Surface Optimal Path Planning Using An Extended Dijkstra Algorithm

Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. Th...

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Vydáno v:IEEE access Ročník 8; s. 1
Hlavní autoři: Luo, Min, Hou, Xiaorong, Yang, Jing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Delaunay triangulation
Dijkstra algorithm
Dijkstra's algorithm
Euclidean distance
Finite element method
Mobile robots
Multiple robots
Optimization
Optimization methods
Path planning
Planning
Routing
Surface
Task analysis
Transportation
ISSN:2169-3536, 2169-3536
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Shrnutí:Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. This paper proposes an extended Dijkstra algorithm. We utilize the Delaunay triangulation to model the surface environment. Based on keeping the triangle side length unchanged, the triangle mesh on the surface is equivalently converted into a triangle on the two-dimensional plane. Through this transformation, we set up the two-dimensional developable passable channel of the surface and solve the optimal route on this channel. Traversing all the two-dimensional developable and passable paths of the surface, we can get the shortest route among all the optimal paths. Then the inverse transformation from the two-dimensional plane coordinates to the corresponding surface coordinates obtains the surface optimal path. The simulation results show that, compared with the traditional Dijkstra algorithm, this method improves the accuracy of the surface optimization path in single-robot single-target and multi-robot multi-target path planning tasks.
Bibliografie:ObjectType-Article-1
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content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3015976