Design and Qualification of a Latching System for Experimental Undercarriage System for 6000 m depth

Janarthanan, C.; sunder, M. Shyam; Sundaramoorthi, V.; Vishwanath, B. O.; Chandran, V.; Rajesh, S.; Muthuvel, P.; Ramesh, N.R.; Gopkumar, K.; Ramadass, G. A.; Atmanand, M. A.

doi:10.1109/oceans.2018.8604818

Cited by 2 publications

(4 citation statements)

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“…The algorithm combines a deep learning network based on the Q-learning algorithm and approximates the Q-value function through a neural network. Q-learning is a classical reinforcement learning algorithm, and its parameter update formula is shown in Equation (2). In Equation (2), 𝛼 represents the learning rate, 𝛾 is the discount factor, and Q(s t , a t ) signifies the Q-value associated with different actions in various environments.…”

Section: The Dqn Algorithmmentioning

confidence: 99%

“…Q-learning is a classical reinforcement learning algorithm, and its parameter update formula is shown in Equation (2). In Equation (2), 𝛼 represents the learning rate, 𝛾 is the discount factor, and Q(s t , a t ) signifies the Q-value associated with different actions in various environments. The implementation principle of Q-learning is to select an action a t and execute it according to a greedy algorithm based on the current state s t , and finally, obtain the corresponding reward r t .…”

Section: The Dqn Algorithmmentioning

confidence: 99%

“…Currently, a deep‐sea mining system consisting of a surface mining ship, buffer station, pump, lifting steel pipe, flexible hose, and tracked submarine mining vehicle is internationally recognized as the best deep‐sea mining solution. [ 2 ] A submarine mining vehicle carries mining equipment on the seafloor for mobile collection operations and transfers minerals to buffer stations by flexible hoses. Then the buffer station uses pumps to transport the collected minerals from the seafloor to the mining ship at the surface by lifting a steel pipe.…”

Section: Introductionmentioning

confidence: 99%

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An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Xing,

Wang,

Liu

2024

Advcd Theory and Sims

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This paper proposes a deep reinforcement learning algorithm to achieve complete coverage path planning for deep‐sea mining vehicle clusters. First, the mining vehicles and the deep‐sea mining environment are modeled. Then, this paper implements a series of algorithm designs and optimizations based on Deep Q Networks (DQN). The map fusion mechanism can integrate the grid matrix data from multiple mining vehicles to get the state matrix of the complete environment. In this paper, a preprocessing method for the state matrix is also designed to provide suitable training data for the neural network. The reward function and action selection mechanism of the algorithm are also optimized according to the requirements of cluster cooperative operation. Furthermore, the algorithm uses distance constraints to prevent the entanglement of underwater hoses. To improve the training efficiency of the neural network, the algorithm filters and extracts training samples for training through the sample quality score. Considering the requirement of cluster complete coverage mission, this paper introduces Long Short‐Term Memory (LSTM) based on the neural network to achieve a better training effect. After completing the above optimization and design, the algorithm proposed in this paper is verified through simulation experiments.

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Section: The Dqn Algorithmmentioning

confidence: 99%

Section: The Dqn Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Xing,

Wang,

Liu

2024

Advcd Theory and Sims

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“…However, because the sediment is easy to lay on the spiral groove during travel, it leads to serious walking slip, difficult turning, low bearing capacity, and large disturbances to the seabed. Additionally, the carrying capacity is relatively low, and the disturbance to the seabed is large [38][39][40][41].…”

Section: Time Phases and Progressmentioning

confidence: 99%

Status and Prospects of the Development of Deep-Sea Polymetallic Nodule-Collecting Technology

et al. 2023

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The deep-sea is rich in mineral resources, and deep-sea polymetallic nodules are considered to be the most likely resource for commercial exploitation. Since the discovery of polymetallic nodules by mankind, researchers around the world have made long and arduous explorations in the exploitation of deep-sea polymetallic nodules and have proposed various mining methods, such as the dragging bucket type, the continuous bucket rope type, the automatic shuttle boat type, and the pipeline -lifting type, and have carried out technical verification accordingly. In the collection of seabed polymetallic nodules, the development and testing of towed type, spiral-driven type, crawler self-propelled type, and suspended type technologies have been carried out, basically realizing the mining technology verification of seabed polymetallic nodules and providing technical support for commercial development. However, according to the demand for commercial development, there are still many technical difficulties in polymetallic nodule-collecting technology, and more focus needs to be placed on the efficiency, environmental protection, intelligence, safety, and reliability of the collecting system in the future. This paper compares the existing progress in collection technology and equipment, and provides ideas and references for the research and development of deep-sea polymetallic nodule-mining technology and equipment.

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Design and Qualification of a Latching System for Experimental Undercarriage System for 6000 m depth

Cited by 2 publications

References 1 publication

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

An Algorithm of Complete Coverage Path Planning for Deep‐Sea Mining Vehicle Clusters Based on Reinforcement Learning

Status and Prospects of the Development of Deep-Sea Polymetallic Nodule-Collecting Technology

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