An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

Ni, Jianjun; Liu, Yang; Wu, Liuying; Fan, Xiaopeng

doi:10.1007/s40815-017-0395-x

Cited by 36 publications

(15 citation statements)

References 34 publications

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“…Path planning plays a significant role in autonomous driving and has been extensively studied for decades. There are lots of methods used for path planning, such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and so on [77,78]. However, these conventional planning algorithms are not very suitable for the path planning task of self-driving cars under complex environments.…”

Section: Path Planningmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

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118

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Self-driving cars are a hot research topic in science and technology, which has a great influence on social and economic development. Deep learning is one of the current key areas in the field of artificial intelligence research. It has been widely applied in image processing, natural language understanding, and so on. In recent years, more and more deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. This paper presents a review of recent research on theories and applications of deep learning for self-driving cars. This survey provides a detailed explanation of the developments of self-driving cars and summarizes the applications of deep learning methods in the field of self-driving cars. Then the main problems in self-driving cars and their solutions based on deep learning methods are analyzed, such as obstacle detection, scene recognition, lane detection, navigation and path planning. In addition, the details of some representative approaches for self-driving cars using deep learning methods are summarized. Finally, the future challenges in the applications of deep learning for self-driving cars are given out.

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Section: Path Planningmentioning

confidence: 99%

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

Chen

et al. 2020

Applied Sciences

Self Cite

118

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“…With the rapid development of computer technology and sensor technology, the research and application of robots have reached a new height [1][2][3][4]. For mobile robots, when facing an unknown environment, they need to use their own sensor devices to sense the surrounding environment, build an environment map by moving, and determine their positions in the map; this is called robot simultaneous localization and mapping (SLAM) problem [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

Gong

et al. 2020

Computational Intelligence and Neuroscience

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The robot simultaneous localization and mapping (SLAM) is a very important and useful technology in the robotic field. However, the environmental map constructed by the traditional visual SLAM method contains little semantic information, which cannot satisfy the needs of complex applications. The semantic map can deal with this problem efficiently, which has become a research hot spot. This paper proposed an improved deep residual network- (ResNet-) based semantic SLAM method for monocular vision robots. In the proposed approach, an improved image matching algorithm based on feature points is presented, to enhance the anti-interference ability of the algorithm. Then, the robust feature point extraction method is adopted in the front-end module of the SLAM system, which can effectively reduce the probability of camera tracking loss. In addition, the improved key frame insertion method is introduced in the visual SLAM system to enhance the stability of the system during the turning and moving of the robot. Furthermore, an improved ResNet model is proposed to extract the semantic information of the environment to complete the construction of the semantic map of the environment. Finally, various experiments are conducted and the results show that the proposed method is effective.

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“…The algorithm solves the hunting of heterogeneous multi-AUV pairs with intelligent targets in a dynamic multi-obstacle underwater environments. Ni et al 22 divided the multi-AUV target hunting into two stages: search and capture. An improved spinal nervous system algorithm to control the search formation in the search phase.…”

Section: Introductionmentioning

confidence: 99%

“…In the capture phase, a dynamic alliance method based on two-way negotiation strategy and a pursuit direction allocation method based on improved genetic algorithm are proposed, which can effectively achieve the pursuit task. The common problem with these algorithms [17][18][19][20][21][22] is that the hunting time increases and the target escapes when some AUVs fail to reach the containment points at the same time.…”

Section: Introductionmentioning

confidence: 99%

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

Cao

2019

International Journal of Advanced Robotic Systems

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As one of the challenging tasks of multiple autonomous underwater vehicles systems, the realization of target hunting is the great significance. The multiple autonomous underwater vehicle target hunting is studied in this article. In some research, because the hunting members cannot reach the hunting point at the same time, the hunting time is long or the target escapes. To improve the efficiency of the target hunting, the leader–follower formation algorithm is introduced. Firstly, the task is assigned based on the distance between the autonomous underwater vehicle and the target. Then, the autonomous underwater vehicles with the same task are formed based on leader–follower mode, and the formation is kept to track the target. In the final capture phase, multiple autonomous underwater vehicle system use angle matching algorithm to round up target. The simulation results show that the proposed algorithm can effectively accomplish the target hunting task, save the hunting time, and avoid the target escape. Compared with the bioinspired neural network algorithm, the proposed algorithm shows better performance.

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An Improved Spinal Neural System-Based Approach for Heterogeneous AUVs Cooperative Hunting

Cited by 36 publications

References 34 publications

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods

An Improved Deep Residual Network-Based Semantic Simultaneous Localization and Mapping Method for Monocular Vision Robot

A leader–follower formation control approach for target hunting by multiple autonomous underwater vehicle in three-dimensional underwater environments

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