A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Ni, Jianjun; Wu, Liuying; Shi, Pengfei; Yang, Simon X.

doi:10.1155/2017/9269742

Cited by 69 publications

(34 citation statements)

References 35 publications

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“…A hierarchical 3D neural network (NN) framework inspired by biological neuro-dynamics is presented by Yan et al [113] for the PP of an AUV. Each neuron in the NN characterizes a distinct subspace in the workspace.…”

Section: Bio-inspired Neuro-dynamicmentioning

confidence: 99%

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A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

141

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The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

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Section: Bio-inspired Neuro-dynamicmentioning

confidence: 99%

“…•Detects the dangerous destinations that have to be avoided with low computational cost •Only simulation results are available Unpredictable Bio-inspired neurodynamics [112,113] Time optimal Achieved Low…”

Section: Achieved Lowmentioning

confidence: 99%

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Panda

Subudhi

Pati

2020

Int. J. Autom. Comput.

141

View full text Add to dashboard Cite

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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

Self Cite

110

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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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“…These algorithms, usually find a solution close to the optimal one and they find it fast. Among the heuristic algorithms, genetic algorithms (GAs) (Davoodi et al, 2015;Jafarzadeh et al, 2017), artificial neural networks (ANNs) (Ni et al, 2017), and ant colony optimization (ACO) (Liu et al, 2017) have been the most popular for solving path planning problems (Masehian and Sedighizadeh, 2007). These algorithms take into account different objectives at the same time and can be adjusted for a wide variety of problems.…”

Section: Literature Reviewmentioning

confidence: 99%

An Exact Geometry–Based Algorithm for Path Planning

Jafarzadeh

Fleming

2018

International Journal of Applied Mathematics and Computer Science

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A novel, exact algorithm is presented to solve the path planning problem that involves finding the shortest collision-free path from a start to a goal point in a two-dimensional environment containing convex and non-convex obstacles. The proposed algorithm, which is called the shortest possible path (SPP) algorithm, constructs a network of lines connecting the vertices of the obstacles and the locations of the start and goal points which is smaller than the network generated by the visibility graph. Then it finds the shortest path from start to goal point within this network. The SPP algorithm generates a safe, smooth and obstacle-free path that has a desired distance from each obstacle. This algorithm is designed for environments that are populated sparsely with convex and nonconvex polygonal obstacles. It has the capability of eliminating some of the polygons that do not play any role in constructing the optimal path. It is proven that the SPP algorithm can find the optimal path in O(nnr2) time, where n is the number of vertices of all polygons and n ̓ is the number of vertices that are considered in constructing the path network (n ̓ ≤ n). The performance of the algorithm is evaluated relative to three major classes of algorithms: heuristic, probabilistic, and classic. Different benchmark scenarios are used to evaluate the performance of the algorithm relative to the first two classes of algorithms: GAMOPP (genetic algorithm for multi-objective path planning), a representative heuristic algorithm, as well as RRT (rapidly-exploring random tree) and PRM (probabilistic road map), two well-known probabilistic algorithms. Time complexity is known for classic algorithms, so the presented algorithm is compared analytically.

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A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles

Cited by 69 publications

References 35 publications

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

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

An Exact Geometry–Based Algorithm for Path Planning

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