Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks

Xie, Shuguo; Garofano, Vittorio; Chu, Xiumin; Negenborn, Rudy R.

doi:10.1016/j.oceaneng.2019.106609

Cited by 64 publications

(21 citation statements)

References 66 publications

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“…Furthermore, the BSAS algorithm introduces additional parameters in the step size strategy and position update strategy, which makes it more likely to find the global optimal value. Because of the advantages mentioned above, the BSAS algorithm is widely used in many fields [ 26 , 27 , 28 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor α , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

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Section: Theoretical Backgroundmentioning

confidence: 99%

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Wang¹,

Gao²,

Wu³

et al. 2020

Entropy

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“…where x represents the angle that the own ship needs to adjust during avoiding collision, d E sum dt indicates that the variation of the midpoint field energy between this time and the previous time is mainly affected via the distance between own ship and obstacle ship, the field energy has negative correlation increasing with distance; sign is a symbolic function, whose function ensures that the smaller of field energy change, the better the evaluation function value; d is the minimum safety encounter distance, which refers to the minimum encounter distance needed to ensure the safe passage when an obstacle ship passing, and the value is given via the empirical formula in reference; 37 t is the time limit for collision avoidance, whose empirical formula is in reference; 30 DCPA (distance to closest point of approach) and TCPA(time to the closest point of approach) refers to the nearest distance and its time encountered by a ship. so the f ( x ) integrates the ship motion trend and the dynamic influence between ships, whose structure evaluates the approaching behavior of the ship from the change of field energy, and estimates the collision avoidance risk rate of the ship encounter distance and time, and takes the minimum value as the optimal collision avoidance decision.…”

Section: The Heuristic Planning Approachmentioning

confidence: 99%

“…In fact, in maritime practice, researchers usually combine those above algorithms to implement collision avoidance. Xie et al 30 Provided a good demonstration combining with COLREGs, the improved Q-learning beetle swarm antenna search algorithm, neural networks and model predictive control, considering both the accurate model and environmental interaction factors to enhance the ability of dealing with uncertain dynamics and achieve better optimization performance. In current, the joint approach with deep learning is still in the initial stage.…”

Section: Introductionmentioning

confidence: 99%

Deep learning structure for collision avoidance planning of unmanned surface vessel

Zheng

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The uncertain dynamics and environmental impacts in collision avoidance planning are always the key issues of autonomous navigation for unmanned surface vessel, due to the strong interactive ability and learning ability of deep learning, the combination of CNN and LSTM provide relevance and memory characteristics, which can extract the potential influence features of collision avoidance and correlation of historical strategies, to gain the more general trend of collision avoidance. Meanwhile, to make sure the real-time problem, the heuristic planning based on improved Electromagnetism-like mechanism algorithm is utilized to seek the optimal strategy with the rolling iteration of CNN–LSTM structure, for obtaining a simple and effective on-line scheme of collision avoidance. The complete algorithm has been tested and validated under the different situation comparing with the deterministic algorithm, it is worth mentioning that the algorithm can potentially be developed to advance collision-free planning especially in multiple vessels situation.

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“…Wu et al [8] used BAS algorithm to optimize obstacle avoidance systems of UAV and robot separately. Xie et al [11] [12], [13] applied BAS to the design and optimization of ship collision avoidance system and precise control of marine diesel engine. In addition, BAS is applied to the design of power system and the construction of stock investment model [9], [14], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

A Hardware Descriptive Approach to Beetle Antennae Search

Yue

Jiang

et al. 2020

IEEE Access

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Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms. The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform. However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required. To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA). Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip. We simulate our Verilog HDL based BAS module with the Modelsim platform. Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS. Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with 132.5 µ s latency for model implementation. INDEX TERMS FPGA, beetle antennae search, Verilog HDL, optimization algorithm.

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Model predictive ship collision avoidance based on Q-learning beetle swarm antenna search and neural networks

Cited by 64 publications

References 66 publications

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm

Deep learning structure for collision avoidance planning of unmanned surface vessel

A Hardware Descriptive Approach to Beetle Antennae Search

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