

Motion Planning of Redundant Manipulator With Variable Joint Velocity Limit Based on Beetle Antennae Search Algorithm

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