The Method Based On Dijkstra Of Multi-directional Ship’s Path Planning

Cheng, Zihui; Zhang, Huajun; Zhao, Qin

doi:10.1109/ccdc49329.2020.9164597

Cited by 12 publications

(4 citation statements)

References 3 publications

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“…Shortest path algorithms such as DFS, BFS, Djikstra & their modifications have been used in a wide array of applications. These range from fast IP rerouting [4], to marine navigation [7], software defined networking [13], maze solving [12] to even optimal planning of sales persons [29]. The limitation of these methods is that the time to find the optimal path is not constant.…”

Section: Shortest Path Algorithmsmentioning

confidence: 99%

Biologically Inspired Neural Path Finding

Li¹,

Khan²,

Tresp³

et al. 2022

Preprint

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The human brain can be considered to be a graphical structure comprising of tens of billions of biological neurons connected by synapses. It has the remarkable ability to automatically re-route information flow through alternate paths in case some neurons are damaged. Moreover, the brain is capable of retaining information and applying it to similar but completely unseen scenarios. In this paper, we take inspiration from these attributes of the brain, to develop a computational framework to find the optimal low cost path between a source node and a destination node in a generalized graph. We show that our framework is capable of handling unseen graphs at test time. Moreover, it can find alternate optimal paths, when nodes are arbitrarily added or removed during inference, while maintaining a fixed prediction time. Code is available here: https://github.com/hangligit/pathfinding

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Section: Shortest Path Algorithmsmentioning

confidence: 99%

Biologically Inspired Neural Path Finding

Li¹,

Khan²,

Tresp³

et al. 2022

Preprint

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show abstract

“…Graph research algorithms are successfully implemented for global search. These algorithms include A‐star, 97 D‐star, 98 and Dijkstra 99 . After graph search, random sampling algorithms are second best for global path planning.…”

Section: Characteristics Of Coverage Path Planning For Uavmentioning

confidence: 99%

“…These algorithms include A-star, 97 D-star, 98 and Dijkstra. 99 After graph search, random sampling algorithms are second best for global path planning. Random sampling includes probability graph algorithm 100 and rapidly exploring random trees algorithm.…”

Section: Analysis Of Overall Classical Algorithms Wrt Area Coveragementioning

confidence: 99%

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Mannan

Obaidat

Mahmood

et al. 2022

Int J Communication

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The unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP-hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state-of-the-art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.

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“…The commonly used graph search algorithms include Dijkstra [29] , A* [30] , D* algorithm [31] etc. Compared with Dijkstra algorithm, A* algorithm has higher efficiency by increasing heuristic estimation, reducing search volume.…”

Section: Figure1 Schematic Diagram Of Mobile Robot Motion Planningmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motionplanning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actorcritic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

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The Method Based On Dijkstra Of Multi-directional Ship’s Path Planning

Cited by 12 publications

References 3 publications

Biologically Inspired Neural Path Finding

Biologically Inspired Neural Path Finding

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

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