The Method Based on Dijkstra of Three-dimensional Path Planning

Zhang, Hongxia; Cheng, Zihui

doi:10.1109/cac51589.2020.9326569

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Deterministic search algorithms, such as depth-first search [49], breadth-first search [50], and Dijkstra's algorithm [51], are pivotal in the realm of path planning. An enhancement to Dijkstra's algorithm was proposed to incorporate diagonal and linear searches at any angle for more effective 3D path planning [52]. A performance study comparing Dijkstra's algorithm, Floyd's algorithm, the A* algorithm, and ant colony optimization (ACO) underscored the superiority of Dijkstra's algorithm in terms of running time, complexity, and path length [53].…”

Section: Graph-based Path Planningmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle Path Planning

Luo,

Tian,

Wang

2024

Drones

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As the technology of unmanned aerial vehicles (UAVs) advances, these vehicles are increasingly being used in various industries. However, the navigation of UAVs often faces restrictions and obstacles, necessitating the implementation of path-planning algorithms to ensure safe and efficient flight. This paper presents innovative path-planning algorithms designed explicitly for UAVs and categorizes them based on algorithmic and functional levels. Moreover, it comprehensively discusses the advantages, disadvantages, application challenges, and notable outcomes of each path-planning algorithm, aiming to examine their performance thoroughly. Additionally, this paper provides insights into future research directions for UAVs, intending to assist researchers in future explorations.

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Section: Graph-based Path Planningmentioning

confidence: 99%

Research on Unmanned Aerial Vehicle Path Planning

Luo,

Tian,

Wang

2024

Drones

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“…Eichhorn [10] constructed an underwater time-varying environment using weighted directed graphs and successfully planned the optimal path for a single AUV in combination with the Dijkstra algorithm. Later, some researchers made a series of improvements to the Dijkstra algorithm [11], [12] based on the existing ones, which have improved the convergence accuracy of the algorithm. However, the improved algorithm still cannot overcome the drawback of the high running cost and low efficiency, as the Dijkstra algorithm requires a comprehensive search of the path nodes.…”

Section: Introductionmentioning

confidence: 99%

Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

2023

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Path planning for autonomous underwater vehicles (AUVs) is a key research focus in the marine domain, requiring consideration of the underwater environment's complexity and the efficiency of the planning algorithms. Firstly, this paper uses the grid method to construct a realistic ocean terrain for the underwater environment and incorporates obstacles and ocean currents. Also, the AUV's movement strategy and posture are illustrated and constrained by a visual search strategy. Secondly, an improved artificial jellyfish search (IJS) algorithm is to overcome the drawbacks of low convergence accuracy and adaptability of the basic algorithm. The IJS algorithm determines the optimal path point by optimizing the objective function. This objective function not only takes into account factors such as path navigation time and path safety but also designs an ocean current disturbance model, which facilitates the AUV to successfully reach the target point while avoiding obstacles and strong side currents. Finally, the optimal smoothed path is output using a cubic spline midpoint interpolation method. Multiple simulation experiments are carried out on the constructed environment model. The comparison results show that the IJS algorithm with a short running time has the optimal time cost and ocean current penalty cost for the planned path. In addition, the IJS algorithm is also shown to be adaptable in the field of multi-AUV movements.

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A Survey on Path Planning for Mobile Robot Systems

Gao

2023

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

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The Method Based on Dijkstra of Three-dimensional Path Planning

Cited by 4 publications

References 3 publications

Research on Unmanned Aerial Vehicle Path Planning

Research on Unmanned Aerial Vehicle Path Planning

Path Planning for Autonomous Underwater Vehicles Based on an Improved Artificial Jellyfish Search Algorithm in Multi-Obstacle Ocean Current Environment

A Survey on Path Planning for Mobile Robot Systems

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